VOLUME 12, ISSUE 9, SEPTEMBER 2024

Abstract: Multivariate imaging advanced in recent years which prompted many applications for detailed understanding in the fields of satellite imaging, medical imaging, and microscopic imaging. To achieve more insights about it, various feature extraction techniques exist which utilize the ample spectral and spatial details in an image. But apart from feature extraction dimensionality reduction (DR) and efficient classification has become a key aspect in multivariate image analysis (MIA). Adding more and more variables in feature space of multivariate image results into high dimensionality which in turn increases the complexity in classification. Therefore, it becomes important to apply DR techniques before classification process. Most widely used DR method is Principal component analysis (PCA) which is linear DR method. The main disadvantage of PCA is that it does not consider the nonlinearity in data. The proposed new methods are invariant to nonlinearity in data. To consider nonlinearity, Geodesic distance measure is used to extract features from multivariate data. Method GGPS performs dimensionality reduction while improving the classification accuracy.

Keywords: Multivariate Image Analysis (MIA), Principal Component Analysis (PCA), Support Vector Machine.

Abstract: The global e-commerce landscape has witnessed exponential growth, revolutionizing how goods and services are bought and sold. With this surge in online transactions comes the imperative need for robust, secure payment mechanisms. In recent years, cryptocurrencies have emerged as a disruptive force in the realm of digital payments, offering decentralized, secure, and transparent alternatives to traditional fiat currencies. This paper delves into the intricacies of integrating cryptocurrency-based secure payment systems into the e-commerce ecosystem. Cryptocurrencies, powered by blockchain technology, provide a decentralized ledger that records transactions across a distributed network of computers. This distributed nature ensures immutability and transparency, reducing the risk of fraud and unauthorized access to sensitive financial data. Moreover, cryptographic techniques employed in cryptocurrencies offer enhanced security, safeguarding transactions against cyber threats and identity theft. However, the adoption of cryptocurrency payments in e-commerce is not without challenges. One significant hurdle is the inherent volatility of cryptocurrency markets, which can pose risks for both merchants and consumers in pricing goods and services. Scalability issues further complicate the integration process, as the current infrastructure struggles to accommodate the growing demands of a global e-commerce ecosystem

Keywords: Bitcoin, Consumer Adoption, Cryptocurrency, Digital Divide, e-Commerce, e- Payment, Payment Gateway- Digital Signature.

Abstract: A Zeta converter is a DC-DC converter which works as a buck-boost converter with a non-inverted output. This paper focuses on a battery charger for an electric vehicle based on zeta converter fed from a pv array. The control technique used here is perturb & observe technique to get the appropriate duty ratio of the gate pulse for the switch of zeta converter and to achieve the maximum power point from pv array. This technique is used for this mission due to its simplicity and its quick response to achieve the mpp and can be easily implemented in low cost system. In this a complete analysis of the zeta converter applied as a photo voltaic battery charger is carried out. The proposed system is simulated using MATLAB Simulink.

Keywords: Battery charger,DC-DC converter, Photovoltaic, MPPT, Zeta converter.

Abstract: A major paradigm shift in the provision and consumption of financial services has been brought about by the integration of Artificial Intelligence (AI) into the financial technology (fintech) industry. In-depth examination of the advantages and difficulties of implementing AI in fintech is given in this paper, which focuses on important technologies like robotic process automation (RPA), natural language processing (NLP), and machine learning. Financial institutions can now use AI to improve fraud detection and risk assessment accuracy, automate repetitive tasks, and customize customer experiences. Large-scale data availability and computing power advancements have sped up the adoption of AI, which has significantly increased customer satisfaction and operational efficiency. This paper also explores the current and future regulatory landscape surrounding AI in fintech, emphasizing the importance of developing ethical AI frameworks and global regulatory harmonization. Recommendations for financial institutions include investing in AI literacy and skills development, fostering collaboration with stakeholders, and implementing continuous monitoring and evaluation mechanisms to ensure the effectiveness and fairness of AI systems.

Keywords: Artificial Intelligence, Fintech, Efficiency, Customer Experience, Security, Data Privacy, Algorithmic Bias, Job Displacement, Regulatory Compliance, Implementation Costs

Abstract: In this paper we have studied the in-depth comparative examination of the temperature-dependent efficiency of solid-state batteries, concentrating on three primary electrolyte materials: Lithium Sulfide (LiS), Garnet-based electrolytes, and Polymer electrolytes. Solid-state batteries are recognized for their superior safety and energy density; however, their performance is notably affected by the operating temperature. The research models the efficiency of these materials over a temperature spectrum of -10°C to 100°C, employing a parabolic degradation model to accurately reflect the behaviour specific to each material. It is found that Lithium Sulfide maintains the highest efficiency retention at elevated temperatures, rendering it particularly suitable for high-temperature applications. It has also been observed that the Garnet-based electrolytes exhibit moderate stability and efficiency within mid-range temperatures, whereas Polymer electrolytes experience a rapid decline in efficiency when operating outside their optimal temperature range, thus making them more appropriate for low-temperature settings. The paper further explores the practical implications for battery applications, potential design enhancements, and the environmental consequences of temperature-induced degradation. Future research avenues include improving electrolyte stability across broader temperature ranges and investigating hybrid electrolyte systems to enhance thermal performance.

Abstract: The primary objective of the study was to compare personality traits between male and female Softball players Total 50 male and 50 female softball players were selected as a subject for the present study. Their age ranged from 21 to 28 years. Data was collected individually through a Eysenck personality inventory from male and Softball Players. To analyze the data mean scores, standard deviation and t-ratio were used to Extraversion, Neuroticism and Psychoticism between Softball and Baseball players. The Results shows Significant differences between Extraversion, Neuroticism and Psychoticism between Softball and Baseball players. Male softball players were less neuroticism and psychotic tendency .