1
| 1. |
{"id":938,"project_id":1302,"title":"CyberHawk: AI-Powered Intrusion Detection and Prevention System with Ransomware & Malware Analysis","prob":"CyberHawk addresses rising cyber threats, including network intrusions, malware, and ransomware, which traditional security systems fail to detect effectively. It leverages AI-driven real-time traffic analysis to identify malicious activities early, preventing data breaches and financial losses. By integrating machine learning models (RF, LSTM, XGBoost) with firewall automation and ransomware mitigation, CyberHawk enhances cybersecurity for businesses and individuals.","description":"CyberHawk is an AI-driven Intrusion Detection and Prevention System (IDPS) that captures real-time network traffic using raw sockets and packet sniffing, analyzes it with machine learning models (RF, LSTM, XGBoost), and detects threats like intrusions, malware, and ransomware. It leverages Snort, Suricata, and Wireshark for traffic analysis, identifies malicious activities through anomaly detection, and mitigates ransomware using honeypots and rollback mechanisms. The system automates firewall rules and provides real-time alerts through a web-based dashboard, ensuring proactive cybersecurity for businesses and individuals.$$\nNetwork Traffic Capture & Preprocessing (Month 1-2)\r\n\r\nPurpose: Capture real-time network traffic for intrusion and malware analysis.\r\nFunctionality: Utilize raw sockets and packet sniffing tools (Wireshark, TCPDump) to collect network packets, categorize traffic types (HTTP, FTP, SSH, etc.), and preprocess data for model training.\r\nFeature Engineering & Model Selection (Month 3)\r\n\r\nPurpose: Identify critical network traffic patterns and choose optimal ML models.\r\nFunctionality: Extract features like packet size, protocol types, encryption behavior, and apply feature selection techniques for IDS, malware, and ransomware detection using models like RF, LSTM, and XGBoost.\r\nModel Training & Evaluation (Month 4)\r\n\r\nPurpose: Train AI models for detecting network intrusions, malware, and ransomware.\r\nFunctionality: Use labeled datasets (CICIDS2017, VirusTotal, EMBER) to train ML models, optimize hyperparameters, and evaluate detection accuracy using performance metrics (Precision, Recall, F1-score).\r\nReal-Time Intrusion & Ransomware Detection (Month 5)\r\n\r\nPurpose: Deploy a real-time threat detection system.\r\nFunctionality: Integrate trained models with a live network monitoring setup, detect abnormal network behaviors, analyze ransomware encryption patterns, and generate security alerts.\r\nWeb-Based Monitoring Dashboard (Month 6)\r\n\r\nPurpose: Provide an interactive interface for real-time threat visualization.\r\nFunctionality: Develop a web dashboard to display detected threats, system logs, and automated firewall rules, ensuring easy monitoring and response.\r\nIntegration of IDS with IPS for Automated Response (Month 7)\r\n\r\nPurpose: Implement automated threat prevention mechanisms.\r\nFunctionality: Configure Snort and Suricata for real-time attack mitigation, implement firewall rule automation, and develop ransomware mitigation techniques (rollback, deception-based honeypots).\r\nFinal Testing, Optimization & Documentation (Month 8)\r\n\r\nPurpose: Ensure system reliability and prepare final reports.\r\nFunctionality: Conduct extensive testing using real-world attack scenarios, optimize detection models, validate mitigation strategies, and complete final project documentation.$$\nAhmed \u2013 Intrusion Prevention and Ransomware Mitigation\r\n\r\nNetwork Traffic Capture & Ransomware Data Collection (Month 1-2)\r\nCollect ransomware datasets (TheZoo, MalwareBazaar).\r\nCapture real-time network traffic using raw sockets and analyze ransomware behaviors.\r\n\r\nFeature Engineering & Prevention Mechanisms (Month 3)\r\nExtract ransomware activity patterns (file encryption, process anomalies).\r\nImplement firewall configurations, IPS rules, and deception-based honeypots.\r\n\r\nModel Training & Real-Time Ransomware Detection (Month 4-5)\r\nDevelop ML models for ransomware detection and prevention.\r\nImplement real-time file encryption monitoring and automated rollback mechanisms.\r\n\r\nWeb Dashboard & IPS Optimization (Month 6)\r\nBuild an interactive web dashboard for real-time security monitoring.\r\nOptimize IPS rules and fine-tune ransomware mitigation strategies.\r\n\r\n\r\nJoint Responsibilities\r\n\r\nIntegration of IDS with IPS (Month 7)\r\nCombine IDS and IPS for real-time threat detection and prevention.\r\nImplement automated security actions (rollback, deception, honeypots).\r\n\r\nFinal Testing & Documentation (Month 8)\r\nConduct end-to-end system validation with real-world attack scenarios.$$\nHassan \u2013 Intrusion Detection & Malware Analysis\r\n\r\nNetwork Traffic Capture & IDS Data Collection (Month 1-2)\r\nCapture real-time network traffic for IDS analysis.\r\nCollect datasets (CICIDS2017, VirusTotal, EMBER) and study network intrusion patterns.\r\n\r\nFeature Engineering & Model Selection (Month 3)\r\nExtract critical network traffic features for IDS and malware detection.\r\nEvaluate ML models (RF, LSTM, XGBoost) for anomaly and malware detection.\r\n\r\nModel Training & Real-Time IDS Deployment (Month 4-5)\r\nTrain IDS and malware detection models with real-time traffic data.\r\nDeploy IDS using Flask\/Django APIs for real-time network monitoring.\r\n\r\nWeb Dashboard & IDS Optimization (Month 6)\r\nDevelop a web dashboard to visualize detected intrusions and malware activities.\r\nOptimize IDS performance to minimize false positives and improve detection accuracy.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-19 00:55:38","updated_at":"2025-03-27 12:58:15","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
CyberHawk: AI-Powered Intrusion Detection and Prevention System with Ransomware & Malware Analysis |
CIIT/SP22-BSE-055/WAH
CIIT/SP22-BSE-057/WAH
|
MUHAMMAD AHMED
HASSAN JAVED
|
Done
|
Accepted
|
| 2. |
{"id":945,"project_id":1305,"title":"DeepHeal HMS","prob":"This project addresses the challenge of Hospital management and mental health assessment by integrating AI-powered depression detection into a full-fledged Hospital Management System (HMS). Traditional hospital management often lacks integrated mental health screening, making depression detection difficult. Additionally, existing mental health systems rely on subjective assessments, leading to inconsistencies and delayed diagnosis.\r\nBy incorporating AI-based depression detection into the HMS, this project ensures a comprehensive healthcare solution, enabling early detection of depression while efficiently managing patient-doctor interactions, medical records, and hospital operations.","description":"The Hospital Management System (HMS) is a web-based application designed to manage patients, doctors, and administrative tasks while providing depression detection as an additional feature. The system includes:\r\n1. Patient Module:\r\n\uf0b7Appointment Booking: Patients can book appointments with doctors.\r\n\uf0b7Medical Record Management: Patients can access their medical history.\r\n\uf0b7Depression Detection: Patients can undergo AI-based speech analysis for depression screening.\r\n2. Doctor Module:\r\n\uf0b7Patient Management: Doctors can view and update patient records.\r\n\uf0b7Depression Analysis Reports: Doctors can analyze depression detection results and provide recommendations.\r\n\uf0b7Prescription Management: Doctors can generate and update prescriptions.\r\n3. Admin Module:\r\n\uf0b7User Management: Admins can add, remove, and manage users (doctors and patients).\r\n\uf0b7Hospital Records: Admins oversee appointments, reports, and patient statistics.\r\n\uf0b7Role-Based Access: Secure access control for different system users.$$\nSystem Modules & Features:\r\nA) Preprocessing Module\r\n\uf0b7Removes background noise and enhances speech clarity.\r\n\uf0b7Normalizes audio for consistent analysis.\r\nB) Feature Extraction Module\r\n\uf0b7Extracts key speech patterns related to depression.\r\n\uf0b7Uses Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms for audio analysis.\r\nC) Machine Learning Model Development Module\r\n\uf0b7Trains deep learning models for depression classification.\r\n\uf0b7Fine-tunes models for accurate predictions.\r\nD) Model Evaluation and Testing Module\r\n\uf0b7Evaluates system performance using precision, recall, and F1-score.\r\n\uf0b7Tests on real-world datasets (e.g., DAIC-WOZ) to improve accuracy.\r\nE) System Deployment Module\r\n\uf0b7Integrates the depression detection system within the HMS.\r\n\uf0b7Patient Dashboard: Patients can record speech for depression analysis.\r\n\uf0b7Doctor Dashboard: Doctors can review depression detection results.\r\n\uf0b7Admin Dashboard: Admins can monitor system performance and manage users.$$\nA) Preprocessing Module B) Feature Extraction Module\r\n\uf0b7Implements preprocessing techniques (noise reduction, silence removal, normalization).\r\nB)Feature Extraction Module\r\n\uf0b7Extracts MFCCs, pitch, tone, and speech rate from audio.\r\n\uf0b7Ensures optimized feature extraction for real-time processing on website\r\nC)Deployment (User Interface Module)\r\n\uf0b7Develops the web based system for real-time stress detection.\r\n\uf0b7Implements API integration for model inference.\r\n\uf0b7Syncs data with database for stress tracking.$$\nC) Model Development Module\r\n\uf0b7Train machine learning models using extracted features.\r\n\uf0b7Fine-tune models for accurate depression classification.\r\nD)Model Evaluation and Testing Module\r\n\uf0b7Implement precision, recall, and F1-score metrics for evaluation.\r\n\uf0b7Test the model on real-world and unseen data for validation.\r\nE)Development Module (Backend Integration)\r\n\uf0b7Implements server-side model integration for real-time predictions on web.\r\n\uf0b7Ensures seamless data exchange between web interface and backend$$\n$$\nHospital Management System For Mehmood Eye Hospital Dera Ismail Khan$$\nDepression Detection using Speech based system$$\nMedical Reports via Email$$\nAppointment Booking based on Depression Detection","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-19 23:22:59","updated_at":"2025-03-21 13:13:26","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
DeepHeal HMS |
CIIT/SP22-BSE-039/WAH
CIIT/SP22-BSE-049/WAH
|
TAIMUR JAN
AZHAR MEHMOOD
|
Done
|
Accepted
|
| 3. |
{"id":943,"project_id":1276,"title":"\"AI-Based Automated Helmet Violation Detection and E-Challan System for Traffic Law Enforcement\".","prob":"Many motorcyclists do not wear helmets, increasing the risk of serious injuries in road accidents. Despite strict traffic laws, many riders ignore helmet regulations, putting their lives at risk. Traffic police manually check for violations, which is a slow, inefficient, and labor-intensive process. Due to limited resources, many violators go unnoticed or escape without paying fines, making law enforcement ineffective. Some riders delay or completely avoid paying their challans, leading to poor compliance with traffic rules.\r\nThis project provides an automated helmet violation detection system using AI-based image processing and OCR technology. Cameras at traffic signals and checkpoints capture images of violators, extract their number plates, and issue e-challans automatically. The system also offers an online payment option, making it easy for violators to pay their fines. Repeated offenses result in stricter actions, such as warnings or license blocking. This system reduces human effort, fine collection, and significantly improves road","description":"The Automated Helmet Violation Detection and E-Challan System is an advanced traffic enforcement solution designed to enhance road safety by ensuring motorcyclists comply with helmet laws. The system leverages real-time image processing and AI-based recognition to detect riders without helmets, capture their images, and take appropriate action.\r\nThe system continuously monitors traffic using high-resolution cameras at signals and checkpoints. The CameraX API in the Android app extracts frames from live video and sends them to YOLOv8 on a cloud server for helmet detection. If a helmet violation is detected, the frame is sent to OCR (Optical Character Recognition) to extract the motorcycle\u2019s registration number from the license plate. The extracted number is checked against the traffic police database to retrieve the rider\u2019s details.