Artificial Intelligence (AI) is revolutionizing Human Resource Management (HRM) by integrating automation, predictive Analytics, and data-driven decision-making. Organizations are increasingly adopting AI-powered solutions to enhance talent acquisition, employee engagement, performance management, and workforce planning. AI-driven recruitment systems streamline hiring by reducing biases, improving candidate-job matching, and expediting the selection process. Additionally, chatbots and virtual assistants are transforming onboarding, training, and employee support by providing real-time assistance and personalized learning experiences. Beyond recruitment, AI plays a crucial role in workforce analytics, helping HR professionals predict attrition risks, optimize talent management, and enhance employee well-being. Machine learning algorithms and sentiment analysis enable organizations to proactively address workforce challenges, implement effective retention strategies, and cultivate a positive workplace culture. Moreover, AI enhances HR operations by automating payroll processing, benefits administration, and compliance tracking, reducing manual errors and ensuring regulatory adherence. This paper covers the evolving role of AI in shaping future HRM strategies, highlighting the impact of AI on HR, opportunities, and challenges. It also emphasizes the significance of a balanced AI-human approach, where AI augments HR capabilities without replacing human judgment. By integrating AI ethically and strategically, organizations can create an agile, data-driven, and employee-centric HRM framework that aligns with future workforce needs.

Keywords : Artificial Intelligence, HRM, HR, Workforce Optimization, HR Strategies, Digital Transformation.

Authors : Bala Bharathi, V.R. Ramakrishna

Title : The Role of Artificial Intelligence in Designing Future Human Resource Management Strategies

Volume/Issue : 2025;7(3 (May - June))

Page No : 48 - 52

Roads make a key role to economic development and growth and take important social benefits. They are of vital importance in order to make a nation grow and develop. In addition, providing access to employment, social, health and education services makes a road network crucial in fighting against poverty. Because of population, economic development, vehicular ownership growth, increasing traffic demands beyond the carrying capacity of the roads which is causing traffic congestion. Pedestrians walking without regard for approaching on the several zebra crossings are causing the traffic mess, especially during rush hours. One of the main causes of traffic jam is pedestrian rushing to cross the road in the morning. The traffic police manning the zebra crossing are stopping vehicles to let even a single pedestrian pass. There should at least be five to six pedestrians waiting before they stop the vehicle. This is the main cause of traffic jam along the busy road. The objective of the present study is to control the traffic jam by proposing the correct choices of the selected road network. The selected study site is the spiritual capital of Andhra Pradesh i.e. Tirupati city

Keywords : Traffic Congestion, Zebra Crossing, Pedestrian pass, Road Network.

Authors : Navya Sree

Title : Influence of Pedestrian on Traffic

Volume/Issue : 2025;7(3 (May - June))

Page No : 46 - 52

The use of precision farming has become a transformative approach to optimizing crop production while ensuring environmental sustainability. Fertilizer application, a critical factor in agricultural productivity, is often performed using conventional methods that lead to inefficient resource utilization, soil degradation, and environmental pollution. This paper presents an AI-based fertilizer recommendation system that leverages machine learning algorithms, remote sensing data, and soil health parameters to provide precise nutrient recommendations tailored to specific crops and field conditions. The proposed model integrates data from soil sensors, weather forecasts, and historical yield patterns to generate accurate fertilizer prescriptions, reducing excess usage and enhancing crop yields. By employing AI- driven predictive analytics, the system optimizes nutrient distribution, minimizes environmental impact, and supports sustainable farming practices. Experimental results demonstrate the effectiveness of the model in improving fertilizer, efficiency while maintaining soil health and maximizing agricultural productivity. This research highlights the potential of AI in precision agriculture and provides insights into how intelligent decision-making systems can contribute to sustainable food production will focus on expanding the datasets, refining predictive models, and integrating real-time monitoring for enhanced accuracy and adaptability.

Keywords : Precision Agriculture, Fertilizer Recommendation, Machine Learning, Sustainable Farming, Soil Health.

