CSE Portal — Boot Sequence
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Project ID = SDP2324 CE F 01
Supervisor: Dr. Hela Chamkhia
Deema Al-Kuwari, Juman Saleh, Maryam Al-Sumati, Sara Al-Kuwari
The desert climate in Qatar makes agriculture difficult due to water scarcity and extreme temperatures that affect the health of crops. The Smart Candy Farm is an innovative solution that addresses the biggest problems faced by Qatari farmers. Enhancing traditional strategies to improve crop productivity for a more efficient and productive farm. The system uses automation and artificial intelligence (AI) to optimize crop growth and resource management by monitoring plant and soil health, moisture management, and temperature control in real time to increase crop productivity while conserving resources. The DSF system automates data collection to make soil testing more efficient through sensors such as temperature, humidity, etc., so that it is easier for the farmer to know what is happening inside the farm quickly, cost-effectively, and accurately. The project uses sensor technologies to improve soil analysis, enabling resource management decisions, agricultural productivity, and sustainability. Major automated irrigation uses soil moisture sensors to reduce water waste and improve moisture levels. Furthermore, temperature sensors continuously scan the environment. To prevent heat stress for crops, the system automatically deploys shade mechanisms when sunlight is harmful to the plant and is higher than the permissible limit, and we can control the speed and direction of shading. We've added a feature where the AI camera measures height and maturity, which impacts harvest schedules and resource distribution. we designed 3D printing for the prototype small field to show how the DSF system works, and the prototype contains all IoT sensors and AI cameras and shading to show how the system works Advanced agricultural technology improves food security, reduces global famines, and lowers input costs for farmers. Ending hunger and mitigating the effects of climate change are in line with the United Nations Sustainable Development Goals. The DSF system ensures this feature by using Internet of Things sensors to measure plant parameters and an artificial intelligence camera to know the health status of the plant by knowing the height, plant health, and crop status. It also provides instant temperature control when sunlight is above minimum. This project is driven by sustainability and the importance of soil in agriculture. Improving and developing the agricultural field, resource use, and sustainability by simplifying soil analysis using technology. The project demonstrates the automated and analytical processes that facilitate farmers' lives required in agriculture and environmental engineering. This effort is transforming agriculture, the environment, and society in Qatar and beyond.
Project ID = SDP2324 CE F 02
Supervisor: Dr. Mohamed Al-Meer
Noor Al-Maadeed, Maryam Al-Majid, Alanoud Al-Marri
Our project addresses the significant global health challenge posed by heart diseases, emphasizing the importance of timely detection and monitoring. Our solution introduces a specialized device equipped with sensors, a microcontroller, and a Processing Hub to swiftly identify and report potential heart-related concerns. Utilizing the power of Artificial Intelligence (AI), specifically 1D Convolutional Neural Networks (1D CNN), our system enhances the accuracy of detection, streamlining the reporting process to specialized medical teams. This not only minimizes clinic wait times for patients, but also improves the responsiveness of medical professionals in addressing emerging issues. To achieve this, we used the PTB-XL ECG dataset, which contains 21,837 clinical 12-lead ECGs from 18,885 patients, with each recording lasting 10 seconds. This comprehensive dataset, annotated by up to two cardiologists, provides a reliable foundation for our model training. Our chosen code, "ECG Detection - 1D CNN", from this dataset serves as a starting point for our model, which has achieved approximately 77% accuracy. We are exploring adaptations to use this with 1-lead ECG, allowing for greater flexibility and broader application. Crucially, we prioritize data security, incorporating robust measures to safeguard sensitive health information. Our Heart Health Vitals Monitoring and Detection device ensures comprehensive data encryption and privacy mechanisms within its architecture. This commitment reinforces patient trust and compliance with stringent data security standards, making it a reliable and secure solution for monitoring and addressing heart-related incidents.
Project ID = SDP2324 CE F 03
Supervisor: Dr. Loay Ismail
Roaa Shady, Noha Elgamal, Habiba Zaky, Tasnim Mushtaha
In response to the critical need for enhanced infection control within healthcare facilities, this senior design project developed an Autonomous UV-C Disinfection Robot to effectively navigate and sterilize hospital environments. The primary objective was to create a reliable, automated solution to maintain high cleanliness standards, crucial for preventing the spread of infections. The project’s main contributions include the integration of UV-C light technology for pathogen deactivation, advanced navigation, and obstacle detection systems, and robust safety mechanisms to protect human health. The robot’s disinfection system ensures comprehensive exposure and effective sterilization of surfaces within its operational range. Our methodology involved designing and implementing five integrated modules: the Disinfection System, Perception Module, Navigation Module, Monitoring System, and Safety Control System. The Perception Module uses sensors to accurately assess and adapt to the environment, while the Navigation Module autonomously maneuvers the robot, avoiding obstacles and following predefined routes. The Monitoring System provides real-time updates on the robot's status and coverage, ensuring alignment with operational standards. The Safety Control System includes mechanisms to prevent human exposure to UV-C light, automatically deactivating the LEDs and shutting down the robot if it detects human presence. The results demonstrate that the Autonomous UV-C Disinfection Robot significantly enhances safety and efficiency in healthcare environments. It provides a scalable, automated disinfection solution, representing a significant advancement in the application of technology for public health protection.
Project ID = SDP2324 CE F 04
Supervisor: Prof. Qutaibah M. Malluhi
Maya Attia, Shaza Elsheikh, Youmna Abdelhamid, Nada Rbie
This project successfully implements a sophisticated system designed to track sunlight throughout the day and redirect it to enhance indoor plant cultivation. By integrating microcontrollers, sensors, and actuators, the system dynamically adjusts sunlight exposure to ensure plants receive optimal lighting conditions. One of the key outcomes of this project is the successful implementation of a user-friendly interface that allows individuals to customize the duration of light exposure based on the specific requirements of different plant species. This empowers users to tailor the lighting environment to suit the needs of their plants, thereby maximizing their healthy growth potential. The reflection system, comprised of pipe and flat mirror subsystems, complements the sunlight tracking mechanism by redirecting and diffusing light, optimizing its distribution. This not only improves the efficiency of light utilization but also addresses potential issues such as uneven growth. Overall, this system enables sustainable indoor gardening practices by reducing reliance on artificial lighting sources and harnessing natural sunlight for optimal plant growth.
Project ID = SDP2324 CE F 05
Supervisor: Dr. Muhammad Arsalan, Co-supervisor: Eng. Suchithra Kunhoth
Noora Al-Naimi, Ghada Al-Jaber, Ghalia Al-Mahmoudi
The current state of road infrastructure significantly influences road safety, vehicle maintenance costs, and overall transportation efficiency. Existing road defect detection methods are often time-consuming, leading to repair delays, inconveniencing commuters, and posing safety hazards. Effectively addressing these challenges is imperative. Traditional road defect detection methods could be more efficient, but they result in delayed repairs and increased safety risks. The need for a more advanced real-time solution that can accurately detect and monitor road defects has become apparent. Our research introduces an innovative Smart Real-Time Road Defect Detection System powered by sophisticated semantic segmentation algorithms, integrating the Jetson Nano 4GB with GoPro Hero 12 cameras. This system aims to revolutionize road maintenance by automating the detection of defects, including potholes, cracks, and surface irregularities. The deep learning model, a pivotal component of our system, undergoes training on extensive datasets of road videos converted into frames. By leveraging semantic segmentation, the system efficiently and precisely identifies defect locations, issuing real-time notifications via the SIM Card for prompt maintenance and heightened road safety. These enhancements not only elevate the accuracy of defect identification but also contribute to the broader goal of creating more intelligent and safer roadways for the future.
Project ID = SDP2324 CE F 06
Supervisor: Dr. Khalid Abualsaud
Najlaa Barqan, Asma Almarri, Fatima AlSheeb, Salma Badawy
Epilepsy is a neurological disease, which randomly affects individuals of all ages and usually starts to appear in childhood, but it can also begin to appear in adulthood. Statistically, epileptic individuals confront a premature death that is three times higher compared to the general population. Furthermore, an overwhelming 80% of those individuals are in countries with limited health resources where it is challenging to reach, making effective seizure management a huge struggle [1]. The concept of this project is proposed in response to the medical challenge of healthcare providers coping with and monitoring epileptic seizures. This device is equipped with integrated parameters that focus on eye blinking, heart rate, and Electroencephalography (EEG) signals. This unique combination offers an effective way to monitor individuals with epilepsy, providing critical data for managing their conditions. Utilizing advanced assistive technology, the proposed device monitors and detects seizure episodes early with high sensitivity, allowing immediate assistance by caregivers and health providers. In addition to data collection, this device is an alternative approach that may lower the chance of harm and facilitate better monitoring and seizure detection. The main advantage of our monitoring device is that it can be used by doctors, caregivers, and the patient himself, where they can constantly check the status of their disease making assistance reachable and easier, bringing hope for a safer and more secure future for epileptic patients.
Project ID = SDP2324 CE F 07
Supervisor: Prof. Sumaya Al-Maadeed
Aldana Aldosari, Gheyoudh Al-Marri, Maryam Al-Marri, Shaikha Alkhayarin
Analyzing food and being aware of its components has become very important in this period, especially for people who are athletes and those interested in a healthy diet or those suffering from diseases such as diabetes and excessive obesity. Athletes and healthy people are very interested in analyzing their foods and knowing how many calories each meal contains. Therefore, this project will serve people interested in food nutrition and healthy lifestyle by implementing a smart device that can recognize food, analyze it and display nutritional estimation to the person who uses this device [12]. The main goal of our project is to create a device and software for recognizing food and nutritional estimation using different food datasets available in the literature. We will create a new dataset and add images of popular-Qatari foods. The dataset was trained and tested by using multi deep learning algorithms to identify foods correctly with high accuracy. The algorithm with the highest recognition rate was deployed in a microprocessor to be used in real-time scenarios. The Raspberry Pi is a single-board computer used as a basic component in the project because it is effective in artificial intelligence processes, such as image classification and detection. The project components in general are normal plate connecting to Raspberry Pi, Camera from the top to get picture of the food, scale to measure the weight of food, and screen to display the final nutritional estimation and calculation based on the food type and weight. If this project is successful, it will be a smart nutrition device that will be used in many areas, such as restaurants and sports clubs, as well as in the homes of people concerned about calories and people who suffer from diseases that require food control, such as diabetes and blood pressure patients.