\r\nOnce the violator\u2019s details are identified, an e-challan is generated and sent via SMS or email and also notify guardians if minors are caught violating helmet rules. The challan includes the captured violation image, offense details, fine amount, and payment deadline. If unpaid within a week, a reminder notification is sent. For three repeated violations, stricter actions such as license blocking are enforced. Parents receive alerts if minors are caught violating helmet rules.\r\nThe system features a mobile and web-based application where users can:\r\nCheck pending challans\r\nView violation details\r\nMake payments securely via JazzCash, Easypaisa, or internet banking\r\nA Statistical Dashboard for Authorities provides analytics on violations per day, top areas, and peak times. Helmet compliance trends are also monitored to enhance enforcement strategies. Additionally, a Gamified Road Safety Quiz & Certification feature promotes responsible driving behavior.\r\nBy the end of the FYP, the Automated Helmet Violation Detection and E-Challan System will be developed, tested, and deployed within the university to demonstrate its practical application.$$\n1. Helmet Violation Detection\r\nThis module is the core of the system and is responsible for detecting whether a motorcyclist is wearing a helmet. It uses AI-based image recognition and deep learning models trained on thousands of images of riders with and without helmets. The system continuously monitors traffic using high-resolution cameras installed at strategic locations such as traffic signals, and checkpoints. Once a motorcycle is detected, the system processes the image to identify the rider\u2019s head and checks for the presence of a helmet. If no helmet is detected, the violation is flagged, and the image is captured for further processing.\r\n2. Number Plate Recognition Module\r\nAfter detecting a helmet violation, the next step is to identify the vehicle involved. This module extracts the motorcycle\u2019s number plate using (OCR) technology. The system isolates the number plate from the captured image, extracts the alphanumeric characters, and converts them into text format. The extracted number plate is then matched with the traffic department\u2019s database to retrieve the vehicle owner\u2019s details, ensuring that the correct violator is identified.\r\n3. E-Challan Generation \r\nOnce the violator\u2019s details are obtained, this module automatically generates an e-challan. The challan includes:\r\nFine amount based on traffic rules\r\nPayment deadline and instructions\r\n4. Violation History Module\r\nThis module maintains a record of each motorcyclist\u2019s violation history. It logs details such as:\r\nTotal number of helmet violations committed\r\nPending challans and their payment status\r\nIf a rider commits multiple offenses, the system applies stricter penalties. After three repeated violations, the system escalates the issue by blocking the rider\u2019s license or applying higher fines.\r\n5. Traffic Police Dashboard\r\nThis module provides a web-based dashboard for traffic authorities and system administrators. The dashboard offers:\r\nReal-time monitoring of traffic violations\r\nAccess to captured images and vehicle details\r\nManagement of issued and paid challans\r\nSearch and filtering options for quick data retrieval\r\nReports and analytics on helmet violations and fine collections\r\nThis module helps police to efficiently manage violations, monitor trends, and take necessary actions against repeat offenders.\r\n6. SMS & Email Notification \r\nThis module is responsible for automatically sending notifications to violators and traffic authorities. The system generates alerts for:\r\nNew challan issuance\r\nWarning messages for repeat offenders\r\nLicense blocking alerts for unpaid fines\r\nNotifications are sent via SMS and email.\r\n7. User Mobile Application\r\nThis module provides a convenient platform for violators to manage their challans. The application allows users to:\r\nLog in securely \r\nCheck pending challans\r\nMake online payments securely using \r\nReceive instant payment confirmation\r\n8. Database Management \r\nThis module is responsible for securely storing and managing all system records, including:\r\nThe database is designed to handle large amounts of traffic data efficiently, ensuring secure storage, quick retrieval, and scalability. \r\n9. Parent Alerts:\r\n Notify guardians if minors are caught violating helmet rules.\r\n10. Statistical Insights: \r\nProvide analytics on helmet compliance trends.\r\n11.Gamified Road Safety Quiz:\r\n Encourage responsible driving behavior.\r\n12.Statistical Dashboard for Authorities: \r\nProvide analytics on helmet violations$$\nI shall developed the following modules:\r\n1. E-Challan Generation\r\nThis module automatically generates an e-challan after identifying the violator. It includes the fine amount, payment deadline. \r\n2. Violation History\r\nThis module logs each motorcyclist\u2019s violation history, tracking total offenses, pending challans, and payment status. For repeat offenders, stricter penalties are applied, such as higher fines or license blocking after three violations\r\n3. Traffic Police Dashboard\r\nA dashboard for authorities to monitor real-time violations, access captured images, manage challans, track fine collections, and generate reports. It provides search and filtering options for quick data retrieval.\r\n4. User Mobile Application Module\r\nThis app allows violators to log in securely, check pending challans, and make secure online payments via multiple payment methods. Users receive instant payment confirmation and notifications.\r\n5.Statistical Dashboard for Authorities:\r\nProvide analytics on helmet violations\r\n6.Statistical Insights:\r\nProvide analytics on helmet compliance trends$$\nI shall developed following modules:\r\n1. Helmet Violation Detection\r\nThis module detects whether a motorcyclist is wearing a helmet using AI-based image recognition and deep learning models. High-resolution cameras at traffic signals capture images, analyze the rider\u2019s head.\r\n2. Number Plate Recognition\r\nOnce a violation is detected, this module extracts the motorcycle\u2019s number plate using OCR technology. It converts the number plate into text and matches it with the traffic department\u2019s database to retrieve the owner\u2019s details.\r\n3. SMS & Email Notification\r\nThis module automatically sends notifications to violators and authorities for new challans, payment reminders, and license blocking alerts via SMS and email, ensuring timely compliance.\r\n4. Database Management\r\nThis module securely stores and manages all records, including violation images, vehicle details, and payment history. \r\n5. Parent Alerts:\r\nNotify guardians if minors are caught violating helmet rules.\r\n6.Gamified Road Safety Quiz:\r\nEncourage responsible driving behavior.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-19 21:33:33","updated_at":"2025-03-21 13:10:28","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
"AI-Based Automated Helmet Violation Detection and E-Challan System for Traffic Law Enforcement". |
CIIT/SP22-BSE-015/WAH
CIIT/SP22-BSE-059/WAH
|
ANAS EJAZ
ZOBIA FIAZ
|
Done
|
Accepted
|
| 4. |
{"id":933,"project_id":1297,"title":"XAI-Driven Pediatric Fracture Diagnosis with Voice Navigation, Image Processing, and Agentive AI for Recovery.","prob":"Pediatric healthcare faces several challenges, including delayed diagnosis, lack of accessibility, and ineffective rehabilitation. Many hospitals, especially in rural areas, lack trained radiologists, leading to misdiagnosed or un-diagnosed fractures that can cause long-term complications. Parents struggle to understand complex medical reports, making informed decision-making difficult. Additionally, individuals unfamiliar with digital platforms find it hard to navigate medical systems. This project solves these issues by using AI-powered image processing for accurate diagnosis, Explainable AI for transparency, Recommendation System for report, h voice navigation for accessibility, and Agentive AI for personalized rehabilitation, ensuring better healthcare for children and aiding medical professionals in learning and diagnosis.","description":"This AI-driven healthcare system helps doctors and patients diagnose pediatric fractures. It integrates multiple advanced technologies, including Image Processing, Natural Language Processing (NLP), Explainable AI (XAI), Agentive AI, and Voice Recognition, to enhance the accuracy of diagnosis and treatment. By utilizing these technologies, the system aims to streamline the medical process, making it more efficient and accessible for both doctors and patients.\r\n\r\nUsers begin by logging into the system, where doctors access patient records, and patients can view medical reports, diagnosis results, and personalized exercise plans. The system ensures a structured approach by guiding users through each step. After providing personal details such as age, medical history, and symptoms, users upload an X-ray or MRI scan of the affected area. The AI then processes the image, detects fractures, and generates a comprehensive medical report with detailed insights.\r\nIf a fracture is detected, the system recommends tailored exercises and treatment plans. The webcam-based AI feature continuously monitors patient movements, providing real-time feedback to ensure correct posture during rehabilitation. Additionally, the voice recognition feature enables hands-free navigation, making it easier for users to interact with the system effortlessly and efficiently.\r\n\r\nCommittee Comments and Responses\r\n\r\nDataset Accuracy: Initially questioned, but later verified to contain data for children aged 3 to 15.\r\n\r\nNLP & XAI Integration: The NLP model generates reports via the ChatGPT API, while XAI enhances transparency in diagnosis by explaining the AI's decisions clearly.\r\n\r\nDataset Age Group: The dataset was confirmed to be suitable for pediatric fracture detection after thorough verification by the research team.\r\n\r\nThis system improves healthcare accessibility, ensuring patients receive timely diagnoses, effective treatment plans, and continuous support for their recovery, ultimately enhancing medical care quality.$$\n\u2022 Admin Dashboard Module: This module provides an interface for managing patient records, monitoring system activities, and overseeing all administrative functions.\r\n\u2022 Image Processing Module: This module processes medical images, such as MRI scans, to detect fractures and other conditions using AI. It ensures accurate disease classification and assists doctors in diagnosis.\r\n\u2022 Explainable AI and Recommendation system: This module extracts key details from medical reports and provides understandable summaries. Explainable AI helps doctors and patients interpret AI-driven diagnoses with clear justifications.\r\n\u2022 Agentive AI for Real-Time Exercise Monitoring: This module uses a webcam-based AI system to track patient exercises, providing real-time feedback and guidance to ensure correct posture and prevent further injury.\r\n\u2022 Voice Recognition Module: This enables voice-based navigation and form-filling, allowing users to interact with the website hands-free, making it accessible to those unfamiliar with technology.\r\n\u2022 Patient Activity Tracking and Notification Module: This module keeps records of patient activities, sends reminders for missed exercises or follow-ups, and notifies users about updates on their health progress.\r\n\u2022 User Interface (UI) Module: This module ensures an intuitive and user-friendly experience, making navigation seamless for both doctors and patients. It integrates all features into a visually appealing and accessible design.$$\nIn HMS, I shall develop Pediatric Fracture Diagnosis which includes: \r\n1. Image Processing Module (MRI Image Upload and Disease Detection), 2. Explainable AI and NLP \r\n 3. Voice Recognition System Module\r\n 4. AI for Real-Time Exercise Monitoring$$\nIn HMS, I shall develop Lab Material Inventory module which includes: 1. Admin Panel Module\r\n 2. Notification and Alerts Module , \r\n 3. Frontend User Interface (UI) Design and User Experience (UX)\r\n 4. Patient Activity Tracking and Notification Module$$\n$$\n$$\nRecommendation System$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-18 06:32:17","updated_at":"2025-03-21 13:11:51","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
