International Journal of AI Electrical Civil and Mechanical engineering
Deep Neural Network-Based Air Temperature Prediction Using Temporal and Spatial Feature Learning
Accurate short-term air temperature prediction plays a vital role in agricultural planning, environmental monitoring, and smart climate management systems. Traditional forecasting approaches often struggle to capture nonlinear temporal variations and dynamic weather patterns, resulting in reduced prediction accuracy and inefficient resource utilization. To address these limitations, this paper presents a hybrid deep learning framework for short-term air temperature prediction using Feed Forward Neural Network (FFNN), Long Short-Term Memory (LSTM), and two-dimensional Convolutional Neural Network (CNN2D) models. The proposed system utilizes historical temperature datasets along with temporal attributes such as day, month, year, and time for forecasting future temperature values. Initially, data preprocessing techniques including normalization, feature extraction, and train-test splitting are performed to improve model learning efficiency. FFNN is employed for baseline prediction, while LSTM captures sequential temporal dependencies in timeseries data. Furthermore, CNN2D is integrated to enhance feature extraction capability and reduce prediction error through parameter sharing and efficient learning. The trained models are deployed using a Flask-based web application that enables realtime temperature prediction through a user-friendly interface. Experimental evaluation demonstrates that the proposed CNN2D-enhanced framework achieves superior prediction accuracy with reduced Mean Absolute Error (MAE) and improved forecasting reliability compared to conventional neural network approaches. The proposed system provides an efficient, scalable, and practical solution for real-time agricultural and environmental temperature forecasting applications.
Air Temperature Prediction, Deep Learning, Feed Forward Neural Network (FFNN), Long Short-Term Memory (LSTM), CNN2D, TimeSeries Forecasting, Artificial Neural Networks, RealTime Prediction, Agricultural Forecasting, Climate Monitoring, Flask Web Application, Machine Learning, Temperature Forecasting, Smart Agriculture
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
DESIGN, ANALYSIS AND OPTIMIZATION OF NH3-H2O ABSORPTION CYCLE UTILIZING ENGINE WASTE HEAT AND SOLAR THERMAL ENERGY FOR REFRIGERATED TRANSPORTATION IN ERITREA
The expansion of cold chain logistics in Eritrea is critical for food security and economic growth, yet it is currently hindered by the high operational costs and environmental impact of conventional diesel-powered transport refrigeration units (TRUs). This paper presents the design and feasibility analysis of a novel hybrid Vapor Absorption Refrigeration System (VARS) for a medium-duty (30 m3 ) refrigerated truck, specifically tailored to the Eritrean climate. The proposed system utilizes an Ammonia-Water (NH3- H2O) working pair driven by two renewable thermal sources: waste heat recovered from the vehicle's engine coolant loop and solar thermal energy harvested via roof-mounted Evacuated Tube Collectors (ETC). A thermodynamic model was developed to size the system components based on a calculated peak cooling load of 1.67 kW required to maintain a cargo temperature of 5°C under an ambient temperature of 40°C. The design integrates a 7.9 m2 solar collector array and an optimized 212 kg Phase Change Material (PCM) thermal storage unit to ensure continuous cooling autonomy for up to 3 hours during engine-off periods. The economic analysis demonstrates that the hybrid system eliminates the diesel consumption associated with conventional TRUs, yielding annual fuel savings of approximately 1,144 litres and operational cost savings of $1,522. Environmentally, the system reduces CO2 emissions by nearly 3.0 tonnes annually. With an estimated payback period of 9.8 years, the system offers a sustainable and technically feasible alternative for the Eritrean transport sector, significantly reducing dependency on fossil fuels while improving supply chain resilience.
