Volume 13 | Issue 3 |

 Abstract

Cooperatives have been a vital part of Indian socio economic landscape representing the values of collective responsibility, participatory decision making and inclusive growth. These organizations have been central to uplifting communities especially in rural areas by providing employment opportunities and supporting economic self sufficiency. Cooperatives are organizations owned and managed by their members who have equal say in decision-making processes, typically based on the principle of democratic governance. The objectives of this paper are to explore the Genesis & Evolution of Cooperative movement in India, government initiatives and role of the cooperatives in inclusive growth, to identify the challenges they face, to analyze the challenges faced by cooperatives in India, to explore opportunities that can help strengthen their role in fostering inclusive economic growth. This paper is in descriptive nature and based on the secondary data collected from the magazines, journals and periodicals etc., addressing these challenges can unlock their full capacity to contribute to India's inclusive growth.

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 Keywords

cooperatives, inclusive growth, challenges, opportunities, NAM

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Creative Commons Attribution 4.0 and The Open Definition

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A massive migration of occupations to remote work and their consequent impact on work-life balance was catalysed by the COVID-19 pandemic, particularly in the IT professions. This study aims at the challenges and facilitative factors for Work from Home (WFH) arrangements concerning mental health, job satisfaction, and productivity from the employee perspective. The study surveyed 120 IT professionals and key factors including workload, flexible time management, communication culture, and the blend of operational and personal responsibilities were looked into. This research very much states that in support of employees, psychosocial support for mental health would be helpful, and strong communication back to the employer would facilitate the assistance of making the employees' experience in remote working a better one. All these findings should serve as a basis for developing fair work-from-home policies for productivity and enhancement of employee support.

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 Keywords

Work from home, IT Professionals, Work life-balance, job satisfaction, employees' well-being.

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This research presents a multimodal deep learning approach for brain tumour diagnosis focusing on software-driven solutions for medical image analysis and classification. The system integrates deep learning techniques to process MRI scans and other diagnostic data improving accuracy and efficiency. A Convolutional Neural Network (CNN) is used for image recognition and processing tasks like image classification while ensemble learning methods improve classification performance. The software is designed for automated pre-processing segmentation, and tumour classification ensuring robustness across diverse datasets. The framework is implemented with Python and Tensor Flow and supports real- time inference and can be deployed in clinical decision support systems. To get the result percentage as 95.3% accuracy in the Xception image which shows in the output.

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 Keywords

Multimodal machine learning, Deep Learning, Convolutional Neural Networks, Brain tumour, Magnetic Imaging Resonance, Computed Tomography.

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 Abstract

The wireless communication sector requires inexpensive, small antennas that can resonant for multiband operations with a respectable gain. Metal components in the traditional antenna configuration provide energy loss and conduction loss at higher frequencies. The Dielectric Resonator Antenna (DRA) is a good option for these issues. It is made of dielectric resonator material, which is devoid of metal components. The DRA provides a sufficient and steady response for metrics like return loss, gain, bandwidth, and VSWR at high frequencies because there is no conductive material present. The need for single antennas that can operate across several frequency bands in wireless applications is addressed in this paper using a unique approach. Multiband operation is made possible by the modified U-slot design of a single circular patch antenna. The antenna's usefulness is increased by incorporating these slots, which support WLAN, Extended WiMAX, and International Mobile Communication Systems. However, low gain is a common problem with microstrip patch antennas. In order to address this, the multiband antenna is positioned above a Cylindrical Dielectric Resonator Antenna (CDRA). The CDRA is 1.6 cm in radius and 9 cm in height, and it is made of polypropylene, which has a dielectric constant of 2.2. Through simulations using the EM Flow solver HFSS, the Modified U-Slot Multiband Circular Patch Antenna with Cylindrical Dielectric Resonator Antenna meets the required return loss criteria across its working frequencies and exhibits enhanced gain performance. The ultimate goal of the Honey Badger Algorithm (HBA) is to improve the efficiency, dependability, and range of this proposed cylindrical dielectric resonator antenna. This algorithm is based on the behavior of honey badgers, which use their sense of smell and honey guide birds to navigate toward the honeycomb.

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 Keywords

Microstrip Patch, Cylindrical Dielectric Resonator Antenna, WLAN, WIMAX, Honey Badger Algorithm

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores integrating solar energy in Unmanned Aerial Vehicles (UAVs) to extend flight endurance and reduce reliance on conventional power sources. It examines the use of solar panels, lightweight batteries, and energy management systems for optimal performance. Challenges such as weight, energy efficiency, and environmental factors are discussed. The integration of solar power is used, which offers a sustainable solution for long-duration UAV operations. Due to solar and battery hybrid systems, it enhance its efficiency and flight time.