Project ID = SDP2324 CE F 08
Supervisor: Dr. Khalid Abualsaud
Noor AlMaadeed, Nadeen Alanazi, Aldana alMeadhadi, Maytha AlSuwaidi
In recent years, significant advancements have been made in the field of assistive devices designed to aid those with visual impairments. Our project is in line with these developments and is divided into two stages. The first stage is dedicated to creating an initial prototype of a smart stick. The second stage, which we plan to undertake later, will focus on its full-scale implementation and thorough testing with users. This structured approach allows us to move systematically from the initial design concept to its real-world use, tackling the unique needs and challenges present in the worldwide landscape of visual impairment. In the initial stage of our project, we focused on designing and developing a prototype smart stick. This effort aimed to overcome the shortcomings of conventional aids like the white cane. Our design was guided by findings from Qatar's ‘National Blindness and Visual Impairment Survey,’ which emphasized the demand for improved assistive tools. The prototype includes an ultrasonic sensor for detecting obstacles, groundwork for incorporating GPS and GSM modules, and the initial design of a bracelet utilizing Nordic Radio Frequency (NRF) technology. These components, managed by an Arduino Mega microcontroller and powered by a rechargeable battery, were assembled and underwent preliminary testing. Although the full integration, including the bracelet's operational functionality and in-field testing, is earmarked for the next phase, the initial phase has successfully established the base for a smart stick that promises to significantly improve the independence and safety of visually impaired individuals. The second stage, "Project Evolution: Introducing the Smart Stick Companion App" represents a leap forward, transitioning from the preliminary bracelet concept to a sophisticated, app-based interface. This companion app not only streamlines user interaction but also expands functionality, providing an adaptive and integrated experience that is set to revolutionize mobility aids for the visually impaired community in our forthcoming work.
Project ID = SDP2324 CE F 09
Supervisor: Prof. Elias Yaacoub
Ftoun AlMkhlef, Isra Ali, Mubashshira Matin, NoorAlhoda Elshawadfy
Gas hazards and fires pose persistent threats, resulting in significant property damage and loss of life despite advances in technology. Employees in the oil and gas sectors are particularly vulnerable to the dangers of explosions and fires ignited by combustible gases and vapors. Various industrial sources, including production machinery, trucks, wells, and surface equipment, can release flammable gases, vapors, hydrogen sulfide, and well gases. These hazards are further exacerbated by a multitude of ignition sources, such as static electricity, electrical equipment, open flames, lightning, cigarettes, hot surfaces, and cutting and welding tools. The frequency of fire incidents in recent years underscores the urgency for innovative solutions in fire and gas hazard response. This project is dedicated to addressing these vital concerns through the development of a specialized rescue robot. With advanced sensors, specialized equipment, and enhanced mobility, this robot serves as a lifeline for both rescuers and victims, providing swift and effective response capabilities. The project's core objective is to revolutionize emergency response, offering a cutting-edge tool to efficiently manage gas hazards and fires across a range of industrial and disaster scenarios. The focus is on enhancing safety, reducing risk, and safeguarding lives and property in the face of these persistent challenges.
Project ID = SDP2324 CE F 11
Supervisor: Prof. Junaid Qadir
Hagar Gaber, Noora Al-Qahtani, Maria Jalal
Much research in assistive technologies has been carried out, but with a significant disparity between support for the visually impaired and the hearing-impaired, with very little attention given to the hearing-impaired. Furthermore, an analysis of surveys related to the topic shows the low scientific production of assistive technologies for the hearing-impaired, and to our best knowledge, there is no system specifically dedicated to alert them in case of emergency. In this project our aim is to provide technology to alert hearing-impaired to surrounding sounds. This project introduces a Wearable Sound Detection and Notification System designed to address the specific challenges faced by deaf individuals in their daily lives. The system utilizes advanced technologies, including microphones and signal processing units, to detect crucial sounds in the environment and deliver notifications through visual cues or tactile feedback. The importance of this project lies in its potential to significantly enhance the safety, independence, and overall quality of life for the deaf community by alerting users to nine important sounds and alongside crucial auditory warnings like car horns, sirens, gunshots, drilling, engine idling, jackhammers, children playing, dog barking, and street music. It will notify the deaf about the safety sound surrounding them. The system aims to mitigate safety concerns. The primary beneficiaries of this solution are deaf individuals of all ages, providing them with a tool that guides and participates in various aspects of daily life. The technical contributions of this project include the development of a machine learning sound detection algorithm, integration with existing assistive devices, and customizable features to meet individual user preferences. Through these contributions, the Wearable Sound Detection and Notification System seeks to promote inclusivity, empower the deaf community, and contribute to a more accessible and supportive societal environment.
Project ID = SDP2324 CE F 12
Supervisor: Dr. Khalid Abualsaud
Shaikha Alkuwari, Sara AlGhanim, Reem Lakhan, Fatma Alsai
Deafness is defined as a substantial hearing loss that can vary from partial to complete hearing impairment. Deaf people have several ways to communicate such as sign language, lip-reading, written communication, or innovative use of assistive technology. Therefore, people who are deaf face challenges in communicating with the hearing community, as there is often a barrier in communication arising from a limited understanding of sign language and uncertainty about how to engage with them effectively. The aim of this project is to design and implement a technology device that allows two-way communication between deaf person and the community. The system consists of a touch screen, microphone, and speaker, in support of, different requests that deaf need in the airport. The deaf will start pressing one of the buttons, then the request will display on the screen. In addition, the voice will be displayed by the speaker simultaneously for better understanding. Nonetheless, the device will capture the recipient answer and convert it to sign language. This device sums up with several benefits; one of the main advantages is to facilitate a successful communication between deaf and the normal person. Moreover, the main purpose of the device will be used in Hamad International Airport to help the deaf travel without assistance.
Project ID = SDP2324 CE F 14
Supervisor: Dr. Osama Halabi, Co-supervisor: Dr. Mohammed Al-Sada
Amna AlAhmad, Haya Al-Muhannadi, Amna Al-Siddiqi
In December 2019 the world was stopped where all the daily tasks were stopped in unexpected ways due to covid-19 pandemic. Throughout all the development in technology and science that the world has accomplished the pandemic still paused the world. Almost overnight everything changed, this pandemic created challenges to our lifestyle, jobs, our education and the way of communicating and connecting with each other. The haptic hand was a solution to this problem. Our system is a haptic hand which gives the user the illusion that they are in another environment. Allowing us to stimulate the real-world scenario of being physically present within the environment you want to be in. The robotic haptic hand stimulates the human sense of touch with tactile and force feedback of the real world, it allows the user to establish a stronger connection with the distant world. Significant improvements have been done to the robotic hand design; all wires are neatly organized, and sensors are strategically placed in dedicated slots. Moreover, the sensors that are attached to the robotic hand, achieve high sensitivity, and obtain data accurately. The final product of the haptic robotic hand allows the user to control the robotic hand while wearing the glove. The robotic hand is able to lift objects that are less than 300g. Our system enables users to move beyond ordinary limitations and establish an experimental standard where our daily objectives and tasks are continued without any restrictions or limitations. It represents a future full of improvement and the ability to achieve communication, education, and much more without any delays and not stopped by any obstacles. Furthermore, by providing the tools to interact, operate, and communicate in the local and remote fields without reducing our sense of physical connection, the robotic haptic hand will allow us to improve our ability to change in navigating through future risks. The haptic hand is not simply a technological achievement; it also establishes the way we could live without physical barriers or major world events solving obstacles and creating a tool giving us the ability to accomplish tasks, work together, and learn.
Project ID = SDP2324 CE F 15
Supervisor: Dr. Ahmed Badawy
Eiman Alkubaisi, Noof Albuhendi, Fatma Almulla, Sara Almansoori
The field of Search and Rescue (SAR) has witnessed significant advancements with the integration of drone technology. This project introduces an innovative drone designed for emergency response, particularly in fire scenarios. Equipped with autonomous flight capabilities, the drone utilizes a thermal camera to identify individuals in need of assistance. A distinctive feature of this drone is its ability to provide immediate aid by deploying oxygen masks to people trapped in fire-affected areas. The drone's Raspberry Pi computer, along with various sensors, enables efficient navigation and operation. It also facilitates real-time communication and sharing of the indoor mapping data with firefighters on the ground. This integration of technology, including obstacle detection, results in a rapid and effective response mechanism, crucial for life-saving operations in inaccessible locations. The drone serves as an aerial support unit in critical rescue missions, particularly in smoke-filled and low-visibility conditions. With real-time communication established, the drone acts as a vital link between the remote environment and the firefighters on the ground, facilitating coordinated and informed decision-making based on the shared indoor mapping data.
Project ID = SDP2324 CE F 16
Supervisor: Dr. Abdulaziz Al-Ali
Amany Maklad, Aya Moustafa, Ghada Theyab, Jawaher Al-hay
This project proposes CartoDrone, an innovative indoor autonomous drone designed to revolutionize 3D mapping within indoor areas. The primary objective of CartoDrone is to autonomously generate a 3D map of unrecognized indoor environments using a stereo camera and a 2D LiDAR. This project utilizes simultaneous localization and mapping (SLAM) technology to enable real-time creation and continuous updates of a 2D map by the LiDAR as the drone explores an indoor area. Next, it uses the 2D map as a base for the navigation to perform 3D mapping missions and produce a detailed 3D map of the environment using a stereo camera. Furthermore, CartoDrone's autonomous capabilities and integration of mapping technology will pave the way towards developing applications of autonomous indoor drones. This engineering solution impacts the global, economic, environmental, and societal sectors by providing a real-life solution that can impact multiple areas such as emergency response, architecture, construction, and facility management and planning. It provides never-before-seen information for effective planning, navigation, and decision-making in indoor areas. The project has successfully developed and integrated cutting-edge technologies such as simultaneous localization and mapping (SLAM) enabling real-time mapping updates and navigation Market research and analysis of related work surveyed in this report show a clear identification of substantial gaps in indoor mapping solutions, driving advancements in autonomous drone systems. The successful start of the project, marked by the assembly, configuration, and testing of the CartoDrone prototype, demonstrated tangible progress toward achieving the project's goals. The project successfully implemented 3D mapping, 2D mapping, and navigation features for indoor environments. The project's findings indicate that CartoDrone is effective for indoor mapping tasks. It uses stereo cameras and 2D LiDAR sensors to generate detailed 3D maps of indoor areas and navigate autonomously. The project also identifies areas where CartoDrone should be improved and offers future research direction to enhance its performance in various indoor environments. CartoDrone, as a cutting-edge engineering solution, opens the way for further improvements in autonomous indoor drones that are capable of efficiently addressing real-world difficulties.
Project ID = SDP2324 CE F 17
Supervisor: Prof. Elias Yaacoub
Almaha Al-kuwari, Yara Ahmed, Afeefa Muskan , Hissa Almerekhi
Within the current realm of unmanned aerial vehicle (UAV) technology, our project addresses the key challenges of real-time data communication and secure transmission in drone applications. Our hardware module is designed to establish a secure visual data communication channel, to ensure that the captured data remains confidential and protected against external threats. The primary objective involves the implementation of advanced encryption standards, specifically AES-GCM, a robust encryption algorithm used for securing data confidentiality and integrity through its hashing and authentication mechanisms. Furthermore, we enhanced the security of our communication system by integrating RSA key exchange functionality to securely share encryption keys between the drone and the control station. The algorithms are implemented on the Raspberry Pi attached to the drone, functioning as the processing and transmission unit. The project employs a dual transmission approach, in which separate segments of the data are transmitted via Wi-Fi and Bluetooth concurrently. This technical approach enhances data security by strategically dividing and transmitting segments through both channels simultaneously, which adds an extra layer of security to the data transmission process.