Vapor Absorption Refrigeration (VARS), Waste Heat Recovery, Solar Thermal Energy, Cold Chain Logistics, Ammonia-Water, Eritrea
Dr. G Bhaskara Rao1 , Dr. Gedion Habtay Gebremicheal2 ,Mr.Tedros Asefaw gebremeskel3 Mr.ghirmai Fkadu Weldezghi4 , Gebreamlak Andemariam arha5
DESIGN VALIDATION OF TRUCK MOUNTED CHEMICAL TANKER FOR ACCELERATION LOADS USING FEA TECHNIQUES
The industries use truck mounted chemical tankers for transporting liquid chemicals. These chemicals could be either hazardous or non-hazardous. While the liquid chemical is transported, the tanker experiences dynamic loading due to acceleration, braking, cornering & vibration. If the design of the tanker is not proper, these dynamic actions can cause excessive stress, excessive deformation, fatigue failure, and carry failure and safety risk. The goal of this project is to verify the structural design of a truck-mounted chemical tanker using advanced FEA techniques, particularly taking into account any acceleration loading that may occur. A 3D model of the tanker is prepared and analyzed using appropriate FEA software. The tank base underwent applications of different acceleration load cases. The results of the analysis provide valuable insights about the stress, displacement, and stability of the tankers’ body and support structure’ s strength. The stress analysis of the chemical tanker under acceleration load establishes that the developed chemical tanker is safe. The current paper exposes that applicating FEA techniques will help to improve tanker safety, reduce design error and optimize design of tanker before fabrication.
Truck Mounted Tanker, Chemical Transportation, Finite Element Analysis, Acceleration Loads, Structural Validation, Stress Analysis, Deformation, Safety Analysis
Mr. Sourabh Kalmegh, Prof Swapnil Choudhary
Exam Hall Allocation & Seating Arrangement System
The Exam Hall Allocation and Seating Arrangement System is a web-based application developed to automate the process of managing examination seating arrangements in educational institutions. In many colleges, exam hall allocation and invigilator assignment are done manually, which is time-consuming and may lead to errors such as duplicate seat allocation, improper hall utilization, and difficulty in handling large student data. This project provides a digital solution by storing student details, hall information, and faculty details in a database and automatically generating seating arrangements based on hall capacity and predefined rules. The system also assigns invigilators to each hall and allows easy viewing of seating plans and student seat details through search functionality. Developed using Python, Flask, SQLite, HTML, and CSS, the system reduces manual work, improves accuracy, saves time, and ensures smooth and efficient examination management.
1Mrs.B.Archana, 2M.Varsha, 3K.Priyanka, 4G. Malleswari, 5M. Sowjanya
SmartHR: AI-Based Employee Retention and Exit Prediction System Using Machine Learning
Employee turnover is a significant problem for contemporary businesses, because it raises operating expenses and reduces organizational productivity. Losing seasoned workers results in higher costs for hiring, onboarding, and training new personnel. Organizations need intelligent technologies that can identify workers who might quit the company soon in order to lower these risks. This study presents Smart HR, an artificial intelligence-based solution for employee retention and exit prediction that uses machine learning approaches to assess attrition probability and evaluate employee-related data. The system analyzes a dataset that includes characteristics like age, department, performance rating, work happiness, wage level, and number of projects issued. Prior to training the predictive models, data preprocessing methods such as cleaning, normalization, and feature selection are used. Employees are then categorized according to the probability of retention or resignation using machine learning algorithms. The created model is incorporated into an interactive application that enables HR managers to input personnel information and obtain real-time forecasts and suggestions for enhancing retention. Predictive analytics can successfully identify trends linked to employee behavior and help businesses make well-informed human resource decisions, according to experimental investigation. By assisting companies in identifying attrition concerns early on and putting appropriate retention tactics into place, the suggested method promotes proactive workforce management.
