http://202.88.229.59:8080/xmlui/handle/123456789/2092 2026-04-14T20:00:48Z http://202.88.229.59:8080/xmlui/handle/123456789/5818 AI and Iot Integrated Techniques for Detection and Prediction of Brain Diseases Sreejit Rama Krishnan Brain is the controlling centre of our body. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of brain diseases. The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. 2024-07-01T00:00:00Z http://202.88.229.59:8080/xmlui/handle/123456789/5733 Predicting Employee Performance Levels Using Machine Learning Algorithms: Enhancing HR Decision-Making through AI Akhil P Shaji This study presents a machine-learning framework to predict employee performance levels, empowering HR professionals with data-driven insights for eTective talent management. Leveraging a comprehensive dataset encompassing demographics, job roles, engagement metrics, training history, and historical performance ratings, the research explores multiple algorithms, including LightGBM, XGBoost, XGBoost with SMOTE, and Random Forest. To address class imbalance, the Synthetic Minority Over sampling Technique (SMOTE) was implemented, generating synthetic samples to enhance prediction accuracy across all classes. Feature selection and importance analysis identified key performance predictors, such as tenure, engagement scores, work-life balance, and satisfaction levels. Among the evaluated models, Random Forest achieved the highest accuracy (94%) with balanced class performance, making it the preferred choice for deployment. This research underscores the transformative role of machine learning in HR practices, providing actionable insights to design targeted development programs, optimize employee performance, and improve organizational outcomes. 2024-11-01T00:00:00Z http://202.88.229.59:8080/xmlui/handle/123456789/5724 CREDIT CARD FRAUD ANALYSIS AND DETECTION METHOD Dr.Meenu Suresh Electronic payment (e-payment) has transformed financial transactions, offering speed and convenience. However, it has also brought about significant challenges, notably in credit card security. This paper explores the landscape of e-payment, focusing on credit card fraud detection—a crucial aspect of financial security systems. Advanced algorithms and machine learning techniques are employed to analyse transaction data for patterns indicative of fraudulent activity. Despite these advancements, credit cards face various security threats, including card skimming, phishing scams, and data breaches. Cybercriminals continuously adapt their tactics, necessitating ongoing advancements in credit card protection. This paper highlights the importance of evolving security measures to safeguard users' financial information in the ever-changing digital landscape. 2024-04-01T00:00:00Z http://202.88.229.59:8080/xmlui/handle/123456789/5723 PNEUMONIA DETECTION USING DEEP LEARNING Dr.Meenu Suresh This paper introduces an advanced deep learning approach for pneumonia detection using convolutional neural networks (CNNs). Trained on a large dataset of annotated chest X-ray images, our model leverages transfer learning and addresses class imbalance through data augmentation and weighted loss-functions. Visualization techniques, including Grad-CAM, enhance interpretability, aiding clinicians in understanding the model's focus. Evaluation on a benchmark dataset demonstrates superior sensitivity and specificity compared to traditional methods. Our findings highlight the model's robustness across diverse demographics, emphasizing its potential for early diagnosis and improved patient outcomes. The study underscores the transformative impact of deep learning on pneumonia diagnosis, providing a valuable tool for efficient and accurate healthcare practices. 2024-04-01T00:00:00Z