Project ID = SDP2324 CE F 18
Supervisor: Prof. Sumaya Al-Maadeed
Aldana Alnaimi, Moza Almaslamani, Almayasa Alkuwari, Sara Alshaiba
Nowadays, hospitals require specialized equipment that allows them to track the sleeping positions of their patients and assess their health. SomniaSense is a cutting-edge system intended to track and evaluate patients' general health and sleep habits in a hospital setting. Its main goal is to gather information for precise vital sign measurement, sleep apnea incident detection, motion estimation, sleeping position changes, and facial expression capture in supine positions. Furthermore, when patients are positioned in these directions, two specialized devices—one on each side of the bed—that are fitted with mmWave sensors concentrate on tracking heart rate and breathing rate. Based on the patient's posture, this special function enables accurate tracking of physiological indicators. This system's capacity to condense voluminous sleep data into brief films is another noteworthy feature. Furthermore, when patients are positioned in these directions, two specialized devices—one on each side of the bed—that are fitted with mmWave sensors concentrate on tracking heart rate and breathing rate. Based on the patient's posture, this special function enables accurate tracking of physiological indicators. This system's capacity to condense voluminous sleep data into brief videos is another noteworthy feature. Healthcare practitioners can improve patient care by using SomniaSense, which combines multi-sensor data fusion, sleep apnea detection, facial expression analysis, sleep position estimation, and summary motion movies. It is outfitted with a variety of sensors, including noise, temperature, and humidity sensors, mmWave radar, and high-resolution Webcam cameras.
Project ID = SDP2324 CE F 19
Supervisor: Dr. Mohammed Al-Meer
Kholoud Al-Mesleh, Fatima Al-Qattali, Noof Al-Amoudi, Fatima Al-Ali
Drowsy driving is a major cause of road accidents. More than 40% of drivers admitted they have fallen asleep while driving [1]. To address this issue, a novel driver drowsiness detection system was developed, employing a deep learning model to track eyes (open/close) and detect drowsiness in real time by using Raspberry Pi. The system achieved remarkable accuracy, reaching 97% during training and 93% when evaluated on an external dataset. This high performance was attributed to meticulous hyperparameter tuning, utilizing 20 epochs, a learning rate of 0.001, a batch size of 16, and the Adam optimizer. The system's effectiveness was further evaluated using various dataset decision ratios for the deep learning model. The optimal ratio was found to be 80% for training data and 20% for testing data, highlighting the importance of a balanced dataset for achieving optimal model performance. After converting the model to TensorFlow Lite and implementing it on the Raspberry Pi 5, we achieved 96.68% accuracy. The system's generalizability was also demonstrated by its ability to effectively detect drowsiness across different datasets, even with limited data.
Project ID = SDP2324 CE F 20
Supervisor: Dr. Hela Chamkhia
Sara Al Dossari, Deema Al Samra, Baina Subaih
The goal of this project is to create a miniaturized version to simulate a smart wheelchair that meets the needs of people who have mobility issues. The wheelchair has voice recognition for easy control, webcam for obstacle detection and avoidance, and location identifying capability. Enhancing users' independence, safety, and general well-being to a great extent is the main goal. The smart wheelchair's integration of webcam allows it to recognize obstacles in its environment and automatically modify its path to prevent collisions. In addition to increasing user safety, this sophisticated obstacle avoidance feature gives users more assurance and security when navigating difficult situations. Also, the wheelchair has voice recognition technology to improve user control, enabling users to direct its movements with spoken commands. Without the need for manual controls, this user-friendly control interface makes operating the wheelchair more comfortable and accessible for people with mobility impairments. The voice-activated feature increases user independence and fosters a smooth, joyful user experience. In addition, the wheelchair keeps an eye on the user's location. If it can’t get the current location, it will send the last location available guaranteeing assistance. Moreover, in recognition of the difficulties presented by noisy surroundings, the mini wheelchair has an extra feature: joystick control. Users can choose to use joystick control instead of voice commands when the surrounding noise level increases. This gives users exact control over the wheelchair's movements, guaranteeing their safety and offering a backup navigation method in such circumstances. Furthermore, Innovative features that improve dependability and safety are emphasized, such as cameras that can detect obstacles and send their location. The Raspberry Pi, webcam, voice recognition module, Arduino Uno, L298N motor driver, batteries, and Fona 808 position tracking tracking are examples of hardware components. Python, OpenCV for obstacle detection and avoidance, Arduino code is used as software components. Through methodical testing and troubleshooting, compatibility, and code debugging challenges were resolved. Throughout the process, improvements in project management, testing, and prototype creation were made. A methodical approach is necessary to implement improvements and address deficiencies in the solution. User-centered design, interdisciplinary cooperation, taking advantage of technology breakthroughs, and putting quality assurance and testing procedures into place are some strategies. It is essential to make training and capacity building investments in team members. All things considered, the wheelchair project offers a solution that combines joystick control, position tracking tracking, voice recognition, and a webcam to meet the unique requirements of people with mobility impairments. The wheelchair seeks to greatly enhance user independence, safety, and general quality of life by integrating these cutting-edge features.
Project ID = SDP2324 CE F 21
Supervisor: Dr. Muhammed Moazam Azeem
Maha Al-Naimi, Salma Saker, Fatima AlKaabi
In an era marked by rapidly evolving security concerns, the necessity for advanced and responsive technologies has reached new heights. This project presents a trailblazing solution: a system that has a destructive mechanism that utilizes the power of radio frequency transmission. This system holds the capability to transmit real-time data, encompassing live video feeds, altitudes information, and target location, all seamlessly relayed to a central base station. It is positioned to enhance security measures, enabling not just immediate monitoring but also stringent control and accountability. What makes this system exceptional is its intrinsic safeguard—the drone's capacity to receive radio signals from the base station, thereby enabling remote shutdown in predetermined circumstances or when unauthorized activities are detected. This dual functionality positions the system as a potent asset for enhancing security measures across a wide range of applications. The project's core innovation lies in the integration of the HackRF device, which enables the transmission and reception of data in the form of radio frequency signals, ensuring robust and secure communication. The core objectives of this project are multi-faceted. First and foremost, it aims to engineer a destructive system in drones that stands at the forefront of technological advancement. This includes outfitting the drone with cutting-edge technology that enables it to not only acquire but also transmit data seamlessly. The pinnacle of its capabilities lies in its ability to facilitate real-time data transmission. This encompasses the live streaming of video feeds, allowing for immediate visual assessment of the drone's surroundings, as well as audio data capture for auditory insights into the monitored area. Additionally, the drone relays precise altitude information, thereby enhancing situational awareness, and employs GPS technology to offer real-time location tracking, ensuring that the drone's whereabouts are continually monitored. Security, both in terms of data integrity and confidentiality, constitutes another pivotal facet of this project's objectives. The development of a remote shutdown mechanism is crucial (e,g., disconnect power to the motors of drones and propellers will cause the drone to fall). This safeguard enables the base station to transmit radio signals, initiating a controlled self-destruct sequence should the drone deviate from its designated path or encounter unauthorized activity. This mechanism prevents the drone from falling in the wrong hands or becoming a threat. Furthermore, the project explores diverse security applications, fostering integration possibilities with security agencies, military utilization, and private security firms, thereby enhancing surveillance capabilities across various domains.
Project ID = SDP2324 CE F 22
Supervisor: Dr. Muhammed Moazam Azeem, Co-supervisor: Prof. Sumaya Al-Maadeed
Fajer Ahmed Robelah, Abeer Mohammed AlMarri, Hila Al-Dosari
The Drone-based Infrastructure Inspection and Data Transmission extend points to address the required productive and secure review of basic infrastructure such as bridges, power lines, and pipelines [24]. Conventional review strategies regularly require manual labor and can posture security dangers to laborers [20]. In addition, the manual collection of information can be time-consuming and inclined to mistakes[23]. To overcome these challenges, our project proposes the improvement of a drone framework that will revolutionize foundation assessment [21]. The drone will be equipped with sensors and cameras to capture high-resolution pictures and recordings of the infrastructure [24]. These pictures and recordings will be transmitted safely to a central server for advance examination[22]. One of the key goals of our venture is to guarantee the secure transmission of information[21]. We are going to execute encryption algorithms to ensure the privacy and judgment of the transmitted information [23]. Also, verification instruments will be utilized to guarantee that as it were authorized people can get to the collected information [20]. In addition, our project will incorporate automated analysis techniques to manage the gathered data [23]. We identify potential fundamental flaws or anomalies in the evaluated infrastructure by utilizing computer vision and machine learning methods [24]. This will facilitate the prompt identification of possible problems, leading to easy assistance and the avoidance of terrible setbacks [22]. To improve the efficacy and security of infrastructure evaluation, the Drone-based Infrastructure Inspection and Information Transmission extends points in outline [21]. Drones and secure information transfer allow for the expediting of inspection preparation, cost reduction, and improvement of information accuracy [20]. This project has the ability to completely change how basic infrastructure is maintained and evaluated [21].
Project ID = SDP2324 CE-CS F 23
Supervisor: Prof. Elias Yaacoub
Aisha Salem Al-Kaabi, Khawla Abdulaziz Buhindi, Shaikha Faisal Alawadi, Aisha Mubarak AlNaemi
The objective of this project is to develop a life-saving watch designed to assist individuals in precarious situations, whether in pools, oceans, or remote and challenging-to-reach locations. The core purpose of this watch is to accurately detect the wearer's location and promptly send emergency alerts to their family members, while also notifying rescue teams through SMS. Furthermore, the watch will continuously monitor vital health data like heart rate and transmit this information. This innovation is crucial because it offers universal utility; anyone, anywhere, and at any time can benefit from this watch, making them aware of its significance in potentially dangerous situations. While there are some rudimentary drowning prevention solutions in development, such as bracelets equipped with inflatable balloons that surface when pressure increases during a drowning incident, these solutions lack the precision of location and depth tracking. Moreover, they are limited in their application, primarily intended for small pools rather than open waters like seas. Our proposed solution, on the other hand, relies on sensor technology, and data transmission, making it versatile and dependable across diverse environments, as it utilizes signals available in any location.
