مساحة للاحتفاء بالعقول الجامعية العراقية ومشاريعها المبتكرة. اكتشف تفاصيل الفرق الفائزة وأفكارها العلمية، وشاهد العروض والملفات الأصلية للمشاريع.
تصفّح المشاريع بحسب المسار أو الجامعة، واضغط على أي مشروع لعرض ملفه الكامل.
المركز الأول
المسار الزراعي
Sanabel is an integrated smart AgriTech platform for monitoring corn crop health in Iraqi agricultural fields, combining UAV technology, artificial in…
فريق صناع الأثر — جامعة النهرين
Sanabel is an integrated smart AgriTech platform for monitoring corn crop health in Iraqi agricultural fields, combining UAV technology, artificial intelligence, and IoT-based sensing into a unified system. The platform uses a Parrot ANAFI 4K drone to capture high-resolution aerial imagery of the field, which is analyzed through a YOLOv8-based AI model for early detection of corn diseases such as Northern Leaf Blight, Maize Streak Virus, and Gray Leaf Spot. The system integrates solar-powered ground stations equipped with RS485 soil sensors that measure humidity, temperature, and nutrient levels (NPK), communicating wirelessly via LoRa to a central Raspberry Pi 5 processing unit. Aerial imagery is further processed using WebODM to generate orthophoto maps and NDVI-based vegetation health analysis. Tested on a real corn field at the University of Baghdad, Sanabel achieved an 89.4% photogrammetric reconstruction success rate and identified Northern Leaf Blight as the dominant disease with 88.0% average detection confidence across the monitored field. The project aims to empower farmers with early disease detection, optimize water usage, and increase crop productivity, offering a practical and sustainable solution suited to Iraq's challenging environmental conditions.
المركز الأول
المسار الصناعي
This project demonstrates the architectural design, and manufacturing processes, and implemented AI tools involved in the proposed automated four-axis…
Orbit Team — الجامعة التكنولوجية
This project demonstrates the architectural design, and manufacturing processes, and implemented AI tools involved in the proposed automated four-axis computer numerical control (CNC) hotwire foam cutting machine. The aim is to cut extruded polystyrene (EPS/XPS) foam three-dimensions (3D) profiles in an optimal time while keeping consistent precise cuts. The machine was designed using SOLIDWORKS software and fabricated via different techniques including wire-cutting, sheet metal bending, and welding. The Grbl Hot Wire software is used to control the proposed machine; however, it is not open source and therefore cannot be modified. To overcome this limitation, the Cursor IDE AI tool is employed to enhance the original software by adding an extension window, integrated with the Gemini chatbot, alongside the Grbl interface. This enhancement enables the conversion of images, CAD models, and other supported file formats into editable and customizable G-code. The generated G-code is then transmitted to the 4-axis machine through an Arduino Mega controller, which can be readily upgraded to accommodate future enhancements and additional functionalities. A Molybdenum hotwire was chosen for the cutting based on the criteria of being the cheapest and available yet most suitable option for this application. Furthermore, other calculations were implemented to make sure that all the energy consumption, wire elongation and ideal cutting temperature match the desired goal. The final outcome of this project is a machine moves within four-dimensional coordinate framework (X-Y-Z-A) which can easily be upgraded into 5-axis or more, that allows it to execute asymmetrical, highly tapered and transitionary geometries like aerodynamic and airfoils. While the machine presents an outstanding dimensional accuracy, it is also considered a full platform cost-effective, upgradable, highly scalable and an adequate rapid prototyping choice.
المركز الأول
المسار الطبي
Abstract Liver disease is a "silent killer" whose complex early diagnosis often leads to physician burnout. To address this, we developed a comprehens…
دقة — الجامعة التقنية الشمالية
Abstract Liver disease is a "silent killer" whose complex early diagnosis often leads to physician burnout. To address this, we developed a comprehensive, AI-Based Liver Disease Diagnosis System that is both highly accurate and user-friendly for clinical settings. Methodologically, we engineered a "multi-model ecosystem." After initially testing the Random Forest algorithm, we selected XGBoost as our primary engine for its superior speed and accuracy, pairing it with strict deterministic logic. Guided by a "Data Quality over Quantity" philosophy, we rigorously cleaned our datasets and removed statistical noise. This approach proved that AI learns far better from small, verified clinical facts than from massive, messy data. As an outcome, this rigorous approach yielded exceptional performance across all stages of the diagnostic pipeline. The system’s "Gate Model" used for initial patient triage achieved an accuracy of 97.41%. For specific disease pathways, the deterministic Fatty Liver (NAFLD) model achieved 100.00% accuracy. The specialized XGBoost engines successfully achieved 95.24% accuracy in predicting Hepatitis C (HCV) complications, 71.43% in assessing HCV mortality status using strictly verified data, 62.50% in classifying complex HCV fibrosis stages, and 94.00% in evaluating Hepatocellular Carcinoma (Liver Cancer) risk. Ultimately, by integrating these models into a seamless full-stack web application, this research provides a highly reliable, practical tool that helps shift liver healthcare from reactive treatment to proactive, life-saving prevention.