1Venkat Rao Ubbala, 2 Pasupuleti Usha Laxmi, 3Chokkam Niharika, 4G Kavitha, 5 Voriganti Bhavana
EFFICIENT MISSING CHILD DETECTION USING DEEP LEARNING AND INTELLIGENT SVM CLASSIFICATION
The increasing number of missing child cases across the world has become a major social and security concern, demanding efficient and intelligent identification systems for rapid child recovery. Traditional child identification methods rely heavily on manual investigation, public reporting, and human surveillance, which are often time-consuming and less effective in crowded environments. To address these challenges, this paper proposes an efficient missing child detection system using deep learning and intelligent Support Vector Machine (SVM) classification techniques. The proposed framework utilizes facial recognition technology combined with deep learning algorithms to automatically detect and identify missing children from images and video surveillance data. Convolutional Neural Networks (CNNs) are employed for feature extraction and facial representation learning, while a multiclass Support Vector Machine (SVM) classifier is used for accurate child identification and classification. The system processes facial images collected from surveillance cameras, public datasets, and uploaded records to compare them with stored missing child databases in real time. Image preprocessing techniques such as face detection, normalization, resizing, and feature enhancement are applied to improve recognition accuracy under varying environmental conditions including lighting variations, occlusions, and facial pose differences. The integration of deep learning with intelligent SVM classification enhances detection precision, reduces false identification rates, and improves computational efficiency. Experimental analysis demonstrates that the proposed system achieves high accuracy, fast response time, and reliable real-time performance compared to conventional identification approaches. The proposed framework can assist law enforcement agencies, child protection organizations, and smart surveillance systems in rapidly identifying missing children and improving public safety. Furthermore, the system supports scalable deployment in crowded public areas such as railway stations, airports, shopping malls, and educational institutions. Overall, the proposed intelligent identification system provides an effective, automated, and scalable solution for missing child detection and recovery.
Missing Child Detection, Deep Learning, Multiclass SVM, Facial Recognition, Convolutional Neural Network (CNN), Intelligent Surveillance, Image Processing, Artificial Intelligence, Face Classification, RealTime Monitoring.
AI DRIVEN FREELANCING PLATFORM WITH INTEGRATED CAREER GUIDANCE
Some freelancers struggle to find relevant jobs that they are interested in and also some clients cannot find freelancers with the same expertise. Currently, existing freelancing platforms neither match skills accurately nor optimize growth of an individual’s freelance career. The proposed platform is an AI- driven system that matches skills and interests to discover freelance opportunities and also provide personalized career recommendations. This platform helps users in skill-development and potential career paths. The main aim is to provide suitable freelance projects while also guiding them in choosing the right career directions according to their interests, skills, experience and work preferences. It suggests relevant courses by identifying skill gaps to help them grow professionally. The system uses machine learning algorithms and data analytics techniques to analyse freelancer profiles, that include technical skills, educational background, professional experience, portfolio content, interests and salary expectations. It results in optimized matching accuracy, reduced hiring time and improved career growth.
1 Mrs. K. Sandhya Rani, 2 T. Radhika, 3 V. Srivani, 4 P. Archana, 5 T. Manishka
TheSmartTouristSafetyMonitoring&IncidentResponseSystemisanadvanceddigitalinitiativedesignedtoensure the safety, security, and seamless travel experience of tourists, with a special focus on the North Eastern Region of India.ThesystemintegratesArtificialIntelligence(AI),Geo-Fencing,andBlockchain-basedDigitalIdentitytocreate areliableandtransparentecosystemfortouristmanagement.ThroughAI-drivenmonitoringandpredictiveanalytics, theplatformcanassesspotentialrisks,detectunusualactivity,andprovidereal-timealertstoauthoritiesandtourists. Geofencing technology enables location-based safety measures by tracking tourist movement within designated zones,triggeringautomaticalertswhenindividualsenterrestricted,high-risk,oremergency-proneareas.Furthermore, a Blockchain-based Digital ID ensuressecure, tamper-proof identity verification, enabling trusted access to services such as accommodations, transport, medical care, and emergency response.
Mrs.A.Eenaja¹,bellialekhya²,shaikhmuskan³,gandicherlatejaswini⁴,Konda Vidya⁵
Urban Urban areas are growing fast because of urbanization. As more people move to citiesthey need civic infrastructure and public services. This growth causes cities to face problems like potholes on roads bad waste management, water leaks, broken streetlights, blocked drainage systems and other infrastructure issues. These problems affect citizens daily lives create safety risks, health concerns and environmental problems. So city authorities must. Resolve theseissues efficiently and on time. With digital technology advancing and a focus on city development a more efficient approach to managing civic complaints is needed. This project supports governance by using technology to improve communication between citizens and authorities. The platform aims to make the complaint reporting system faster and more reliable, for citizens and administrators.
