Projects
IP Masking Detection using Shodan and WHOIS
This project is designed to detect whether an IP address is masked using VPNs, proxies, or anonymization services. It utilizes Shodan to gather internet-wide intelligence about exposed services and network behavior. WHOIS lookup is used to analyze IP ownership, ISP details, and registration information. By correlating data from both sources, the system identifies inconsistencies in location and hosting patterns. The project helps determine if an IP is residential, corporate, or data-center based. It improves visibility into hidden network identities and suspicious activities. The solution is valuable for cybersecurity monitoring and digital forensics. Overall, it enhances awareness and control over IP-level threats and anonymity risks.
Drowsiness Detection System
Drowsiness Detection System is a real-time computer vision project designed to prevent road accidents caused by driver fatigue. The system continuously monitors the driver’s face using a camera and detects eye closure, blinking rate, and yawning patterns. It uses Python, OpenCV, and machine learning algorithms to analyze facial landmarks in live video streams. When signs of drowsiness are detected, the system generates instant audio or visual alerts to wake the driver. This project demonstrates the practical implementation of artificial intelligence in transportation safety. It is widely used in smart vehicles, driver monitoring systems, and road safety applications. The solution improves driver awareness and reduces the risk of fatigue-related accidents. Overall, this project is ideal for students and professionals exploring real-time AI and computer vision projects.
C6 – Analytical Approach for Future Blockchain Forensic Investigation of Bitcoin Transaction Network
C6 – Analytical Approach for Future Blockchain Forensic Investigation of Bitcoin Transaction Network focuses on identifying illicit activities within the Bitcoin blockchain using advanced data analysis techniques. The project analyzes Bitcoin transaction networks to trace fund flows, detect suspicious patterns, and uncover hidden relationships between wallets. It applies graph theory, clustering algorithms, and network analytics to improve transaction traceability. Machine learning models are used to classify normal and anomalous transaction behaviors. This approach enhances digital forensics by providing deeper insights into decentralized financial systems. The project supports law enforcement and cybersecurity teams in combating fraud, money laundering, and cybercrime. It demonstrates real-world applications of blockchain analytics, big data processing, and forensic investigation. Overall, this project highlights the future of blockchain forensics in securing cryptocurrency ecosystems.
Image Classification Using CNN (Convolutional Neural Networks)
Image Classification Using CNN (Convolutional Neural Networks) is a deep learning project that automatically identifies and categorizes images based on their visual content. The system uses convolutional neural network architecture to extract features such as edges, shapes, and textures from images. It is implemented using Python with TensorFlow or Keras for efficient model training and evaluation. The project trains the CNN model on labeled image datasets to achieve high classification accuracy. Data preprocessing techniques like image resizing, normalization, and augmentation are applied to improve performance. This project demonstrates the practical use of deep learning in computer vision applications. It is widely used in fields such as medical imaging, facial recognition, and autonomous systems. Overall, this project is ideal for learners exploring real-time AI and deep learning projects.
AI-Powered System Quantifies Suicide Indicators and Identifies Suicide-Related Content in Online Posts
AI-Powered System Quantifies Suicide Indicators and Identifies Suicide-Related Content in Online Posts is an advanced natural language processing project focused on mental health risk detection. The system analyzes online posts from social media and forums to identify linguistic and emotional patterns associated with suicidal behavior. Machine learning and deep learning models are used to quantify suicide risk indicators with high accuracy. Text preprocessing techniques such as tokenization, sentiment analysis, and feature extraction are applied. The project helps in early detection and prevention by flagging high-risk content in real time. It demonstrates the ethical application of artificial intelligence in mental health monitoring. This system can support counselors, healthcare professionals, and crisis intervention teams. Overall, the project highlights the role of AI in saving lives through intelligent content analysis.
An Application of IoT-Based Smart Electric Meters for Electricity Theft and Cyber-Attacks Detection
An Application of IoT-Based Smart Electric Meters for Electricity Theft and Cyber-Attacks Detection focuses on securing smart grid systems using intelligent monitoring techniques. The project uses IoT-enabled smart meters to collect real-time electricity consumption data from users. Advanced data analytics and machine learning algorithms are applied to detect abnormal usage patterns and power theft. The system also identifies potential cyber-attacks targeting smart meter communication networks. It enhances grid reliability by providing early alerts and automated reporting mechanisms. This project demonstrates the integration of IoT, cybersecurity, and smart energy management. It is highly relevant for power utilities, smart cities, and energy providers. Overall, the project showcases how AI and IoT can protect critical infrastructure from fraud and cyber threats.
An Integrated Approach for Detecting Diabetes and Recommending Diet Plans in Healthcare Big Data Clouds Using Ensemble Framework
An Integrated Approach for Detecting Diabetes and Recommending Diet Plans in Healthcare Big Data Clouds Using Ensemble Framework is an intelligent healthcare analytics project. The system analyzes large-scale medical data stored in cloud environments to detect diabetes at an early stage. Ensemble machine learning algorithms are used to improve prediction accuracy and reliability. Based on the diagnosis results, the system recommends personalized diet plans for effective diabetes management. Big data technologies enable scalable storage and fast processing of healthcare datasets. The project demonstrates the integration of cloud computing, data analytics, and artificial intelligence in healthcare. It supports doctors and patients with data-driven decision-making and preventive care. Overall, this project highlights the role of AI-powered big data solutions in modern digital healthcare systems.
B10 – Detection of Parkinson's Disease through Analysis of Image and Speech Data
B10 – Detection of Parkinson's Disease through Analysis of Image and Speech Data is a healthcare AI project focused on early diagnosis of neurological disorders. The system analyzes facial images and speech signals to identify patterns associated with Parkinson’s disease. Image processing techniques are used to detect facial expression and motor abnormalities. Speech signal analysis extracts vocal features such as pitch, tremor, and frequency variations. Machine learning and deep learning models classify patients based on combined image and speech data. This multimodal approach improves diagnostic accuracy compared to single-data methods. The project supports clinicians with automated and data-driven diagnostic insights. Overall, it demonstrates the effective use of AI in medical image and speech analysis for healthcare applications.
Recommendation System Based on Customer Reviews Using AI and NLP
Recommendation System Based on Customer Reviews Using AI and NLP is an intelligent system that suggests products or services based on user feedback. The system analyzes customer reviews using natural language processing techniques to understand sentiment and preferences. Machine learning algorithms classify reviews as positive, negative, or neutral. Key features and opinions are extracted from textual data to improve recommendation accuracy. The project uses Python with NLP libraries such as NLTK, spaCy, or transformers. It helps businesses personalize user experience and increase customer satisfaction. This system is widely used in e-commerce, travel, and online service platforms. Overall, the project demonstrates how AI and NLP can convert unstructured text into actionable recommendations.
C9 – NLP-Based Extended Lexicon Model for Sarcasm Detection with Tweets and Emojis (SAK)
C9 – NLP-Based Extended Lexicon Model for Sarcasm Detection with Tweets and Emojis (SAK) focuses on identifying sarcastic content in social media text. The system analyzes tweets by combining textual data and emoji-based sentiment cues. An extended lexicon model is used to capture contextual polarity and sarcasm indicators. Natural language processing techniques such as tokenization, sentiment scoring, and feature extraction are applied. Machine learning classifiers improve sarcasm detection accuracy in short informal texts. This project addresses challenges of ambiguity and mixed emotions in social media analytics. It is useful for opinion mining, brand monitoring, and social sentiment analysis. Overall, the project highlights the importance of advanced NLP models in understanding human language nuances.