A Study on Entrepreneurial Schemes of NBCFDC

The National Backward Classes Finance and Development Corporation (NBCFDC) plays a pivotal role in promoting the economic empowerment of backward classes in India through various enterprise development programs. Established under the Ministry of Social Justice and Empowerment, NBCFDC provides financial support, skill development, and other resources to foster entrepreneurship among economically disadvantaged communities. The corporation offers a range of financial products, including loans, subsidies, and venture capital funds, aimed at improving the economic conditions of backward classes. This paper explores NBCFDC’s mission, the types of entrepreneurial schemes it supports, and its impact on the socio-economic development of backward classes. With a focus on providing access to financial resources and skill training, NBCFDC helps individuals and communities achieve financial independence and long-term sustainability through entrepreneurship

Production and Performance of Bio-Diesel from Pongamia Oil Methyl Ester

Diesel engines are widely used for different applications in industrial power plant, transportation, agriculture etc. despite these advantages, environmental pollution, cost increment, depletion of crude oil becomes a major concern throughout the world. A methyl ester of pongamia was prepared and blended with diesel in four different compositions varying from 25% to 100%. Methyl esters of pongamia oils has several outstanding advantages among other new renewable and clean engine fuel alternatives and can be used in any diesel engine without modification.  The engine performance and emission characteristics of pongamia bio-diesel (Pongamia Oil Methyl Ester) and its blends with petro-diesel are presented. The engine tests are conducted on a 4-Stroke Tangentially Vertical (TV1) single cylinder kirloskar engine, throughout the experiment under steady state conditions at full load condition. From the test results, it could be observed that the B25 blend gives optimum performance like higher brake thermal efficiency lower specific fuel consumption and lower emissions like lower in smoke density and oxides of nitrogen. The research findings show that B25 gives lowest emissions which make it a good alternative fuel to operate diesel locomotives without any modification in existing diesel engine

Leveraging Artificial Intelligence for Enhanced Internet of Things Applications

This paper explores the symbiotic relationship between Artificial Intelligence (AI) and the Internet of Things (IoT), highlighting the significant role that AI plays in enhancing IoT applications. The paper begins by providing an overview of both AI and IoT technologies and their individual capabilities. It then delves into the ways in which AI augments IoT systems, including data analytics, predictive modeling, anomaly detection, and autonomous decision-making

DEX – Digital Employee Experience at Digital Workplace; Challenges and Strategic Implications for Organization Practicality

Digital Employee Experience (DEX) refers to the experience of employees while interacting with the workplace technology, digital tools, information technology systems and infrastructure. The experience is not only focusing the technology infrastructure and applications, but also mainly focusing on the intuitiveness, supportiveness and efficiency of the technology tools in helping the employees performing everyday duties and tasks. The study plans to discuss the employee experience in the digital workplace and its importance for efficient organization functioning and to discuss the practicality of the organizations in challenges. There are a few studies has dealt with DEX in a qualitative way of discussion. This study applied qualitative approach using literature survey method to collect the existing literatures related to DEX and sustainable digital workplace for sustainability of the organizations. The study adopted qualitative literature review content analysis using summative method for discussing and reporting. The study explored the digital workplace tools for Collaboration and communication – Slack, Google Hangouts, Face book in workplace, for Project Management – Base camp, Asana, Trello, Write, for Remote Desktop – AnyDesk, Chrome Remote Desktop, Remote PC, for Time Management –Toggl, Clockify, Harvest, for Screen Sharing and recording –Team Viewer, Screen leap, Join .me,  for Video Conferencing & Tele working – Skype, Zoom, Cisco Webex, and for Cloud Storage - Google Drive, Google Docs, Dropbox, One Drive. The study identified challenges like integrating business needs with business operations and functions, techno stress among employees, competence needs, security issues, Tele – working technological issues and lack of end user experience among employees. Further it discussed the implications for DEX are Setting the infra-structure, Tele-working necessary model equipment, Creating Positive DEX by streamlining digital tools and updates, Provision of end user experience to employees and competency needs among employees

Forensic Asset Tracing Efficacy on Fraud Detection of Nigerian Listed Firms

This study examines the efficacy of forensic asset tracing in detecting fraud in Nigerian listed firms. With rising corporate scandals, forensic accounting has become a crucial tool for fraud prevention and detection. This research employs a mixed-method approach, combining qualitative and quantitative data to assess forensic asset tracing\u27s impact on fraud detection. Primary data were collected through structured questionnaires distributed to forensic accountants, auditors, and regulatory officers, while secondary data were sourced from financial reports and fraud cases. The study employs regression analysis to evaluate the effectiveness of forensic asset tracing mechanisms in curbing fraudulent activities. Findings reveal that forensic asset tracing significantly enhances fraud detection, particularly when integrated with robust regulatory frameworks and corporate governance practices. The study identifies key challenges, including regulatory bottlenecks, limited forensic expertise, and inadequate technological adoption, which hinder forensic asset tracing\u27s full potential. The research contributes to literature by providing empirical evidence on the role of forensic accounting in fraud detection in Nigeria. It recommends strengthening forensic accounting practices through capacity building, regulatory enhancements, and the integration of advanced digital forensic tools. The study concludes that forensic asset tracing is an effective tool for fraud detection but requires a supportive regulatory environment and skilled professionals. Future research should explore the role of artificial intelligence in forensic asset tracing and fraud detection