Project ID = SDP2324 CS F 24
Supervisor: Dr. Saleh Al-Hazbi
Iffat Said, Ikram Chebbak, Kulsum Kader, Ridita Razzak Ridi
During the COVID-19 pandemic and in subsequent years, the number of Qatar University students who struggle to find relevant information on the website has increased, as evident by the increasing number of questions posted daily by students on student social media groups. This phenomenon can be attributed to the lack of face-to-face meetings with advisors during the pandemic, as well as a complicated official website structure, both of which make access to crucial information unnecessarily problematic. Hence, this project was proposed. Murshidi is an AI-powered chatbot which makes use of Langchain and OpenAI’s APIs in order to provide a seamless question-and-answer experience for students, alongside other unique features (to be added) that differentiate this project from similar chatbots in the market. For instance, Murshidi is able to support voice-recognition for English, text capabilities for both Arabic and English and in the future, its mobile application will offer the user a personalized user experience based on their usage history. Not only does this proposed solution tackle the issue students face, but it also solves problems from the perspective of advisors. The research conducted suggests that Qatar University advisors struggle with students wasting entire appointments for trivial questions and a large number of assigned students per advisor – which reduces the amount of time that can be dedicated to high-risk students. By offering a reliable, updated, and easy-to-use chatbot on the official Qatar University website and other such universities in the region, the impact of the aforementioned issues can be minimized considerably. However, the proposed solution has its limitations such as the heavy reliance on OpenAI’s API for the backend query processing which poses a future risk to Murshidi, should OpenAI decide to discontinue their service or make any changes to their API usage as well as pricing strategy, which will then directly affect the mentioned respective aspects of Murshidi. Furthermore, Murshidi minimizes ai generation and focuses more on using AI for information retrieval. This has been done to reduce inaccurate responses as the consequence of False positives and False negatives is high in the context of Murshidi. Furthermore, as we plan to include voice querying for Murshidi, OpenAI is not the best choice for such a development as Arabic is a complex language including many different accents and a vast dictionary of words including spoken slang, this would require Murshidi to move towards using better open-source software that meets the needs of users. Lastly, as OpenAI’s models learn from current and future user queries, this does not pose an issue with the web-based version of Murshidi, however, it would be a high risk once the mobile app is in development that would directly fetch personal data that belongs to the user. In that case, OpenAI’s models will be learning from conversations that contain personal information such as GPA, age, grades and current status of students. This data can then be at risk of being leaked to the public. These drawbacks encourage us to constantly improve Murshidi and find better alternatives to meet the needs of the market without compromising on performance and security.
Project ID = SDP2324 CS F 25
Supervisor: Prof. Cagatay Catal
Hanan Othman Al-Qaaod, Aya Ahmad Al nsairat, Hanan Mohammed Alyafei, Israa Elsayed Mohamed
This senior project report presents Yad B Yad web application which is a software solution designed to address the challenges faced by special needs students in university settings. The project team conducted a thorough analysis of the problem and identified the need for a customizable, user-friendly software solution that could assist students with various disabilities. The report outlines the project's objectives, including developing a software solution that can improve the learning experience for students with special needs by connecting them with other students who will assist them in their studies and facilitate their effective integration into the university community. The report also details the software development process, including the selection of the Scrum Model as the primary development methodology. The report concludes with a discussion of the project's main conclusions, highlighting the strengths and shortcomings of the solution, as well as its key contributions and novel aspects. The report also outlines potential future developments and enhancements for the software solution and discusses its potential implementation in other universities and educational institutions. This project represents a significant contribution to the field of special needs education and has the potential to improve the lives of countless students with disabilities.
Project ID = SDP2324 CS F 26
Supervisor: Dr. Tamer Elsayed
Mayar Elsayed, Najwa Al-Majid, Naematul Zannat, Noora Al-Marri
The Arabic Dialect Translator App (لهجتي) is a fresh, user-friendly tool that breaks down language barriers among various Arabic dialects. It harnesses the power of advanced systems like GPT from OpenAI and Gemini from Google API. These systems use advanced machine learning technology, enabling the app to interpret and translate a wide array of Arabic dialects. The app features Android's SpeechRecognizer for converting speech into text and employs Google Cloud Services for converting text into speech. This dual feature enhances the app’s accessibility, catering to users with diverse needs. The app can translate numerous Arabic dialects, ranging from Levantine (شامي) to Egyptian, making it a versatile communication tool. In a nutshell, the Arabic Dialect Translator App is a game-changer when it comes to communication within the Arab world. By using new technology for accurate translations, the app paves the way for better understanding and more meaningful conversations across different Arabic dialects. It’s a simple yet professional tool, making it an essential asset for anyone Wanting to understand the different dialects of the Arabic language.
Project ID = SDP2324 CS F 27
Supervisor: Dr. Saleh Al-Hazbi
Aisha H Al-Malki, Aisha M Al-Shahwani, AlReem Al-Emadi, Jameela Al-Hajri
In the rapidly evolving educational landscape, the dramatic increase in student enrollment presents significant challenges, particularly in the realm of English learning and writing. As academic and professional success increasingly depends on proficient English communication skills, the limitations of traditional teaching methods are becoming more apparent, posing substantial hurdles for both instructors and learners. In response to these challenges, the Nibras project has been developed as a pioneering solution, aimed at redefining the English education process. This project is designed with the explicit goal of enhancing the English grading process for educators and improving the learning experience for students by integrating cutting-edge technologies. Utilizing the advanced capabilities of Artificial Intelligence (AI) and Large Language Models (LLMs), specifically through the deployment of ChatGPT-4 and the Open AI Assistant API, Nibras introduces a novel approach to grading typed English writing assignments. These technologies are harnessed to analyze the structure, grammar, style, and content of student submissions, offering a sophisticated alternative to traditional grading methods. To test the efficacy of this innovative system, a pilot study was conducted with 17 handwritten assignments from students in the Department of English. These assignments were digitized into text files and processed through Nibras. The AI system evaluated each assignment, assigning grades based on predefined linguistic and syntactic criteria. The results were then compared with grades given by human instructors to measure consistency and accuracy. The findings from this trial indicated that Nibras could provide not only consistent and objective grading, but also detailed feedback tailored to individual student needs. This feedback mechanism is designed to identify specific areas of strength and weakness, thereby guiding students in their learning process. The variance in grading between Nibras and human instructors highlighted the AI’s potential to supplement traditional grading, offering a dual system where AI and human insights are combined for optimal educational outcomes. Furthermore, the use of ChatGPT-4 and the Open AI Assistant API ensures that Nibras remains at the cutting edge of technological advancements in education. These tools enable Nibras to learn from interactions, thereby continuously improving its grading algorithms based on real-world educational data. By integrating sophisticated AI technologies into its core functionality, Nibras not only addresses the immediate challenges faced by educators and learners but also sets a precedent for the future of digital education tools. This approach positions Nibras as a transformative solution, promising to reshape the landscape of English education by bridging the gap between traditional methodologies and modern technological innovations.
Project ID = SDP2324 CS F 28
Supervisor: Dr. Abdelkarim Erradi
Khadija Fakhrul-Huda, Dana Al-Jalham, Anfal Anfws, Noora Al-Mohammed
This project addresses the challenges faced by students at Qatar University in navigating the campus bus schedule, including class lateness, extended waits in hot weather, and uncertainty about bus arrival times. The proposed solution, a mobile application named "Rakoub," focuses on improving the static bus schedule by providing real-time tracking capabilities, trip planning, and crowd-counting features. "Rakoub" aims to create a user-friendly app that allows users to easily view and search for bus schedules and routes. The app will reduce users' waiting times by providing real-time bus location and estimated time of arrival, enabling efficient planning of journeys. Additionally, the implementation of crowd-counting techniques will allow users to estimate bus crowdedness for a safer and more comfortable commute. The project's objectives include designing a trip planning app for campus-level transportation, implementing an interface for accessing static bus routing and schedules, deploying real-time bus tracking, and introducing a crowd-counting technique. By achieving these objectives, the project aims to bring consistency to students' everyday bus journeys, reducing stress and improving the overall campus experience. Expected benefits and impacts of "Rakoub" include a reduction in time taken to reach destinations, increased safety through knowledge of bus crowdedness, decreased wait times in hot weather, and improved academic performance through effective time management. The project also aims to reduce in-campus traffic during peak hours by promoting dependency on buses over personal vehicles. Ultimately, "Rakoub" represents a unique and innovative approach to campus transportation by implementing a GPS system on a smaller scale, specifically tailored for Qatar University. The project strives to enhance transportation efficiency and convenience, offering a comprehensive solution to the challenges associated with static bus schedules on campus.
Project ID = SDP2324 CS F 29
Supervisor: Prof. Saeed Salem
Aisha AlHitmi, Reem Shehata, Aisha Alkhayarin, Dalal Nayem
Agriculture is heading towards a bright future full of modern technology, where machine learning can be innovatively integrated with agricultural science to achieve greater efficiency and sustainability in crop production. Agricultural crops are the basis of food and vital resources for humanity, so focusing on improving their cultivation is crucial. By leveraging technology and machine learning techniques. In this project, we seek to help individuals who want or are amateurs to carry out home gardening work by establishing small gardens or agricultural reserves inside the house. Our focus will be on the Solanaceae group such as (peppers, tomatoes, eggplants, etc.). Therefore, we have created a mobile application called "ZAD", which includes valuable information about agricultural crops that can be grown inside the home, providing users with simple and easy-to-use solutions to grow their agricultural products and take care of them to preserve them from diseases, as the application provides an immediate diagnosis of plant diseases and treatment recommendations through image classification model in computer vision which is a type of machine learning model that analyzes and categorizes images into predefined classes or categories based on their visual features. To start, the user starts scanning the plant to determine if the plant is diseased or healthy, if the plant is sick (identify the disease), instructions will be given on caring for the plant and how to prevent agricultural diseases and pests. Moreover, there is general information about this plant such as the scientific name and plant family. There is also valuable information about how to farm from zero, taking into place climatic conditions, the agricultural season, the type of plant, the sunlight it needs, the amount of water needed, and the type of agricultural soil.
Project ID = SDP2324 CS F 30
Supervisor: Prof. Khaled Bashir Shaban
Khaloud AlYafei, Noora Al-Emadi, Fajar AL-Mohannadi, Haya Al-mohannadi
In our senior project, we are developing a competition management solution. This solution enables the dynamic creation of events, rubrics, participants, and judges, providing flexibility in the evaluation process. We will create a web application to ensure accessibility from various platforms. The system will encompass multiple rubric forms containing information such as group number, evaluator name, score, weight, and the total score. Access to each form will be restricted to assigned examiners who may assess multiple competition groups and each group can be evaluated by multiple judges. This approach ensures a comprehensive and balanced review of each project, promoting fairness and accuracy in the evaluation process. When compared to the current traditional evaluation method, the suggested dynamic solution for the senior project greatly improves efficiency, accuracy, and user experience. This creative solution changes evaluation management by increasing efficiency, decreasing mistakes, providing user guidance, supporting multiple criteria evaluation, and involving the audience with real-time score displays. All these features ensure a more significant and effective event while keeping close oversight over the evaluation process. Dynamic describes something that is constantly changing, active, or moving forward. Being dynamic concerning an application or system refers to its ability to change, develop, or react in real-time or as required to different inputs, conditions, or requirements. Our system has real-time score calculation dynamically computing scores means evaluating immediately or responding to new or changing data. For example, the system provides real-time feedback by processing and updating ratings immediately as evaluators submit evaluations.
