Promising Projects

Project Title: MedGuard AI: A Multimodal Clinical Decision Support System for ICU Monitoring Using NEWS2 and Deep Learning-Based Chest X-Ray Analysis

Description:
This project addresses a significant challenge in the healthcare field, particularly in Intensive Care Units (ICUs), where continuous patient monitoring and early detection of critical conditions are essential, especially for patients with respiratory diseases. The proposed solution integrates artificial intelligence (AI), the National Early Warning Score 2 (NEWS2), deep learning-based chest X-ray analysis, explainable artificial intelligence (XAI), robotic assistance, and automated Telegram notifications to provide real-time clinical decision support and improve patient safety. The expected outcomes include enhanced diagnostic accuracy, faster emergency response, reduced workload for healthcare professionals, and more efficient and reliable monitoring of ICU patients.

Uploaded At: 2026-07-02 10:54:27

Project Title: CareMate

Description:
يتناول هذا المشروع مشكلةً جوهريةً في مجال الرعاية الصحية في العراق، حيث يعاني المرضى من صعوبة العثور على الأدوية النادرة والتبرع بالدم في الحالات الطارئة. ويستند الحل المقترح إلى تطبيق جوال يدمج تقنية نظام تحديد المواقع الجغرافية، والذكاء الاصطناعي، وأنظمة الإشعارات الفورية بهدف ربط المرضى بالصيدليات والمتبرعين بالدم في المناطق القريبة منهم فورياً

Uploaded At: 2026-07-05 04:11:25

Project Title: Design and manufacture of an elbow rehabilitation system

Description:
مشروعنا هو عبارة عن جهاز طبي مؤتمت وقابل للارتداء (Wearable Exoskeleton) مصمم خصيصاً لإعادة تأهيل وقياس كفاءة مفصل المرفق. يتكون الجهاز من هيكل حديدي متين وقابل للتعديل ليتناسب مع أبعاد الذراع المختلفة للمرضى، ويتم التحكم بحركته وزواياه عبر منظومة تحكم مغلقة الحلقة (Closed-Loop) تجمع بين متحكم الأردوينو، وخوارزمية PID عتادية لضمان حركة سلسة ودقيقة للمفصل.
​يقوم الجهاز بأتمتة الجلسات العلاجية وتخصيصها تدريجياً عبر ثلاث مراحل أساسية: (الحركة السلبية المستمرة CPM، الحركة النشطة المساعدة، ومرحلة المقاومة)، مع توفير نظام أمان فوري لحماية المريض، مما يجعله حلاً مبتكراً ومميزاً من الناحية الفنية والسعرية مقارنة بالأجهزة المستوردة المكلفة.

Uploaded At: 2026-07-01 05:56:14

Project Title: AI-Powered System for Early Breast Cancer Classification

Description:
يقدم هذا البحث نظامًا لتحليل صور الماموغرام مدعومًا بالذكاء الاصطناعي لتصنيف سرطان الثدي في مراحله المبكرة باستخدام شبكة عصبية التفافية (CNN) لتصنيف الصور إلى فئتين: حميدة وخبيثة. تم تطوير النموذج باستخدام Tensor Flow وKeras، بينما استُخدم Streamlit لبناء تطبيق ويب تفاعلي لتحليل صور الماموغرام، وإدارة بيانات المرضى، وأرشفة الصور.

Uploaded At: 2026-07-04 19:08:33

Project Title: ArabicMindGuard: Real-Time Intelligence System for Mental Wellbeing Monitoring and Classification Using Social Media Contents

Description:
This project proposes an automated detection system for depression status from Arabic social media content using Natural Language Processing (NLP) and MARBERT transformer model. The architecture is designed to continuously collect social media streams, conduct text preprocessing, store processed data in a database and apply text classification model to classify user contents into depressed and non depressed content while estimating depression severity levels. The analysis results are dynamically visualized via dashboard to offer real-time mental wellbeing insights.

Uploaded At: 2026-06-30 12:18:36

Project Title: Implementation of a Voice Controlled Intelligent Robotic Prosthesis Hand