Dr.D.Shanthi ¹Professor&HOD,S.Harshini², C.Sreshta³,T. Nithyani⁴,B. Srija⁵
CAPTURING NON-MANUAL FEATURES OF INDIAN SIGN LANGUAGE AND CONVERTING IT INTO TEXT
Sign Language Recognition is one of the most growing fields of research area. Many new techniques have been developed recently in this area. The Sign Language is mainly used for communication of deaf-dumb people. This paper shows the sign language recognizing of 26 hand gestures in Indian sign language using MATLAB. The proposed system contains four modules such as: pre-processing and hand segmentation, feature extraction, sign recognition and sign to text. By using image processing the segmentation can be done. Some of the features are extracted such as Eigen values and Eigen vectors which are used in recognition. The Linear Discriminant Analysis(LDA) algorithm was used for gesture recognition and recognized gesture is converted into text and voice format. The proposed system helps to dimensionality reduction.
A.Rajini Devi1 , M. Krishnaveni2 , B. Dhanalaxmi3 , M. Prasanna4 , M. Sathwika5
GAMIFIED LEARNING PLATFORM FOR RURAL EDUCATION
Rural Learn is an educational platform that enhances learning for rural studentsthrough interactive, gamified content. It converts lessons into story-based narratives, offers quizzes as engaging challenges, and provides a chatbot for personalized guidance. Students’ progress is tracked to encourage consistent learning. The platform also promotes social interaction by allowing friend requests, chat, and collaboration, creating a supportive virtual community. By integrating gamification, technology, and social engagement, Rural Learn makes learning more effective, accessible, and motivating.
Mrs.D.Kousari Kumari1 , P. Soumya2 , Sai Swetha3 , K. Akshaya4, V.Chinmayee5
Real-Time Fatigue Monitoring For Drivers
Driver drowsiness is one of the leading causes of road accidents, posing a significant threat to human life and road safety. This project presents an intelligent, real-time Drowsiness Detection and Accident Prevention System that continuously monitors the driver’s alertness using a webcam-based vision approach. The system analyzes facial features, particularly eye movements, to detect signs of fatigue using parameters such as Eye Aspect Ratio (EAR). Upon detecting drowsiness, the system immediately alerts the driver through audio and visual warnings to regain attention. If the driver fails to respond, the system adopts a multi-level safety mechanism by notifying predefined primary contacts through instant messaging. In critical situations where there is no response from both the driver and contacts, the system utilizes GPS tracking to obtain the driver’s real-time location and identifies nearby emergency services such as hospitals or police stations. By integrating real-time monitoring, communication, and location-based emergency response, the proposed system goes beyond simple detection and acts as a preventive safety solution. It is costeffective, scalable, and has the potential to significantly reduce accidents caused by driver fatigue, thereby enhancing overall road safety.
1Mrs. K. Sandhya Rani, 2D. Nikhitha, 3V. Ishta, 4P.Sri Sai Manaswini, 5G.Veena
AI-Based Internship Recommendation Engine for PM Internship Scheme
Many students from rural or underserved communities who want to apply for an internship do not have much information or guidance available to them that will help them identify and choose a suitable internship opportunity. Additionally, given that many of the students come from a rural area, they have limited exposure and experience to using digital media; therefore, they may not be aware of how to successfully apply for internships through a website that requires some technical expertise. This paper presents an internship recommendation engine (IRE), which utilizes artificial intelligence (AI), to provide customized recommendations for potential candidates based on their education, skills, interests, and desired location. The proposed AI-based IRE system would provide candidates with accurate recommendations based on the candidate’s education and skills/abilities and the candidate’s interests and the parameters in which the candidate wants to receive a recommendation for an internship. Once the IRE has completed the analysis, it would display the recommended internship to the candidate through their account on the IRE system. In order to provide the candidate with the best internship recommendation, the IRE system employs a hybrid recommendation engine approach—encompassing both content-based filtering and collaborative filtering—to yield accurate recommendations.
