Volume 14 | Issue 5 |

 Abstract

The rapid growth of social media platforms and digital communication channels has increased the spread of fake news across the world. False information shared through websites, blogs, and social media applications creates confusion among users and negatively affects society, politics, education, and public health. Manual verification of news articles is time-consuming and difficult due to the large amount of online content generated every day. This research paper presents a Fake News Detection Web Platform that uses Machine Learning and Natural Language Processing (NLP) techniques to identify whether a news article is real or fake. The proposed system allows users to enter news content through a web interface, after which the system analyzes the text using trained machine learning models. The platform provides accurate and fast predictions with a user-friendly interface. The research also discusses system architecture, methodology, dataset processing, algorithms used, advantages, limitations, and future improvements. The developed platform can help users verify online information and reduce the spread of misinformation.

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 Keywords

Fake News Detection, Machine Learning, NLP, Web Platform, Artificial Intelligence, Text Classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The deployment of fifth-generation (5G) networks marks a transformative milestone in telecommunications, delivering unprecedented speed, ultra-low latency, and the capacity to connect billions of devices simultaneously.

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 Keywords

Computing Risks Edge, Data Privacy, AI-Driven Threat Detection, 5G Networks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

"Many language, many creeds, many customs, let there be unity and diversity " LGBTQIA+ is a large fluence spectrum where intersex is a variation in sex characteristics that doesn't go with the definition of ordinary male or female. After birth boys and girls are given a particular colour, like boys for blue and girls for pink, all of that happens. There is also a kind of understood identity which they realise that they are not either of male or female. They are in between.

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Educational status of third gender in West Midnapore

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: The present study investigates changes in soil nutrient status and physico- chemical characteristics of agricultural lands in a semi -arid region of Maharashtra. Soil samples were collected from fifteen representative survey no. from a depth of 0-15 cm. Key parameters such as soil temperature, pH, electrical conductivity, organic carbon, and essential nutrients (N, P, K, Ca, Mg, and S) were analyzed using standard laboratory methods. The soils were found to be clay-rich with moderate to high fertility levels. The findings reveal moderate alkalinity, low to moderate organic carbon levels, and adequate macronutrient availability, suggesting balanced but improvable soil fertility conditions. Spatial variability highlights the need for site-specific soil management strategies.

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 Keywords

Keywords: Soil nutrients, Soil fertility, Nutrient management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Compression ratio in engines refers to the ratio of the total cylinder volume when the piston is at the bottom of its stroke (known as the "bottom dead center" or BDC) to the total cylinder volume when the piston is at the top of its stroke (known as the "top dead center" or TDC). In other words, it is the ratio of the maximum to the minimum volume within a cylinder during the engine's operation. Compression ratios of 10, 11, 11.5, 12, 12.5 & 13 and engine speeds of 1000 to 5000 rpm, in increments of 100 rpm, were utilized. In the present study, the effect of different compression ratios was studied and optimum compression ratio was established for a dedicated CNG engine. The results show that with the increase in compression ratio, the performance characteristics viz. power, torque, brake thermal efficiency and brake specific fuel consumption are improved. The CNG engine has established itself as a viable alternative to diesel engines in both heavy commercial and passenger transport applications. The expansion of CNG distribution networks and the implementation of strict emission regulations have prompted Original Equipment Manufacturers (OEMs) to focus on the development of CNG vehicles across various market segments. As the global automotive industry continues its relentless pursuit of sustainable and environmentally friendly transportation solutions, compressed natural gas (CNG) has emerged as a promising alternative to traditional gasoline and diesel fuels. CNG, composed mainly of methane, offers several advantages, including lower greenhouse gas emissions and reduced reliance on fossil fuels. In this context, dedicated CNG engines have gained significant attention as they are purpose-built to maximize the potential benefits of this alternative fuel source. Among the myriad factors that influence the performance of CNG engines, the compression ratio stands out as a pivotal parameter deserving comprehensive investigation. The compression ratio, defined as the ratio of the cylinder's maximum and minimum volumes, plays a fundamental role in determining an engine's efficiency, power output, and emissions characteristics. It is a parameter of paramount importance in internal combustion engines, irrespective of the fuel type. However, its influence on dedicated CNG engines, specifically tailored to harness the unique properties of natural gas, has not been explored in depth. Understanding the intricate relationship between compression ratio and CNG engine performance is essential for optimizing engine design and achieving the desired balance between efficiency, power, and emissions.

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 Keywords

CNG engine, Thermal Efficiency, Compression Ratio, emission control, CO-NO?-THC-CH? emissions, ECU calibration.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: Education has been considered instrumental in fostering economic development of a country. How well a State promotes and handles the issue of education is a subject of deep study, as the present and future of generations depend largely upon it. Educational policy research is a dynamic and complex field that play a pivotal role in policy analysis, evidence-based policy reforms, identifying gaps, prioritizing issues and deciding government funding for research and development. Policy research in education is intended to generate knowledge about underlying values that shape various educational processes and to develop a better insight of educational practices and their outcomes.