XAI-Driven Pediatric Fracture Diagnosis with Voice Navigation, Image Processing, and Agentive AI for Recovery. |
CIIT/SP22-BSE-027/WAH
CIIT/SP22-BSE-056/WAH
|
AHTESHAM ASIF
TAUQEER AHMED
|
Done
|
Accepted
|
| 5. |
{"id":949,"project_id":1313,"title":"SmartVision: An AI-Powered Mobile Assistant for Visually Impaired \r\nIndividuals","prob":"The visually impaired individuals face significant challenges in their daily lives \r\ndue to the inability to recognize objects read printed text, and navigate their \r\nsurroundings without external assistance. This lack of independence affects \r\ntheir ability to perform essential tasks such as identifying household items, \r\nreading signboards, and seeking help in emergencies. While some assistive \r\ntechnologies exist, they rely on expensive hardware or require constant \r\nhuman intervention, making them impractical for real-time use. To address \r\nthis issue, our project aims to develop an AI-powered mobile application that \r\nprovides real-time object recognition, text-to-speech conversion for printed \r\ntext, and hands-free voice command navigation. Application will utilize \r\nadvanced AI techniques to process visual and textual information, ensuring a \r\nseamless and interactive user experience. Additionally, it will feature an \r\nautomatic flashlight activation system for low-light conditions. This solution will \r\nempower visually impaired individuals, promoting greater autonomy in their \r\neveryday activities while enhancing safety and accessibility.","description":"The Blind Vision App is an AI-driven mobile application designed to provide \r\nreal-time assistance to visually impaired individuals by helping them \r\nrecognize objects, read printed text, and navigate their surroundings \r\nindependently. \r\nThe application functions through an intuitive interface where users can \r\ninteract via voice commands, allowing for hands-free operation. Upon \r\nactivation, the camera captures the surrounding environment, and the AI\r\npowered object detection system identifies and announces the objects in \r\nview. \r\nThe integrated OCR module further enables users to scan and convert \r\nprinted text into speech, providing a seamless way to read signboards, \r\ndocuments, or labels. To ensure usability in various lighting conditions, the \r\napp includes an automatic flashlight feature that activates in low-light \r\nenvironments. \r\nThe Blind Vision App merges the power of Flutter for a user-friendly \r\nexperience with advanced AI for object recognition and text processing, \r\nultimately offering an accessible, reliable, and intelligent solution for \r\nvisually impaired users to navigate their daily lives with greater \r\nindependence and safety.$$\n1.Camera & Object Detection Module: \r\n\u2022 Image Processing: Captures and processes images from the camera. \r\n\u2022 Real-Time Object Recognition: Detects objects and announces them using AI. \r\n2. Voice Command Module: \r\n\u2022 Speech-to-Text Processing: Converts spoken commands into actions. \r\n\u2022 Hands-Free Navigation: Enables users to control the app using voice commands. \r\n3. Text Recognition (OCR) Module: \r\n\u2022 Text Extraction: Reads printed text from images (e.g., signboards, labels). \r\n\u2022 Text-to-Speech Conversion: Converts extracted text into audible speech output. \r\n4. Auto Flashlight Module: \r\n\u2022 Low-Light Detection: Detects ambient light levels. \r\n\u2022 Automatic Flashlight Activation: Turns on the flashlight in dark conditions. \r\n5. Mobile Application Interface Module: \r\n\u2022 User Authentication: Implements sign-in, sign-up, and profile management. \r\n\u2022 User-Friendly UI: Ensures accessibility for visually impaired users. \r\n\u2022 Navigation & Settings: Provides easy-to-use controls and app settings. \r\n6. AI Model Training & Optimization Module: \r\n\u2022 Custom AI Model Training: Develops an object detection model for better \r\naccuracy. \r\n\u2022 Performance Optimization: Ensures the AI model runs efficiently on mobile devices. \r\n7. Backend & Database Management Module: \r\n\u2022 Cloud Storage: Manages user data and preferences using Firebase Firestore. \r\n\u2022 Data Communication: Facilitates interaction between the mobile \r\napp and AI models.$$\nCamera & Object Detection Integration \u2013 Implement camera functionality and send images to \r\nAI model. \r\n User Authentication Module: Implements sign-up, login, and user profile management using \r\nFirebase\/Auth API. \r\n Voice Command & Speech Processing Module: Integrates speech-to-text functionality, \r\nallowing users to control the app using voice commands. \r\n Backend & API Development: Implements REST APIs to communicate between the mobile \r\napp and AI models. \r\nBackend & Database Management \u2013 Set up Firebase Firestore for data storage and \r\ncommunication. \r\nDeployment & Testing: Ensures the app is tested on multiple devices and \r\nplatforms before launch.$$\n1. Develop AI\/ML Models: \r\no Train object detection models (e.g., YOLO, TensorFlow Lite) for real-time object \r\nrecognition. \r\no Build an OCR model (e.g., Google ML Kit, Tesseract) for text extraction from \r\nimages. \r\n2. Optimize Models: \r\no Optimize models for mobile devices to ensure efficiency and low latency. \r\n3. Integrate with Flutter: \r\no Use tflite_flutter to integrate models into the app. \r\n4. Audio Feedback: \r\no Convert detected objects and text into audio using flutter_tts. \r\n5. Testing: \r\no Test models in real-world scenarios and evaluate performance.$$\n$$\nAn AI based mobile app working as a sight for blind and visually impaired$$\n1. Multi-Language Support: Recognises and speaks text in multiple languages.$$\n2. Voice-Controlled Navigation: Allows users to control the app entirely through \r\nvoice commands.$$\n3. Auto Flashlight in Low Light: The app will automatically turn on the flashlight \r\nin low-light conditions to ensure proper object detection.","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-21 15:05:34","updated_at":"2025-04-07 13:09:31","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
SmartVision: An AI-Powered Mobile Assistant for Visually Impaired
Individuals |
CIIT/SP22-BCS-005/WAH
CIIT/SP22-BCS-044/WAH
|
MUHAMMAD QASIM
MUHAMMAD IMRAN
|
Done
|
Accepted
|
| 6. |
AI-Based X-ray Fracture Detection System with Voice-Assisted Reporting |
CIIT/SP22-BCS-033/WAH
CIIT/SP22-BCS-063/WAH
|
MUSFARAH WAJID
SYED BAQIR HUSSAIN SHAH
|
Done
|
Accepted
|
| 7. |
{"id":934,"project_id":1289,"title":"MediPredict system (Volunteer Doctor & Disease Prediction System)","prob":"Millions worldwide, especially in remote areas, lack access to timely medical care due to doctor shortages, overcrowded hospitals, and high costs, leading to delayed diagnoses and severe complications. Even in urban areas, identifying symptoms before consulting a doctor can cause unnecessary panic or delays. The MediPredict AI bridges this gap by offering a web-based platform where patients connect with volunteer doctors for free or low-cost consultations. It features AI-driven disease prediction, live chat and video consultations, secure medical record management, appointment scheduling, and data encryption, ensuring accessible, efficient, and secure healthcare for all.","description":"The MediPredict AI is a web-based healthcare platform that enables patients to connect with volunteer doctors for free or low-cost consultations. The system integrates AI-powered disease prediction based on symptoms.\r\n\r\nThe platform allows patients to input symptoms, which an AI model analyzes to predict three diseases: Diabetes, Heart Disease, and Parkinson\u2019s Disease. The datasets for these predictions will be sourced from Kaggle, and the models used will be Logistic Regression and SVM Classifier. It supports live chat and video consultations, ensuring effective doctor-patient communication. Additionally, the system provides secure medical record management, enabling users to store and access prescriptions, reports, and consultation history. Appointment scheduling ensures seamless doctor-patient interactions, while data encryption and security protocols protect patient privacy.\r\n\r\nObjectives:\r\nProvide accessible and affordable healthcare through volunteer doctors.\r\n\r\nEnable AI-powered disease prediction for Diabetes, Heart Disease, and Parkinson\u2019s Disease using Logistic Regression and SVM Classifier.\r\n\r\nSupport secure video & chat consultations for remote healthcare access.\r\n\r\nOffer medical record management for prescriptions and reports.\r\n\r\nEnsure data privacy and security through encryption and access controls.\r\n\r\nBy integrating AI-driven diagnosis and telemedicine services, the system improves healthcare accessibility and early disease detection.$$\nIn the MediPredict system the modules are as follows:\r\n\r\n1: Doctor & Patient Dashboard\r\n2: Appointment Scheduling\r\n3: Live Chat & Video Consultation\r\n4: Notification System\r\n5: AI-Powered Disease Prediction\r\n6: Medical Records & Prescription Management\r\n7: User Profile Management\r\n8: Patient Feedback & Ratings$$\nIn the MediPredict system I shall develop the following modules\r\no\tAppointment Scheduling\r\no\tLive Chat & Video Consultation\r\no\tNotification System\r\no\tAI-Powered Disease Prediction$$\nIn the MediPredict system I shall develop the following modules\r\no\tDoctor & Patient Dashboard\r\no\tMedical Records & Prescription Management\r\no\tAdmin Profile Management\r\no\tPatient Feedback & Ratings$$\n$$\nVolunteer online doctor system$$\nAI-Powered Disease Prediction$$\nMedical Records & Prescription Management$$\nNotification System","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-18 15:28:10","updated_at":"2025-03-27 12:53:46","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
MediPredict system (Volunteer Doctor & Disease Prediction System) |
CIIT/SP22-BCS-018/WAH
CIIT/SP22-BCS-032/WAH
|
HAMMAD ALI
MUHAMMAD AJMAL
|
Done
|
Accepted
|
| 8. |
{"id":951,"project_id":1284,"title":"Garb & Go Auto Self Serve","prob":"This project introduces a fully automated, AI-powered retail solution that eliminates the need for cash transactions, checkout lines, and human attendants. Users gain access by identifying themselves, pick their desired items, and leave\u2014while the system automatically detects the selections and deducts the total from their virtual wallet. Operating 24\/7, it provides a fast, secure, and convenient shopping experience while reducing operational costs. By allowing only registered users with a minimum balance, it ensures controlled access and prevents unauthorized usage.\r\nThe system specifically targets standard-sized (one-size) items to simplify detection and inventory management. Using computer vision and object detection, it accurately recognizes products without relying on weight-based identification. Instead of RFID tags, which require tagging every product, or barcodes, which need manual scanning, AI-powered cameras enable seamless detection. With a one-time setup cost, this approach is more scalable, cost-effective, and ensures high accuracy in tracking purchases while eliminating additional expenses.","description":"The system begins when a registered user scans to gain access. It verifies their identity and ensures they have the minimum balance required in their virtual wallet. Once verified, access is granted, allowing the user to pick items.\r\nAI-powered camera detect the selected items, updating the virtual cart in real time. After the last item is picked, the system waits five minutes before automatically deducting the total amount from the user's virtual wallet. A payment confirmation is then sent via the mobile app.\r\nAs soon as a user picks an item, the system automatically updates the stock count. If the stock falls below a predefined threshold, an alert is sent to the admin panel for restocking.$$\n1. Customer Module:\r\n\u2022\tUser Registration & Authentication:\r\nImplement user signup, login, and authentication via the mobile app.\r\n\u2022\tVirtual Cart:\r\nThe cart updates automatically in real time as items are picked up or returned to the shelf.\r\n\u2022\tVirtual Wallet:\r\nUsers can add funds via payment method.\r\n2. Admin Module:\r\n\u2022\tAdmin Login & Registration:\r\nSecure login and registration for managing system operations.