This project builds a system that detects unauthorized access and cyber-attacks on smart vehicles using advanced machine learning techniques. It analyzes network traffic data in real-time to identify threats early. Python is used to implement various classification algorithms to improve detection accuracy. The system enhances vehicle security by preventing malicious intrusions.
The system monitors and manages electricity distribution efficiently for power companies. It detects faults, optimizes load distribution, and reduces power losses using smart sensors and data analytics. The solution helps power companies maintain stable supply and improve customer service. Real-time alerts and reports aid quick decision-making.
This project uses a graph neural network that accounts for connectivity patterns to detect driver drowsiness in real-time. It processes sensor and video data to identify fatigue signs and issues alerts. The system aims to reduce road accidents by enhancing driver safety. The neural network adapts to connectivity changes for accurate detection.
A hybrid machine learning approach combined with explainable AI techniques is developed to detect cyber terrorism activities. The system analyzes network traffic and suspicious patterns to identify threats. Explainable AI ensures transparency in decisions, allowing security analysts to understand detection reasons. This project strengthens cybersecurity defenses against terrorism.
This simulation model predicts container handling and operation times at terminals to optimize scheduling and resource allocation. It uses discrete event simulation techniques to replicate real terminal workflows. Predictions help improve operational efficiency and reduce delays. The system supports decision-making for terminal management.
The project analyzes user comments on YouTube videos to determine viewer sentiment using artificial intelligence. It classifies comments into positive, negative, or neutral categories using natural language processing models. This helps content creators understand audience feedback better. The system supports automated moderation and trend analysis.
This AI-based solution recommends suitable crops to farmers based on soil, weather, and historical data. It forecasts crop yield and rainfall to assist agricultural planning. Explainable AI models provide transparent reasoning behind recommendations, increasing farmer trust. The system promotes sustainable and data-driven farming practices.
The project develops a system that monitors behavior and network traffic to detect ransomware attacks at early stages. Adaptive algorithms learn normal system behavior to identify anomalies quickly. Early detection helps prevent data encryption and loss. This solution enhances cybersecurity in enterprise environments.
This system automates the payroll processing for state civil service employees, including salary calculation and deductions. It reduces errors and ensures timely payments through a computerized solution. The system maintains detailed records and generates reports for accountability. It streamlines financial operations in government offices.
A web-based framework is developed to detect cervical cancer using machine learning models analyzing medical images. The system offers easy access for doctors and patients to upload and review results. It improves early diagnosis accuracy and aids clinical decision-making. The framework integrates seamlessly with healthcare workflows.
This project uses active machine learning techniques to classify diverse physical activities from smartwatch sensor data. The system reduces labeling effort by selecting informative samples for training. It supports accurate recognition of heterogeneous activities for health monitoring. The approach improves personalized fitness tracking.
The system generates digital cash receipts instantly during supermarket transactions. It stores transaction records securely for both customers and management. This solution speeds up billing processes and reduces paper usage. It supports easy retrieval and auditing of sales data.
This project creates a secure electronic voting system that ensures voter privacy and data integrity. It uses encryption and authentication techniques to prevent tampering and fraud. The system supports transparent vote counting and quick result publication. It aims to modernize traditional voting processes.
Multiple deep learning models are combined to predict the health status of vehicle engines. The system analyzes sensor data to detect faults before breakdowns occur. Ensemble learning improves prediction accuracy and robustness. This helps in scheduling timely maintenance and reducing repair costs.
This project monitors intranet network traffic to detect behavioral anomalies indicating attacks. Machine learning models analyze patterns to distinguish between normal and malicious activities. The system strengthens internal network security for organizations. It provides real-time alerts to prevent damage.
The project creates digital twin models representing real EV batteries to predict their health and performance. Data-driven approaches combined with explainability allow understanding battery degradation factors. This enables better battery management and extends lifespan. The system supports electric vehicle reliability.
This system uses historical forex data and machine learning to predict Euro to Dollar exchange rate trends. It categorizes market movements to assist investors and traders. The project improves decision-making in foreign exchange markets. Time-series and regression models are implemented for forecasting.
