Software product is a kind of software whose development is done to accomplish a precise requirement. Meanwhile, engineering is branch which is related to develop a product based on explicit technical fundamentals and techniques. There are diverse phases executed to predict the defect in software such as to employ the data for input, pre-process it, extract the attributes and classify the defect. This research work presents numerous algorithms, namely gaussian naive bayes (GNB), Bernoulli NB, random forest (RF) and multi-layer perceptron (MLP), for predicting the software defect. This work focuses on developing an ensemble algorithm to enhance the efficacy of predicting the defects. This ensemble consisted of Principal Component Analysis (PCA) algorithm with class balancing. Python is executed for simulating the introduced model. Diverse parameters such as accuracy, precision and recall are employed for analysing the results.

This paper focuses on the transportation sector's increasing greenhouse gas emissions, mostly caused by the consumption of fossil fuels, are an important cause of global warming. Electric cars (EVs) provide a sustainable substitute, but their widespread use depends on reliable and affordable charging techniques. The present study proposes an ideal Charging Strategies constant temperature-constant voltage (CT-CV) charging method that uses a PID controller with a feedforward current term to optimize charging efficiency. The CT-CV approach reduces charging durations by 22% and 42% at SOC levels of 0% and 80%, respectively, according to simulations carried out using MATLAB/Simulink with different states of charge (SOCs). Improved battery health and efficiency are shown by the results, which inspire EV adoption and aid in international efforts to reduce emissions.

Technical companies supplying financial services went through fast digital change that led to global adoption. Modern banking customers depend on high-end Fintech solutions ranging from mobile banking to digital wallets and peer-to-peer lending with block chain-based financial products to contact their banks. Nevertheless, consumer acceptance of Fintech fluctuates because too many behavioral, psychological, and environmental influences. This study combined quantitative testing with qualitative research elements for its research design structure. The main data collection process for this study depends on survey research combined with intensive interviews. The study reveals how end-users adopt Fintech services through their trust levels and risk perceptions alongside socio-economic characteristics alongside attitudes. The research study receives support from diverse data sources that include financial statements in conjunction with academic publications and marketplace intelligence. The study evaluates Fintech adoption enablers and barriers because this research helps leaders from both financial institutions and Fintech companies together with policy makers to create accessible user solutions. Organizations must study how customers behave to enhance trust-based security solutions which create robust digital financial service protection systems.

COVID-19 pandemic situation and calculates executed by Government changes the investment behaviour of the people. It is the process of setting aside a portion of present day earning for future use, or the flow of money accumulated in this way over a given period of time. Saving will help to increases in bank deposits, purchases of securities, gold and silver or increased cash stock. Investing is a kind of activity i.e. engaged in by people who have savings. In accordance with to Statista Research Department, as a result of the lockdown, many people were able to save money during the year 2020, and majority of the people were lost their savings and their jobs because of pandemic situation. This research paper is based on the investment pattern and behaviour of the people in Erode city. Investing is likewise essential for secured future of human life. This study has made an attempt to analyse the Savings and Investment pattern of Rural Women during Covid-19 pandemic. The data has been collected from 756 sample respondents by adopting convenience sampling technique. The statistical tools like Percentage Analysis and Henry Garret Ranking Technique are used. Consequently, the researcher has concluded that Fluctuation in value has the first problem in the study area.

The integration of Distributed Energy Resources (DERs) into microgrids has become essential for enhancing energy reliability, reducing costs, and promoting sustainability. Effective management of these resources is critical to ensure optimal operation and energy efficiency. This paper presents a comparative analysis of two prominent optimization approaches for managing DERs in Microgrid Energy Management Systems (EMS): the Heuristic State Machine (HSM) strategy and the Linear Programming (LP) optimization approach. The Heuristic State Machine strategy offers a flexible and adaptive method for dynamic decision-making, while the Linear Programming approach provides a more structured, mathematically rigorous optimization framework. We evaluate both strategies based on their ability to minimize energy costs, improve system reliability, and maximize the utilization of renewable energy within microgrids.

The analysis considers multiple operational scenarios, including varying energy demand, generation capacities, and environmental conditions. Results indicate that while the Heuristic State Machine approach excels in handling uncertainty and real-time adaptability, the Linear Programming method demonstrates superior performance in terms of computational efficiency and optimal resource allocation under defined constraints. This study provides valuable insights into the strengths and limitations of both methods, offering recommendations for their practical implementation in modern EMS for microgrids. Ultimately, the findings aim to guide future research and assist energy planners in selecting the most appropriate optimization strategy for enhancing the operation of microgrids with integrated DERs.

Software testing plays a critical role in ensuring and evaluating the quality of software by verifying that it functions as intended and does not produce unintended behaviors. Despite advancements in software development methodologies and programming languages, testing remains a crucial component of the development process. While numerous techniques have been proposed to automate software testing, many fail to achieve satisfactory performance in terms of accuracy. This research introduces a reinforcement learning-based method for software testing, which demonstrates an impressive accuracy of 96%, offering a significant improvement over traditional testing approach.