المركز الأول
مسار التربية والتعليم
Problem Statement: Students in the Cybersecurity Department often struggle to get immediate answers to course-related questions outside office hours. …
فريق الأمن السيبراني — الجامعة التقنية الوسطى
Problem Statement: Students in the Cybersecurity Department often struggle to get immediate answers to course-related questions outside office hours. There is no dedicated support system available at the department entrance, leading to delays in learning and reduced student engagement. Proposed Solution/Innovation: CyberGuard AI is an AI-powered kiosk robot placed at the department entrance. It provides instant, accurate answers to students' academic questions in both Arabic and English — by voice or text — 24/7, without requiring human intervention. Method/Implementation: The system uses Retrieval-Augmented Generation (RAG): course materials are ingested into a local ChromaDB vector database. When a student asks a question, the most relevant content is retrieved and passed to an LLM (Groq Cloud API with local fallback) to generate a grounded answer. The entire pipeline runs on a Raspberry Pi 5. Key Components (HW/SW/AI): Hardware: Raspberry Pi 5 (8GB), touchscreen, USB microphone, speaker, and Pi camera. Software: PySide6 UI, LangChain + ChromaDB (RAG), Groq API (Llama 3.3 70B online), LlamaCpp (Llama 1B offline), Face-ID authentication, and a full security layer against injection attacks. Results/Impact: The robot successfully answers cybersecurity course questions in real time, recognizes registered students via Face-ID, operates in kiosk mode preventing unauthorized system access, and continues functioning during internet outages using the local AI model. Demo Summary: The 60-second demo shows a student approaching the kiosk, logging in via Face-ID, asking a cybersecurity question in Arabic by voice, and receiving a spoken answer — then switching to English text input to demonstrate bilingual capability.
المركز الأول
مسار خدمة المجتمع
ٌThis project addresses a significant problem in human-robot interaction and campus academic orientation. The proposed solution is based on "Dalil," a…
FANTASTIC 4 — الجامعة التقنية الشمالية
ٌThis project addresses a significant problem in human-robot interaction and campus academic orientation. The proposed solution is based on "Dalil," an autonomous, open-source robotic assistant utilizing a hybrid processing architecture that pairs a Raspberry Pi 5 with an ESP32 microcontroller, with the aim of deploying YOLOv8 facial recognition and natural language processing to guide university visitors and independently resolve repetitive academic inquiries. The expected outcomes include achieving highly optimized response latencies under 3 seconds, attaining a speech processing accuracy exceeding 90% in the localized Iraqi dialect via secure API connections, and delivering a substantial reduction in the routine administrative burden imposed on university personnel.
The increasing demand for intelligent robotic systems capable of operating in hazardous, remote, and inaccessible environments has accelerated the int…
Early Detection and Rescue Team — جامعة بابل
The increasing demand for intelligent robotic systems capable of operating in hazardous, remote, and inaccessible environments has accelerated the integration of robotics, embedded computing, and artificial intelligence technologies. This project presents the design and implementation of a remotely operated mobile robotic rover that enhances operator safety while providing continuous situational awareness. The proposed platform combines a Zynq FPGA-based processing system with a Raspberry Pi 5 for edge AI processing and advanced sensor integration. The rover incorporates wireless telemetry, GPS positioning, real-time video transmission, and a tracked crawler chassis to ensure reliable mobility over uneven terrain. System control and sensor interfacing are implemented using MicroPython, enabling efficient development and integration of embedded functionalities. To enhance autonomous monitoring capabilities, a Convolutional Neural Network (CNN) model is developed and optimized using TinyML techniques for deployment on the Raspberry Pi 5. The integrated platform, which also includes a live video stream and a six-degree-of-freedom (6DOF) robotic arm, an MQ-2 gas sensor, and a firefighting hose, provides an effective solution for remote surveillance, environmental monitoring, industrial inspection, emergency response, and AI-driven research applications.
المركز الثاني
المسار البيئي
Global plastic production exceeds 400 million tons annually with under 9% recycled, making manual waste collection inefficient and hazardous. NatureCl…
MSM — جامعة الموصل
Global plastic production exceeds 400 million tons annually with under 9% recycled, making manual waste collection inefficient and hazardous. NatureCleanerBot is an autonomous mobile robot designed to detect and collect plastic bottles in semi-structured outdoor environments. Its mechatronic architecture integrates a tracked platform with a custom five-degree-of-freedom robotic arm and 3D-printed gripper. A Raspberry Pi 5 central controller fuses YOLO11n-OBB object detection with a dual ultrasonic sensor system facilitating both obstacle avoidance and precise depth measurement for “Scan-and-Verify” navigation and inverse-kinematics retrieval. Evaluations showed 85.1% precision, 89.5% recall, 91.3% mAP50, and 9.0 ms inference speed. Field testing validated stable operation, though detecting vertically oriented bottles remains a limitation. Results prove the feasibility of combining edge AI and robotics for sustainable waste management.
المركز الثاني
المسار الزراعي
Application of autonomous Unmanned Aerial Vehicle (UAV) systems and the use of artificial intelligence in precision agriculture are increasing and are…
فريق الذكاء الطائر — جامعة النور
Application of autonomous Unmanned Aerial Vehicle (UAV) systems and the use of artificial intelligence in precision agriculture are increasing and are important tools for better crop monitoring, reduction of human labor, and enhancement of agricultural productivity. Although the above-mentioned challenges exist, real-time strawberry detection is still difficult under different illumination or unseen fruits and complicated backgrounds. This paper presents an edge artificial intelligence (AI) model improved by using the YOLOv8 model to detect strawberries and monitor them in real time, and introduces an autonomous unmanned aerial vehicle (UAV) system for strawberry monitoring. The model to be used is optimized for the YOLOv8 model, with the addition of edge computing on an NVIDIA Jetson Orin Nano, autonomous flight control using a Pixhawk controller, and an intelligent dashboard for flight mission planning and crop analysis. To meet the requirement of detecting strawberry ripeness stages, plant health conditions, and common diseases under complex field environments, it is proposed that feature extraction methods be strengthened to generate better feature representations, multi-scale feature fusion methods be adopted to fuse the information from different scales, and lightweight attention mechanisms be introduced to achieve effective detection. The model was trained on more than 20,000 annotated images from 12 classes and tested based on the Precision, Recall, and mean Average Precision (mAP) metrics. Results from this experiment showed 90% Precision, 83% Recall, 88% mAP@50, and 74% mAP@50-95, with real-time inference at around 9 fps on Jetson Orin Nano, with no dependency on the cloud. The full AgriFlyAI suite also produces crop health analysis, disease hotspot maps, and crop yield predictions to aid data-informed farming decisions. The outcomes prove that the suggested model is an efficient, scalable, and practice-oriented solution to the problem of intelligent UAV-based precision agriculture.