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Policy Research, Higher Education, Legislation, Challenges in formulation

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Creative Commons Attribution 4.0 and The Open Definition

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Economic infrastructure plays a critical role in fostering regional development and reducing spatial inequalities. The Kalyana Karnataka region of Karnataka has historically remained underdeveloped due to structural and institutional constraints. This study evaluates the status and progress of economic infrastructure development in the region, focusing on transportation, energy, irrigation, and industrial infrastructure. The study adopts a descriptive and analytical research design using secondary data sourced from the Economic Survey of Karnataka (2023-24), KKRDB reports, and government publications. The findings indicate that while infrastructure expansion has occurred across sectors, development remains uneven, with transportation and energy sectors performing relatively better than irrigation and industrial infrastructure. The study identifies significant inter-district disparities and a gap between infrastructure creation and utilization. Policy recommendations are provided to enhance balanced and inclusive regional development.

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 Keywords

Economic infrastructure, regional disparity, Kalyana Karnataka, inclusive development, public investment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

he Sutlej-Yamuna Link (SYL) Canal dispute represents one of the most complex and prolonged interstate river water conflicts in India. Originating after the reorganization of Punjab in 1966 and the creation of Haryana, the dispute revolves around the allocation and sharing of Ravi-Beas river waters between Punjab and Haryana. The proposed 214-kilometer SYL Canal was intended to facilitate the transfer of water from the Sutlej River to the Yamuna basin, enabling Haryana to access its allocated share. However, political resistance, regional identity, environmental concerns, constitutional debates, and judicial intervention transformed the issue into a major challenge to Indian federalism. This research paper critically examines the historical evolution, constitutional framework, judicial pronouncements, political dimensions, and environmental implications of the dispute. The paper also evaluates the role of the Supreme Court, the Union Government, and interstate tribunals in resolving river water conflicts. Further, it analyzes contemporary developments and suggests policy measures for sustainable water governance and cooperative federalism. The study concludes that the SYL dispute is not merely a legal disagreement over water sharing but a broader reflection of India's evolving federal structure, ecological crisis, and regional politics.

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 Keywords

: Sutlej-Yamuna Link Canal, interstate water dispute, Punjab, Haryana, federalism, river water sharing, Supreme Court, Ravi-Beas waters, constitutional law, cooperative federalism.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid emergence of Generative Artificial Intelligence (GenAI) marks a paradigm shift in Human Resource Management (HRM), transitioning the function from traditional administrative support to a data-driven strategic partnership. This research systematically synthesizes contemporary academic literature and industry case analyses to investigate the impact of GenAI integration on recruitment efficiency and employee engagement. Findings indicate that GenAI-enabled workflows significantly optimize the talent acquisition lifecycle; documented evidence shows that automating sourcing, screening, and job description generation can reduce recruitment cycles by up to 90% while cutting operating costs by 20% to 40%. Furthermore, the study examines the technology's influence on the internal employee experience, revealing that intelligent conversational agents and adaptive learning pathways facilitate hyper-personalization, resulting in a 105.5% increase in feedback frequency and a 35.5% improvement in mental well-being scores. However, the integration of Large Language Models (LLMs) introduces critical organizational challenges, including persistent risks of algorithmic bias, the "black box" transparency-opacity paradox, and heightened data privacy concerns under frameworks such as the EU AI Act. Drawing on the Resource-Based View (RBV) and Ability-Motivation-Opportunity (AMO) theory, this paper argues that while GenAI serves as a transformative strategic enabler, its success is contingent upon robust ethical governance and a "human-in-the-loop" mandate to preserve relational integrity. The research concludes by proposing a structured implementation roadmap that balances technological efficiency with the human-centric values essential to modern workforce management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Generative AI, HR Automation, Recruitment Efficiency, Employee Engagement, HR Analytics, Talent Acquisition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Currently, smartphones are used for nearly all activities, including chatting, watching videos, studying, and using various applications. To conserve battery power, phones use power-saving features like sleep mode, but these methods are mostly reactive. This means they only start working when the battery begins to drain, which can slow down apps and delay background tasks. Because of this, users may experience lag or reduced performance. To solve this, this paper presents an Intelligent Predictive Framework for Dynamic Mobile Energy Optimization. The proposed framework adopts a lightweight predictive approach for intelligent and efficient mobile energy optimization. Instead of simply turning apps on or off, the system analyzes user behavior, app usage, system load, and phone temperature in real time. Based on this, it predicts what needs to be done and manages apps smartly. Applications are divided into three states: Active, Throttled, and Deep Hibernate. Important apps run smoothly, while unnecessary apps are controlled to save battery. The system also manages device temperature to avoid overheating. Results show improved battery savings, smoother performance, and a more comfortable user experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Machine Learning, Mobile Energy Optimization, Task Scheduling, Thermal Management, Predictive Systems.

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Creative Commons Attribution 4.0 and The Open Definition