Project ID = SDP2324 CS F 31
Supervisor: Prof. Khaled Bashir Shaban
Aya Muhanad Mahmoud Hassan, Fatma Magdy Elsaid Elhusseiny Elnahas, Hissa Abdulla Al-Muhannadi, Salma Shady Mohamad Talaat Hashem El-Etreby
Cephalograms, X-ray images of the head, play a crucial role in aiding orthodontists in understanding patients’ conditions and facilitating accurate diagnoses. In the journey of a dentistry student, mastering the interpretation of cephalograms is essential. Furthermore, the diagnosis of patients through the analysis of facial, profile, and oral cavity images is an important part of the learning journey. This project aims to offer tools that assist instructors in teaching cephalometric analysis and provide students with the opportunity for practice assessments beyond traditional classroom settings. The project has three primary objectives: 1. Integration of the solution into a Learning Management System (LMS): The project focuses on seamless integration into an LMS, enabling instructors to leverage its functionalities, such as creating classes and automatically grading assessments. After assessing various options for integration with Blackboard and Moodle LMS, the decision to employ two Moodle plugins is detailed in the report. 2. Development of a Cephalogram Annotation Machine Learning (ML) Model: This tool empowers instructors to upload images and have them processed by an ML model. The capabilities of the ML model advance beyond other cephalometric analysis models by reducing the average distance error of the predicted points. The model's benefits are far-reaching as it uses a single network to identify each point and its label in one go. This reduces the computation requirements and allows more accurate predictions. The model returns annotations in the form of coordinate arrays to one of the Moodle plugins. 3. Creation of Diagnostic ML Models: The proposed solution extends beyond image annotation to include predefined diagnostic questions that are processed by several ML models. The models are uniquely developed convolutional neural networks, as their availability for diagnosis on facial images is limited. One of the technical advantages of this type of model, in comparison to other models, is its ability to detect even the most subtle changes in image characteristics. This feature is highly recommended for diagnosis as slight differences in the facial images need to be detected. The models connect by returning the answers to one of the Moodle plugins based on the patient’s images provided by the instructors. The tool’s overarching benefit lies in reducing instructors’ assessment preparation time, allowing them to allocate efforts to other essential tasks. In addition, automatic image annotation through an ML model mitigates the potential for human errors and overall significantly enhances assessment accuracy.
Project ID = SDP2324 CS F 32
Supervisor: Dr. Khaled Khan
Aisha Abdulaziz Al-Kaabi, Haya Turky Al-Subaey, Maryam Abdulla Al-Kuwari, Moudhi Mohammed Al-Zeyara
The idea of QThrift first came to us when we heard about students who were struggling to find academic materials that are required in their courses for several reasons, like not being able to afford those items due to unaffordable prices, and the university textbook section does not have enough items for all students. Therefore, we thought about creating a website to aid students and others to acquire academic materials. Our website is nonprofitable, which will allow users to sell or donate their used or new items to other users who need those items at affordable prices, which will also help reduce waste and help the environment. Moreover, to expand on our idea, we thought of adding more items like CDs, DVDs, research papers and other items. Our website has features like buying, selling, donating, and borrowing all in a one-stop service, a chatroom for users to communicate and discuss topics they are interested in. These allows users to give their feedback about their experience while using the Qthrift website. We can then consider that feedback to create a more accessible and user-friendly website and we also allowed users to give recommendations on which items they need and would like to get. In addition, a reward point system for users to gain points in order to get special deals and offers on their future purchases. In conclusion, our overall aim is to create a space for people to buy items that they need and desire for a much lower price and to reduce waste and help to improve the environment.
Project ID = SDP2324 CS F 33
Supervisor: Dr. Moutaz Saleh
Taqwa Ellabad, Khadija Khedr, Asma Bahabarah, Amani Mamiche
With the technological advances in education where access to knowledge is more vital than ever, educational revision website applications have risen to the forefront as essential tools. For education, consolidating and structuring knowledge has always been the essential bedrock for effective learning. However, the exhausting task of preparing revision content drives learners away, discouraging them from attempting to adopt this important step of the learning process. As well as the complexity of existing platforms that needs some sort of familiarity to not consume time, generally makes learners not attempt to rely on them for their revision. Therefore, to address this, our project studies the difficulties users face when using current revision platforms and aims to assist in modernizing the creation of revision material through AI integration into an accessible, and all-round platform. The focus of our project is on developing a user-friendly, scalable web application capable of handling vast amounts of data while ensuring accurate and helpful assistance. The platform seeks to maximize the benefits derived from active learning techniques, by offering students a user-friendly, comprehensive revision website equipped with text summarization, flashcards, question extraction, the Feynman technique, and mind-mapping functionalities that are AI-generated from uploaded personal notes of users with an interactive and engaging user interface. Therefore, providing personalized revision material according to the cognitive needs of learners and helping in reducing the complexities associated with existing platforms. Additionally, the project does not only seek to improve the individual learner’s experience but also supports educational institutes in their teaching methodologies and provides a safe professional environment to share and accept different revision materials. Finally, in this digital age, educational revision website applications stand as a symbol of progress, empowering students of all ages and backgrounds to achieve their educational goals with exceptional ease and effectiveness.
Project ID = SDP2324 CS F 34
Supervisor: Dr. Tamer Elsayed
Fatima Elzahra Mahgoub, Fatima Zakaria, Somaya Al-Saad, Haleema Shamil
Information charts are common in research. They represent and visualize data, However, accessing and understanding graph-based information is extremely difficult for visually impaired individuals. They often require external assistance, whether in the form of a person explaining it to them, which subjects them to the availability of sighted individuals, or a non-visual representation of the graph. Research has been done on producing template-based descriptions of graphs, but these often contain trivial, standard information such as mean, minimum and maximum values. Further research has been done on training models to understand the intent of the graph, not just the values, and generating summaries based on what the graph seems to imply, but this is limited to a few graph types. This project aims to build the first web-based application that allows users to be provided with a graph’s summary and data points and ask questions about the graph through searchable statistics, for basic bar, line, and pie graphs. This was done by integrating computer vision and image processing techniques with an open-source project, SeeChart. SeeChart is a web browser extension that takes in Scalable Vector Graphic (SVG) images and returns a graph summary, an interactive graph, as well as some other features. This project aims to extend this functionality to pixel-based images to empower visually impaired users by providing independent access to, and understanding of, graph-based information. Hence, promoting inclusivity and equal opportunities in education as well as allowing blind individuals to actively participate in data-driven environments.
Project ID = SDP2324 CS F 35
Supervisor: Prof. Rehab Mustafa Mohammad Duwairi
Sara Ahmed Babiker Ahmed, Shouq Abdulkareem M A Alyafei, Hour Tamer Ibrahim Elsayed Elbahnasawi, Nousseiba Boudaia
Recently, with the increasing number of chronically ill people, more awareness is being raised regarding informative dietary choices and healthy lifestyles. Chronic diseases are a serious problem and a long-lasting condition that needs continuous care and attention. Among the daily challenges faced by those with chronic conditions is the uncertainty about which food products are suitable for their health, leading to overwhelming and stressful situations. Advancements in technology and the introduction of new tools have facilitated the maintenance and monitoring of good health. Our app, Moyeen, offers a solution to the dilemma of choosing suitable food products. Users can simply take a picture of the barcode, nutrition facts label, or manually input the required data, simplifying the process of making informed dietary decision. Subsequently, Moyeen determines the suitability of products based on the user's dietary restrictions to ensure they are compatible with the user's health condition. If a scanned product is not suitable, Moyeen suggests alternative products. The application also delivers daily insights to enhance users' awareness of dietary choices. Furthermore, it contains disease articles, providing guidance on managing and coping with the user's specific health condition.
Project ID = SDP2324 CS F 36
Supervisor: Prof. Cagatay Catal
Yosra Elshayeb, Zena Dhailieh, Marah Alsagheer, Sara Ali
The goal of this project is to address the pressing need for a mental health support system tailored for college students. In today's academic environment, students often face challenges with different mental health problems such as depression, anxiety, and high levels of academic stress. The desired methodology is developing a web application to help college students share and address mental health struggles in a secure environment. With personalized features and background videos, students can improve their academic experiences and happiness. A mental health podcast raises awareness and offers support to students navigating mental health challenges. One crucial feature of our platform is the inclusion of a journal, which allows students to openly express their experiences, emotions, and sentiments in a private space. This function in journaling promotes self-reflection and self-expression, enabling students to examine and analyze their thoughts in a safe and accepting setting. To provide instant help, our platform offers 'Relief AI-Chatbot'. This chatbot utilizes a pre-existing large language model designed for providing mental health assistance. Moreover, a feature promoting student engagement in controlled breathing exercises is implemented to help with anxiety management and reducing low blood pressure for better overall health. Furthermore, we have added a functionality for students to observe and keep track of their mood shifts over time. Finally, students can book appointments with a mental health counselor to receive immediate support on how to address their mental health concerns.
Project ID = SDP2324 CS F 37
Supervisor: Prof. Rehab Duwairi
Noora Al-Thani, Noor E.Ahmednooh, Noor Neama, Amina Al-Mukhani
Thanks to technology, Hamad Medical Center has revolutionized patient access to various services, such as upcoming appointments and test results. However, private health centers do not always have the same level of comfort. Each private facility has its own database, which requires patients to create a new profile each time they visit another facility. This process is not only time consuming but also annoying. Patients often have to physically visit or call a clinic to make an appointment, which can be difficult for those who don't have money, time or phone anxiety. Our application aims to simplify these processes and improve healthcare patients who visit private clinics. Through our platform, patients can instantly book appointments at various centers, rate doctors and hospitals, and much more. By consolidating information into a single database, our application facilitates communication between private hospitals and patients.
Project ID = SDP2324 CS F 38
Supervisor: Dr. Khaled Khan
Fatima Al-Sada, Leen koree, Asra Marzughi, Noor Al-Sadi
In response to the limited digitization of hospital services in Qatar, our project goal is to address the pressing need for a comprehensive hospital app. The current reliance on manual and outdated systems for appointment scheduling, medical record management, prescription processes, and patient communication leads to inefficiencies such as long administrative processes and reduced accessibility to healthcare services. Despite the existence of comprehensive hospital apps globally, none specifically serve Qatar's healthcare needs . Our project's objective is to implement a comprehensive, user-friendly digitization app within the healthcare sector that can be integrated into any hospital system, whether it has one or multiple branches. By achieving our objective, we aim to enhance the overall patient experience, streamline operations, and improve accessibility to healthcare services. The proposed mobile application automates healthcare services, such as appointments, and lab tests, eliminating the need for third-party intermediaries in healthcare processes. The key functionalities of the app include a secure registration process, a listing of clinics, doctors, and schedules, and an appointment management system that allows patients the flexibility in scheduling, canceling, and viewing their appointments. The application employs a notification system, lab tests, and prescription refills. Furthermore, the application provides a medical history with detailed insights into past appointments and tests, facilitating healthcare journey tracking. Another feature is the Health Vital Monitor, which allows patients to input and monitor their vital health data, to encourage active engagement in healthcare and to provide healthcare workers with valuable information for better patient health management. In conclusion, our project not only addresses the need for a comprehensive hospital app in Qatar but also sets the stage for a transformative shift towards a modern healthcare ecosystem. By aligning with digitization trends observed in other sectors, our initiative seeks to create lasting improvements in both patient experience and healthcare service delivery. The successful implementation of this app is anticipated to contribute to the creation of a modern healthcare landscape in Qatar. Despite challenges, our commitment to robust solutions ensures the app's success, enhancing the overall quality and accessibility of healthcare services in the country. Our goal is to improve healthcare practices and make healthcare in Qatar more efficient and patient focused.