المركز الثاني
المسار الصناعي
تُعدّ الطاقة الشمسية الكهروضوئية من أسرع تقنيات الطاقة المتجددة نموًا على مستوى العالم، إلا أن كفاءة وموثوقية هذه الأنظمة تتأثر بشكل كبير بالأعطال الم…
القدرة والتحكم — جامعة كربلاء
تُعدّ الطاقة الشمسية الكهروضوئية من أسرع تقنيات الطاقة المتجددة نموًا على مستوى العالم، إلا أن كفاءة وموثوقية هذه الأنظمة تتأثر بشكل كبير بالأعطال المختلفة، مثل تراكم الغبار، والتظليل الجزئي، والبقع الساخنة، والتشققات، والتآكل، وتسرب الرطوبة، وأعطال الثنائيات، والتي تؤدي إلى انخفاض إنتاج الطاقة وارتفاع تكاليف الصيانة. يعالج هذا المشروع تحدي الكشف الآلي عن الأعطال وتصنيفها وتحديد مواقعها في حقول الطاقة الشمسية الكهروضوئية من خلال تطوير نظام مراقبة ذكي يعتمد على الذكاء الاصطناعي والتعلم. يعتمد الحل المقترح على استخدام الشبكات العصبية الالتفافية) لتحليل الصور المرئية والحرارية الملتقطة للألواح الشمسية، مما يتيح التعرف التلقائي على أنواع متعددة من الأعطال وتصنيفها وتحديد مواقعها بدقة عالية. ويهدف المشروع إلى تحسين موثوقية وكفاءة تشغيل محطات الطاقة الشمسية، وتطوير عمليات الصيانة من خلال تقليل الاعتماد على الفحص اليدوي، وخفض زمن اكتشاف الأعطال، وتقليل التكاليف التشغيلية. وقد أظهرت النتائج التجريبية أن النموذج المقترح حقق دقة تصنيف بلغت 95.3%، مما يؤكد كفاءته في التشخيص الذكي للأعطال. كما يوفر النظام إطارًا عمليًا وقابلًا للتوسع لدعم المراقبة الآنية، والصيانة التنبؤية، مع إمكانية التكامل مستقبلًا مع الطائرات المسيّرة وتقنيات إنترنت الأشياء. ومن خلال تعزيز قدرات تشخيص الأعطال وتحسين استراتيجيات الصيانة، يسهم المشروع في تطوير أنظمة إدارة ذكية للطاقة المتجددة ودعم التشغيل المستدام لمحطات الطاقة الشمسية الكهروضوئية واسعة النطاق
المركز الثاني
المسار الطبي
Patients with severe speech and motor impairments often face difficulty expressing basic needs, especially when they cannot use speech, hands, or conv…
IT Babylon Team — جامعة بابل
Patients with severe speech and motor impairments often face difficulty expressing basic needs, especially when they cannot use speech, hands, or conventional input devices. This project presents Amal, an AI-based assistive communication system that enables patients to communicate with caregivers using eye blink detection and sign language recognition. The system uses the mobile camera to detect short and long eye blinks and convert them into dot/dash patterns. These patterns are mapped to caregiver-defined commands, allowing an unlimited set of personalized requests according to the patient’s daily needs.
المركز الثاني
مسار التربية والتعليم
Across Iraq, learners from primary school to university face fragmented study tools, intermittent connectivity, weak support for colloquial Iraqi Arab…
فريق المستقبل الذكي — جامعة الصفوة
Across Iraq, learners from primary school to university face fragmented study tools, intermittent connectivity, weak support for colloquial Iraqi Arabic, and weak privacy safeguards for a mostly-minor user base. This paper presents Ulfana, a privacy-first educational platform that serves all tiers (school, institute, university, vocational) from a single Arabic right-to-left application, unifying capabilities usually scattered across separate tools. It contributes a multi-tier adaptive architecture; a study-feed ranker governed by an education-specific 'golden rule' with per-post transparency; action-linked gamification with an anonymised leaderboard; a cross-tier Academic Passport and a Smart Transition Bridge that schedules prerequisite review to peak on the enrolment date; and the Speaking Library, an offline Arabic Retrieval Augmented Generation (RAG) assistant that answers each student from their own books by fusing BM25 and FAISS HNSW retrieval with cross-encoder re-ranking and a per-user BM25 sub-index, local Qwen2.5 generation, Iraqi-dialect post-processing, and offline neural speech. The Speaking Library runs fully offline on commodity CPUs; the implementation was verified through 60+ application and 161 backend tests.