Dr. D. Shanthi¹, Ch. Sneha², M. Ishwarya³, N. Vanishka⁴, Ch. Sai Harshitha⁵
Analysis on Marketing-Spending Restaurants for Zomato
The food-technology aggregator industry in India has witnessed exponential growth over the past decade, with Zomato emerging as the dominant platform connecting consumers with over 350,000 restaurant partners across 800 cities. Within this highly competitive marketplace, restaurant marketing expenditure — encompassing platform advertising, promotional discounting, sponsored listings, and loyalty programmes — has become a critical determinant of visibility, order volume, and revenue generation. This study analyses the relationship between marketing spending patterns of restaurants listed on the Zomato platform and their performance outcomes including customer acquisition, order frequency, ratings, and revenue growth. Using a structured questionnaire administered to 120 restaurant owners and marketing managers, the study examines current marketing budget allocation practices, the perceived return on investment of platform versus offline marketing channels, and the impact of Zomato-specific promotional tools on restaurant business performance. Data is analysed using percentage analysis, weighted average ranking, and chi-square testing. Findings reveal that Zomato-platform spending — particularly sponsored listings and discountbased promotions — delivers higher measurable returns than traditional offline marketing for digitally active restaurants, yet significant ROI perception gaps exist across restaurant size categories. Recommendations include data-driven budget allocation frameworks, optimised promotional calendar strategies, and targeted investment in Zomato Gold and Pro membership programmes for maximising customer retention.
Bamini Aravind1 , Nellutla Shiva2 , Tahara Jabeen3 , Vislavath Srivani4 Ms. S. Ramya5
A STUDY ON TALENT ACQUISITION AND RECRUITMENT STRATEGIES AT GENPACT
Talent acquisition and recruitment strategy are foundational pillars of organisational capability in the professional services and business process management (BPM) industry, where human capital is the primary driver of service quality, client satisfaction, and competitive differentiation. Genpact, a global professional services firm headquartered in New York with major delivery operations in Hyderabad and Bengaluru, employs over 125,000 professionals across 30 countries and processes recruitment at significant scale to support its rapidly expanding digital transformation, analytics, and finance and accounting service lines. This study examines the talent acquisition strategies, sourcing channels, selection frameworks, and employer branding initiatives deployed at Genpact India, with specific reference to its Hyderabad operations. Primary data was gathered through structured questionnaires administered to 100 respondents comprising Genpact HR and talent acquisition professionals (60) and recently recruited employees (40) in Hyderabad. Secondary data was sourced from Genpact Annual Reports, LinkedIn Talent Insights, NASSCOM workforce publications, and academic literature on strategic recruitment. Findings reveal that Genpact's campus recruitment contributes 34% of annual new hires, that employee referral programmes generate 28% of hiring volume at 42% lower cost-per-hire than external sourcing, and that structured competency-based interviews improve quality-of-hire scores by 31% over unstructured methods. Key challenges include talent scarcity in niche digital skills (AI/ML, cloud, advanced analytics), high attrition in BPM industry averaging 35-40% annually, and intense competition from peer organisations including Wipro BPS, Infosys BPO, and Concentrix. Recommendations include strengthening internal talent mobility, AI-powered candidate screening, and purpose-driven employer branding to attract and retain high-quality talent.
Muddemari Bhargavi¹, Kamjula Vaishnavi Reddy², Benjaram Vikas Reddy³, Ms. P. Amulya⁴
A STUDY ON THE RELATIONSHIP BETWEEN LEVERAGE AND PROFITABILITY WITH REFERENCE TO ASIAN PAINTS
he relationship between financial leverage and corporate profitability is one of the most extensively examined questions in corporate finance, with theoretical frameworks ranging from the Modigliani-Miller irrelevance proposition through the trade-off theory and pecking order hypothesis offering contrasting predictions about the direction and magnitude of the leverage-profitability association. Asian Paints Limited, India's largest paints and coatings manufacturer and a constituent of the Nifty 50 index, represents a distinctive case study in this regard: the company has maintained a nearzero leverage posture throughout its history, relying almost entirely on internal accruals and equity financing while simultaneously delivering among the highest return on equity ratios in Indian corporate history. This study examines the relationship between financial leverage and profitability at Asian Paints over the five-year period FY 2018-19 to FY 2022-23, employing leverage ratio analysis, profitability ratio computation, DuPont decomposition, and multivariate regression to document and quantify the leverage-profitability relationship. Secondary data sourced from Asian Paints Annual Reports and NSE disclosures forms the empirical basis. Findings reveal that Asian Paints' Debt-toEquity ratio declined from 0.14x in FY19 to 0.06x in FY23 while Return on Equity simultaneously improved from 39.8% to 47.1%, confirming a negative leverageprofitability relationship consistent with the pecking order theory. The regression D/E coefficient of -18.42 (p = 0.031) confirms that lower leverage independently predicts higher ROE after controlling for margin, asset utilisation, and scale effects.
