Category Archives: Uncategorized

Authors: Acharjee Partho Protim, Wei Zhang

Abstract: Lightweight design is essential in modern automotive systems to improve energy efficiency, reduce emissions, and enhance performance. This study presents a simulation-driven framework for the lightweight design of an automotive reducer housing using finite element analysis (FEA) and topology optimization (TO). A baseline reducer housing is analyzed under multiple load conditions, including maximum torque, emergency braking, and cornering. Stress distribution and deformation behavior are evaluated to identify structurally redundant regions. A Solid Isotropic Material with Penalization (SIMP)-based topology optimization method is applied with a volume reduction constraint to minimize compliance while maintaining stiffness. The optimized topology is reconstructed into a manufacturable design considering casting constraints. Comparative FEA validation shows significant mass reduction while preserving structural integrity, safety factor, and stiffness. The proposed methodology provides an effective and practical framework for lightweight automotive component design.

DOI: https://doi.org/10.5281/zenodo.19880845

Published by: vikaspatanker

Authors: Khadija Elkattany, Md Mutasim Billa

Abstract: This paper presents a hybrid machine learning framework that addresses scalability and accuracy challenges in retail inventory management by integrating real-time demand forecasting with anomaly detection, evaluated using Walmart’s historical sales data. Traditional approaches face a trade-off: maintaining individual models for each product category is computationally prohibitive, while generalized models often underperform for dissimilar items, resulting in stock outs or overstocking. To address this, we propose a department-level aggregation strategy that balances specificity and generalization, combined with a hybrid methodology: ARIMA for linear trend and seasonality modeling, cubic spline interpolation to capture nonlinear residual patterns, and neural networks for complex interactions. The framework dynamically adjusts predictions using real-time sales streams and applies residual-based anomaly detection with threshold triggers to identify sudden demand spikes or supply disruptions. Experiments on a filtered Walmart dataset (12 months, 15 departments) indicate an 18% reduction in mean absolute error (MAE) compared to exponential smoothing baselines, while spline-enhanced neural networks achieve a 24% improvement over standalone ARIMA. The anomaly detection module identifies 92% of simulated irregularities with a 7% false-positive rate. The proposed framework provides three principal advantages: (1) scalable department-level modeling without per-product customization, (2) real-time adaptability to fluctuating demand, and (3) cost-efficient inventory optimization through integrated anomaly alerts. This work offers a practical blueprint for retailers to enhance forecasting precision, mitigate supply chain risks, and reduce operational costs in volatile markets.

DOI: https://doi.org/10.5281/zenodo.19880805

Published by: vikaspatanker

Authors: K. Sagar, K.Ashok, G. Vijay Kumar

Abstract: This investigation explains the effect of addition of polypropylene fibers and Nano Silica into concrete. This investigation is divided into two phases. First Phase deals with calculations of Compressive and Split Tensile strength. Here we have done compressive strength tests for calculating the optimum percentage of Nano Silica with variation from0% to 3% of cement which is replaced with cement in concrete. Now polypropylene fiber is added to concrete from 0% to 1.4% of cement and those specimens were tested for compressive and Split Tensile strength and obtained the maximum percentage of fiber at which strengths maximum. In second phase, a modal equation is developed using Multiple Regression Analysis (MRA) for compressive and Split Tensile strength based on experimental results which are found in phase one. By using obtained modal equations we will calculate Predicted strength and their residuals and their graphical representation is shown.

DOI: https://doi.org/10.5281/zenodo.19879606

Published by: vikaspatanker

Authors: Rathod Neha, Mojidra kirtika, khandhediya Isha, Harkishan Gohil

Abstract: Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, and the four primary techniques for enhancing privacy in FL; Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and hardware based Trusted Execution Environments (TEE), is provided. We will review aggregation protocols, determine the strength of FL systems against poisoning and inference attacks, and compare various FL systems implemented in three industries; healthcare, mobile communication and finance. A detailed review of FL reveals research issues related to; statistical heterogeneity, communication overhead, system heterogeneity and fairness. Finally, this review presents a prioritized set of research objectives for the next ten years, with an emphasis on situating FL within the larger context of privacy-preserving ML and potential regulatory developments.

DOI: https://doi.org/10.5281/zenodo.19878825

Published by: vikaspatanker

Authors: Sanskriti Solse, Swarali Karkar, Saneeya Shaikh, Shubham Patil, Ms. Manila Gupta, Prof. Mohammad Juned, Dr. Varsha Shah

Abstract: The escalating cost of academic textbooks in India presents a significant financial burden to engineering students, many of whom purchase books for a single semester only to leave them unused thereafter. This paper presents ReBook, a full-stack, location-aware web platform that facilitates the buying, selling, and donation of second-hand academic books among students. The system employs Java Spring Boot for the backend REST API, MySQL 8.0 for persistent storage, and a JavaScript single-page application for the frontend. Key innovations include GPS-based distance sorting using the Haversine formula, WhatsApp seller integration, a pincode-level hyper-local search filter, and an AI-powered camera-based book condition detection module built on the Claude API (Anthropic). All ten planned feature modules were implemented and verified functional across Chrome, Firefox, and Android mobile browsers. ReBook directly addresses the affordability and sustainability challenges of academic publishing for students across India.