المركز الثاني
مسار خدمة المجتمع
More than 3,000 preventable deaths occur on Iraqi roads each year, partly due to the lack of integration between road hazard detection, geospatial map…
Teana team — جامعة تكنولوجيا المعلومات والاتصالات
More than 3,000 preventable deaths occur on Iraqi roads each year, partly due to the lack of integration between road hazard detection, geospatial mapping, and emergency response systems. Existing CCTV networks, citizen-reporting applications, and standalone machine-learning solutions operate independently, limiting their effectiveness in supporting timely decision-making. This paper presents RASID (Road AI Surveillance Intelligence Detection), an integrated MLOps and GIS platform for real-time road hazard monitoring and fleet intelligence. The proposed system streams image frames and GPS coordinates from fleet vehicles to a centralized server, where a YOLO11s detection model performs hazard detection and stores validated events in a PostGIS database as geotagged records. Detected events are distributed through a WebSocket-based GIS platform, enabling real-time notifications for drivers, maintenance teams, and emergency responders. In addition, an active-learning pipeline supports continuous model improvement through automated retraining and deployment. A pilot deployment involving ten vehicles achieved 78.0% [email protected], 74.7% precision, and 81.1% recall, while sustaining up to 120 frames per second of server throughput and maintaining sub-second event dissemination latency. The results demonstrate the feasibility of combining computer vision, geospatial intelligence, and MLOps into a unified platform for intelligent road safety management.
المركز الثالث
المسار البيئي
The traditional outdoor misting systems are known to have high ongoing energy use and significant water loss due to inefficient spraying. Moreover, tr…
soul tech — الجامعة التكنولوجية
The traditional outdoor misting systems are known to have high ongoing energy use and significant water loss due to inefficient spraying. Moreover, traditional methods often create nuisance to pedestrians by unwanted wetting. In an attempt to overcome these inefficiencies, this paper presents SMART ECO-MISTER Breeze, a solar based urban cooling system with the support of Edge AI. The architecture proposed consists of a Raspberry Pi 4, AMG8833 Thermal Camera and BME280 sensor to enable ambient monitoring and human heat detection in real-time. The system uses AI algorithm to determine the amount of mist needed for each person and uses a 50W monocrystalline solar panel, along with a 12V LiFePO4 battery and MPPT controller to operate off the grid. The 60cm 3D printed physical prototype includes a 12V high pressure diaphragm pump, 0.1mm brass nozzles, solenoid valves and closed loop water re-cycling system. Further, the system includes a LoRa MESH wireless network, which allows to coordinate multiple cooling poles in a decentralised way across the city. Experimental validation shows that the system can provide targeted and personalized cooling without wetting pedestrians, if they do not want to be wet. In so doing, this smart architecture requires much less energy and water than traditional methods, providing a highly sustainable paradigm for urban climate adaptation.
المركز الثالث
المسار الزراعي
تناول هذا المشروع مشكلةً جوهريةً في مجال الزراعة وإدارة الموارد المائية، حيث يواجه المزارعون صعوبة في تشخيص أمراض النباتات واكتشافها مبكراً نتيجة غياب…
فريق الاشاوس — الجامعة التقنية الجنوبية
تناول هذا المشروع مشكلةً جوهريةً في مجال الزراعة وإدارة الموارد المائية، حيث يواجه المزارعون صعوبة في تشخيص أمراض النباتات واكتشافها مبكراً نتيجة غياب المراقبة اللحظية للبيانات الحيوية، بالإضافة إلى أضرار الري العشوائي الذي يسقي جميع المحاصيل بكميات متساوية متجاهلاً عتبة الرطوبة الخاصة بكل نبتة، مما يؤدي إلى هدر المياه واختناق الجذور. ويستند الحل المقترح (منصة بستاني+) إلى تقنيات إنترنت الأشياء (IoT) والذكاء الاصطناعي، من خلال دمج متحكم دقيق (ESP32) وحساسات بيئية مع نظام ري آلي متصل سحابياً، ووحدة رؤية حاسوبية، بهدف توفير بيانات مراقبة شاملة ولحظية لجميع النباتات، وتقديم إرشاد زراعي رقمي متكامل يُمكّن المزارعين -حتى المبتدئين منهم- من العناية بمحاصيلهم باحترافية. وتشمل النتائج المتوقعة أتمتة عملية الري بالكامل وترشيد استهلاك المياه بناءً على الاحتياج الفعلي للنبات، والتشخيص الآلي للأمراض لاقتراح خطط العلاج المناسبة، بالإضافة إلى حماية معدات الضخ من التلف، مما يؤسس لبيئة زراعية ذكية ومستدامة.