Mitte Arunjyothi¹, Yekula Praveen Kumar², C Vinesh³, Kakunoori Karthik⁴ Ms. P. Amulya⁵
LEVERAGING HR ANALYTICS FOR ENHANCED WORKFORCE PLANNING AND DECISION MAKING AT HETERO DRUGS LIMITED
Human Resource Analytics— the systematic application of data analysis, statistical modelling, and predictive intelligence to workforce data—has emerged as a transformative capability for HR functions seeking to transition from administrative service delivery toward strategic business partnership. In the pharmaceutical manufacturing sector, where workforce competency, regulatory compliance, and operational productivity are directly linked to product quality and business performance, the strategic deployment of HR analytics offers particular value for workforce planning accuracy, talent retention, and human capital decisionmaking quality. Hetero Drugs Limited, one of India's largest generic pharmaceutical manufacturers with a workforce exceeding 9,600 employees across manufacturing, R&D, quality assurance, regulatory affairs, and commercial operations, provides a rich organisational context for examining how HR analytics can enhance workforce planning and decision-making effectiveness. This study examines the application of HR analytics across four maturity levels— descriptive, diagnostic, predictive, and prescriptive—at Hetero Drugs, drawing on primary survey data from 100 HR managers and employees and secondary data from organisational workforce reports and pharmaceutical industry HR benchmarks. Findings reveal that HR analytics adoption at Hetero Drugs has driven measurable workforce outcomes: attrition rate declined from 18.4% in FY22 to 12.4% in FY24, time-to-fill improved from 46 to 31 days, internal promotion rate increased from 22% to 31%, and employee Net Promoter Score improved by 18 points. However, implementation challenges including fragmented HR data systems (66%), data quality gaps (62%), and analytics talent scarcity (58%) constrain the advancement toward fully predictive and prescriptive HR analytics maturity.
Gandla Sahithi¹, Mittapalli Reshma², Sardar Manisha Kour³, Syed Nabil Hamed⁴ Ms. A. Swathi⁵
A STUDY ON BUSINESS ANALYTICS APPLICATIONS IN DIGITAL MARKETING SYSTEMS
Business analytics has emerged as the defining competitive capability of the digital marketing era, enabling organisations to transform the vast streams of behavioural, transactional, and contextual data generated by digital interactions into actionable intelligence that drives campaign optimisation, audience personalisation, and marketing resource allocation. Digital marketing systems—encompassing search engine marketing, social media advertising, email campaigns, programmatic display, content marketing, and influencer platforms—generate unprecedented volumes of granular performance data that, when analysed through appropriate analytical frameworks, yield insights unavailable through traditional marketing research methodologies. This study examines the applications, adoption patterns, business impact, and implementation challenges of business analytics across digital marketing systems, drawing on primary survey data from 100 digital marketing and IT professionals and secondary data from industry analytics reports including Gartner, McKinsey, eMarketer, and HubSpot State of Marketing Reports. The research employs descriptive analytics framework mapping, survey-based adoption analysis, and impact quantification to document how descriptive, diagnostic, predictive, prescriptive, and cognitive analytics capabilities are applied across seven major digital marketing channels. Findings reveal that 86% of respondents agree that analytics improves campaign ROI, predictive analytics adoption reaches 70% among surveyed practitioners, and real-time bid optimisation delivers an average 31% improvement in Return on Ad Spend. However, significant implementation challenges persist including data silos (68%), privacy regulation compliance (62%), and analytics talent scarcity (58%). The study provides a comprehensive framework for understanding analytics maturity in digital marketing and offers strategic recommendations for organisations seeking to advance their analytical capabilities.