\r\n\u2022\tStock Management:\r\nAutomatically updates inventory when an item is sold. Sends low-stock alerts to the admin for restocking.\r\n\u2022\tSales Reports:\r\nGenerates daily sales and revenue reports.\r\n3. Smart Access Control:\r\n\u2022\tSecure Entry System:\r\nUsers authenticate themselves through a secure access method before gaining entry.\r\n\u2022\tAuthentication Validation:\r\nEnsures only registered users with a sufficient balance can access the system.\r\n4. AI-Powered Object Detection Module:\r\n\u2022\tObject Detection:\r\nUses AI-powered camera to detect which item is picked.\r\n\u2022\tVirtual Cart Updates:\r\nUpdates the cart dynamically in real time based on AI detection.\r\n\u2022\tStock Tracking :\r\nUpdates stock after an item is purchased.\r\n5. Payment Module:\r\n\u2022\tAutomated Payment Processing:\r\nAfter the last item is picked, the system waits five minutes before deducting the amount from the user's virtual wallet.\r\n\u2022\tBill Generation & Confirmation:\r\nGenerates the final bill and sends a digital receipt to the user via the mobile app.$$\nIn the \"Grab&Go Auto Self Serve,\" I will develop the following parts:\r\na) User Registration & Authentication: Implement user signup, login, and authentication via the mobile app.\r\nb) Virtual Cart:Users can see the cart update automatically in real-time as items are picked up or returned to the shelf.\r\n c) Virtual Wallet: User can choose a payment method to add amount in their wallet.\r\n d) Object Detection: Identify different items placed on the shelf and checks which item is being picked in real time. Trains the AI model for first half of the products handling dataset collection, labeling, and\u00a0YOLO\u00a0training.\r\ne) DB Management :\r\nEnsure seamless data flow between system modules, managing database operations for user authentication and product details while maintaining accuracy\u00a0and\u00a0efficiency.\r\nf) Bill Generation: Generates and displays the final bill.\r\ng)Digital Receipt: Sends a receipt to the user\u2019s app after successful payment.$$\nIn the \"Grab&Go Auto Self Serve,\" I will develop the following parts:\r\na) Admin Login & Registration: Secure login and registration for admin access to manage operations.\r\nb) Stock Management: Monitors inventory levels in real time and updates stock automatically as items are purchased \r\n c)Daily Sales Report: Generate daily reports on sales, and revenue.\r\n d) Restock Alerts: Notifies admin when stock is low.\r\n e) Object Detection: Identify different items placed on the shelf and checks which item is being picked in real time. Trains the AI model for the second half of the products handling dataset collection, labeling, and\u00a0YOLO\u00a0training.\r\n f) Smart Access Control: Users authenticate themselves through a secure access method before gaining entry. Ensures only registered users with a sufficient balance can access the system.\r\n g) Payment Processing: Automatically processes payment through user\u2019s virtual wallet.$$\n$$\nBILLING AUTOMATION SYSTEM FOR GENERAL STORES$$\n1.Checkout Process\r\nPrevious : Customers still go to checkout counter where multiple items are scanned at once.\r\nGrab&Go: Eliminates checkout counter entirely\u2014items are detected as they are picked.$$\n2. Payment Mechanism\r\nPrevious: Customers pay after scanning at the counter.\r\nGrab&Go: Payment is automatically deducted via a virtual wallet upon exit, enabling cashless and card less shopping.$$\n3.Availability\r\nPrevious: Require staff and are not available 24\/7. \r\nGrab&Go: Unmanned and operates 24\/7 with AI-powered camera for accurate tracking.","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-22 11:35:14","updated_at":"2025-04-07 13:09:57","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
Garb & Go Auto Self Serve |
CIIT/SP22-BSE-013/WAH
CIIT/SP22-BSE-042/WAH
|
FATIMA RAFAQAT
SHEHNAZ HAIDER
|
Done
|
Accepted
|
| 9. |
OptiAI |
CIIT/SP22-BCS-007/WAH
CIIT/SP22-BCS-008/WAH
|
MUHAMMAD NADIR
ANAS ALI
|
Done
|
Accepted
|
| 10. |
Qisflex: Interactive Learning Platform for Quantum Computing |
CIIT/SP22-BCS-047/WAH
CIIT/SP22-BCS-015/WAH
|
UMAIR AHMED YOUNAS
QAMAR ZAMAN
|
Done
|
Accepted
|
| 11. |
Assist Network Windows Service |
CIIT/SP22-BSE-008/WAH
CIIT/SP22-BSE-038/WAH
|
ABDULLAH
IBRAHIM ALI SHAH
|
Done
|
Accepted
|
| 12. |
{"id":948,"project_id":1309,"title":"Driver Awareness System for Road Safety (DASRoS)","prob":"Road accidents due to driver fatigue remain a major safety issue. In Pakistan, a significant percentage of accidents result from drowsy driving, causing injuries and fatalities. Existing solutions, such as manual monitoring and expensive infrared-based systems, are either ineffective or inaccessible to the general public. The Driver Awareness System for Road Safety (DASRoS) will provide an affordable and real-time drowsiness detection and alert system to reduce accidents caused by driver fatigue.","description":"The Driver Awareness System for Road Safety (DASRoS) is a real-time AI-powered drowsiness detection system designed to reduce road accidents caused by driver fatigue. The system will use computer vision and deep learning to analyze a driver's facial expressions, eye movements, blink rate, and head position to detect signs of drowsiness.\r\n\r\nThe system will utilize OpenCV and a pre-trained deep learning model (CNN or Haar Cascades) to track facial features. If prolonged eye closure or fatigue symptoms are detected, an alert mechanism (sound or vibration) will notify the driver.\r\n\r\nAdditionally, the system will store driver drowsiness data, which can help transport companies analyze patterns and improve driver schedules to prevent fatigue-related accidents. The proposed solution is designed to be cost-effective, working with standard webcams or mobile cameras without the need for expensive infrared sensors.\r\n\r\nKey features of DASRoS:\r\n\u2705 Real-time face and eye-tracking using OpenCV & Deep Learning\r\n\u2705 Drowsiness detection based on eye closure duration & head movement\r\n\u2705 Alert system (sound\/vibration) for instant driver notification\r\n\u2705 Data logging for fatigue analysis\r\n\u2705 Works with standard webcams, making it cost-effective\r\n\r\nBy implementing DASRoS, we aim to contribute to Pakistan\u2019s Vision 2025 by enhancing road safety through AI-driven innovation.$$\n1: Face and Eye Tracking Module\r\nUses OpenCV and Deep Learning (CNN\/Haar Cascades) to detect facial landmarks, track eye movement, and identify drowsiness symptoms.\r\n\r\n2: Alert System Module\r\nGenerates alerts (sound\/vibration) when prolonged drowsiness is detected.\r\n\r\n3: Data Logging & Reporting Module\r\nStores drowsiness events and generates reports for fatigue analysis and driver behavior insights.\r\n\r\n4: Mobile App \/ Web Interface (Optional)\r\nProvides real-time monitoring, drowsiness alerts, and historical data analysis for fleet management companies.$$\n\ud83d\udd39 Sadan Aleem (CIIT\/SP22-BSE-026\/WAH) is responsible for developing following: \r\n\u2705 Responsible for Face & Eye Tracking Module\r\n\u2705 Implements OpendCV, CNN, and Deep Learning Models$$\n\ud83d\udd39 Muhammad Abdullah Ashraf (CIIT\/SP22-BSE-033\/WAH) is responsible for developing following: \r\n\u2705 Responsible for Alert System & Data Logging Module\r\n\u2705 Works on sound\/vibration alerts and database management$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-21 14:49:29","updated_at":"2025-03-27 13:01:09","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
Driver Awareness System for Road Safety (DASRoS) |
CIIT/SP22-BSE-026/WAH
CIIT/SP22-BSE-033/WAH
|
SADAN ALEEM
MUHAMMAD ABDULLAH ASHRAF
|
Done
|
Accepted
|
| 13. |
{"id":944,"project_id":1304,"title":"RAG-Based Generative Story Writing: A Social Media Platform That Merges Retrieval with AI-Driven Narrative Creation","prob":"Modern AI-powered story-writing tools often produce narratives with broken plots, inconsistent characters, and missing background details. Additionally, they lack a platform for users to share their stories. This project addresses these issues by developing a system that not only generates high-quality stories using AI but also provides a social platform for users to share and explore content. The AI enhances storytelling by incorporating key literary elements such as character archetypes, popular themes, and structured plots while allowing user input for customization. This solution empowers writers, educators, and content creators to generate, refine, and share engaging stories within a dedicated storytelling community.","description":"This project develops a mobile application that enhances automated story writing by combining a retrieval system with a generative AI model. Users can enter a story prompt, and the AI generates a narrative using additional story elements like common character types, popular themes, and proven plot structures. The platform also allows users to post their generated stories and explore those shared by others, fostering a community of storytellers. By integrating well-established literary components and social interaction, the application improves character development, plot coherence, and overall story quality while creating an engaging space for writers, educators, and content creators.$$\n1.\tUser Input Module:\r\n\u2022\tAllows users to enter a story prompt.\r\n2.\tPrompt Analysis and Retrieval Module:\r\n\u2022\tRetrieves relevant literary cues to guide the narrative generation process.\r\n3.\tGenerative Module:\r\n\u2022\tIntegrates a fine-tuned generative AI model that processes the prompt along with the identified elements.\r\n4.\tStory Composition and Post-Processing Module:\r\n\u2022\tMerges and formats the output from the generative module to ensure coherence and readability.\r\n\u2022\tApplies post-processing tasks such as grammar checks and stylistic adjustments.\r\n5.\t User Interface Module:\r\n\u2022\tProvides an intuitive user interface for input, output, and interaction.\r\n\u2022\tEnsures a smooth user experience with clear navigation and feedback display.\r\n6.\t Settings and Feedback Module:\r\n\u2022\tAllows users to customize parameters (e.g., story length, tone) and adjust preferences.\r\n\u2022\tCollects user feedback to improve story quality and overall application performance.\r\n7.\t Version Control Module:\r\n\u2022\tMaintain history and revisions of generated stories for user review and editing.\r\n8.\t Content Moderation Module:\r\n\u2022\tAutomatically screen generated content to enforce quality and safety guidelines.\r\n9.\tSocial Media Module:\r\n\u2022\tEnables users to post their generated stories and share them with the community.\r\n\u2022\tAllows users to like, comment, and follow other storytellers.\r\n10.\t Script-Based Short Video Module:\r\n\u2022\tConverts generated stories into script-based short videos.\r\n\u2022\tAllows users to share generated short videos within the platform.$$\nMustafa Amanullah will develop the Prompt Analysis and Retrieval Module, Generative Module, Settings and Feedback Module, Version Control Module, and Social Media Module. These modules will enable the system to extract literary cues from user input, generate coherent narratives using AI, and allow users to customize parameters such as story length and tone while providing feedback for improvements. Additionally, the version control system will help users review and edit previous drafts, while the social media module will create an interactive platform where users can share, like, comment, and engage with other storytellers.$$\nUsman Ijaz will develop the User Input Module, User Interface Module, Story Composition and Post-Processing Module, Content Moderation Module, and Script-Based Short Video Module. These components will handle user inputs, provide an engaging user interface, refine generated stories for coherence and quality, and enforce content safety standards. Additionally, the script-based short video module will convert generated stories into structured video scripts, allowing users to create and share short videos within the platform.$$\n$$\n$$\nGenerative AI$$\nRetrieval Augmented Generation (RAG)$$\nContent Moderation","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-19 22:26:27","updated_at":"2025-03-21 13:12:18","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
RAG-Based Generative Story Writing: A Social Media Platform That Merges Retrieval with AI-Driven Narrative Creation |