Authors : Sandhyarani

Title : Precision Agriculture: AI based Fertilizer Recommendations For Sustainable Crop Production

Volume/Issue : 2025;7(3 (May - June))

Page No : 40 - 45

This paper presents a comprehensive conceptual design for a mobile application aimed at revolutionizing the tourism experience by centralizing and simplifying access to a wide range of essential tourism facilities and services. As global tourism continues to expand, travellers increasingly demand streamlined and user-friendly solutions for discovering local attractions, securing accommodations, exploring dining options, planning transportation, and receiving personalized recommendations tailored to their individual preferences. Addressing these needs, the proposed mobile application integrates advanced technologies such as location-based services (LBS), real-time data processing, and personalized content algorithms, ensuring a highly customized and efficient experience for users. The app's core design prioritizes accessibility and convenience, featuring an intuitive, user-friendly interface that caters to a diverse global audience. Multilingual support ensures seamless communication for international travellers, while offline capabilities provide uninterrupted functionality even in areas with limited internet connectivity. To further enrich the user experience, the application incorporates powerful tools, including an interactive map for smart navigation, a booking engine for seamless reservations, real-time notifications to keep users informed, and access to local events and experiences to foster deeper cultural engagement. A standout feature of the app is its integration of cutting-edge technologies such as artificial intelligence (AI) and augmented reality (AR). AI-driven algorithms analyze user preferences, travel patterns, and real-time data to deliver personalized recommendations and create dynamic itineraries. Meanwhile, AR functionality transforms exploration by overlaying immersive visual content—such as historical insights or scenic route highlights—onto the user’s real-world surroundings, bridging traditional tourism with modern innovation. This mobile application also emphasizes sustainability by promoting eco-friendly practices, such as walking routes, public transportation options, and green-certified accommodations, aligning with responsible tourism trends. By connecting travellers with local businesses and events, the platform fosters economic growth within host communities while offering users an authentic and meaningful connection to their destination.

Keywords : Online Markrting, Stock, Social Media, Web, Internet, E-Commerce.

Authors : Dillibabu

Title : Sustainable Smart Tourism: A Mobile Application Framework with AI, AR, and LBS Integration

Volume/Issue : 2025;7(3 (May - June))

Page No : 35 - 39

In this era of modern technological world, Internet plays a vital role in human life. Internet has become an essential part of offices and homes. The number of people spends their time online in all over the world. This demand is driving Marketing Researchers to develop new platforms for interactive and two-way communication between Marketing firms and consumers. The Internet is a brilliant future among the favourite tools of Marketing Research. Online Marketing refers to a set of powerful tools and methodologies used for promoting products and services through the Internet. Online Marketing is also known as Internet Marketing, Web Marketing, Digital Marketing and Search Engine Marketing (SEM). Online Marketing is the exchanging values between the seller and buyer and it is done online. Online Marketing has sub sold traditional advertising in recent years and continues to be a high growth industry. Online marketing refers to Advertising and Marketing efforts that use the Web and E-mail to drive direct sales via E-Commerce. Online Marketing consists of three specialized areas like E-mail, Web Marketing and Social Media Marketing. The paper focuses on Online Marketing, Methods of Online Marketing, Benefits and Limitations of Online Marketing and Recent Trends and Issues in Online Marketing.

Keywords : Online Markrting, Stock, Social Media, Web, Internet, E-Commerce

Authors : K. Pavithra

Title : Online Marketing: Emerging Trends and Issues

Volume/Issue : 2025;7(3 (May - June))

Page No : 30 - 34

The primary objective of the present paper is to assess the growth of economic sectors in the Indian Economy over a long period time with time series analysis which brings out the emerging trends in the growth of economic sectors in the Indian Economy. The economy of India has three basic sectors namely, Primary Sector, Secondary Sector and Services Sector. The paper found that the Gross Value Added (GVA) at basic prices has increased at lower growth rates of less than 5% per year during the period from 1950-51 to 1979-80. Prof. Raj Krishna called this low growth period as “Hindu Rate of Growth” because the annual average growth rate of Gross Value Added (GVA) at basic prices never crossed 5% during this period. From Sixth Five Year Plan onwards, the annual average growth rate of Gross Value Added (GVA) at basic prices has been increased at a greater than 5% in all the plan periods and it is the highest at 7.4% during Post-COVID Annual Plans period. The paper found that all the three sub-sectors of the Tertiary Sector namely, Trade, hotels, transport & communication, Financing, real estate and professional services, public administration, defence and other services have higher growth rates than the growth rates of Primary and Secondary Sectors. Therefore, it is clear that both Primary and Secondary Sectors of Indian economy have been growing with a slow growth rates than the Services Sector. The paper found that there is a continuous downward trend in the contribution of Primary Sector to the Gross Value Added (GVA) of India with a negative slope of 0.646, which indicates that for every one year increase in the time period there will be a decline in the contribution of Primary Sector to the GVA of India by 0.646 percentage points. On the other hand, it is observed that the contributions of both Secondary and Service Sectors to the GVA of India have depicted upward trend with a positive slopes 0.190 and 0.456 respectively for Secondary and Service Sectors to the GVA of India. Therefore, it is inferred that the changes in the contribution of Service Sector is at a greater speed than the changes in the contribution of Secondary Sector to the GVA of India.

