Promising Projects

Project Title: نظام المراقبة الذكي للمحاليل الوريدية Intelligent Intravenous Infusion Monitoring System

Description:
This project addresses a significant problem in medical field related to manual
intravenous (IV) infusions. The proposed solution is based on an integrated Artificial
Intelligence (AI) and Internet of Medical Things (IoMT) approach, using a smart
weight sensor and a mobile application, with the aim of continuously measuring the
remaining fluid, calculating the actual flow rate and using an AI-based flow-anomaly
model to detect leakage or occlusion, while instant alerts are sent expected when the
fluid reaches low level and also when leakage or occlusion is detected. Additionally,
automated medical summary reports are generated for each infusion session,
eliminating the need for manual documentation. Among the expected outcomes are a
reduced nursing workload and ensured patient safety. AI is used in this project to
reduce the risks that may arise from traditional monitoring


يتناول هذا المشروع مشكلةً جوهريةً الطبي تتعلق بالمحاليل الوريدية اليدوية. ويستند الحل المقترح إلى نهج متكامل يجمع بين الذكاء في مجال
االصطناعي وانترنت األشياء الطبية باستخدام حساس وزن ذكي وتطبيق هاتفي، بهدف قياس كمية المحلول المتبقية بصورة مستمرة وحساب معدل
التدفق الفعلي، واستخدام نموذج الذكاء االصطناعي الكتشاف الشذوذ في التدفق والكشف عن التسريب أو االنسداد ، مع إرسال تنبيهات فورية عند
َّ انخفاض مستوى المحلول أو عند اكتشاف تسريب أو انسداد. عالوة على ذلك، د
تُول تقارير طبية موجزة ومؤتمتة لكل جلسة تسريب، مما يُغني عن
الحاجة إلى التوثيق اليدوي. وتشمل النتائج المتوقعة تخفيف عبء العمل على الكادر التمريضي وضمان سالمة المرضى. ويُستخدم الذكاء االصطناعي
في هذا المشروع للحدّ من المخاطر التي قد تنجم عن المراقبة التقليدية.

Uploaded At: 2026-07-04 19:24:23

Project Title: An Intelligent Closed-Loop Prosthetic Rehabilitation Platform for Lower-Limb Recovery

Description:
This project presents an intelligent closed-loop prosthetic rehabilitation platform for lower-limb recovery. The system connects the prosthetic limb, artificial intelligence, EEG monitoring, VR training, and a mobile application into one adaptive rehabilitation workflow. The prosthetic sensing layer collects pressure, orientation, and ankle-motion data, while the AI module analyzes gait behavior and classifies the gait state as normal, imbalance, or abnormal. In parallel, EEG brainwave indicators are used to estimate attention and fatigue, allowing the system to respond not only to movement errors but also to the user’s readiness during training.
The VR environment includes dedicated scenes for rehabilitation exercises, real-time sensor-reading visualization, corrective feedback, and relaxation. When EEG indicators show fatigue or reduced attention, the system automatically changes the active VR scene to relaxation mode, replacing the need for the patient to manually request a pause. The mobile application supports therapist monitoring by showing the patient’s condition, session progress, exercise history, pressure-distribution heatmaps, rehabilitation feedback, and patient–therapist chat.
A pilot evaluation collected 11,533 multimodal records, achieving 92% recall, 94% precision, about 5% false-positive rate, and 453 ms mean end-to-end latency. The platform demonstrates a practical step toward safer, more personalized, and more engaging prosthetic rehabilitation.

Uploaded At: 2026-07-04 19:46:53

Project Title: MedBox: An AI-Powered Smart Medical Assistant and IoT Medication Management System

Description:
This project addresses a significant problem in home healthcare, specifically the
management of medication schedules for elderly patients and individuals with chronic
diseases. The proposed solution is an integrated smart medical system (MedBox)
based on a dual-processor architecture combining Arduino and Raspberry Pi, coupled
with Groq AI cloud intelligence, with the aim of preventing medication errors,
monitoring drug storage conditions, and enabling voice-interactive assistance. The
expected outcomes include a reliable IoT medication management device that alerts
patients at scheduled medication times, monitors temperature and humidity to prevent
drug degradation, and responds intelligently to voice queries using an AI-powered
assistant.