Description:
Project Comprehensive Description
1. Problem Statement
Upper-limb amputees and individuals with neuromuscular disorders often rely on prosthetic devices to regain a degree of functional independence in their daily lives. However, many existing prosthetic hands are controlled using EMG signals generated by residual muscles. While this approach can be effective for some users, it may not be suitable for everyone. Patients with severe nerve damage, muscle weakness, paralysis, neurological disorders, or insufficient residual muscle activity may experience difficulty generating stable muscle signals required for reliable prosthetic control. As a result, these individuals may face limitations in operating conventional prosthetic devices and performing basic tasks such as grasping, holding, and manipulating objects. The inability to effectively control assistive devices can reduce independence, restrict participation in daily activities, and negatively impact overall quality of life.
2. Proposed Solution / Innovation
This project proposes a voice-controlled AI robotic prosthetic hand integrated with a custom-manufactured carbon socket and powered by a Raspberry Pi, utilizing the VOSK toolkit for offline speech recognition. The system operates by capturing real-time vocal inputs via a microphone connected directly to the system. The captured audio is processed offline using the VOSK speech recognition library. When a valid command is identified, the controller instantly triggers the dedicated servo motors to perform the corresponding mechanical gestures, such as opening, closing, holding, or displaying the "OK" and "Victory" signs. To enhance system flexibility, a dedicated mobile application is developed to allow the user to dynamically manage, add, or modify these voice commands.
This proposed approach effectively addresses the limitations of other active prostheses in a low-cost system and a lightweight carbon socket prosthesis. The novelty of this project lies in a smart voice-controlled robotic hand with a low-cost lightweight and robust prosthesis. Compared to existing solutions, the proposed system overcomes the EMG based problems and mitigates the training needs significantly.
3. Method / Implementation
The project is implemented through four stages:
• Requirement Analysis, where the Raspberry Pi 4B, high-torque servo motors, and the local VOSK API is selected.
• System Design, which focuses on engineering a lightweight, durable carbon socket using advanced casting alongside optimized wiring and active thermal management.
• Development and Implementation, where the five-fingered robotic hand is assembled, its actuators are mapped to the GPIO pins, the VOSK speech recognition software (utilizing MFCC, PLP, and TDNN models) is developed, and a dedicated mobile application is built for dynamic command management.
• Testing and Evaluation, where the fully assembled system is successfully verified using actual voice commands like open, close, hold, OK, and victory, with continuous software refinements to drastically reduce latency and maximize physical gesture accuracy.
4. Key Components (HW/SW/AI)
• Hardware (HW): Raspberry Pi 4B (Central Unit), MG996R Servo Motors (Actuators), and a USB Microphone (Input).
• Software (SW): VOSK API, Python 3.x, Linux-based Raspbian OS, Flutter application framework, and Dart programming language.
• Artificial Intelligence (AI): VOSK API selected since it utilizes advanced MFCC (Mel-Frequency Cepstral Coefficients) and PLP (Perceptual Linear Prediction) audio feature extraction and TDNN (Time Delay Neural Network).
5. Expected Results / Impact
• Results: The implemented voice-controlled prosthetic hand demonstrated encouraging results, successfully executing five essential gestures ("open", "close", "hold", "OK", and "victory") with quick, precise mechanical transitions supported by a lightweight, durable custom carbon socket. Powered entirely offline by a standard 5V rechargeable lithium battery, the system relies on the VOSK toolkit to deliver rapid, real-time speech recognition, seamlessly translating vocal inputs into immediate actuator movements. Furthermore, the accompanying mobile application connects wirelessly to the controller, enabling the user to dynamically add, modify, or delete custom voice commands that the system remembers and executes instantly without requiring an internet connection.
• Impact: The project empowers individuals with limb impairments by providing an intuitive, hands-free interface. It enhances autonomy and psychological well-being by offering a high-tech smart prosthesis.
6. What the 70-second Video Demonstrates
The video shows the implemented voice-controlled prosthetic hand and the developed app in action:
• Demonstration: The user issues live vocal commands such as open, OK Sign, and victory, and the prosthetic hand performs the required gestures. Then the developed companion app is shown, which can connect to the prosthetic hand and allow users to add, edit, and delete hand gestures, in addition to recording new voice commands.
• Validation: Upon issuing a voice command, the robotic hand executes these movements in real-time, demonstrating the efficiency of the AI model (pre-trained/offline) and the reliability of the mechanical assembly under actual operating conditions.

Uploaded At: 2026-07-03 12:04:59

Project Title: Design and Development of an Intelligent Assistive White Cane with Microcontroller-Based Obstacle Detection and Real-Time GPS/GSM Telemetry for the Visually Impaired

Description:
This project addresses a significant problem in the field of assistive technologies and medical device engineering, focusing on the autonomous navigation of visually impaired individuals. The proposed solution is based on an integrated embedded hardware system utilizing an Arduino/ESP32 microcontroller, ultrasonic sensors, a GPS tracking module, and a GSM cellular transceiver with the aim of providing non-contact hazard detection and remote location monitoring. The expected outcomes include a fully functional, lightweight smart stick prototype that accurately calculates obstacle proximity, triggers immediate haptic and auditory alerts for the user, and automatically transmits precise live geographical coordinates via SMS to caregivers during emergencies, thereby significantly enhancing the user's safety and independence.