CIIT/SP22-BSE-004/WAH
CIIT/SP22-BSE-051/WAH
|
MUSTAFA AMANULLAH
USMAN IJAZ
|
Done
|
Accepted
|
| 14. |
LittleGenius - AI Interactive Learning Hub |
CIIT/SP22-BCS-003/WAH
CIIT/SP22-BCS-010/WAH
CIIT/SP22-BCS-046/WAH
|
SONIA WAJID
TAYYABA SAJID
LARAIB BIBI
|
Done
|
Accepted
|
| 15. |
AI-Powered Smart Finance Manager |
CIIT/SP22-BCS-030/WAH
CIIT/SP22-BCS-055/WAH
|
MUHAMMAD MUNEEB IQBAL
ALI ASJAD
|
Done
|
Accepted
|
| 16. |
{"id":926,"project_id":1300,"title":"The Educators: A Smart Solution for Automated Learning & School Management","prob":"Our FYP solves the problem of inefficient school management and communication gaps between students, teachers, and parents. Currently, parents have to call the school for leave requests, check homework manually, and visit the school for fee payments. Teachers struggle with tracking attendance, sharing homework, and managing test reports. Admins face difficulties in handling fee challans, student promotions, and sending important notices. Our LMS will automate these tasks, making school operations smoother. Parents will receive updates via the mobile app, students will access homework, recorded lectures, and study materials online, and admin tasks like fee management and exam results will be fully automated.","description":"The Educators LMS is a web-based platform for teachers\/admin and a mobile app for parents, designed to automate and streamline school management tasks for students, teachers, parents, and administrators. The system allows parents to apply for student leave online, with automatic approval notifications via the mobile app. Teachers can upload homework, which is sent to parents after school. Monthly test reports, holiday notices, and fee challans are automatically generated and shared with parents. The system also includes a smart fee management module, applying late fees and restricting access if payments are not made on time. A class leaderboard rewards top performers with fee waivers, and an automated student promotion system ensures smooth transitions between grades. Students can access recorded lectures, study notes. The LMS simplifies school operations, improves communication, and enhances learning accessibility for students.$$\nIn the Educators LMS, the modules are (i) Auth Module, providing secure login, registration and role-based access for parents, teachers, and admins, ensuring data privacy and seamless user management; (ii) Automated Attendance & Absence Notifications, where teachers mark daily attendance, and parents receive immediate notifications via the mobile app if their child is absent; (iii) Student Leave Management System, allowing parents to request student leave online, which can be approved or rejected by admins or teachers, and once approved, attendance is automatically updated with a confirmation sent to parents via app; (iv) Digital Diary & Homework Notifications, where teachers upload homework on the LMS, parents receive notifications via app, and students can access their daily homework online; (v) Monthly Test Reports, enabling teachers to upload test results, notify parents via app, and display performance trends such as improvement or decline; (vi) Holiday & Important Notices System, which allows the admin to send school-wide notifications delivered via app and the LMS dashboard; (vii) Smart Fee Challan & Late Fee Handling, which auto-generates monthly fee challans with due dates, applies a 5% late fee after the grace period, and enables the admin to track the fee status as paid, unpaid, or late; (viii) Automated Detention System for Late Fee, which restricts access to class materials and exams for students with unpaid fees; (ix) Student Performance Leaderboard & Fee Waivers, displaying the top students in class and school-wide rankings while automatically granting fee waivers of 100%, 75%, or 50% to the top three performers; (x) Auto-Generated Exam Results & Class Ranks, which calculates total marks, percentages, and class rankings, generating student performance reports; and (xi) DLP & Scheme of Work Sharing System, where the principal can enter a Digital Lesson Plan (DLP) or Scheme of Work link into the system, which is then sent automatically to all teachers via WhatsApp, allowing them to access or view the shared document instantly.$$\nIn the LMS, I shall develop various modules, including (i) autogenerated exam results and class ranks to automate student assessments and rankings, (ii) a DLP and scheme of work sharing system to facilitate structured lesson planning and collaboration among teachers, (iii) a digital diary and homework notification module to enhance communication between teachers, students, and parents, (iv) a smart fee challan and late fee handling system to automate fee management and track late payments efficiently, and (v) a student performance leaderboard and fee waivers module to encourage academic excellence by rewarding top performers with financial assistance, (vi) Admin Module, providing secure login, registration and role-based access for parents, teachers, and admins, ensuring data privacy and seamless user management;$$\nIn the LMS, I shall develop various modules, including (i) a student leave management system to streamline the process of requesting and approving leaves, (ii) a monthly test reports module to generate and share student performance reports efficiently, (iii) a holiday and important notices system to keep students and staff informed about upcoming holidays and crucial announcements, (iv) an automated detention system for late fee management, ensuring timely payments by enforcing necessary actions, and (v) an automated attendance and absence notification module to track student attendance and send real-time alerts to parents and administrators.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document. In 8th semester, the FYP MUST be 90% complete in the 14th week. Your website, if any, MUST be online and your App, if any, MUST be in AppStore when you come for internal viva. Your hardware MUST be packed in such a way that it gives the look of a sellable product.","isDraft":0,"status":2,"created_at":"2025-03-13 14:48:36","updated_at":"2025-03-17 11:40:42","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
The Educators: A Smart Solution for Automated Learning & School Management |
CIIT/SP22-BSE-020/WAH
CIIT/SP22-BSE-035/WAH
|
BAREERA
REEMAL IMTIAZ
|
Done
|
Accepted
|
| 17. |
{"id":950,"project_id":1298,"title":"AI-Powered Automated Market Research and Trend Analysis","prob":"Businesses struggle to track market trends, product demand, and industry shifts in real time. Traditional market research is slow, expensive, and outdated, relying on static reports that miss fast-changing trends.\r\n\r\nThis project builds an AI-powered market research system that automates data collection, detects trends, and predicts future demand. It gathers data from Google Trends (search interest), e-commerce platforms (Amazon, Shopify sales), industry reports (startup funding, patents), and customer reviews. AI then analyzes consumer sentiment, identifies top-selling products, and forecasts which trends will grow.\r\n\r\nThe system focuses on three major trend types:\r\nSearch Trends: What people are searching for.\r\nIndustry Trends: Business investments, startup funding.\r\nConsumer Product Trends: Best-selling products & customer sentiment.\r\n\r\nUnlike existing tools, our system combines multiple data sources, predicts future trends, and suggests business actions to help companies stay ahead of competitors.","description":"This project is an AI-powered market research and trend analysis system that automates data collection, trend analysis, sentiment detection, and predictive forecasting to help businesses make data-driven decisions.\r\n\r\nStep 1: Data Collection & Storage\r\nThe system collects data from:\r\nGoogle Trends \u2192 Tracks search interest over time.\r\nE-commerce Platforms (Amazon, Shopify) \u2192 Identifies top-selling products & consumer demand.\r\nMarket & Financial Reports \u2192 Tracks industry investments and funding trends.\r\nNews & Business Articles \u2192 Extracts business trends & expert insights.\r\nCustomer Reviews & Online Discussions \u2192 Analyzes sentiment from Trustpilot, Google Reviews, and relevant forums.\r\nSocial Media (Twitter, Reddit, LinkedIn) \u2192 Extracts business-related discussions & consumer sentiment (where access is available).\r\n\r\nStep 2: Data Processing & Storage\r\nThe collected data is cleaned, structured, and stored in a MongoDB\/PostgreSQL database.\r\n\r\nStep 3: Trend Detection & Sentiment Analysis\r\nAI analyzes emerging trends, classifies consumer sentiment as positive, negative, or neutral, and detects patterns in industry movements.\r\n\r\nStep 4: Predictive Market Forecasting\r\nAI models forecast future product demand and industry shifts using time-series analysis (LSTM, ARIMA, Prophet).\r\n\r\nStep 5: AI Business Recommendations\r\nThe system generates actionable insights for businesses based on detected trends.\r\n\r\nStep 6: Web Dashboard\r\nInsights are displayed in a React.js-powered UI, allowing users to filter and explore trends with interactive graphs and AI-driven recommendations.$$\nModules:\r\nData Collection Module:\r\nFunction: Gathers data from multiple sources, including Google Trends, e-commerce platforms, news articles, customer reviews, and social media.\r\nTechnologies Used: Python, Scrapy, BeautifulSoup, API integrations.\r\nWhy It\u2019s Needed: Ensures real-time and diverse data collection for accurate market research.\r\n\r\nData Processing & Storage Module:\r\nFunction: Cleans, structures, and stores collected data for efficient analysis.\r\nTechnologies Used: Pandas, NumPy, MongoDB, PostgreSQL.\r\nWhy It\u2019s Needed: Eliminates noise and irrelevant data while ensuring proper database storage.\r\n\r\nTrend Detection & Sentiment Analysis Module:\r\nFunction: Identifies emerging trends and classifies sentiment as positive, negative, or neutral.\r\nTechnologies Used: NLP (spaCy, BERT, VADER), Scikit-learn.\r\nWhy It\u2019s Needed: Helps businesses understand market shifts and consumer reactions.\r\n\r\nPredictive Market Forecasting Module:\r\nFunction: Uses AI models to forecast future product demand and industry trends.\r\nTechnologies Used: LSTM, ARIMA, Facebook Prophet.\r\nWhy It\u2019s Needed: Helps businesses prepare for future market conditions.\r\n\r\nAI Business Recommendation Module:\r\nFunction: Provides actionable insights based on trend analysis and forecasting.\r\nTechnologies Used: GPT-4 API, rule-based decision models.\r\nWhy It\u2019s Needed: Helps businesses make data-driven decisions and strategic plans.\r\n\r\nWeb Dashboard Module:\r\nFunction: Displays trend insights and analytics in an interactive format.\r\nTechnologies Used: React.js, Chart.js, FastAPI.\r\nWhy It\u2019s Needed: Allows users to explore data visually and interactively.\r\n\r\nnote: This project can have estimated cost of around $50 .$$\nData Collection & Processing:\r\nResponsible for gathering and storing data from different sources, such as search trends, e-commerce platforms, news articles, and customer reviews. Tasks include web scraping, API integration, data cleaning, and efficient database management to ensure seamless data retrieval for analysis.$$\nTrend Analysis, AI Models & Dashboard:\r\nResponsible for analyzing collected data to identify trends, classify sentiment, and forecast future market shifts. Tasks include implementing AI models, trend detection algorithms, and designing the web dashboard to present insights visually and interactively for end users.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-21 22:00:02","updated_at":"2025-04-17 12:55:14","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
AI-Powered Automated Market Research and Trend Analysis |
CIIT/SP22-BSE-029/WAH
CIIT/SP22-BSE-044/WAH
|
MUHAMMAD MUNEEB AHMED
MUHAMMAD ABID HUSSAIN
|
Done
|
Accepted
|
| 18. |