Uploaded At: 2026-06-30 14:07:34

Project Title: NATIONAL HEALTH STOCK: AN ADVANCED ASSET MONITORING & CITIZEN SYSTEM FOR NATIONAL DRUG SECURITY

Description:
Abstract: The stability and security of national pharmaceutical supply chains are critical components of a resilient healthcare infrastructure. This paper introduces the "National Health Stock," an Advanced Asset Monitoring and Citizen System designed to aggregate, analyze, and manage medical inventory on a national scale. By leveraging centralized database architectures, modern web frameworks, and advanced Artificial Intelligence (AI) and Computer Vision algorithms, the system addresses systemic vulnerabilities such as regional stock discrepancies, drug expiration wastage, and hazardous pharmacological interactions. The platform provides a secure, multi-tenant environment featuring rigorous cybersecurity frameworks, equipping the Ministry of Health with real-time geospatial tracking and automated anomaly-driven crisis simulation. Simultaneously, it empowers citizens with transparent medication geolocation and AI-driven clinical safety checks. Ultimately, this system ensures equitable drug distribution, maximizes asset utilization, and establishes a robust, tamper-proof digital framework for national drug security.

Uploaded At: 2026-06-30 09:14:55

Project Title: Al-Rehab Glove: An Artificial-Intelligence-Based Rehabilitation Glove for Monitoring Hand Tremors in Parkinson’s Disease Patients

Description:
This work presents the design and development of “Al-Rehab Glove,” a wearable smart-glove system that monitors and analyzes hand tremor in Parkinson’s patients using inertial sensing and artificial intelligence. The glove embeds an MPU6050 accelerometer-gyroscope module and an Arduino Nano microcontroller to record three-axis motion signals, which are stored on an SD card and later processed in MATLAB. After noise reduction and feature extraction, three supervised machine-learning algorithms—K-Nearest Neighbor (KNN), Decision Tree, and Naïve Bayes—are applied to classify tremor severity as mild, moderate, or severe. The classification result then drives a pneumatic actuation mechanism, built from flexible air tubes and finger holders, that assists finger movement at a treatment level matched to the detected severity. Experimental evaluation on motion data collected from Parkinson’s patients showed that the Decision Tree algorithm achieved the highest classification accuracy among the three models, confirming the feasibility of the proposed system as an objective, continuous tool for tremor monitoring and rehabilitation support.

Uploaded At: 2026-07-02 13:20:03

Project Title: HerbalDr AI: Intelligent Herbal Medical Consultation with Multi-Dialect NLP and Dual-AI Routing

Description:
HerbalDr AI is a cross-platform intelligent medical consultation system supporting 13 Arabic dialects and 5 English variants. The system uses a dual-AI engine that dynamically routes requests between Google Gemini 2.5 Flash and Meta Llama 3.3 70B, delivering culturally-sensitive herbal medicine guidance to 422 million Arabic speakers. Key innovations include: (1) a Firestore-backed dynamic prompt management engine enabling zero-downtime behavioral updates, (2) a quantitative Risk Scoring Algorithm classifying patient severity on a 0–100 scale, (3) a 3-tier automated ban escalation system, and (4) a multi-layer security architecture with WAF, server-side API isolation, and role-based access control. The system achieves 99.9% uptime through API key rotation and dual-model failover, with response times under 5 seconds. Built with React Native (Expo SDK 52) and Next.js 15, supporting SDG 3, SDG 9, and SDG 10.

Uploaded At: 2026-06-26 05:06:56

Project Title: NeuroVoice: An Intelligent Brain–Computer Interface for Real-Time Communication Between Thoughts and Voice Using Artificial Intelligence

Description:
Some examples of severe neurological disorders that still conserve cognition, but detract from the ability to communicate include Locked-In Syndrome (LIS), Amyotrophic Lateral Sclerosis (ALS) and advanced stroke. In this context, typical communication technologies are constrained by slow interaction speed, high implementation costs or the necessity of invasive surgical procedures, and thus there is an urgent need for a practical and accessible assistive communication technology.
This work presents NeuroVoice, a sophisticated non-invasive Brain–Computer Interface (BCI) framework using EEG signals and Artificial Intelligence that decodes imagined speech into real-time spoken language. This work thus introduces and uses a new framework composed of EEG signal acquisition, advanced preprocessing techniques, WST for feature extraction, and machine learning algorithms to enhance imagined speech recognition. They convert the decoded commands into audible speech, which can be supplemented with mobile communication and emergency assistance services.
The experimental assessment showed that the proposed system provides a real-time performance with a classification accuracy of 90.3%, low computational complexity, and an inexpensive hardware platform. Architected with future growth in mind, it provides a scalable, non-invasive and cost-effective platform that minimizes hardware changes.
NeuroVoice is a major advance towards the next generation of assistive healthcare technologies that converts neural intentions into intelligible communication and thus, enhances autonomy and quality-of-life for patients with severe speech and motor impairment.