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Authors: Assistant Professor Mr. Eknath Prasath M, Mr. Vasanth Kumar S.

Abstract: Marketing strategies play a significant role in influencing consumer behaviour and shaping purchasing decisions in modern markets. Businesses operating in competitive environments must adopt effective marketing techniques to attract customers and maintain long-term relationships. Understanding consumer behaviour is essential for organizations to develop products, pricing strategies, promotional activities, and distribution systems that satisfy customer needs. This study aims to analyze marketing strategies and examine consumer behaviour with special reference to Coimbatore district. The research focuses on the factors that influence consumer buying decisions, the role of digital marketing, and the effectiveness of promotional strategies adopted by businesses in the region. The study mainly relies on secondary data collected from journals, research articles, and online sources. The findings suggest that marketing strategies such as branding, advertising, pricing policies, and digital promotions significantly influence consumer purchase behaviour in Coimbatore. Factors such as brand reputation, product quality, promotional offers, and social media marketing play a crucial role in shaping consumer preferences. The study also highlights the growing importance of digital platforms in influencing consumer decision-making.

DOI: https://zenodo.org/records/19865755

Published by: vikaspatanker

Authors: Prof. Vikas More, Sagar Mahajan, Yash Kadam, Yuvraj Singh, Yashraj Khandar

Abstract: In the modern banking environment, customer experience has become a critical factor influencing customer satisfaction, loyalty, and overall business performance. With the rapid advancement of digital technologies and increasing competition in the financial sector, banks are required to move beyond traditional service models and adopt customer-centric approaches. This study focuses on enhancing customer experience in the Indian banking sector through the application of business analytics and the development of a personalization framework. The primary objective of this research is to analyze how business analytics can be used to understand customer behavior and improve service delivery. The study also aims to identify customer expectations regarding personalized banking services and to examine the existing gaps in service quality. Primary data for the study was collected through a structured questionnaire administered to 50 respondents. The collected data was analyzed using simple statistical tools such as percentages and pie charts to derive meaningful insights. The findings of the study reveal that while customers are generally satisfied with banking services, there is a significant demand for personalized services. Most respondents expressed that banks do not fully understand their needs and expect more customized offerings. The study also highlights that digital banking, particularly mobile banking, is widely preferred due to its convenience and accessibility.

Published by: vikaspatanker

Authors: Dr. G. Arutgeevitha, Mr. M. Kavin Kumar

Abstract: The role of Artificial Intelligence in customer segmentation and target marketing has gained significant importance in the modern business environment. This study examines how AI technologies such as machine learning, predictive analytics, and data mining enhance the accuracy and efficiency of identifying customer groups and delivering targeted marketing strategies. AI enables organizations to analyze large volumes of customer data, identify patterns, and predict consumer behavior, leading to more personalized marketing efforts. The study also explores the benefits and challenges associated with AI adoption in marketing practices, highlighting its impact on improving customer engagement, conversion rates, and overall marketing performance.

DOI: https://zenodo.org/records/19865248

Published by: vikaspatanker

Authors: Ashita Chhabra, Ansh Popli, Jaskirat Kaur, Ansh Rehan

Abstract: The rising popularity of AI chatbots in the sphere of education is transforming the learning experience of children and the manner in which educators provide academic assistance. They offer personalized feedback and flexible assistance, interactive and interactive learning experiences, which can increase motivation and increase accessibility of learning. However, their growing incorporation into child,oriented education brings about myriad of moral, mental and safety issues. Other ethical concerns of the use of AI chatbot also feature in the paper as comprising academic dishonesty, unfairness and algorithm favoritism. It however, also researches a mental outlook on the excessive use of automatic reactions, and the loss of critical analytic faculties, creativity, and independent learning capability, in society. Finally, the paper concludes with security issues, which are the vulnerabilities of data security, misinformation and transparency in AI. The findings suggest that in the realm of digital learning, there is need to employ conscientious methods of AI technology to ensure the well,being of children is not jeopardized and this could be made possible through appropriate data protection, ethical practices and child-centered policies. Although the positive side of the application of AI chatbots in education is obvious, it is also important to define the way the latter should be applied in a responsible manner.

Published by: vikaspatanker

Authors: Assistant Professor Ms.G. Revathi, Mr. Gowtham MR

Abstract: The transformation of food consumption patterns has become a significant subject of study in the field of marketing and consumer behavior. This research investigates the shift in consumer preference from traditional staple grains such as rice and wheat to nutrient-rich millets in Coimbatore city. The study aims to analyze awareness levels, key influencing factors, and consumer purchasing behavior towards millet-based products. A descriptive research design has been adopted, with primary data collected from 100 respondents through structured questionnaires. The findings indicate that increasing health consciousness, rising incidence of lifestyle diseases, medical recommendations, and media influence are the primary drivers behind this shift. Consumers perceive millets as a healthier alternative due to their high fiber, mineral content, and low glycemic index. Despite growing acceptance, challenges such as pricing, limited accessibility, and lack of awareness in certain segments still persist. The study concludes that millets possess strong market potential and represent an emerging segment in the food industry. Strategic marketing, awareness campaigns, and supply chain improvements can further accelerate this transition

DOI: https://zenodo.org/records/19864623

Published by: vikaspatanker