{"id":942,"project_id":1296,"title":"Schedulo - Online Appointment Booking Platform for Mechanics and Service Providers","prob":"Schedulo is an online appointment scheduling platform designed specifically for mechanics and service providers in Canada. The platform will enable customers to book appointments with registered mechanics and service providers, reducing wait times and improving service efficiency. This project is initially area-specific (mechanics and services) but will be enhanced to support multiple industries.","description":"This is industrial paid project and client in from Canada. Canada lacks a dedicated platform for mechanics to register services and for users to find and book appointments easily. The platform enables users to locate nearby providers, schedule via a calendar system with QR codes, and receive automated reminders. It supports cash and online payments, while admins oversee registrations, approvals, transactions, and performance analytics.\r\nRevision 1: Payment & False Scheduling Management\r\n\u2022\tFalse Scheduling Prevention: Service providers will verify customer details and make a confirmation call before finalizing an appointment.\r\n\u2022\tAdvance Payment: Customers must pay 10% of the service charge upfront via Stripe, ensuring serious bookings.\r\n\u2022\tRescheduling: Shops and service providers will have the authority to allow or decline reschedules.\r\nRevision 2: Area-Specific Implementation\r\n\u2022\tSchedulo will initially focus on mechanic services in Canada, creating a mechanic marketplace where shops can register and users can easily find and book them.\r\n\u2022\tFuture enhancements will include more industries.\r\nRevision 3: Comparison with Similar Platforms\r\n\u2022\tA similar platform, Appointa, exists but is not focused on mechanics and services.\r\n\u2022\tSchedulo differentiates itself by targeting a niche industry and offering customized solutions for mechanics, including specific tools like service package listings, workshop availability, and auto-reminders.\r\nRevision 4: Review & Requirement Updates\r\n\u2022\tOur revised requirements now focus more on area-based service management, advanced payment verification, and a dedicated mechanic marketplace to address gaps in existing solutions.\r\nRevision 5: Use Case Elaboration\r\n\u2022\tCustomer Use Case: A customer wants to book a mechanic for car repair. They search for nearby workshops, check availability, pay 10% advance, and receive confirmation.\r\n\u2022\tMechanic Use Case: A mechanic registers, updates availability, accepts appointments, and manages payments via the platform.\r\n\u2022\tAdmin Use Case: The admin verifies mechanics, handles disputes, and ensures smooth financial transactions.$$\nUser Authentication & Dashboard \u2013 Ensures secure login and registration for customers and service providers. The dashboard provides an overview of bookings, payments, and notifications.\r\n\r\nService Directory & Categorization \u2013 Organizes services into different categories, allowing users to browse and filter based on their needs.\r\n\r\nLocation-Based Service Discovery \u2013 Uses geolocation to help users find service providers within a designated radius, improving accessibility and convenience.\r\n\r\nAppointment Booking & Scheduling \u2013 Provides an interactive calendar for scheduling appointments, with booking links and QR codes for easy confirmation.\r\n\r\nNotifications & Reminders \u2013 Sends automated reminders and alerts via email or SMS to reduce missed appointments and keep users informed.\r\n\r\nPayment & Transaction Management \u2013 Supports cash and online payments, with transaction monitoring and security measures handled by the admin.\r\n\r\nUser Interaction & Feedback \u2013 Allows users to leave reviews, ratings, and feedback, helping others choose reliable service providers.\r\n\r\nProfile & Settings Management \u2013 Enables users and service providers to update their profiles, manage service details, and customize notification preferences.\r\n\r\nAdmin Control & System Oversight \u2013 The admin oversees user registrations, service approvals, financial transactions, and system performance analytics.$$\nIn Schedulo, I will develop the following modules:\r\n\r\n(1) User Authentication & Dashboard \u2013 Ensuring secure login and registration, along with personalized dashboards for users and service providers.\r\n\r\n(2) Service Directory & Categorization \u2013 Organizing services into structured categories to enable easy browsing and filtering for users.\r\n\r\n(3) Notifications & Reminders \u2013 Implementing automated alerts and reminders to keep users informed about their appointments and updates.\r\n\r\n(4) Payment & Transaction Management \u2013 Supporting both cash and online payments, with transaction monitoring to ensure secure and smooth financial processing.\r\n\r\n(5) Profile & Settings Management \u2013 Enabling users to update their profiles, customize notification preferences, and manage account settings efficiently.$$\nIn Schedulo, I will develop the following modules:\r\n\r\n(1) Location-Based Service Discovery \u2013 Enabling users to find nearby service providers within a designated radius, using location-based filtering for better accessibility.\r\n\r\n(2) Appointment Booking & Scheduling \u2013 Allowing customers to book, reschedule, and manage their appointments through a seamless calendar-based system.\r\n\r\n(3) User Interaction & Feedback \u2013 Implementing a review and communication system where users can share feedback and interact with service providers.\r\n\r\n(4) Admin Control & System Oversight \u2013 Managing user registrations, service approvals, financial transactions, and tracking platform performance through analytics.$$\n$$\nTampo Salon System$$\nMulti-Service Platform \u2013 Unlike the Tampo Salon System, which is salon-specific, our platform will support various industries.$$\nSmart Service Discovery \u2013 Users can search and filter services based on location, availability, and ratings, ensuring they find the best providers nearby.$$\nEnhanced Booking & Payments \u2013 Features like QR-based confirmations, multiple payment methods, and automated reminders will improve the booking experience for both customers and service providers.","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-19 14:00:27","updated_at":"2025-03-21 13:11:27","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
Schedulo - Online Appointment Booking Platform for Mechanics and Service Providers |
CIIT/SP22-BCS-043/WAH
CIIT/SP22-BCS-012/WAH
|
NAIMA AZHAR
SAJEELA KHAN
|
Done
|
Accepted
|
| 19. |
Sparta Academy |
CIIT/SP21-BSE-069/WAH
CIIT/SP22-BSE-001/WAH
|
MUHAMMAD SAAD SHUJA
IQRA NOOR
|
Done
|
Accepted
|
| 20. |
{"id":946,"project_id":1291,"title":"SheinReview","prob":"SheinReview helps buyers make informed purchasing decisions by providing reliable and verified reviews of Shein products. Many customers rely on biased or insufficient reviews, leading to poor choices. This platform ensures that buyers get authentic user experiences before making a purchase, reducing fraud risks and increasing consumer confidence.","description":"SheinReview is a web-based platform specifically designed for Shein buyers. It collects and analyzes product reviews from Shein's official store to provide insightful sentiment analysis. The platform ensures that buyers make informed decisions by offering unbiased and research-backed knowledge extracted from real user reviews.\r\nCustomers prefer visiting our website before exploring other platforms because we provide aggregated sentiment insights, helping them understand general customer satisfaction trends regarding Shein products. By offering a centralized source of unbiased information, buyers can compare different options before finalizing their purchase.\r\nUsers can search for Shein clothing and accessories, view sentiment-based insights, and access summarized review trends. The platform is dedicated exclusively to Shein products. Additionally, AI-driven sentiment analysis will be integrated to assess review authenticity and overall sentiment trends, ensuring buyers receive reliable feedback.$$\nKey Functionalities:\r\n\u2022\tData Scraping & Aggregation (extracting reviews from Shein\u2019s official store)\r\n\u2022\tAI Sentiment Analysis (evaluates review sentiment to help users make informed choices)\r\n\u2022\tReview Authentication System (detects fake or spam reviews using AI techniques)\r\n\u2022\tSearch & Filtering (filter by category, size, price, and sentiment trends)\r\n\u2022\tTrending & Popular Products Section (highlights top-rated Shein products based on AI analysis)\r\n\u2022\tAdmin Dashboard (manages sentiment analytics and data visualization)$$\nAqsa Fayyaz shall develop the following modules:\r\n\r\nSetting up Laravel (PHP) framework for backend operations.\r\nManaging API endpoints for review retrieval, sentiment analysis results, and authentication.\r\nImplementing the database schema in MySQL\/PostgreSQL.\r\nHandling user authentication and role management (admin and user).$$\nNazir Hussain shall develop the following modules:\r\n\r\nDesigning the UI\/UX using Bootstrap, HTML, and CSS.\r\nImplementing responsive web design for a user-friendly experience.\r\nDeveloping pages for product search, filtering, and review display.\r\nIntegrating trending and popular products sections.\r\nImplementing user authentication UI.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-20 17:15:55","updated_at":"2025-03-27 12:55:16","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
SheinReview |
CIIT/FA21-BSE-023/WAH
CIIT/SP21-BCS-068
|
AQSA FAYYAZ
Nazir Hussain
|
Done
|
Accepted
|
| 21. |
{"id":953,"project_id":1290,"title":"ScholarMatch: A Scholarship and Admission Recommendation System for BS , MS and PhD Programs","prob":"Many students struggle to find suitable universities and scholarships for BS, MS, and PhD programs abroad due to the overwhelming amount of information and lack of personalized guidance. This leads to inefficiency, missed opportunities, and stress. ScholarMatch aims to solve this problem by providing an app-based recommendation system that leverages data to offer filter-based recommendations and scholarship opportunities based on the student\u2019s academic background, interests, and career goals. Additionally, the platform incorporates AI-powered recommendations, analyzing the student\u2019s academic scores and comparing them with university admission probabilities to provide more accurate and realistic options. ScholarMatch also integrates web scraping to gather real-time admission and scholarship data from university websites, ensuring that students have access to the latest and most relevant information. The system will further provide detailed insights on admission requirements, program details, and scholarship opportunities, making the university search process more efficient and accessible.","description":"ScholarMatch\u00a0is a\u00a0app-based recommendation system\u00a0designed to assist students in identifying local universities that offer scholarships and admissions for BS ,\u00a0MS and PhD programs. The system leverages\u00a0data \u00a0to provide\u00a0personalized recommendations\u00a0based on the student\u2019s academic background, interests, and career goals.\r\nThe platform will allow students to create profiles, input their academic details, and specify preferences such as program type, and scholarship requirements. The system will analyze this data and recommend universities that match the student\u2019s profile. Additionally, the platform will provide detailed information on admission requirements, program details, and scholarship opportunities.\r\nTo further support students in their decision-making process, ScholarMatch enables users to connect with alumni from specific universities. This feature allows students to gain firsthand insights and feedback from those who have studied at their preferred institutions. By engaging with alumni, students can ask questions, understand the academic and cultural environment, and get advice on scholarships and career prospects.\r\nNew features : \r\n\u00b7 Web Scraping: Automatically gathers real-time admission and scholarship data from university websites.\r\n\u00b7 Preferenced Model : Unlike conventional platforms, ScholarMatch tailors recommendations based on user preferences, ensuring that students only see programs offered by their student given preferences.\r\n\u00b7 AI-Powered Recommendations: Filters program options based on the student's selected program and student academic scores, ensuring relevant results.$$\n1 : User Profile & Preferences Module\r\n2 : University Recommendation Module\r\n3 : Scholarship & Financial Aid Module\r\n4: Alumni Module\r\n5 :Interactive Visualization \r\n6 :Feedback & Review System Module\r\n7 : Web Scraping\r\n8 : AI-Based Filtering Module$$\nWeb Scraping, AI-Based Filtering Module ,University Recommendation Module( student will get recommended specific universities)\r\n , Scholarship and Financial Aid Module ( student will get to know the scholarship opportunity in specific university)\r\n , Alumni Module( student can connect with Alumni and alumnii can share experiences)\r\n will be developed by Fazal-ur-Rehman$$\nUser Profile & Preferences Module , Interactive Visualization , Feedback & Review System Module , Scholarship & Financial Aid Module\r\nWill be developed by Muhammad Huzaifa Sarfraz$$\n$$\nEducational Websites$$\nAI-Based Filtering Module$$\nAlumni Module$$\nWeb Scraping","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-30 17:42:21","updated_at":"2025-04-07 13:10:51","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