The project predicts the duration of patient hospital stays based on clinical and demographic data. Explainable models provide transparency for healthcare providers. Predictions help optimize hospital resource management and planning. The system supports better patient care and cost control.
Multiple sensors’ data are fused and analyzed via machine learning to detect movements in patients using trunk orthosis. The system aids in monitoring rehabilitation progress accurately. It provides real-time feedback to therapists and patients. This enhances physical therapy outcomes and safety.
This project integrates explainable AI with federated learning to detect financial fraud without sharing sensitive data. It ensures privacy preservation across distributed data sources. Explainability builds trust in automated fraud detection systems. The approach balances security with data confidentiality.
Deep learning models are concatenated to improve prediction accuracy of ischemic stroke occurrence. The system also interprets key features contributing to stroke risk. It supports early diagnosis and clinical intervention. This research advances AI applications in healthcare.
Adaptive active learning techniques are used to classify different levels of student anxiety from behavioral data. The system minimizes labeled data requirements while maintaining accuracy. It helps educators identify and support anxious students early. The project promotes mental health awareness.
This project automates the generation of updated document versions using deep learning. It identifies changes and integrates them seamlessly for collaborative writing. The system improves document management efficiency. It supports version control in professional and academic contexts.
The system uses sensors and imaging technology to detect human presence during fire emergencies. Real-time detection aids rescue teams in locating trapped individuals quickly. It enhances survival rates in disaster scenarios. The project integrates with existing fire safety systems.
A comparative study of YOLOv7 and YOLOv8 object detection models is conducted to improve victim detection accuracy. The system is designed for disaster rescue operations to locate survivors efficiently. Performance metrics guide model selection and optimization. This aids faster and safer rescue missions.
This project converts handwritten documents into editable digital text using convolutional and recurrent neural networks. It supports digitization and archival of historical and personal records. The system improves recognition accuracy for diverse handwriting styles. It reduces manual transcription effort.
The project detects emotions in speech by incorporating gender-specific features for improved accuracy. It uses deep learning models trained on annotated audio datasets. Applications include customer service and healthcare monitoring. The system enhances human-computer interaction.
The system detects and classifies fish species in underwater images using advanced object detection models. It supports marine biology research and sustainable fisheries management. Real-time processing helps in automated monitoring. The project compares model efficiency and accuracy.
Social network graphs are analyzed to identify key influencers in online communities. The system uses data mining techniques to evaluate user influence and reach. It aids marketing strategies and information dissemination. Insights improve targeted advertising campaigns.
Machine learning models analyze transaction patterns to identify fraudulent Unified Payments Interface (UPI) activities. The system enhances digital payment security by detecting anomalies. It helps banks and payment platforms prevent financial losses. Real-time alerts improve response times.
This project monitors Industrial Internet of Things (IoT) networks to detect cyber threats using smart sensors and AI. It analyzes data streams for anomalies indicating attacks. The system safeguards critical infrastructure and industrial control systems. It supports proactive cybersecurity measures.
Various ML models predict rainfall amounts and patterns using historical and sensor data. Accurate forecasts support agriculture and disaster management planning. The project enhances preparedness for extreme weather events. It improves resource allocation during floods or droughts.
This research extracts personal details from anonymized purchase histories by identifying behavioral patterns. It highlights privacy vulnerabilities in shared data. The system uses clustering and inference to reconstruct user identities. The project calls for stronger data anonymization techniques.
An ensemble of deep learning models is pruned for efficient detection of Distributed Denial of Service (DDoS) attacks in IoT environments. The system balances accuracy with computational resources. It strengthens IoT network security against volumetric attacks. Real-time detection enables swift mitigation.
The project develops a fast and secure image encryption algorithm suitable for cloud storage. It uses innovative key generation techniques to enhance security. The lightweight design supports resource-constrained devices. Encryption protects user privacy and data integrity.
This system securely eliminates duplicate textual files in cloud storage without exposing sensitive information. It uses cryptographic techniques to maintain data confidentiality. Deduplication optimizes storage and reduces costs. The project improves cloud storage efficiency.
