Unbiased Circular Leakage Centered Adaptive Filtering Control for Power Quality Improvement of Wind-Solar PV Energy Conversion System
Published Date - 17 Mar
Due to the intermittent and unpredictable nature of solar and wind energy, greater integration of these sources into the existing power grid could pose significant technological difficulties, particularly for weakened grids or standalone systems lacking adequate and sufficient storage. The impact of the fluctuating nature of solar and wind resources can be partially alleviated by integrating the two renewable resources into an optimal combination, and the whole system becomes more reliable and cost-effective to run. In this study, the opportunities and problems of hybrid solar PV and wind energy integration systems are reviewed. Major power quality challenges for both grid-connected and stand-alone systems include harmonics, voltage and frequency fluctuation, and they are more severe in weak grid situations. This can be dealt to a significant extent by having adequate design, improved rapid reaction control capabilities, and good hybrid system optimization. The primary research projects related to optimal size design, power electronics topologies, and control are reviewed in this study. The study provides an overview of the current state of hybrid solar and wind systems that are both grid-connected and stand-alone.
Review of Literature in Research Methodology: Its Importance & Guidelines for researchers
Published Date - 17 Mar
This paper discusses review of literature as a methodology for conducting research and offers a direction about its objectives, purpose, relevance and importance. It also gives guidelines on how to write Review of Literature.
Machine Learning Strategies for Fraud Prevention in Financial Data
Published Date - 15 Mar
The rapid expansion of the E-Commerce industry has led to an exponential surge in credit card usage for online transactions. Unfortunately, this growth has also resulted in an increase in fraudulent activities. Detecting fraud within credit card systems has become increasingly difficult for banks. Machine learning techniques play a pivotal role in identifying credit card fraud during transactions. To predict these fraudulent activities, banks employ various machine learning methodologies, leveraging historical data and incorporating new features to enhance predictive accuracy. In this study, we evaluate the effectiveness of three machine learning models—*Logistic Regression, **Decision Tree, and **Support Vector Machine (SVM)*—for credit card fraud detection. Our dataset comprises 3,925,159 credit card transactions sourced from Kaggle. Transactions are labeled as either "genuine" (denoted by "0") or "fraudulent" (denoted by "1"). With 3,921,920 genuine transactions and 3,239 fraud cases, the dataset is imbalanced. To address this, we create a new balanced dataset with 3,239 samples for training and testing the models. Our evaluation focuses on accuracy. The results indicate the following accuracy rates for the three models:- Logistic Regression: 92.47%, Decision Tree: 99.21%, SVM : 85.57% Comparatively, the Decision Tree outperforms both Logistic Regression and SVM. This research contributes to the ongoing efforts to combat credit card fraud using predictive analytics, artificial intelligence, and machine learning in real-time applications.
Self Heal: AI-Enhanced Conversational Therapy Bot for Mental Wellbeing
Published Date - 15 Mar
Self-Heal stands as an innovative initiative dedicated to delivering accessible mental health support through a cutting-edge application. In response to the escalating challenges in mental health, the work centers around an AI-enhanced conversational therapy bot, uniquely empowered with voice-enabled capabilities. Leveraging advanced natural language processing and machine learning algorithms, this therapeutic companion engages users in empathetic and personalized conversations, transcending the boundaries of traditional text-based interactions. Self-Heal integrates cutting-edge Language Models (LLMs) to continuously enhance its conversational capabilities. These LLMs are trained on vast datasets of human interactions, enabling the therapy bot to understand nuanced language nuances and respond with even greater empathy and effectiveness. The integration of sentiment analysis further enriches the application by offering deep insights into users' emotional states. Complemented by a resourceful hub for education on mental health, Self-Heal envisions contributing significantly to the well-being of individuals by providing a stigma free, technologically advanced platform for those seeking emotional support and guidance.