ScholarMatch: A Scholarship and Admission Recommendation System for BS , MS and PhD Programs |
CIIT/SP22-BCS-062/WAH
CIIT/SP22-BCS-064/WAH
|
FAZAL UR REHMAN
MUHAMMAD HUZAIFA SARFRAZ
|
Done
|
Accepted
|
| 22. |
{"id":928,"project_id":1288,"title":"AI & RL-Driven Distributed Intrusion Detection System (DIDS) for the Banking Sector","prob":"The banking sector is increasingly targeted by sophisticated cyberattacks, leading to financial losses and compromised customer trust. Traditional security measures often fail to detect and respond to these evolving threats in real-time, especially across distributed infrastructures like branches, ATMs, and online platforms. Our project aims to address this critical issue by developing an AI & RL-Driven Distributed Intrusion Detection System (DIDS). Leveraging Artificial Intelligence (AI), Machine Learning (ML), and Reinforcement Learning (RL), DIDS will analyze network traffic to detect anomalies and respond to threats instantly. By implementing a microservices-based architecture using Docker and Kubernetes, DIDS ensures scalability, resilience, and efficiency in protecting banking networks. This solution will help banks prevent financial losses, maintain customer trust, and improve overall cybersecurity resilience against evolving cyber threats.","description":"The AI & RL-Driven Distributed Intrusion Detection System (DIDS) is a cybersecurity solution designed to protect the banking sector from evolving cyber threats. Traditional Intrusion Detection Systems (IDS) rely on static rules and signatures, making them ineffective against zero-day attacks. This system integrates Machine Learning (ML) and Reinforcement Learning (RL) to create an adaptive, self-learning cybersecurity framework that detects anomalies and automates responses.\r\n\r\nDIDS operates across multiple banking entities, including branches, ATMs, online banking platforms, and cloud-based financial services. The system captures network traffic, detects suspicious activities, and prevents fraud and cyberattacks such as phishing, unauthorized access, insider threats, and malware intrusions.\r\n\r\nTo ensure the AI model is trained on modern cybersecurity threats, the following datasets will be used:\r\n\r\nCICIoT2023 \u2013 Covers modern IoT-based attacks in financial environments. \r\nUNSW-NB23 \u2013 Contains updated malware, phishing, and banking fraud patterns. \r\nTON_IoT 2024 \u2013 Real-world IoT and edge computing attacks from smart banking devices. \r\nLive Attack Data \u2013 Generated using Kali Linux, Metasploit, and penetration testing.\r\n\r\nThe system follows a structured AI development lifecycle, including data collection, feature extraction, anomaly detection (Autoencoders, Isolation Forest), and RL-based threat response (Deep Q-Network, PPO). The DIDS system is deployed on Azure Kubernetes Service (AKS), Docker, and Terraform, ensuring scalability and real-time security monitoring.\r\n\r\nThe AI model continuously learns from past incidents, improving detection accuracy and reducing false positives. Reinforcement Learning automates security responses by deciding the best course of action, such as blocking malicious IPs or limiting transaction rates. This enhances banking cybersecurity by providing real-time, adaptive protection against both known and unknown cyber threats while ensuring compliance with financial regulations.$$\n1. Traffic Capture & Preprocessing\r\nDescription: Collects and processes network traffic from banking systems, including ATMs, branches, and online platforms.\r\nKey Features:\r\nCaptures network packets and transaction logs. \r\nFilters and normalizes extracted features for AI analysis. \r\nTechnologies Used: Wireshark, Zeek, Kafka, Python, Pandas\r\n\r\n2. AI & RL-Based Anomaly Detection\r\nDescription: Detects unusual activities in network traffic and transactions using Machine Learning (ML) and Reinforcement Learning (RL) models.\r\nKey Features:\r\nIdentifies fraud using Autoencoders, Isolation Forests, and XGBoost. \r\nLearns from attacks using Deep Q-Network (DQN) and PPO. \r\nDetects zero-day threats without predefined patterns. \r\nDatasets Used:\r\nCICIoT2023 \u2013 Covers modern IoT-based attacks in financial environments. \r\nUNSW-NB23 \u2013 Contains updated malware, phishing, and banking fraud patterns. \r\nTON_IoT 2024 \u2013 Real-world IoT and edge computing attacks from smart banking devices. \r\nLive Attack Data \u2013 Generated using Kali Linux, Metasploit, and penetration testing. \r\nTechnologies Used: Scikit-Learn, TensorFlow, PyTorch, OpenAI Gym, Python\r\n\r\n3. Signature-Based Detection\r\nDescription: Uses predefined attack signatures to detect known cyber threats such as phishing, malware, and brute-force attacks.\r\nKey Features:\r\nRecognizes attack patterns using Snort and Suricata IDS. \r\nIdentifies brute-force login attempts, DDoS, and SQL injections. \r\nLogs detected threats for forensic analysis. \r\nTechnologies Used: Snort, Suricata, Zeek IDS, Bash, Python\r\n\r\n4. Threat Intelligence Integration\r\nDescription: Fetches real-time cyber threat intelligence to track banking fraud techniques and attack vectors.\r\nKey Features:\r\nCross-verifies transactions against blacklisted IPs and fraud indicators. \r\nFetches updates from IBM X-Force, Open Threat Exchange (OTX), and FraudNet. \r\nEnhances AI\u2019s predictive capabilities using live security feeds. \r\nTechnologies Used: IBM X-Force API, OTX API, FraudNet, Python\r\n\r\n5. Reinforcement Learning (RL) Automated Response\r\nDescription: Automates security actions based on detected threats, continuously improving from past incidents.\r\nKey Features:\r\nUses RL models (DQN, PPO) to decide security actions (block, monitor, alert). \r\nLearns from past attack outcomes to optimize responses. \r\nReduces false positives by adapting to normal transaction behavior. \r\nDatasets Used:\r\nCICIoT2023 attack scenarios for RL-based adaptive decision-making. \r\nLive Banking Fraud Dataset (2024) \u2013 Real-time data from financial cybercrime investigations. \r\nTechnologies Used: TensorFlow RL, OpenAI Gym, Python\r\n\r\n6. Regulatory Compliance & Security Auditing\r\nDescription: Ensures compliance with banking security standards such as PCI-DSS, GDPR, and ISO 27001.\r\nKey Features:\r\nEncrypts sensitive banking data. \r\nAutomates compliance checks and security audits. \r\nGenerates regulatory compliance reports. \r\nTechnologies Used: Compliance Checker API, Security Logs, Python\r\n\r\n7. Dashboard & Reporting Module\r\nDescription: Provides a real-time monitoring dashboard for security analysts and bank administrators.\r\nKey Features:\r\nDisplays live threat detection statistics. \r\nProvides fraud analysis reports and alerts. \r\nVisualizes cyber threats in interactive dashboards. \r\nTechnologies Used: Kibana, Grafana, Flask API, Python\r\n\r\n8. Cloud-Based Microservices Deployment\r\nDescription: Deploys and manages system components using Docker and Kubernetes for scalability and resilience.\r\nKey Features:\r\nRuns AI models and IDS sensors in Azure Kubernetes Service (AKS). \r\nSupports auto-scaling for high banking transactions. \r\nEnsures system uptime during cyberattacks. \r\nTechnologies Used: Docker, Kubernetes, Terraform, Azure Kubernetes Service (AKS), Bash$$\nI will be responsible for developing the cybersecurity and DevOps modules, ensuring secure deployment, compliance, and real-time threat detection. My key responsibilities include:\r\nTraffic Capture & Preprocessing: Collecting, cleaning, and normalizing network logs from banking infrastructure. \r\nSignature-Based Detection: Implementing Snort\/Suricata for recognizing known attack patterns. \r\nThreat Intelligence Integration: Connecting with global fraud detection databases for real-time security insights. \r\nRegulatory Compliance & Security Auditing: Implementing PCI-DSS, GDPR, and ISO 27001 compliance mechanisms. \r\nCloud-Based Microservices Deployment: Using Docker and Kubernetes to ensure system scalability, fault tolerance, and efficient security monitoring.$$\nI will be developing the AI, ML, and RL-based Anomaly Detection and Automated Response modules to enhance the system\u2019s intelligence and security response.\r\nAI & RL-Based Anomaly Detection: Developing Machine Learning (ML) models (Autoencoders, Isolation Forests, XGBoost) to detect fraudulent transactions and cyber threats. \r\nAutomated Threat Response & Incident Management: Implementing Reinforcement Learning (Deep Q-Network, PPO) for adaptive security decision-making. \r\nDashboard & Reporting Module: Creating a visual representation of detected threats and system performance using Kibana and Grafana. \r\nBusiness Planning & Revenue Forecasting: Assisting in designing the Banking Security-as-a-Service (B-SaaS) model for financial institutions.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document. In 8th semester, the FYP MUST be 90% complete in the 14th week. Your website, if any, MUST be online and your App, if any, MUST be in AppStore when you come for internal viva. Your hardware MUST be packed in such a way that it gives the look of a sellable product.","isDraft":0,"status":2,"created_at":"2025-03-14 19:18:56","updated_at":"2025-03-17 11:47:01","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
AI & RL-Driven Distributed Intrusion Detection System (DIDS) for the Banking Sector |
CIIT/SP22-BSE-007/WAH
CIIT/SP22-BSE-023/WAH
|
UMAIR AHMED
ALI SHAN WASIM
|
Done
|
Accepted
|
| 23. |
{"id":936,"project_id":1287,"title":"Automated Multilanguage Invoice OCR System","prob":"The fintech industry is making a lot of progress and providing solutions for different problems. Different sectors still relies on the manual invoice reimbursements. This requires trained human resources along with time for processing. It is highly important to design an automated system that can automatically understand the invoices and make reimbursements online. However, this fintech solution has many challenges as these invoices are from different sectors and in different formats. These invoices not only have variations in format but also in typed computerized text and handwritten text. To handle all these variations to design an automated solution remains a challenging task. In this system, the objective is to automate the process of reading and extracting invoice data (Typed and handwritten text) from images or PDFs using latest deep learning techniques. This will significantly reduce the manual effort and time required to process large volumes of invoices.","description":"The Automated Invoice Processing System is a web-based fintech solution designed to address the challenges of manual invoice reimbursements by automating the extraction and processing of invoice data from images and PDFs using advanced deep learning techniques. Many industries still rely on human effort for invoice validation and reimbursement, which is both time-consuming and resource-intensive. This system aims to reduce manual workload, improve processing efficiency, and accelerate reimbursements by automatically extracting key invoice details from diverse invoice formats, including both typed and handwritten text.\r\nUsers can upload invoices in image or PDF format, and the system utilizes AI-driven OCR technology to analyze and extract essential details such as invoice number, date, item descriptions, prices, taxes, and total amounts. Since invoices vary significantly across industries in structure and format, the system is trained to handle these variations effectively, ensuring high precision in recognizing and organizing invoice data.