المركز الثالث
المسار الصناعي
Road congestion and a rapidly growing delivery economy make last-mile logistics in Iraqi cities slow, costly, and unreliable. This paper presents our …
Metalhead — الجامعة التكنولوجية
Road congestion and a rapidly growing delivery economy make last-mile logistics in Iraqi cities slow, costly, and unreliable. This paper presents our project: a low-cost autonomous delivery drone built on the Holybro S500 V2 quadcopter with a Pixhawk 6C Mini flight controller running ArduPilot, supervised by a Raspberry Pi 5 companion computer with a 13-TOPS Hailo-8L AI accelerator. The system performs the complete delivery mission autonomously—takeoff, obstacle-aware cruise, arrival notification, operator-confirmed package release, and automatic return to launch—while a custom web ground-control dashboard provides the live map, camera and LiDAR feeds, telemetry, and a complete audit log. Onboard edge AI runs object detection in real time, and the navigation layer uses smart algorithms including advanced robot path planning, Dijkstra-based route selection, and 360° 2-D LiDAR obstacle avoidance with hard-stop and side-step policies. The full mission pipeline was validated end-to-end over the University of Technology campus in Baghdad, demonstrating a practical, affordable path toward delivery drone services in Iraq
يهدف هذا المشروع إلى تطوير نظام أولي منخفض التكلفة للكشف المبكر عن سرطان الرئة بطريقة غير جراحية، من خلال تحليل هواء الزفير باستخدام مصفوفة من حساسات …
اوميكاترون — جامعة الموصل
يهدف هذا المشروع إلى تطوير نظام أولي منخفض التكلفة للكشف المبكر عن سرطان الرئة بطريقة غير جراحية، من خلال تحليل هواء الزفير باستخدام مصفوفة من حساسات الغازات من نوع MOS (Metal Oxide Semiconductor) وتقنيات الذكاء الاصطناعي. تعتمد فكرة المشروع على أن الخلايا السرطانية تُنتج مركبات عضوية متطايرة (VOCs) تختلف عن تلك الموجودة في الأشخاص الأصحاء، ويمكن الاستفادة منها كمؤشرات حيوية للكشف المبكر عن المرض. تعمل آلية النظام على التقاط استجابة الحساسات عند تعرضها لهذه المركبات، ثم إرسال البيانات إلى متحكم Arduino لمعالجتها مبدئيًا، قبل تحليلها باستخدام خوارزميات التعلم الآلي في بيئة Python، والتي تتضمن معالجة الإشارة، وتحليل المكونات الرئيسية (PCA)، وتصنيف الأنماط باستخدام خوارزمية SVM. ويتيح ذلك التمييز بين الأنماط المختلفة للمركبات العضوية المتطايرة وإظهار نتائج الفحص بصورة سريعة وغير جراحية. أثبت النموذج الأولي إمكانية استخدام الحساسات منخفضة الكلفة مع تقنيات التعلم الآلي في تحليل أنماط المركبات العضوية المتطايرة، مما يمثل خطوة واعدة نحو تطوير أنظمة ذكية وسريعة للكشف المبكر عن سرطان الرئة. وتتمثل المرحلة المستقبلية للمشروع في التحقق من أداء النظام باستخدام عينات زفير حقيقية من المرضى لتقييم دقته في التطبيقات السريرية.
Design and implementation of an interactive assistive system to help children with Autism Spectrum Disorder (ASD) to perform the activities of daily l…
تأهيل اطفال التوحد — جامعة تكريت
Design and implementation of an interactive assistive system to help children with Autism Spectrum Disorder (ASD) to perform the activities of daily living independently is presented in this project, which is named Autism Care System. This system utilizes an ESP32 microcontroller and also involves a TFT display, audio stimulation using a DFPlayer Mini module, touch and sound sensors, and physical interaction buttons, integrating a multisensory learning environment. The robot guides and teaches step by step using visual and auditory cues for everyday tasks like handwashing and dressing, and offers instant positive feedback with sounds and animation to keep the children engaged and motivated in learning. The proposed system aims to assist children with ASD within the home and educational context to become more independent, improve adherence to tasks, and enhance interactive learning, while being low-cost, portable, and child-friendly.
المركز الثالث
مسار خدمة المجتمع
يعتمد المشروع على تصميم رادار بصري ذكي باستخدام تقنيات الرؤية الحاسوبية والذكاء الاصطناعي، حيث تم تدريب النموذج على أكثر من 15 ألف صورة للطائرات المسي…
Sky zone — الجامعة التكنولوجية
يعتمد المشروع على تصميم رادار بصري ذكي باستخدام تقنيات الرؤية الحاسوبية والذكاء الاصطناعي، حيث تم تدريب النموذج على أكثر من 15 ألف صورة للطائرات المسيّرة ليتمكن النظام من اكتشاف الدرونات وتتبعها وتحليل حركتها والتنبؤ بمسارها المستقبلي. بعد اكتشاف الهدف يقوم النظام بتكبيره وعرض بياناته، ثم يرسل المعلومات إلى درون اعتراضية ذكية مدعومة بالذكاء الاصطناعي لملاحقة الهدف. تم اختبار السوفتوير على درون DJI Tello مفتوحة المصدر، وبعد نجاح الاختبارات تم تطوير الهاردوير لبناء نموذج عملي، يتضمن درون اعتراضية عالية السرعة مستوحاة من نماذج درونات مستخدمة فعلياً في ساحات القتال، بالإضافة إلى تطوير درون يحمل سلاح مدعوم بالذكاء الاصطناعي لتنفيذ مهام الاعتراض والتعامل مع عدة أهداف دون خسارة المنصة. المشروع حالياً مكتمل من ناحية التصميم والبرمجيات وجاهز للاختبار العملي، ويحتاج فقط إلى بعض القطع الإلكترونية لإكمال الدرون الاعتراضية، بالإضافة إلى استخدام كاميرا ذات مواصفات أعلى من المستخدمة حالياً لزيادة مدى الكشف ورفع دقة التتبع. النتائج المتوقعة: • إنشاء منظومة متكاملة لكشف وتتبع ومواجهة الطائرات المسيّرة بالذكاء الاصطناعي. • القدرة على اكتشاف الدرونات والتنبؤ بمسارها والتعامل معها بسرعة عالية. • تطوير حلول دفاعية قابلة للتوسع لحماية المنشآت والمناطق الحساسة. • الاعتماد على تقنيات حديثة أصبحت من أهم توجهات الجيوش حول العالم، بعد إثبات فعالية أنظمة مكافحة الدرونات المدعومة بالذكاء الاصطناعي في الاستخدامات الحديثة