Project ID = SDP2324 CE M 39
Supervisor: Prof. Amr Mohamed
Aiman Saad Baig, Mohammed Ibrahim, Mohammad Hilou, Albaraa Aloush
The field of medical diagnostics is witnessing a paradigm shift with the introduction of advanced robotic systems. Our project embodies this shift. The current traditional methods involve the need for professional endoscopists taking long durations to navigate through the Esophagus and the unsatisfactory range of view provided. Our project aims to enhance the precision and safety of gastrointestinal diagnostics. It integrates robotics, machine learning, and medical imaging to innovate endoscopic procedures. The project's goal is to develop an endoscope with a flexible, navigable tip. This tip is designed for autonomous navigation through the Esophagus to the stomach. Such autonomy is essential to reduce patient discomfort and increase diagnostic accuracy. The tip, equipped with two degrees of freedom, captures live images. These images are relayed in real-time to Microsoft HoloLens VR headset worn by the physician. This setup ensures continuous monitoring and control during the procedure. A key feature of this project is the use of machine learning for esophageal center detection. This detection guides the endoscope along a safe path. The integration of AI in this context is a notable advancement in medical robotics. It demonstrates the potential of machine learning in medical applications. The endoscope's tip movements are controlled by Maxon ECX SP13 motors, operated via EPOS4 controllers. The coordination between the AI system and these motors is critical. It ensures precise navigation and alignment of the endoscope within the Esophagus. This coordination is crucial for the safety and effectiveness of the procedure. This report focuses on the project's initial phase, the proof of concept for the center detection and motor control integration. The testing of this integration, using live video feedback, has shown promising results. These results validate the feasibility of the proposed system and its potential in autonomous medical navigation. The full implementation of this system has significant implications for medical diagnostics. It not only aims to improve gastrointestinal endoscopies but also sets a precedent for AI and robotics in healthcare. The Advanced Navigational Endoscopy System is a pioneering project. It marks a significant step towards technology-driven, patient-centric medical care.
Project ID = SDP2324 CE M 40
Supervisor: Dr. Mahmoud Barhamgi
Omar Ali, Mohammed Darwish, Tamim Diab, Ali- Al marzouqi
Aquaponics is a sustainable combination of aquaculture and hydroponics, it presents a promising solution to modern agricultural challenges. This integrated system connects the mutual relationship between fish and plants, where fish waste provides essential nutrients for plant growth, and plants help purify the water, making it safe for the fish. While the benefits of aquaponics are great, maintaining the balance between fish and plant needs is complex. This project addresses this challenge by introducing an automated, sensor-driven aquaponic system managed by a microcontroller. The primary objective of this project is to design and implement an intelligent system capable of real-time monitoring and active response to ensure the optimal growth environment for both fish and plants. By using sensors, the system continuously monitors parameters such as pH level, water temperature, dissolved oxygen, water level, electrical conductivity, and the concentrations of ammonia, nitrite, and nitrate. The microcontroller processes this data and based on pre-defined parameters, actuates pumps, heaters, and solenoid valves to maintain the desired system conditions. Furthermore, data logging and analysis features have been combined. This not only aids in the optimization of the system over time but also enables more functionalities, potentially predicting system limitations before they occur. A user-friendly dashboard is also planned, allowing users even with minimal technical knowledge to monitor, interact with and understand the health and status of the aquaponic system. In conclusion, this project enhances the sustainability and efficiency of aquaponic systems by employing automation. It simplifies the complicated process of system maintenance and ensures the wellbeing of both fish and plants, and provides a clear understanding for future sustainable agricultural activities.
Project ID = SDP2324 CE M 41
Supervisor: Prof. Qutaibah Malluhi
Abdullah Mahran, Abdulrahman Marwan Aboumadi, Waleed Marwan Hamada, Ghassan Bankasli
Our senior project develops a novel system designed to prevent cheating during exams by employing a blend of hardware and software components. Central to this system is a microcontroller-based keylogger integrated with a server. The keylogger captures students' keystrokes during exams, temporarily storing them before transmission to the server based on either a predefined time interval or word count. Once on the server, the captured keystrokes undergo analysis using a predefined list of cheating keywords, including commonly cheating-associated phrases like "ChatGPT," "how to solve," "Chegg," and "solution of." Additionally, the system incorporates typing pattern authentication to counter impersonation attempts, gathering details about users' typing habits, such as button press, and timing of button presses and releases. This data fuels the training of a machine learning model during student account configuration, enabling storing students’ typing pattern in a model to use it in exams to distinguish genuine users from impostors attempting to impersonate and solve an exam that is not theirs. Furthermore, the system provides an observer interface, allowing administrators to monitor students' statuses during exams, with real-time alerts to notify them of cheating behavior. These alerts make it easy to quickly respond and classify student situations, such as "connected," "engaged in cheating behavior," or "impersonation trial," simplify monitoring and management processes. Overall, this innovative solution provides educational institutions and test centers with a robust tool to preserve academic integrity, increasing their reliability and promoting the production of quality graduates, which ultimately benefits society.
Project ID = SDP2324 CE M 43
Supervisor: Dr. Noora Fetais
Aly Okasha, Mohammad Rayyan, Ibrahim koubeisi
Sport holds paramount importance as a holistic contributor to health, establishing a symbiotic relationship between physical activity and overall well-being. Marathons, one of the most famous sports, allow people to participate easily, featuring numerous events worldwide that attract participants at different skill levels. However, organizing marathons poses various challenges, especially concerning participant safety and data collection. In our project, we aim to address these challenges by focusing on solutions tailored for marathon organizers. Our chosen application scenario is a marathon event organized by Qatar University where we tested the product. The primary objective is to design a product capable of collecting diverse information, such as position of each participant, fainting state, cheating state, speed, and finish time. Additionally, the product will track the route of players and include features to detect signs of distress, like a potential collapse, by using a sensor that measures the players’ movements and records both acceleration and gyroscopic data. Moreover, a GPS chip will be used to ensure that players do not take shortcuts. This is achieved by identifying the exact zone of the marathon track, and the player will be detected when there is an attempt to cheat. The collected data for each player will be transmitted to a google firebase, facilitating comprehensive analysis to evaluate individual performances. This holistic approach aims to enhance the overall marathon experience, making participation more enjoyable and ensuring the well-being of the participants.
Project ID = SDP2324 CE M 44
Supervisor: Dr. Mohamed Hashim M A Al-Meer
Ezeddin Mohamad Naji Ezeddin, Manaf Ahmed Abduljabbar, Obada Mhd Bashar Alhomsi, Mahmoud Ammar Barodi
The State-of-Health (SoH) of lithium-ion batteries (LIBs) has gained significant attention in recent years, especially in the domains of electric vehicles (EVs), portable electronics, and grid storage systems. SoH is a critical parameter that describes the overall health and functionality of a battery and is a key determinant in predicting its life expectancy and performance. Factors influencing SoH include cycle life, depth of discharge, temperature effects, and internal resistances, among others. This project provides a comprehensive overview of the SoH assessment methodologies for LIBs. The commonly employed techniques for estimating SoH, such as counting the electric charge and checking the voltage. There are also some more complex methods for instance a method called "impedance spectroscopy," which measures how hard it is for electrons to move through the battery. Furthermore, recent advancements in the integration of artificial intelligence and machine learning techniques for predictive SoH modeling are highlighted. By understanding and accurately predicting SoH, stakeholders can optimize battery management systems, ensure the safe operation of batteries, and enhance their lifespan and efficiency.
Project ID = SDP2324 CE M 45
Supervisor: Prof. Junaid Qadir
Deya Aldeen Abdelbaset, Emran Al-Horani, Jamil Sabbagh, Khaled Ahmed Alsuwaidi
The Faheem AI system ushers in a new era for fitness, blending Virtual Reality with Artificial Intelligence (AI) to craft a customizable, mixed-reality training experience. With Meta's Oculus glasses and Unity's VR integration. Faheem AI's core includes Machine Learning (ML) algorithms and a specialized Large Language Model (LLM), which together offer real-time, precise feedback on form and performance to mitigate injury risk and optimize exercise efficacy. Beyond fitness instruction, Faheem AI's LLM serves as a comprehensive health advisor, engaging users in discussions about well-being and nutrition, providing a healthier life choice. Faheem AI's adaptability caters to diverse fitness preferences, supporting personalized and safe workouts. The integration of AI facilitates an interactive, educational exercise session, where users receive feedback and actively learn the nuances of proper form and safety. Positioned at the intersection of technology and physical training, Faheem AI represents the future of personal fitness—intelligent, responsive, and tailored to individual needs. It exemplifies the convergence of technology with human expertise, promising a superior, engaging training experience. Our project envisions expanding this technology across different sports disciplines, leveraging VR to enhance coaching and contribute to sports science and human-computer interaction. Faheem AI is a transformation of the fitness landscape, setting the stage for a smarter, safer, and more interactive approach to personal health and sports training.
Project ID = SDP2324 CE M 46
Supervisor: Prof. Uvais Qidwai
Mubarak Al-Hajri, Khalifa Al-Malki, Ahmet Dia Alkhattaf, Ahmed Miqdad
The ICU Assistant project introduces an advanced robotic system designed to enhance patient care in Intensive Care Units (ICUs) while addressing the high risk of infection transmission—a challenge exacerbated by frequent physical interactions between healthcare professionals and medical devices, not directly with patients. This innovative robotic assistant mitigates infection risks by minimizing direct human contact, and crucially, it employs ultraviolet laser cleaning for rapid, eco-friendly sanitation. This approach aligns with the primary goals of enhancing safety and reducing operational costs by avoiding direct patient interaction. Equipped with cutting-edge robotics including stepper motors, Arduino controllers, servo motors, and high-definition cameras, the ICU Assistant functions as a practical extension of medical staff. It enables doctors to remotely monitor patient vital signs and interact with ICU equipment through a specialized arm capable of pushing buttons and turning knobs, thereby maintaining high standards of patient care without requiring physical entry into the ICU. Operated through a comprehensive control setup featuring a joystick, screen, and onboard computing, the robot efficiently executes a variety of routine tasks with enhanced mobility and operational range due to its transition from AC to battery power. This design not only supports the ICU Assistant's adaptability within dynamic healthcare environments but also underscores its cost-effectiveness by reducing the need for multiple devices and personnel. The system’s integration into existing healthcare frameworks has been meticulously crafted to ensure it enhances patient care without disrupting established routines. Specific feedback from healthcare professionals during trials has demonstrated successful integration and acceptance, validating its practical utility and ease of adoption. Risk assessments focusing on patient safety and the prevention of cross-contamination have been pivotal, with the ICU Assistant adhering to stringent healthcare standards for hygiene and infection control. In conclusion, the ICU Assistant represents a significant advancement in healthcare technology, addressing crucial challenges in ICU care by reducing infection risks and operational costs. Its deployment highlights the robot's role in promoting safer, more efficient ICU environments, setting a new benchmark for future technological developments in the sector with its innovative non-patient-direct interactions and device manipulation capabilities.