\r\nOnce extracted, the invoice data is displayed on a user-friendly web interface, allowing users to review and validate the information before processing reimbursements. Additionally, the system enables businesses to export extracted data in Excel format, facilitating easy storage, retrieval, and financial analysis.\r\nTo further enhance accuracy, the system integrates a continuous learning mechanism, allowing the AI model to adapt to different invoice styles over time. By leveraging deep learning and OCR advancements, the system improves its ability to correctly interpret invoices, reducing errors and enhancing automation in financial workflows.\r\nAs a web-based application, the system is accessible from any device with an internet browser, eliminating the need for specialized software installations. This ensures flexibility and ease of use, making it a valuable tool for businesses handling large volumes of invoices. Overall, this project aims to streamline invoice management, minimize human intervention, and enhance financial automation in the fintech sector.$$\n1. Invoice Upload Module:\r\n\u2022\tFile Handling: Enables users to upload invoices in different formats such as PDF, JPEG, and PNG.\r\n\u2022\tPreprocessing: Applies image enhancement techniques, noise reduction, and skew correction to improve OCR accuracy.\r\n2. Data Annotation Module:\r\n\u2022\tData Labeling for English: Annotates typed and handwritten English invoices to train the AI model.\r\n\u2022\tData Labeling for Arabic: Annotates typed Arabic invoices to enable multi-language support.\r\n3. Text Detection Module:\r\n\u2022\tText Detection for English and Arabic\r\n\u2022\tBounding Box Generation\r\n4. Text Recognition Module:\r\n\u2022\tText Recognition for English and Arabic\r\nDeep Learning Model Training Module:\r\n\u2022\tTraining for Typed English\r\n\u2022\tTraining for Handwritten English\r\n\u2022\tTraining for Typed Arabic:\r\nModel Testing Module:\r\n\u2022\tTesting for Typed English: Evaluates the accuracy of text recognition for typed English invoices.\r\n\u2022\tTesting for Handwritten English: Measures the model\u2019s effectiveness in extracting handwritten English text.\r\n\u2022\tTesting for Typed Arabic: Tests recognition capabilities for typed Arabic invoices.\r\n5. Data Analytics:\r\n\u2022\tAlgorithm Design\r\n\u2022\tInsights & Visualizations\r\n\u2022\tKey Metrics: Track total invoice amounts, frequently invoiced products\/services, and payment cycles.\r\n\u2022\t Dashboards: Provide interactive visualizations with filters for date range, company, and invoice type.\r\n6. Data Display Module:\r\n\u2022\tWeb Interface: Provides a user-friendly dashboard to display extracted invoice data.\r\n7. API Integration Module:\r\n\u2022\tInvoice Upload API\r\n\u2022\tData Processing API\r\n\u2022\tResult Display API\r\n8. Data Export Module:\r\n\u2022\tExcel, PDF & CSV Export\r\n\u2022\tStructured Report Generation\r\n\r\nOCR Detection & Recognition Process\r\nDetection (Language-Independent)\r\nData Labeling: Invoices are labeled using object detection, with text as bounding boxes and the rest as the background.\r\nLanguage-Independent Labeling: All text, whether typed or handwritten, is labeled as \"Text\" without language differentiation.\r\nModel Training & Testing: YoloV10 is used for text detection.\r\nText Localization: The trained model identifies text by drawing bounding boxes.\r\nRecognition (Language-Specific)\r\nData Labeling: Text is labeled per language (e.g., English, Arabic) and stored in CSV files. Arabic text is labeled using the Lexilogos online keyboard.\r\nTraining the Recognizer: The model is trained to recognize text within detected regions.\r\nModel Architecture: Uses VGG16 + BiLSTM + CTC with fine-tuning on a custom invoice dataset.\r\nNote:Arabic recognizer training and testing will be done later.\r\nDataset Availability\r\nProvided by an industrial partner, including diverse invoices from healthcare, garages, and other sectors.\r\n\r\nArabic Tokenizers:\r\nYes, Arabic tokenizers will be used as preprocessing steps for model training. It breaks down Arabic text into smaller, manageable parts called tokens (like words or phrases). This step is crucial for improving the overall process of understanding and processing Arabic text in tasks like OCR and other language applications.\r\n\r\nModel Development:\r\nYes, instead of converting any language text to English (common language) for training, which would require an extra language conversion step, we will train different recognizers for each language. This way, we will avoid the conversion process and each model works better because it is designed for its specific language.$$\n\u2022\tData Labeling for Arabic: Handles annotation of typed Arabic invoices.\r\n\u2022\tText Detection : Implements text detection algorithms for invoices.\r\n\u2022\tDeep Learning Model Training (Typed Arabic & English Detection): Develops AI models for Arabic invoices and English detection improvements.\r\n\u2022\tModel Testing for Arabic (Typed): Evaluates and improves recognition performance for Arabic invoices.\r\n\u2022\tData Analytics: Implements Data analytics, reports, trends.\r\n\u2022\tAPI Development for Data Processing & Result Display: Implements structured API endpoints for data handling.\r\n\u2022\tUser Authentication & Security: Implements role-based access control and encryption.$$\nAbdul Basit:\r\n\u2022\tInvoice Upload & Preprocessing: Implements file handling and image preprocessing.\r\n\u2022\tData Labeling for English: Annotates typed and handwritten English invoices.\r\n\u2022\tText Detection : Implements text detection algorithms.\r\n\u2022\tDeep Learning Model Training (Typed English & Handwritten English): Develops AI models for typed and handwritten English invoices.\r\n\u2022\tModel Testing for English (Typed & Handwritten): Evaluates text recognition accuracy for English invoices.\r\n\u2022\tAPI Development for Invoice Processing: Implements REST APIs for invoice extraction and integration.\r\n\u2022\tExport Module & Structured Report Generation: Develops functionalities for Excel, PDF, and CSV export.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-18 20:47:00","updated_at":"2025-03-27 12:52:48","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
Automated Multilanguage Invoice OCR System |
CIIT/SP22-BCS-035/WAH
CIIT/SP22-BCS-036/WAH
|
ALI MUSTAFA
ABDUL BASIT
|
Done
|
Accepted
|
| 24. |
{"id":929,"project_id":1274,"title":"Wedding Aura","prob":"The wedding fashion industry doesn\u2019t have a single online platform where people can find luxury wedding dresses, styling, and beauty services all in one place. Brides and grooms often struggle to choose the right outfits, accessories, and beauty services for their big day. There\u2019s also no smart system to help with an easy way to rent wedding dresses and accessories. This project aims to solve these problems by creating a complete online marketplace that offers wedding fashion, beauty services, rental options, and order management\u2014all in one convenient system.","description":"Wedding Aura is a web-based platform designed to offer a seamless experience in luxury wedding fashion, beauty services, and rentals. It aims to simplify the process for brides, grooms, and fashion enthusiasts by providing everything they need in one place. The system is built using the MERN stack (MongoDB, Express, React, Node.js) to ensure efficiency and smooth functionality.\r\n\r\nUsers can browse and purchase bridal, groom, and fancy dresses, along with matching accessories such as jewelry, handbags, and footwear. To make wedding fashion more affordable, the platform also offers rental services for dresses and accessories. In addition to fashion, beauty and styling services are a key feature, allowing users to book bridal and groom makeup, hairstyling, skincare treatments, and mehndi services through professional service providers.\r\n.\r\n\r\nFor convenience, the platform includes a shopping and order management system where users can add products to their cart, save items to a wishlist, and place orders. They can also track their orders and bookings for a hassle-free experience. Bridal and groom packages further streamline wedding preparations by bundling dresses, makeup, and accessories into a single, customizable package.\r\n\r\nThe admin panel plays a crucial role in managing the platform, allowing administrators to oversee products, orders, beauty service bookings. Wedding Aura creates an all-in-one digital solution for luxury wedding fashion and styling, making wedding planning easier and more accessible with beauty features.$$\n1. User Management Module\r\n\r\n User Registration & Login\r\n Profile Management\r\n Wishlist & Order History\r\n Appointment & Rental History\r\n\r\n2. Clothing Collection & Rentals Module\r\n\r\n Bridal & Groom Attire\r\n Party & Formal Wear\r\n Kids\u2019 Dresses\r\n Rental System for Clothing\r\n\r\n3. Parlor & Beauty Services Module\r\n\r\n Bridal & Groom Makeup Booking\r\n Hair & Skincare Treatments\r\n Mehndi (Henna) Services\r\n Appointment Scheduling System for Beauty Professionals\r\n\r\n4. Jewelry & Accessories Module\r\n\r\n Bridal & Casual Jewelry\r\n Fashion Accessories (Handbags, Watches, etc.)\r\n Rental System for Jewelry\r\n\r\n5. Shoes & Footwear Module\r\n\r\n Bridal & Groom Footwear\r\n Traditional & Casual Shoes\r\n\r\n\r\n6. Online Shopping & Order Management Module\r\n\r\n Shopping Cart & Checkout\r\n Order Tracking\r\n Payment Gateway Integration (Cash on Delivery, Credit\/Debit Cards, etc.)\r\n Wishlist Management\r\n\r\n7. Bridal & Groom Packages Module\r\n\r\n Customizable Wedding Packages\r\n Combination of Dresses, Jewelry, Makeup, & Accessories\r\n\r\n8. Rental Services Module\r\n\r\n Dress & Jewelry Rental\r\n Rental Request & Approval System\r\n Rental Tracking & Return Management\r\n\r\n9. Flower Collection Module\r\n\r\n Bridal Bouquets & Floral Jewelry\r\n Fresh & Artificial Floral Accessories\r\n\r\n11. Admin Panel\r\n\r\n Product Management (Add, Update, Delete)\r\n Order Processing & Tracking\r\n Beauty Service & Rental Management\r\n\r\n User & Vendor Management\r\n\r\n12. Vendor Management Module (if third-party vendors are involved)\r\n\r\n Vendor Registration & Login\r\n Product Listing & Order Fulfillment\r\n Service Provider Profile Management\r\n\r\n13. Review & Rating Module\r\n\r\n Product & Service Reviews\r\n Star Ratings for Vendors & Services\r\n\r\n14. Notification & Communication Module\r\n\r\n Email & SMS Notifications\r\n Order & Appointment Reminders\r\n Chat Support (Optional)\r\n\r\n15. Reports & Analytics Module\r\n\r\n Sales Reports & Revenue Tracking\r\n User Engagement Analytics\r\n Order & Rental Reports$$\nUser Management \u2013 Handles user registration, profiles, wishlist, and history.\r\nClothing Collection & Rentals \u2013 Manages wedding and fancy dress sales and rentals.\r\nParlor & Beauty Services \u2013 Enables booking for makeup, hair, and skincare services.\r\nJewelry & Accessories \u2013 Provides bridal and casual jewelry for purchase and rental.\r\nShoes & Footwear \u2013 Offers bridal, groom, and casual footwear.\r\nBridal & Groom Packages \u2013 Combines dresses, makeup, and accessories into packages.\r\nFlower Collection \u2013 Manages floral accessories, including bouquets and handmade jewelry.$$\nOnline Shopping & Order Management \u2013 Handles cart, checkout, payment, and order tracking.\r\nRental Services \u2013 Manages rental requests and returns for dresses and accessories.\r\nAdmin Panel \u2013 Controls product management, orders, and services.\r\nVendor Management \u2013 Allows vendors to manage their products and services.\r\nReview & Rating \u2013 Enables customers to review products and services.\r\nNotification & Communication \u2013 Sends order updates, reminders, and alerts.\r\nReports & Analytics \u2013 Tracks sales, engagement, and performance metrics.$$\n$$\n$$\n$$\n$$\n","comments":" $$ In 7th semester, you should have implemented at least one major use case other than user logins and user registrations along with the newly modified designed SRS document.","isDraft":0,"status":2,"created_at":"2025-03-17 13:33:54","updated_at":"2025-03-19 10:53:36","isReviewDraft":1,"isInternalDraft":0,"isExternalDraft":0,"reviewedDate":null,"markedDate":null}
Wedding Aura |
CIIT/SP22-BCS-019/WAH
CIIT/SP22-BCS-039/WAH
|
MEHAK ZAFAR
AHSAN MAJEED
|
Done
|
Accepted
|
| 25. |
OpportuNest – (A nest of opportunities) |
CIIT/SP22-BCS-002/WAH
CIIT/SP22-BCS-006/WAH
|
MUHAMMAD FAKHAR-UL-HASNAIN
ABDULLAH JAVED
|
Done
|
Accepted
|