Uploaded At: 2026-06-29 16:35:14

Project Title: Innovative Brain-Controlled Wheelchair Using NeuroSky

Description:
This project presents a hybrid smart wheelchair designed to assist individuals with severe physical disabilities by integrating Brain-Computer Interface (BCI) technology with head-motion sensing. The proposed system uses the NeuroSky MindWave headset to acquire electroencephalogram (EEG) signals and determine the user’s attention level. MATLAB processes these signals in real time and generates movement commands that are transmitted to a microcontroller. Forward and backward movements are controlled using EEG attention values, while left and right movements are achieved using an MPU6050 motion sensor mounted on the headset to detect head tilt. The microcontroller controls two DC motors through BTS7960 motor drivers to drive the wheelchair. Experimental evaluation demonstrated reliable performance with stable communication between software and hardware, fast response time, and accurate movement control. The proposed system provides a practical, affordable, and efficient assistive mobility solution for people with severe motor disabilities

Uploaded At: 2026-07-05 14:46:18

Project Title: Development of an Intelligent Integrated Medical Device for Elderly Patient Monitoring in Digital Health Systems

Description:
This project addresses a significant problem in terminal disease patients; The proposed solution is based on an integrated medical device with the aim of registering the patient’s vital signs. The expected outcomes include the progression (or deterioration) of the patient’s medical status.
Keywords: SIMD (Smart Integrated Medical Device), ICU (Intensive-Care Units), CCU (Coronary- Care Units), RCU (Respiratory-Care Units), HIV (Human Immunodeficiency Virus), HV (Hepatitis Virus).

العنوان: تطوير جهاز طبي ذكي ومدمج لمراقبة المرضى وكبار السن في نظم الرعاية الصحية الرقمية
الخلاصة
يتناول هذا المشروع مشكلةً هامةً لدى المرضى المصابين بأمراضٍ عضال، ويعتمد الحل المقترح على جهازٍ طبيٍّ متكاملٍ يهدف إلى تسجيل العلامات الحيوية للمريض. وتشمل النتائج المتوقعة تطور (أو تدهور) الحالة الصحية للمريض.

Uploaded At: 2026-07-05 23:14:01

Project Title: Design and Development of a Vision-Based 6-DOF Robotic System for Intelligent Drug Dispensing in Smart Pharmacies

Description:
This project addresses a critical challenge in pharmacy automation by developing a vision-guided six-degrees-of-freedom (6-DOF) robotic arm for automated medication dispensing. The system employs a camera and optical character recognition (OCR) to read prescription information and identify the required medications. The robotic arm then retrieves and dispenses the medication automatically with high accuracy. The proposed system aims to reduce dispensing errors, shorten waiting times, and improve the efficiency and reliability of pharmacy operations.

Uploaded At: 2026-06-29 17:58:46

Project Title: Software and Artificial Intelligence in Accelerating Medical Diagnosis

Description:
This project addresses a fundamental problem in the medical field: delays in the diagnosis of medical conditions. The proposed solution involves developing an integrated software platform powered by artificial intelligence models trained on specialized medical datasets to aid in the early detection and diagnosis of pathological patterns. Expected outcomes include enabling physicians and healthcare providers to make accurate treatment decisions in record time by providing immediate, automated diagnoses that contribute to improving the quality of healthcare.

Uploaded At: 2026-07-05 01:31:22

Project Title: Sleepy: An Intelligent IoT-Based Sleep Assistance System Using Random Forest for Personalized Sleep Recommendations

Description:
The quality of sleep is affected by many environmental factors, such as temperature, humidity, air quality, and lighting conditions. However, current methods of sleep assistance do not have sufficient functionality and are lacking personal adaptation to user preferences. In order to solve this problem, this paper introduces "Sleepy", an AI-powered system for monitoring, assessing, and adjusting the sleeping environment. This system utilizes a set of environmental sensors, adaptive RGB lighting, a touchscreen interface, and a mobile application to provide monitoring and intelligent interaction with the user. The Random Forest Machine Learning algorithm, which was trained on real-world data, is used to assess environmental parameters and user preferences and provide recommendations about the sleep environment and the suitable time to go to bed. The presented prototype showed promising results in terms of real-time environmental monitoring, adaptive adjustment of parameters, and interaction between the device and the mobile application. Testing conducted on a sample of 40 users confirmed a high level of user satisfaction with usability and comfort.

Uploaded At: 2026-06-30 11:44:40