An encryption scheme is designed to securely send messages to two recipients without relying on pairing operations. It offers provable security guarantees. The system simplifies multi-receiver encryption protocols. Applications include confidential communications.
This project applies elliptic curve cryptography (ECC) to enhance data security in cloud computing. ECC provides strong encryption with smaller key sizes. The system improves performance and reduces computational load. It is suitable for modern cloud environments.
Automates the scheduling of medical staff duties in hospitals and clinics. It ensures fair allocation and avoids scheduling conflicts. The system improves staff satisfaction and operational efficiency. It maintains detailed duty records and supports shift management.
Digitally tracks key economic indicators to monitor regional or national growth. Provides real-time analytics and reporting tools. Supports policymakers in making informed decisions. The system enhances transparency and data accuracy.
Maintains a digital directory of business locations and contacts within Enugu Urban. Facilitates easy search and management of commercial data. Supports urban planning and economic development. The system updates business records regularly.
Enables secure online money transfers and payment processing. Tracks transaction status and history in real-time. The system reduces fraud and improves financial accessibility. It supports digital banking services.
Automates the evaluation of civil service employees’ performance. Ensures objective assessments and transparent criteria. Supports human resource development and promotions. The system generates comprehensive performance reports.
Coordinates ambulance assignments and dispatches based on emergency calls. Minimizes response times through optimized routing. Improves emergency medical service efficiency. The system tracks ambulance locations in real-time.
Controls and monitors access to windows in buildings electronically. Enhances security by logging entry and exit events. Allows remote control and alerts for unauthorized access. Suitable for residential and commercial properties.
Automates vehicle and driver scheduling to maximize fleet utilization. Optimizes route planning and reduces downtime. Enhances transport company operational management. The system generates schedules and tracks assignments.
Generates graphical charts from statistical data automatically. Supports various chart types for effective data visualization. Improves reporting and analysis processes. User-friendly interface enables easy data input.
Manages employee information including attendance, leave, and payroll. Streamlines HR operations and record-keeping. Enhances communication between management and staff. The system improves organizational productivity.
Automates the assessment and processing of employee promotions. Ensures fairness based on predefined criteria. Supports career development within civil services. The system maintains promotion histories and eligibility records.
Facilitates online course enrollment and registration for students. Automates exam result processing and publication. Enhances academic administrative efficiency. The system supports easy student access and record management.
Manages hotel room bookings and availability online. Processes customer information and payments securely. Improves reservation accuracy and customer experience. The system generates reports for hotel management.
Automates sales transactions and inventory tracking. Generates receipts and daily sales reports. Enhances retail store management and accuracy. Supports fast billing and stock monitoring.
Connects job seekers with employers via an online platform. Manages job listings, applications, and interview scheduling. Facilitates campus recruitment and placement activities. The system provides real-time updates and notifications.
Digitizes land records and property ownership information. Facilitates quick access to land data for legal and administrative use. Supports land management and urban planning. The system improves record accuracy and security.
Provides detailed weather forecasts tailored for aviation needs. Supports flight planning and safety management. Integrates real-time sensor data with historical models. The system enhances operational decision-making in airports.
Handles employee data including recruitment, attendance, and leave management. Automates HR workflows to reduce manual errors. Provides comprehensive reports for management decisions. The system improves organizational efficiency.
Delivers personalized educational content adapting to student learning pace. Provides instant feedback and performance tracking. Enhances student engagement and knowledge retention. Useful for remote and self-paced learning environments.
Develops AI-based expert systems to aid medical diagnosis and treatment decisions. Integrates local healthcare knowledge with global medical data. Increases accessibility to quality healthcare services. Supports medical practitioners with decision support tools.
Collects, analyzes, and disseminates market data for business decision-making. Supports real-time updates on prices, demand, and supply trends. Enables businesses to strategize effectively. The system improves market transparency.
Creates a secure electronic system to monitor window openings and closings. Logs all access events to enhance security. Allows remote monitoring and control through digital interfaces. Suitable for integration in smart building systems.