Bale Supraja¹, Nakka Raju², Enukonda Narender³ Mr. G. Mahesh⁴
A STUDY ON WEALTH CREATION OF AN INDIVIDUAL ACCORDING TO THEIR RISK APPETITE AT KOTAK MAHINDRA BANK
Wealth creation is the systematic process by which individuals grow their net worth over time through disciplined saving, strategic investment across diversified asset classes, and effective risk management tailored to their unique financial capacity and psychological tolerance for loss. Individual risk appetite—the degree of variability in investment returns an investor is willing to accept in pursuit of higher wealth accumulation—serves as the foundational parameter for constructing investment portfolios that balance growth aspirations with loss tolerance constraints. Kotak Mahindra Bank's wealth management division, operating through Kotak Private Banking and Kotak Securities, provides riskprofiled investment advisory and portfolio management services to retail, high-networth individual (HNI), and ultra-high-networth individual (UHNI) segments, offering asset allocation frameworks aligned with conservative, moderate, and aggressive risk profiles. This study examines the relationship between individual risk appetite and wealth creation outcomes at Kotak Mahindra Bank, analysing risk profiling methodology, asset allocation frameworks across risk categories, investment product selection, portfolio performance outcomes, and financial planning effectiveness. Primary data was collected through structured questionnaires administered to 110 respondents comprising Kotak Bank wealth advisors, relationship managers, and clients across different risk profile categories. Secondary data was sourced from Kotak Mahindra Bank annual reports (2021– 2024), SEBI investor education publications, AMFI industry data, and academic literature on risk tolerance and wealth management. Findings indicate that aggressive-risk-profile investors at Kotak achieve 5-year annualised portfolio returns of 14.8–18.4% compared to 7.2–8.6% for conservative-profile investors, while goal-based financial planning integration improves 10-year wealth accumulation by an estimated 34% compared to product-centric advisory approaches. Recommendations address risk profiling accuracy enhancement, goal-based planning adoption, digital wealth management capability, and financial literacy investment for improving individual wealth creation outcomes.
Jakkula Padma1 , Kanakam Deepika2 , Kasagoni Sharanya3 , Bandi Pallavi4 , A. Jyothsna5
A STUDY ON IMPACT OF TARIFFS IMPOSED BY US GOVERNMENT ON INDIAN STOCK MARKET
The imposition of tariffs by the United States government under the Trump administration's trade policy framework has emerged as a significant macro-financial risk factor for emerging market economies, with India's stock market exhibiting measurable sensitivity to US trade policy announcements and tariff escalation events. India's bilateral trade relationship with the United States, valued at USD 129 billion in FY 2023–24 and representing a USD 45.7 billion trade surplus in India's favour, places India among the primary targets of US reciprocal tariff policy, with the April 2025 announcement of 26% reciprocal tariffs on Indian exports generating an immediate and significant equity market reaction. This study examines the impact of US government tariffs on the Indian stock market, analysing Sensex and Nifty 50 index reactions to major tariff announcement events, sectoral vulnerability differential analysis across IT, pharmaceuticals, textiles, automotive, and metals, FII outflow patterns, INR/USD exchange rate dynamics, and broader macroeconomic transmission channels from US trade policy to Indian equity valuations. Primary data was collected through structured questionnaires administered to 100 respondents comprising equity analysts, portfolio managers, and institutional investors. Secondary data was sourced from BSE and NSE historical price data, RBI financial stability reports, DGCI&S trade statistics, OECD trade policy publications, and academic literature on tariff-equity market linkages. Findings indicate that US tariff announcement events have generated average Nifty 50 single-day declines of 1.8–2.4% in event windows, with export-oriented sectors including textiles (−8.2%), metals (−6.7%), and auto components (−5.9%) recording the sharpest sectoral corrections. Recommendations address portfolio hedging strategies, sector rotation frameworks, and policy advocacy priorities for mitigating US tariff impact on Indian market participants.