Keywords : Economic Development, Hindu Rate of Growth, Structural Change.

Authors : M. Anjankumar

Title : Emerging Trends in the Growth of Economic Sectors in the Indian Economy from 1950-51 to 2022-23

Volume/Issue : 2025;7(3 (May - June))

Page No : 19 - 24

An effective energy management system is introduced for a hybrid microgrid that integrates wind, solar, and battery-based power sources. The development encompasses the creation of wind and solar energy conversion systems, a battery storage system, power electronic converters, control algorithms, and controllers, all designed to assess the functionality of the hybrid microgrid. The solar power generation system is governed by a Fuzzy-based Maximum Power Point Tracking (MPPT) Controller to optimize performance. To accommodate variations in renewable energy generation and load demands, an energy management system is implemented to sustain power balance. This proposed system aims to ensure the reliable and stable operation of a microgrid with multiple Distributed Generators (DGs) and a battery energy storage system through coordinated energy management strategies. By utilizing a Fuzzy-based MPPT Controller, the system optimally extracts power from the solar system. The hybrid microgrid, as suggested, serves as a valuable testing ground for researching and validating algorithms in the realm of smart grid applications.

Keywords : EMS, Hybrid System, Microgrid, Solar Energy, Wind Energy, Fuzzy MPPT Controller

Authors : Malleswara Reddy

Title : Energy Management System for Hybrid Microgrid Using Fuzzy Controller

Volume/Issue : 2025;7(3 (May - June))

Page No : 19 - 25

Handwritten signature classification stands as a fundamental aspect in document verification systems. The paper develops a stable handwritten signature classification framework based on Convolutional Neural Networks (CNN), the lightweight Mobile Net architecture and Alexnet for optimizing signature verification precision and operation speed. Handwritten signature classification represents an intricate process because writing styles consistently exhibit distinct individual characteristics hence requiring systems with sophisticated understanding capabilities. The model design implements CNN extraction of hierarchical image features and Mobile Net structure to achieve both performance excellence and operational compatibility across devices independent of their computing power. The present approach applies data augmentation together with transfer learning methods which improve both the generalization performance and unseen data accuracy of the model. Breeding from our signature classification framework yields promising outcome.

Keywords : Deep Learning, Convolutional Neural Networks (CNN), Mobile net, Image Processing.

Authors : Rajesh Babu

Title : Digital Signature Recognition Using Mobile NET

Volume/Issue : 2025;7(3 (May - June))

Page No : 13 - 18

This system is an advanced AI driven approach that converts textual descriptions into visual images and short videos. This model integrates Stable diffusion, a powerful latent diffusion model, it interprets user input and generates realistic visuals with good quality. This model leverages natural language processing(NLP) and deep learning techniques to understand input text and generate coherent images by refining noise. The integration of Contrastive Language- Image Pretraining(CLIP) helps align textual semantics with visual representations, ensuring meaningful output. With applications in digital art, content generation, the system enables users to generated accurate images efficiently. This system highlights the potential of generative AI in creative and potential domains, opening new possibilities in automated visual content creation.

Keywords : Latent Diffusion Model, NLP, Deep Learning, CLIP, Generative AI, Digital art, Content Creation.

Authors : Rajesh Babu

Title : Design and Integration of DreamShaper: A Unified Framework for Text-to-Image and Video Generation

Volume/Issue : 2025;7(3 (May - June))

Page No : 8 - 12

Student placement prediction is a crucial task for educational institutions and recruiters to optimize campus hiring processes. This study employs a data-driven machine learning approach to predict student placement outcomes based on academic performance, extracurricular activities, certifications, aptitude scores, and soft skills ratings. A dataset comprising 10,000 student records was analyzed, and multiple machine learning models were trained, including LR, SVM, DT, RF, KNN, and GNB. To address data imbalance, the SMOTE was used to ensure robust model performance. Feature selection identified CGPA, aptitude test scores, and placement training as the most influential factors. Among all models, RF achieved the highest accuracy of 79.72%, outperforming traditional statistical methods. Model performance was evaluated metrics and execution time analysis. The findings provide valuable insights for students, helping them understand the key factors influencing placement success, and assist institutions in refining training programs. This study demonstrates the effectiveness of machine learning in enhancing placement prediction accuracy and suggests potential future improvements using deep learning techniques for more precise outcomes.

Keywords : Student Placement Prediction, ML, Campus Recruitment, Data-Driven Analysis, SMOTE, Random Forest.

Authors : Abuthahir

Title : Machine Learning-Based Student Placement Prediction Analysis: A Data-Driven Approach To Enhance Employability

Volume/Issue : 2025;7(3 (May - June))

Page No : 1 - 7