A Detailed Study on Introduction of Computational Intelligence

This article demonstrates an analysis of Computational Intelligence (CI) through its crucial concepts together with principles and implementation examples in the field of artificial intelligence. The core computational models in CI help replicate human reasoning and solve complex problems through neural networks as well as fuzzy systems alongside evolutionary algorithms while hybrid systems interpolate their benefits. The paper demonstrates CI development from inception to present day while focusing on eminent milestones alongside CI adoption across data mining and robotic control and optimal decision making applications. The chapter explores both theoretical perspectives of CI and provides an evaluation of its strengths and limitations. This paper uses different examples to validate the importance and usage of CI in modern technology before establishing directions for future research in this developing field. The obtained outcomes demonstrate that CI methods can reach practical use after implementing improvements. This paper delivers an extensive overview of CI principles to support researchers and practitioners so they can boost innovation alongside cross-disciplinary work in this developing field

FOMO and Impulse Buying: A Behavioral Study of Gen Z in the Fashion Market

Easy availability of online platforms has led to an increase in the number of suppliers and competition between businesses. To attract customers, companies have used the fear of missing out (FOMO) effect through advertising messages that stimulate the fear of missing out in each individual, prompting them to make quick purchasing decisions. In recent years, the fashion industry has undergone a significant transformation, driven largely by the rise of digital technology and social media. Among the most influential consumer segments in this landscape is Generation Z (Gen Z). Gen Z means a person born between the mid-1990s and early 2010s. This generation is characterized by its digital connectivity, with a strong affinity for online shopping and social media engagement. As a result, understanding their purchasing behavior, particularly in the context of impulse buying, has become increasingly important for fashion brands and marketers. Additionally, promotional offers play a crucial role in driving impulse purchases. Retailers and fashion brands frequently use strategic discounting, flash sales, limited-time offers, and exclusive deals to create a sense of urgency and scarcity. These marketing tactics leverage the psychological principle of loss aversion, where consumers feel compelled to act quickly to avoid missing out on a good deal. For brands and retailers, there is a significant opportunity to shift away from reliance on aggressive discounting and short-term sales tactics. Instead, they can focus on building long-term relationships with customers by offering rewards for sustainable shopping habits and promoting mindful consumption. Loyalty programs that incentivize conscious purchases, alongside efforts to highlight the environmental impact of fast fashion, can encourage Gen Z to buy with purpose and awareness

Comparative Analysis of Machine Learning Models for Diabetes Prediction

Diabetes is a chronic health condition affecting millions worldwide, and early detection plays a vital role in effective disease management and prevention. In this study, we conduct a comparative analysis of four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Linear Regression—applied to the Pima Indian Diabetes dataset obtained from Kaggle. The dataset comprises diagnostic measurements of female patients aged 21 and above of Pima Indian heritage. Each model is evaluated using key classification metrics, including accuracy, precision, recall, and F1-score. Among the models, Logistic Regression and Gradient Boosting achieved the highest accuracy of 75%, while Random Forest and Linear Regression showed slightly lower performance at 72% and 73.16%, respectively. The study highlights the effectiveness of ensemble methods and traditional classifiers in predicting diabetes outcomes and provides insight into their relative strengths for clinical decision support systems. These results suggest that machine learning can be a valuable tool in aiding early diagnosis and improving patient care strategies

Socio-Economic Impact of Human-Wildlife Conflicts on Agriculture based Livelihood in the Kodagu Karanataka State

Human-wildlife conflicts (HWC) pose significant socio-economic challenges for agriculture-based livelihoods in Kodagu, Karnataka. This study examines the extent and impact of wildlife-related damages on crops, livestock, and farmer well-being, focusing on economic losses, food security, and coping mechanisms. Kodagu, known for its rich biodiversity and proximity to protected areas, experiences frequent conflicts with elephants, wild boars, and other species, leading to substantial financial strain on farmers. Additionally, psychological stress and rural migration trends further exacerbate socio-economic instability. The study highlights the effectiveness of existing mitigation strategies, including compensation schemes, fencing, and community-based conservation efforts. Policy recommendations emphasize the need for sustainable, long-term conflict resolution strategies to balance conservation goals with farmers’ livelihoods

IOT-Based Accident Prevention System: A Model Experiment for U-Turn Curves

In today’s world, the combination of high population density and the widespread use of vehicles has led to a serious concern: the increasing number of road accidents. Every year, thousands of people lose their lives or suffer serious injuries in such incidents. In developing countries like India, road accidents remain one of the leading causes of death. National highways, as well as mountain and hill areas, have dangerous roads and curves that are narrow and single-lane.  Accidents at U-turns commonly occur due to limited sight distance, especially on curved or hilly roads, where drivers cannot see oncoming traffic in time to react safely. Inadequate road signage, poor lighting, and lack of dedicated turning lanes further increase the risk. Additionally, high vehicle speeds, misjudgment of gaps in traffic, and sudden or illegal U-turns made without proper signaling often lead to collisions. In areas with high traffic volume or narrow roads, the risk multiplies as vehicles may not have sufficient space or time to complete a U-turn safely. Addressing these risks requires a combination of improved road design, warning systems, enforcement of traffic laws, and driver awareness. At these curved sections, drivers are often unable to see oncoming vehicles or obstacles, and if their vehicle is not in good condition, it becomes difficult to control, increasing the risk of accidents. To minimize such accidents, we propose a project aimed at preventing collisions at U-turns by alerting drivers to oncoming vehicles. This is done by keeping an ultra sound sonic sensor on both sides of the U-turn and so that if vehicle comes from one end of the curve, then sensor senses and it gives signal to Arduino and Arduino gives command to LED lights of the other side in order to alert the driver