Uploaded At: 2026-07-01 03:59:19

Project Title: نظام ذكي للكشف عن الأمراض الجلدية وتشخيصها واقتراح العلاج المناسب

Description:
Skin diseases are among the most common health problems worldwide, yet access to specialized dermatologists remains limited in many regions. This paper presents an intelligent multi-platform system for detecting and diagnosing fifteen common skin diseases such as eczema, psoriasis, acne, melanoma, and fungal infections using deep learning techniques. A convolutional neural network (CNN) based on EfficientNet‑B3 was trained on a balanced dataset of 3,750 images (250 per disease). The model achieved an overall accuracy of 94.8% and a macro‑average F1‑score of 94.0%, with melanoma detection reaching 97.7%. The trained model was converted to TensorFlow Lite and integrated into Android, desktop, and web applications. Real‑world testing on 50 new images yielded 92.0% accuracy, confirming good generalization. While not a substitute for professional diagnosis, the system provides a valuable assistive tool for patients in underserved areas, medical students, and primary care physicians.

Uploaded At: 2026-06-23 13:03:43

Project Title: DentalScan

Description:
يهدف مشروع DentalScan إلى تسهيل اكتشاف أمراض الأسنان من صور الأشعة باستخدام الذكاء الاصطناعي وتقليل الوقت والجهد اللازمين للتشخيص الأولي. يعتمد التطبيق على نموذج YOLOv8 الذي تم تدريبه على مجموعة بيانات عامة من Kaggle للتعرف على 31 حالة مختلفة مثل التسوس والحشوات والتيجان والزراعات والأسنان المفقودة وعلاج القناة وفقدان العظم. يعمل التطبيق على نظام ويندوز بشكل كامل دون الحاجة إلى اتصال بالانترنت حيث تتم معالجة الصور داخل جهاز المستخدم للحفاظ على خصوصية بيانات المرضى. كما يعرض النتائج مباشرة على صورة الأشعة مع نسبة الثقة لكل حالة مما يوفر دعما سريعا لأطباء الأسنان حيث يساعدهم في التشخيص الأولي دون أن يكون بديلا عن الخبرة الطبية.

Uploaded At: 2026-07-05 21:06:36

Project Title: NeoAlert

Description:
Premature neonates are at high risk because their bodies cannot regulate temperature well. Most older incubators
only react after the baby’s temperature has already changed, using fixed thresholds. This project presents
NeoAlert, an adaptive incubator system designed to predict temperature changes before they happen. The system
utilizes two distinct sensors, namely an infrared thermometer alongside an 8×8 thermal camera — to monitor the
neonate’s skin temperature, along with an ambient sensor for the surrounding air. These readings are processed
by an incremental linear regression model running on an ESP32-S3 microcontroller, which learns each neonate’s
normal temperature pattern over time. When the system predicts that the temperature is starting to move away
from this pattern, it adjusts a Peltier module using proportional PWM control to correct it early. The goal is to
reduce temperature changes and help nurses and decrease the need for constant manual adjustments by nurses.
The system is also designed to support therapeutic hypothermia used in the treatment of hypoxic-ischemic
encephalopathy (HIE).

Uploaded At: 2026-07-04 18:59:33

Project Title: Edge AI-Based Multimodal System for Brain Tumor Analysis

Description:
This project presents an Edge AI-based multimodal system for automated brain tumor analysis, integrating MRI images with structured clinical data to support clinical decision-making through a unified deep learning and machine learning pipeline. MobileNetV2 performs multi-class MRI classification into glioma, meningioma, pituitary tumor, and no-tumor categories, achieving 99.02% accuracy, while YOLOv8n localizes tumor regions within the scans. In parallel, an XGBoost model trained on structured clinical data provides an interpretable binary diagnosis, complementing image-based predictions. A web-based interface exposes all three models to clinicians for real-time upload, classification, localization, and reporting. The system is deployed on a Raspberry Pi 4 as a standalone edge inference server, eliminating cloud dependency and ensuring low-latency, privacy-preserving processing. A LAN with DNS-based access (tumor.ai.local) enables seamless institution-wide access. Overall, the system delivers an accurate, interpretable, and cost-effective diagnostic platform for secure real-time brain tumor analysis in diverse healthcare environments.

Uploaded At: 2026-07-05 00:59:17

Project Title: Augmented Teleoperation Surgical Arm (ATSA)

Description:
يتناول هذا المشروع مشكلة جوهرية في الجراحة المعتمدة على التشغيل عن بعد, حيث تؤثر الرعشات الفسيولوجية لليد لدى الجراح في مستوى الدقة اثناء اداء الاجراءات الجراحية ضمن بيئة تشغيلية تفتقر الى الارشاد. ويستند الحل المقترح الى نظام الذراع الجراحية المعززة بالتشغيل عن بعد ( ATSA) والذي يهدف الى دمج بيئة جراحية مدعومة بالرؤية الحاسوبية من خلال اسقاط مسارات بصرية مثلى على الاهداف الجراحية,الى جانب تطبيق خوارزميات ( Kalman Filter ) بصورة انية لتصحيح الاضطرابات الحركية. ويستند الحل المقترح الى تحقيق كبت كامل للرعشات, وتعزيز الدقة في تنفيذ العمليات الجراحية , اضافة الى توفير اطار عمل موثوق يمهد لتطوير انظمة خياطه جراحية ذاتية التشغيل.

Uploaded At: 2026-07-04 20:41:03