Project ID = SDP2324 CE M 47
Supervisor: Prof. Uvais Qidwai, Co-supervisor: Dr. Ibrahim Rony
Lance Eric Ruben, Khalid Suleman, Mohammad Saleh Farooqui, Ali Kamel Al-Kapti
Project ID = SDP2324 CE M 48
Supervisor: Dr. Mohamed Al-Meer
Mostafa Abdelhamid, Mohammed Alhato, Qais Bakleezi, Saoud Al-Rumaihi
Identifying emotions, thoughts, and social cues through facial expressions is a vital aspect of human communication. In the fields of psychology, artificial intelligence, and computer interfaces, accurately recognizing these expressions is critical. Deep learning, a computer-centered learning method, has made substantial strides in this field. This article explores the significance of facial expressions, the correlation between deep learning and it, and the benefits of utilizing deep learning models to decipher emotions and expressions on faces, all to ensure optimal outcomes. Creating robust systems capable of detecting emotional expressions on human faces has been revolutionized by deep learning. Convolutional neural networks (CNNs) are employed in this process. In our bid to enhance our understanding of facial emotions, we propose a novel approach for utilizing CNNs. With a systematic experimentation of different techniques, our research has led to significant improvements in the recognition rate of facial emotions. Rigorously scrutinizing our outcomes further reinforced this discovery. Understanding how people feel about different things, keeping an eye on mental health, and improving communication between computers and people are just some of the areas that can benefit from the advancements we have made. By enhancing computers' ability to recognize facial expressions, we are continuing to assist in the integration of technology into society.
Project ID = SDP2324 CE M 49
Supervisor: Dr. Ahmed Badawy
Ali Daifalla, Rashid Alyazeedi, Taleb Alkorbi, Saleh Huwail
In the dynamic field of agricultural technology, the introduction of an engineered solution for palm tree disease detection using drone and AI technology marks a significant advancement. This project, rooted in the rich cultural and economic context of Qatar's palm tree industry, showcases an innovative approach to addressing the challenge of maintaining the health and productivity of these valuable trees. The project's cornerstone is the development and integration of a Convolutional Neural Network (CNN) model, expertly designed and trained to accurately identify signs of disease from aerial imagery. The CNN's deployment on a Raspberry Pi 4, coupled with the Coex Clover 4.2 drone, has successfully demonstrated real-time disease detection and automated treatment dispensing. The aim of this fusion of technology is to exhibited high precision in identifying disease symptoms and to offer a swift response mechanism for treatment, revolutionizing traditional agricultural practices. From an academic perspective, the project has been a profound learning experience, amalgamating knowledge from various domains such as machine learning, embedded system design, and IoT integration. The hands-on application of theoretical concepts to a practical challenge has enriched the team's expertise and prepared the ground for future endeavors in smart agriculture. The rigorous testing and evaluation phase has validated the system's efficacy, with the CNN model achieving impressive accuracy and reliability in disease identification. The high confidence levels in predictions affirm the model's readiness for real-world application, while the insights gained from misclassifications present opportunities for further refinement. In conclusion, the project stands as a testament to the potential of integrating cutting-edge technology into agriculture, aiming to boost efficiency, enhance sustainability, and secure food production. It paves the way for future innovations in agricultural practices, setting a benchmark for the digital transformation of the industry. With its potential for scalability and adaptability, this UAV-based disease detection system promises to be a valuable asset in the quest for agricultural self-sufficiency and environmental conservation.
Project ID = SDP2324 CE-CS M 50
Supervisor: Prof. Uvais Qidwai
Amro Moursi, Malek Hamad, Mohamed Taher, Hamad Alansi
This project focuses on the development and implementation of an advanced plant health monitoring system. The primary goal is to create a comprehensive system capable of assessing and monitoring the health of plants through the integration of various components. Our approach involves addressing the critical environmental factors essential for preserving plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, PH, water levels, humidity, as well as the presence of essential nutrients like Nitrogen, Phosphorus, and Potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. This camera emits IR light using an integrated IR sensor that serves to enhance visibility in complete darkness by illuminating the scene with infrared light, allowing the camera to capture clear images even when there is no visible light present. The captured data is then compared against a reference database containing different health status. This comparative analysis is implemented using AI deep learning model which enables us to generate accurate assessments of plant health status. By leveraging this data-driven approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.
Project ID = SDP2324 CE-CS M 51
Supervisor: Dr. Mahmoud Barhamgi
Abdullah Qassim Al-Bukhari, Ahmad Fahad Al-Ahmad, Ibrahim Khalid Al-Mohanadi, Mohamed Ali Abdelhalim Ali
In an age where digital data transfer is universal, ensuring the security of sensitive information is critical. Our "Secure Data Transfer Device" proposes a novel solution to the persistent challenge of data vulnerability during transmission and storage. This device not only secures digital communication between parties but is also designed with the foresight to integrate with sensor systems for automated data acquisition, significantly extending its applicability on the Internet of Things (IoT) domain. The project delivers a standalone hardware solution built upon the Raspberry Pi 4, featuring an intuitive user interface for straightforward operation. It empowers users to perform robust encryption, employing AES 256-bit for group-oriented symmetric encryption and RSA for individual-targeted asymmetric encryption. This flexible approach caters to various security needs, ensuring that sensitive data remains confidential and intact, whether in transit or at rest. The device's development phase saw the creation of encryption and decryption algorithms in Python, which were seamlessly integrated to uphold our envisioned design. With the hardware successfully deployed, the device now stands as a testament to secure communication, with continuous updates ensuring resilience against emerging security threats. Envisioned future work aims to broaden the device's capabilities by enabling direct sensor data input. This will allow real-time, secure collection of environmental or biometric data, making the device a versatile ally in IoT security. The anticipated enhancements include advanced encryption for sensor data, energy optimization for sustainability, and cloud integration for remote accessibility, promising a new horizon in data security where sensitive information is shielded from the source to the recipient.
Project ID = SDP2324 CS M 53
Supervisor: Dr. Moutaz Saleh
Ahmed Ashraf Abdou, Husam Nibal Snober, Mohammed Wafik Saqallah, Walid Ben Ali
In this digital age, we rely on passwords to protect our online accounts and personal information on the web. However, with so many passwords to memorize, many suffer from password fatigue and easily fall into the trap of reusing weak passwords across different sites and writing them down in insecure places. This is a serious security risk, as a single compromised password can give attackers access to many other accounts. To address this issue, we introduce Vaultexe, an open-source self-hosted zero-knowledge password manager that addresses the problem of password fatigue by providing users with a secure and convenient way to manage their secrets. Vaultexe requires users to memorize only a single, strong master password to access their vault. All sensitive information is encrypted client-side with a cryptographic derivation of the master key before being sent to the servers via TLS connection. This means that Vaultexe never sees the user's master password and has zero knowledge of any of their online credentials, making it safe and reliable even if Vaultexe servers were compromised. Vaultexe offers a variety of features to make password management easy and secure, including automatic generation of high entropy passwords for new websites, auto-filling of credentials when logging into websites, tracking of all user accounts for each platform, secure storage of secret notes and textual data, and a unique soft deletion scheme to allow recovery of accidentally deleted secrets within two weeks of the incident. While current state-of-the-art solutions allow users to export vaults in plaintext which may be the greatest source of credential leaks that today's password managers suffer from, Vaultexe allows exporting user vaults into passwords which is the first of its kind in the password manager ecosystem and is expected to pick up a lot of attention on release. As a cloud solution, Vaultexe auto syncs users’ vaults across all of their registered devices and platforms in real-time giving users a consistent and reliable experience. Vaultexe also offers a unique self-hosting feature that allows users to deploy the entire cloud architecture on their own servers behind their own firewalls using our own UNIX based CLI tool in no time. This makes Vaultexe ideal for users who need full control over their password manager or who need to comply with their industry regulations that may require data to be kept on premises.
Project ID = SDP2324 CS M 54
Supervisor: Dr. Mucahid Kutlu
Mohammad Hassan, Hassan Adil Khan, Nafin Mahmoud, Ifran Ahmed Rafi
The world is in a phase of major digital transformation which has led people from different parts of the world to connect easily with each other, regardless of race, gender, identity, and such other factors. This digitization indeed is a positive step but has come forward with a lot of cons amongst which a major one is Online Hate Speech. This report highlights the problems associated with Online Hate Speech in today’s era and discusses a solution in detail to combat the crisis caused by it. Additionally, in order for the solution to prevail in the global market, business analysis as well as feasible models have been outlined. The Solution focuses on a Chrome Extension that works as a guide, continuously monitoring text inputs across multiple websites in real-time. During execution, the extension tests inputs from users in real time by assessing it with the help of an advanced AI model trained using hate speech datasets and uses a server for quicker processing. This AI model quickly analyses the text and reports its results to the extension which causes a prompt notification to appear on the user’s screen if hateful speech is diagnosed. The goal is to create a smart AI tool that promotes positive online interactions while protecting user privacy, so that the AI model only tests essential text data for analysis and does not store any user-specific information. In essence, The Google Chrome Extension solution aims to contribute to a more welcoming and respectful digital environment by using AI to prevent the spread of Online Hate Speech.
Project ID = SDP2324 CS M 55
Supervisor: Prof. Saeed Salem
Ahmed Aledamat, Ziad Abdelazim, Taleb Watan, Abd Errahmane Maham
Nowadays, in a world that is progressively more connected, the demand for adaptable and effective event-sharing platforms has become greater than ever before. This abstract introduces "Qvent," a smart phone application shaped to transform the way individuals and groups share and post events. The project aims to tackle the limitations of existing event-sharing platforms by supplying an easy-to-use, versatile, and feature-packed solution. Currently, event-sharing platforms often lack user-friendliness as well as flexibility which cater to wide-ranging user requirements., some apps that is used to share events lack some features like allowing individuals to share the event they want, volunteering in a particular event and some other features that will be explained in the report. The app offers flexibility by empowering users with the ability to personalize event particulars including date, time, location along with event category “app name” goal is to bridge this gap by offering an all-inclusive solution to these problems and would surely solve make sharing events simpler to everyone in the community.