المركز الرابع
المسار البيئي
River water pollution is a grave threat to the health of the public, the integrity of ecosystems and sustainable development, especially for countries…
Byte Force — جامعة النور
River water pollution is a grave threat to the health of the public, the integrity of ecosystems and sustainable development, especially for countries like Iraq where the Tigris River is one of the main sources of water for millions of residents. Traditional monitoring methods based on manual sampling are labor-intensive, spatially limited and lack the ability to acquire spatiotemporal data in real time. In this paper, we present Tigris Eye, a novel low-cost autonomous surface robot (ASR) system for continuous GPS-tagged AI-powered river water quality monitoring. The system adopts a passive drift navigation strategy, where a fleet of robots passively drifts through the river current without any propulsion, but with directional control only via a bio-inspired fin rudder Each robot is equipped with an ESP32-S3 microcontroller for sensor data acquisition (TDS, turbidity, laser distance), an ESP32-CAM module for periodic visual capture, a GPS module for geotagging every reading, and a hydrokinetic waterwheel mounted at the bow to harvest supplementary electrical energy from river current. All data are sent in real-time over a secure WebSocket (WSS/TLS) to a cloud-based Node.js server hosted on Railway, where YOLOv8-based visual detection identifies floating waste, algal blooms, oil spills, and colour anomalies, and GPT-4o Vision generates structured nine-field pollution assessment reports. An interactive web-based dashboard displays live sensor readings, AI analysis results, GPS-tagged pollution maps and rudder control. The fleet deployment model, multiple low-cost units operating in parallel, guarantees a complete coverage of the river and enables source-localisation of pollutants. Field validation on the Tigris River demonstrates the system's effectiveness in detecting pollution events with sub-10-second latency.
المركز الرابع
المسار الزراعي
This project addresses a significant problem in the field of soft robotics and automated manipulation for Fruit Harvesting, which is the unstable gras…
انامل — جامعة العين العراقية
This project addresses a significant problem in the field of soft robotics and automated manipulation for Fruit Harvesting, which is the unstable grasping of smooth or low-friction objects. The proposed solution is based on a cable-driven logarithmic spiral soft robot (SpiRob) integrated with 3D-printed flexible contact membranes and reusable directional nano-adhesion tape, with the aim of maximizing payload stability without requiring excessive mechanical clamping force. The expected outcomes include a substantial increase in grasping reliability for 3-arm robotic arrays, allowing the system to securely hold smooth geometries by combining morphological wrapping with physical van der Waals adhesion.
المركز الرابع
المسار الصناعي
Industrial factories face increasing cyber threats targeting programmable logic controllers (PLCs), sensors, cameras, and human-machine interfaces (HM…
Beta5G — جامعة الموصل
Industrial factories face increasing cyber threats targeting programmable logic controllers (PLCs), sensors, cameras, and human-machine interfaces (HMIs). This paper presents a distributed, real-time intrusion detection system (IDS) for smart factories, built on a three-tier Fog–Cloud–Dashboard architecture deployed via Docker. The Fog layer applies a tuned Decision Tree (DT v3, Recall = 99.89%) combined with an Isolation Forest anomaly detector to filter network traffic locally, forwarding only suspicious packets to the Cloud. The Cloud layer uses an XGBoost classifier (Accuracy = 76.15%) trained on the UNSW-NB15 dataset with SMOTE oversampling and adversarial augmentation to classify nine attack types, while SHAP values provide human-interpretable explanations for each decision. Two web dashboards serve distinct roles: a technical dashboard for security managers and a simplified bilingual (Arabic/English) observer interface. Live tests demonstrate a detection rate exceeding 97% under stress conditions, with sub-second response latency.
المركز الرابع مكرر
المسار الصناعي
This project presents a machine-learning system that detects malicious Android applications through static analysis alone, without executing the appli…
Machine learning for detection threats on mobile applications — الجامعة المستنصرية
This project presents a machine-learning system that detects malicious Android applications through static analysis alone, without executing the application. Using the TUANDROMD dataset of 4,465 real Android applications described by 241 static features, the pipeline applies a custom Random Forest model for feature selection and Principal Component Analysis for dimensionality reduction, then classifies each application as malware or goodware using a Double Q-Learning reinforcement-learning model with an asymmetric reward structure that penalizes missed malware more heavily than false alarms. The trained model achieves 94.89% accuracy and a low false-negative rate of 5.39%, and is deployed through a simple interactive desktop application.