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 Keywords

Solar cell, UAV, Drone Circuit and Components, Battery and Solar Characteristics

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of social media has transformed how businesses engage with consumers, particularly in the e-commerce sector. This study investigates the impact of social media engagement on brand loyalty, analyzing how various digital interactions contribute to consumer trust and long-term relationships with brands. With platforms like Instagram, Facebook, and Twitter becoming essential for business marketing, brands increasingly rely on real-time engagement, influencer collaborations, and user-generated content to foster loyalty. Utilizing a mixed-methods research approach, this study incorporates surveys and case studies from leading e-commerce platforms to examine consumer behaviors and brand interactions. The findings suggest that businesses with strong social media engagement experience higher customer retention and repeat purchases. Personalized advertising, direct customer interactions, and influencer marketing are identified as key factors driving brand loyalty. Additionally, user-generated content enhances authenticity and encourages wider consumer participation, strengthening brand credibility. The study concludes that e-commerce brands must strategically optimize their social media presence by focusing on interactive and personalized engagement. Understanding the nuances of digital consumer behavior can help businesses refine their social media strategies to build stronger customer relationships and drive sustained loyalty in an increasingly competitive market.

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 Keywords

Social Media, Brand Loyalty, E-Commerce, Customer Engagement, Online Marketing, Consumer Trust, Digital Branding

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Creative Commons Attribution 4.0 and The Open Definition

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Virtual Try-On (VTO) technology is becoming popular in India, allowing people to see how clothes, glasses, or makeup look on them using AI and AR. While this makes shopping easier, it also raises concerns about how personal data is collected and used. This study explores how comfortable Indian consumers are with sharing their data for VTO, focusing on their awareness, opinions, and privacy concerns. A survey of people aged 18-35 helps understand if they prefer convenience over data security. The results will help businesses build trust and find a balance between personalization and privacy. By looking at consumers' attitudes, the study aims to guide companies on how to handle data responsibly. Understanding these concerns will also help businesses improve VTO features while ensuring privacy is respected. This balance can lead to better user experiences and more successful adoption of VTO technology in India.

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 Keywords

Virtual Try-On (VTO), Augmented Reality (AR), Artificial Intelligence (AI), Privacy, Data Security, Personalization vs. Privacy, E-commerce, Online Shopping, Data Usage, Virtual Shopping Experience, Indian Consumers, Privacy Concerns, Data Sharing Preferences, Online Fashion Shopping.

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This study explores the level of awareness and understanding of Systematic Investment Plans (SIPs) among investors in Coimbatore and examines the impact of SIP investments on financial planning. The study also analyzes the relationship between investors' awareness and the growth of SIPs while identifying the challenges faced by potential investors in understanding the benefits and risks associated with SIPs. Statistical tools such as the test Chi-square are used to determine the association between demographic factors and SIP awareness. One-way ANOVA is applied to analyze differences in satisfaction levels across various demographic groups, and Factor Analysis helps identify key factors influencing SIP adoption. The findings offer valuable insights for financial institutions and policymakers to develop targeted awareness programs and enhance financial literacy, thereby promoting disciplined investment practices in Coimbatore.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Systematic Investment Plan (SIP), Investor Awareness, Financial Planning, Chi- square Test, One-way ANOVA, Factor Analysis, Investment Challenges, Coimbatore City.

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Creative Commons Attribution 4.0 and The Open Definition

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The Vehicle Data Prediction is an innovative web-based application designed to classify vehicle movement data as either legitimate or falsified using Machine Learning. With the increasing need for reliable traffic monitoring, logistics optimization, and fraud detection, this system provides a user-friendly interface where users can input key vehicle parameters such as position, speed, and acceleration. The entered data is then processed through two powerful machine learning models Logistic Regression and Random Forest Classifier which yields accuracy of 77.63% and 89.08% which analyze and predict the authenticity of the data.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning Algorithms; Random Forest; Logistic Regression; Web Application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Samajik Prushthbhoomi me mahila police ki sahbhagita evam chunautiyan

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Samajik Prushthbhoomi me mahila police ki sahbhagita evam chunautiyan

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Creative Commons Attribution 4.0 and The Open Definition