Project ID = SDP2324 CS M 56
Supervisor: Dr. Mohammad Saleh
Muhammad Mahdiyan, Ridhwan Athaullah, Mohamad Allaham
As the number of road accidents increases, understanding their causes is crucial. Human factors like speeding or reckless driving, alongside vehicle defects have significant impact on the causes of accidents. The combination of these factors can lead to serious consequences. Vehicles are complex machines with hundreds of parts working together to operate in harmony. Dur to this complex network, malfunctions are quite common. Thus, drivers must always have routine checks on their vehicles to thorough inspections by mechanics. Vehicle maintenance is important for everyone's safety on the road. With tires being the connection point between the vehicle and the road, well-maintained tires are essential for handling, braking, and overall stability of the vehicle. Neglecting tire care, such as using old or worn-out tires, significantly increases the risk of accidents. In contrast, regular tire checks and proper replacements considerably reduce the likelihood of accidents caused by mechanical failure. However, many drivers overlook tire maintenance due to unawareness, time constraint, or underestimating risks. The proposed solution to this problem is an effective and user-friendly application that can be integrated into the users' lifestyles, ensuring constant awareness of tire checkups, and establishing a tire care routine. TireWise features a classification model analyzing tire images, informing users if tire replacement is necessary, and sending periodic reminders for monthly tire checkups. The application allows users to set customizable reminders and reminders for tire replacements or routine checks. The application provides an organized interface that enables users to review their tire image and condition history as "Tire Scans". It also organizes these results as vehicles with each vehicle having a set of tire scans. The project aims to simplify tire inspection and automate the process within a handheld mobile device, ensuring constant awareness and providing a reliable platform for managing tire care. Offering to the users a comprehensive view of their vehicles and tire classification results, while assisting in informed decisions about tire maintenance.
Project ID = SDP2324 CS M 57
Supervisor: Dr. Mohammad Saleh
Youssef Sherif Aly, Essa Ahmed Kamel Abou Jabal, Khalifa Ebrahim Yousuf, Mohamed Dhia Abdaoui
In today’s digital age, the significance of a password cannot be underestimated, as it is the key that grants users’ authority over their online accounts. Regrettably, a substantial number of individuals have fallen prey to data breaches due to the widespread reliance on weak, default, or repeatedly used passwords. This unfortunate consequence can be traced back to a simple rationale that predictable passwords and password reuse make it easier to remember and convenient for users who might be unaware of the security risks hidden behind that convenience. This vulnerability becomes especially apparent when a single breach in one service jeopardizes multiple other accounts. The prevalence of weak and recycled passwords poses a significant threat to online security. This vulnerability is further compounded by the practice of reusing passwords across multiple accounts, a single breach exposing a multitude of sensitive information. This is where our password manager application comes in, aimed with the sole purpose of addressing the pervasive issues associated with weak, recycled, and default passwords. Our goal is to provide users with a seamless and secure experience that encourages them to protect their privacy and secure their accounts across all the services they use. Our password manager aims to fill in the gap between security and user convenience. Additionally, our application securely stores sensitive documents, providing users with a centralized repository for their important files. This eliminates the need for scattered storage locations and enhances accessibility. With our application at their disposal, users can confidently navigate the landscape of the digital realm, secure in the knowledge that their accounts and sensitive data remain well-guarded. PassGuard offers a robust set of security features, including master password protection where users only need to remember one master password to access all their stored credentials, ensuring convenience without compromising security. Industry-Standard Encryption where Passwords and documents are encrypted using industry-standard encryption techniques, safeguarding sensitive data from unauthorized access. Local storage ensures that all passwords and documents are stored locally on the user's device, giving them complete control over their stored credentials. Furthermore, users can easily export and import their credentials, ensuring backup and portability with other devices. By providing an intuitive interface, secure password and document storage, and the capacity to generate robust passwords, we aim to provide users with a tool that elevates their security without sacrificing the ease and comfort they seek.
Project ID = SDP2324 CS M 58
Supervisor: Dr. Armstrong Nhlabatsi, Dr. Jurlind Budurushi
Khalid Abdullah, Farhan Al Sadi, Hosam Zarouk, Abdelwahab Al Masri
Elections are procedures which involve decision-making based on a group’s majority opinion, which is expressed based on a final tally/sum of every participant’s/voter’s vote. Although elections come in many different contexts, the need to preserve the privacy of every voter’s opinion or choice remains essential across the board. In addition, while the recent emergence of internet voting over traditional paper-based and Direct Recording Electronic (DRE) schemes sounds promising, it suffers countless drawbacks, such as a rising concern in preserving accurate voter identification and privacy, as well as ensuring voters’ ability to cast or verify their votes in a smooth and understandable process. Many of the internet voting systems that have been established relatively recently still suffer certain aspects of these drawbacks, and therefore, contributing to the internet voting community by enhancing existing systems is an essential component in aiding them to reach the same level of security and usability as paper-based and DRE voting schemes. Helios is an end-to-end verifiable open-source internet voting system founded in 2008 which suffers many of the same problems that other internet voting systems experience. Specifically, it faces various deficiencies from both security and usability aspects. To specify, its main security flaws include the use of a single factor authentication process that is easily susceptible to impersonation attacks, in addition to the display of a bulletin board which violates voter participation privacy as it directly links voters’ identities with their respective ballot trackers. On the other hand, deficiencies in usability include a complex vote verification process that is difficult to comprehend by average non-technical users as it involves copying and pasting large pieces of cryptographic texts, as well as a relatively poor user interface that encompasses a range of complex technical terms. Given these deficiencies, as well as the fact that Helios is an open-source and scalable internet voting system in the field, our project aims to bridge the gap that keeps this system from advancing and becoming more popular towards larger groups of audiences who are interested in conducting easy and secure internet voting elections around the world. That is why we aimed to improve Helios from four main aspects which encompass both security and usability enhancements. From a security perspective, we integrated biometric authentication into Helios’s current login process, as well as modified the bulletin board to disassociate voters’ identities from their ballot trackers and therefore achieve higher voter participation privacy. Meanwhile, from the aspect of usability, we improved Helios’s overall user interface, as well as incorporated a QR code mechanism that allows voters to verify their votes much more easily.
Project ID = SDP2324 CS M 59
Supervisor: Dr. Noora Fetais
Abdelaziz Jama, Ammar Elkhatib, Mohammed Salah, Omar Shalaby
In the era of Qatar's rapid technological advancement and the continual proliferation of new real estate websites and properties, the process of finding suitable housing has become both dynamic and challenging. In response, we present the Qatar Real Estate Web Scraping Search Engine, a groundbreaking initiative designed to efficiently extract and organize property data from the myriad of platforms. This project serves as a crucial link between clients and the ever-growing landscape of real estate listings, streamlining the property search process, reducing effort, and saving time. Our focus is not merely to add to the existing array of real estate websites but to redefine the user experience through immediate data presentation and user-friendly features. As Qatar propels forward in technological progress, our forward-looking approach aims to make real estate engagement more accessible and convenient in the digital age.
Project ID = SDP2324 CS M 60
Supervisor: Dr. Armstrong Nhlabatsi
Mahmoud Ismail, Mohamed Hammad, Mohammad Mushahidurrahman, Ibrahim Tahhan
The perennial challenge of managing student attendance in educational institutions necessitates a transformative shift from traditional manual systems towards an advanced, technology-driven solution. This project not only addresses the inherent vulnerabilities and limitations of manual attendance systems but also strives to significantly improve accuracy, efficiency, and user-friendliness. The primary objectives include eliminating inaccurate attendance records to a high percentage, thereby enhancing the integrity of the educational institution. Achieving this involves mitigating human errors in old manual systems to less than 5%, ensuring accurate recording and reporting of student attendance without errors or discrepancies. Efficiency improvements are pursued through the streamlining and simplification of the attendance process, aiming for not more than three user interactions. The integration of technology-driven solutions and efficient procedures aims to save time, with a target of not more than one second for attendance registration. Additionally, the development of a faster mobile application for the automated attendance system enhances overall efficiency. The project adopts advanced technologies, such as biometric authentication and geo-fencing, to address issues like code sharing in previous attendance systems. The conversion of the attendance and absence registration system into an electronic, automated format represents a contemporary approach aligned with modern educational needs. Emphasizing a highly user-friendly aspect, the system strives to be accessible to all students, including those with disabilities or diverse cultural backgrounds. Automated notifications keep students and educators informed about the status, attendance discrepancies, and other relevant information. Seamless integration with existing school databases, student information systems, and other software ensures a cohesive educational environment. Customization options for attendance policies and settings cater to different courses, programs, and institutional requirements, while the fair and effective enforcement of compulsory attendance policies ensures accountability. The proposed project has achieved a 100% in showcasing the specific accomplishments across different user cases (students, teachers, and administrators). This progress underscores the commitment to practical implementation, validating the efficacy of the developed system in real-world scenarios. While the shortcomings and challenges persist, ongoing efforts to address these issues demonstrate a dedication to continuous improvement and excellence in attendance management within educational institutions.
Project ID = SDP2324 CS M 61
Supervisor: Dr. Osama Halabi
Salem Al-Ansari, Mohammed Al-Obaidly, Murshed Al-Muhannadi, Mouadh Ben Abderrahim
In today's digital age, reliance on electronic devices for entertainment is increasing. To exploit this trend, our project leverages Virtual Reality (VR), a medium experiencing rapid growth in popularity and technological development, to create experiences that do more than entertain. Our objective is to enhance cognitive, motor, and social skills through gamified VR experiences that are both engaging and educational. To achieve this, we have adopted an interdisciplinary methodology combining elements of cognitive psychology, game design, and software engineering. This approach involves the gamification of proven cognitive and physical exercises, transforming them into interactive VR games that are intuitive and appealing. We developed three VR games: an escape room for problem-solving skills, a block-building game for creativity, and a block-cutting game using sabers to promote physical activity. Our evaluation strategy consists of user testing sessions to measure the effectiveness of each game in enhancing the targeted skills. We utilized a mixed-methods approach, gathering quantitative data on user performance and qualitative feedback to assess user engagement and learning outcomes. The results to date have been promising, showing significant improvement in users' cognitive and physical skills as measured by pre- and post-test comparisons. The insights gained underscore the critical importance of understanding the educational content being gamified, ensuring that the VR games not only entertain but also achieve their educational purposes effectively. This project demonstrates the potential of VR to transform traditional educational methods, offering a compelling, immersive way to develop crucial life skills in a digital age.
Project ID = SDP2324 CS M 62
Supervisor: Dr. Mucahid Kutlu
Mohammed Al-Eida, Salman Taher, Mohammed Al-Qeraisi
The Picture Exchange Communication System (PECS) has emerged as a valuable tool for facilitating non-verbal communication, particularly among autistic children. In Qatar, the use of PECS has gained prominence; however, current systems do not cater to the Qatari dialect or adequately represent the local culture within the symbols. To address this challenge, our proposed app aims to enhance communication for autistic children in Qatar. The app will feature a dynamically generated voice in the Qatari dialect, providing a more culturally relevant and engaging communication experience. Additionally, the app will introduce symbols that specifically represent elements of the Qatari culture, fostering a deeper connection between individuals and promoting inclusivity. Through these enhancements, our app seeks to bridge communication gaps and contribute to a more inclusive environment for non-verbal individuals in Qatar. We successfully developed a Picture Exchange Communication System (PECS) application named SOLIF specifically tailored for the Qatari culture and its dialect, incorporating culturally relevant symbols and an existing text to speech engine that outputs a Qatari dialect voice. This application enhances communication accessibility through its intuitive interface, allowing phrase formation from symbols that reflect the culture of Qatar and offering predictive text features using an N-Gram module, and creation and customization of new words and categories. SOLIF's design prioritizes user-experience to be easier in terms of navigating the application and using its features, user customization of words, marking significant contributions to PECS applications in Qatar.
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