المركز الرابع
المسار الطبي
Abstract This project addresses the growing problem of poor air quality and its impact on respiratory health, especially among children, older adults,…
PH Coders — جامعة الناجي
Abstract This project addresses the growing problem of poor air quality and its impact on respiratory health, especially among children, older adults, and people with respiratory diseases. The proposed solution is based on combining air quality monitoring with lung sound analysis using gas sensors, signal processing, and artificial intelligence. The main objective is to provide an intelligent system that can monitor environmental air conditions and detect possible respiratory diseases at an early stage. The expected outcomes include air quality assessment, early detection of lung diseases such as asthma, bronchitis, chronic obstructive pulmonary disease (COPD), and pneumonia, continuous health monitoring, and better support for medical decision-making. Keywords: Lung sound analysis, Artificial intelligence, Respiratory disease detection, Air quality monitoring. برنامج ذكاء اصطناعي لتحليل صوت الرئة لأمراض الجهاز التنفسي: الربو، التهاب القصيبات، مرض الانسداد الرئوي المزمن والالتهاب الرئوي الخلاصة يعالج هذا المشروع المشكلة المتزايدة لسوء جودة الهواء وتأثيرها على صحة الجهاز التنفسي، خاصة بين الأطفال وكبار السن والأشخاص المصابين بأمراض الجهاز التنفسي. الحل المقترح يعتمد على دمج مراقبة جودة الهواء مع تحليل صوت الرئة باستخدام حساسات الغاز، ومعالجة الإشارات، والذكاء الاصطناعي. الهدف الرئيسي هو توفير نظام ذكي يمكنه مراقبة حالة الهواء البيئية واكتشاف الأمراض التنفسية المحتملة في مرحلة مبكرة. تشمل النتائج المتوقعة تقييم جودة الهواء، والكشف المبكر عن أمراض الرئة مثل الربو، والتهاب الشعب الهوائية، ومرض الانسداد الرئوي المزمن، والالتهاب الرئوي، والمراقبة الصحية المستمرة، ودعم أفضل لاتخاذ القرار الطبي. الكلمات المفتاحية: تحليل صوت الرئة، الذكاء الاصطناعي، كشف أمراض الجهاز التنفسي، مراقبة جودة الهواء.
المركز الرابع
مسار التربية والتعليم
University students in Iraq face significant gaps in culturally specific educational and emotional support. This project introduces Nibras, an open-so…
NGX — جامعة النور
University students in Iraq face significant gaps in culturally specific educational and emotional support. This project introduces Nibras, an open-source, low-cost (US$31) intelligent robot deployed on a Raspberry Pi 4. Unlike generic AI, Nibras integrates a Claude Haiku 4.5 language model with a specialized computer vision pipeline to estimate emotional cues such as stress and fatigue. Using a voice-first interface, it communicates in native Iraqi Arabic, providing tutoring and emotional wellbeing support. A parent dashboard provides daily summarized reports on mood trends and engagement without compromising student privacy through local-only processing. Testing across 150 cases demonstrates high accuracy in dialect preservation (96%) and crisis detection (100%), proving that sophisticated, hardware-integrated AI can be accessible and culturally resonant. Keywords: Artificial intelligence, AI, computer vision, Iraqi dialect, emotional well-being.
المركز الرابع
مسار خدمة المجتمع
مشروع أثر هو نظام ذكي مساعد لقراءة المخطوطات العربية التاريخية ومراجعتها يجمع بين عدة طبقات ذكية تبدا من نموذج تعرّف الخط اليدوي HTR( دربناه على مجموع…
فينكس — الجامعة التكنولوجية
مشروع أثر هو نظام ذكي مساعد لقراءة المخطوطات العربية التاريخية ومراجعتها يجمع بين عدة طبقات ذكية تبدا من نموذج تعرّف الخط اليدوي HTR( دربناه على مجموعة اوسع من مخطوطات) ، ونموذج تجزئة تخطيط الصفحات، ونموذج لغوي مصغر موجّه لدعم التفريغ، مع أدوات مراجعة بشرية تفاعلية. يهدف المشروع إلى تحويل صور المخطوطات إلى نصوص قابلة للبحث والتحليل، مع الحفاظ على النص الأصلي وإظهار مواضع الشك والقراءات البديلة بدلاً من التصحيح الصامت. كما يدعم النظام الذكاء الاصطناعي في قراءة المخطوطات او شرحها، والتدقيق بالمصادر الرقمية مثل المكتبات الخارجية والبحث في فهارس المخطوطات مما يوفر أداة مساعدة للباحثين والمحققين في التعامل مع المخطوطات العربية المعقدة,
المركز الخامس
المسار البيئي
يتناول هذا المشروع مشكلةً جوهريةً في مجال إدارة حركة المرور الحضري. ويستند الحل المقترح إلى وكيل Double DQN مشترك مدرَّب على شبكة محاكاة مكونة من 50 ت…
البينه — جامعة الصفوة
يتناول هذا المشروع مشكلةً جوهريةً في مجال إدارة حركة المرور الحضري. ويستند الحل المقترح إلى وكيل Double DQN مشترك مدرَّب على شبكة محاكاة مكونة من 50 تقاطعاً، يتعلم زيادة الوقت او انقاص وقت عبور السارات بالتوافق مع الطلب المروري مع دعم تحديد أولويات سيارات الإسعاف عبر GPS، بهدف تقليل وقت انتظار السائقين والحد من الازدحام وخفض الانبعاثات الناتجة عن توقف المركبات. وتشمل النتائج تخفيض وقت انتظار السائقين بنسبة 22%، ووقت انتظار سيارات الإسعاف بنسبة 85%، ورفع معدل مرور السيارات بنسبة 7%، وخفض الازدحام الشديد بنسبة 24%، موثَّقةً عبر 48 سيناريو متطابق الطلب المروري.
المركز الخامس
المسار الزراعي
This project addresses a significant problem in Farming, where water waste is at all times high, especially in Iraq. The proposed solution is based on…
DevSyndrome — الجامعة التكنولوجية
This project addresses a significant problem in Farming, where water waste is at all times high, especially in Iraq. The proposed solution is based on a hardware system that consists of a temperature sensor, humidity sensor, waterflow sensor, and rain sensor. As well as a smart software system and application that gets direct live information from the soil activity via the sensors, and decides when to water the crops and how much water to spend, with the aim of lowering water waste. Additionally, the application has a smart AI assistant that is trained on Iraqi climate. The expected outcomes include, simplifying the farming process for farmers, especially amateurs. On the other hand, it will in high hopes help reduce water waste by up to 70% when it comes to the farming industry.
المركز الخامس
المسار الصناعي
منصة تعمل كوكيل ذكاء اصطناعي (AI Agent) لأتمتة الإنتاج الإعلامي بالكامل. يقوم النظام بتحليل الفيديوهات الطويلة وتحويلها آلياً إلى مقاطع سياقية (قصيرة …
فريق التقنية للنصر — جامعة العين العراقية
منصة تعمل كوكيل ذكاء اصطناعي (AI Agent) لأتمتة الإنتاج الإعلامي بالكامل. يقوم النظام بتحليل الفيديوهات الطويلة وتحويلها آلياً إلى مقاطع سياقية (قصيرة ومتوسطة)، مع توليد الصور المصغرة والعناوين المحسّنة (SEO). تتم جميع عمليات المعالجة والمونتاج عبر سيرفرات سحابية، لينتهي الأمر بنشر وجدولة المحتوى فورياً على أكثر من 8 منصات تواصل اجتماعي من خلال لوحة تحكم مركزية، مما يختصر عمل فريق كامل بضغطة زر.
المركز الخامس مكرر
المسار الصناعي
This paper presents a design and implementation of Geostring, an end-to-end automated string-art platform that converts any digital photograph into a …
مهندسو الاوتار — الجامعة التقنية الشمالية
This paper presents a design and implementation of Geostring, an end-to-end automated string-art platform that converts any digital photograph into a physical artwork composed of a single continuous thread wound around a circular nail frame. The system comprises three integrated components: an interactive web-based 2D generator employing a greedy chord-selection algorithm in polar coordinates, a live Three.js-based 3D mechanical simulator, and an AI-assisted parameter tuning assistant. The physical drawing machine is built around an Arduino UNO R3 running GRBL firmware, two NEMA 17 stepper motors driven by TMC2225 silent drivers, and a GT2 belt-driven linear arm operating over a 290 mm rotating disc. The greedy algorithm iteratively selects the nail-to-nail chord that maximally reduces the residual image darkness, producing sequences of up to 8,000 chords. Experimental evaluation confirms a disc positioning accuracy of 0.08° mean error, thread-shuttle repeatability of ±0.15 mm, and successful autonomous artwork production. The platform bridges computational art with physical mechatronic fabrication, providing an open, modular, and affordable system applicable to engineering education, creative technology, and intelligent automation research.project addresses a significant problem in (mention the field or domain). The proposed solution is based on (briefly describe the idea or technology) with the aim of (state the main objective). The expected outcomes include (summarize the anticipated results or impact).
المركز الخامس
المسار الطبي
يتناول هذا المشروع مشكلة جوهرية في الجراحة المعتمدة على التشغيل عن بعد, حيث تؤثر الرعشات الفسيولوجية لليد لدى الجراح في مستوى الدقة اثناء اداء الاجراء…
فريق ATSA روبوت جراحي مدعم بالذكاء الاصطناعي — جامعة نينوى
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.
المركز الخامس
مسار التربية والتعليم
This project tackles the challenges in EdTech and immersive learning, where abstract concepts can prove hard to comprehend as traditional educational …
مبرمجون بلا حدود — جامعة الموصل
This project tackles the challenges in EdTech and immersive learning, where abstract concepts can prove hard to comprehend as traditional educational materials predominantly use static text and two-dimensional illustrations. The project designs and implements an AI-powered interactive learning platform that automatically converts traditional educational material into experiential learning experiences. It accepts a wide variety of input types, such as PDF, DOCX, PowerPoint presentations and images (which can be in file or image formats), videos and text documents. Mix AI$ and models include OpenAI GPT, Google Gemini, Anthropic Claude, DeepSeek (an engine that is built specifically for the curriculum context), local NPL china's framework to analyze content of each unit in the subjects as a learning bot which learns from your resources ($881.10 quarterly) These resources are split from formative multiple-choice questions (MCQs) to AI-generated mind maps and interactive learning environment through Augmented Reality, Web-based Virtual Reality (WebVR - A-Frame). The graceful degradation of the multi-engine AI architecture, which is proposed in this work, could improve reliability and privacy with installation method because integrated system may detect failure in cloud service usage capability and still also be operational for critical timely operation requirement. Experimental Implementation The proposed solution was successfully validated as an automated curriculum analysis, concept extraction and 3D learning content generation without manually generating the 3D content [7]. The platform also allows for shorter preparation time required of instructors, greater interaction and engagement as well as conceptual understanding through more immersive experiences when compared to traditional ways of teaching. Assimilation of Artificial intelligence with Augmented and Virtual Reality solutions provides a flexible and optimal answer for postmodern modern-day digital education & supports the inherent pressure to implement technology-enabled learning environments .
المركز الخامس
مسار خدمة المجتمع
The project presents an AI-based autonomous fire detection and extinguishing system designed to enhance rapid response to fire incidents. The system u…
innovation — الجامعة التكنولوجية
The project presents an AI-based autonomous fire detection and extinguishing system designed to enhance rapid response to fire incidents. The system utilizes computers. vision algorithms integrated with surveillance cameras to detect fire in real time. Once a fire is identified, an autonomous drone is deployed to the exact location of the detected event. The drone performs additional onboard visual analysis to precisely locate the fire source. Upon confirmation, a servo-actuated mechanism activates a fire extinguisher to suppress the fire efficiently. This approach aims to reduce response time, minimize human risk, and improve fire control in hazardous or challenging-to-reach environments.
جرّب تغيير كلمة البحث أو إعادة ضبط الفلاتر لعرض جميع المشاريع الفائزة.