Malepu Sai Chandana1 , Dandu Janardhan2 , Boda Ravi3 , Pendyala Vivekvardhan4 , K.V. Chiranjeevi5
A Study of Green Finance and Sustainable Investing at HDFC Bank
Green finance has emerged as a pivotal mechanism for channeling capital towards environmentally sustainable and socially responsible projects. This study investigates the green finance strategies and sustainable investing practices adopted by HDFC Bank, one of India's leading private sector banks. Using secondary data spanning from 2019– 20 to 2023–24, this paper analyzes green bond issuances, ESG portfolio performance, renewable energy financing, and their impact on financial returns. The findings reveal that HDFC Bank's ESG score improved significantly from 64.2 to 78.7, while green loan portfolios expanded by over 194% during the study period. Regression analysis confirms that ESG scores, green bond volumes, and renewable energy finance share are positively correlated with return on investment (ROI). The paper concludes with policy suggestions to strengthen green finance frameworks in India's banking sector.
Mohd Sameer1 , Gona Neha2 , Sudini Abhishek Reddy3 , Gaddam Jagan Mohan4 Dr. Y. Azith5
A Study on Analysis of Cash Flow Statement: Madhucon Sugar and Power Industry Pvt. Ltd.
an indispensable tool for evaluating a company's liquidity position, operational efficiency, and financial sustainability beyond what profit and loss accounts and balance sheets reveal. For capital-intensive industries such as sugar and power generation, where working capital cycles are long, seasonal revenue patterns are significant, and infrastructure investment requirements are continuous, the cash flow statement provides the most transparent window into actual financial health. Madhucon Sugar and Power Industry Pvt. Ltd., headquartered in Nalgonda, Telangana, operates integrated sugar manufacturing and co-generation power facilities, making its cash flow dynamics particularly complex due to cane procurement seasonality, power purchase agreement cycles, and debt-heavy project financing. This study analyses Madhucon Sugar and Power Industry's cash flow statements over the five-year period FY 2019-20 to FY 2023-24, examining operating cash flows, investing cash flows, financing cash flows, and derived ratios including the operating cash flow ratio, cash flow coverage ratio, and free cash flow trend. Primary data was gathered through structured interviews with five finance officials of the company. Secondary data was sourced from the company's published financial statements, Ministry of Corporate Affairs filings, and CRISIL industry research on the Indian sugar sector. Findings reveal a persistent gap between reported profits and operating cash generation in early study years, driven by working capital absorption during cane procurement seasons. FY23 and FY24 show marked improvement in operating cash flow conversion as debt restructuring reduced interest outflows and operational efficiency improved sugar recovery rates.
Nelapati Sharon¹, Samreen Begum², Baindla Akshay Kumar³, Gaddam Anjali⁴, Dr. B. Narmada⁵
THERMAL STRESS & FATIGUE LIFE ANALYSIS OF ENGINE CYLINDER HEAD
The engine cylinder head is exposed to severe thermal and mechanical conditions during operation, making it highly susceptible to thermal stress and fatigue failure. This project investigates the thermal behavior and fatigue life of an engine cylinder head using numerical simulation techniques. A three-dimensional model of the cylinder head is developed in CATIA and analyzed using ANSYS. A steady-state thermal analysis is performed to determine temperature distribution under combustion and cooling conditions. The resulting temperature field is then used to evaluate thermal stresses and deformation through structural analysis. Fatigue analysis is conducted by applying cyclic thermal loads to estimate the life of the component using material S–N curves. The results indicate that maximum thermal stresses occur near critical regions such as the combustion chamber, valve seats, and bolt holes. These areas are identified as potential sites for crack initiation due to repeated thermal cycling. The study highlights the importance of effective cooling design, material selection, and stress reduction techniques to enhance the durability and performance of the cylinder head. This work provides valuable insights into improving engine reliability and serves as a foundation for further optimization and advanced thermal fatigue studies.
1Tatikonda Syam Kumar,2Dr. Siva Bhaskara Rao Devireddy,3Dr. Lakshmi Tulasi Chavali,4B. Ravi Kumar
A Grid Connected Single Phase Inverter Controlling Two Solar Pv Arrarys Operating Under Different Atmospheric Conditions
This project presents a grid-connected single-phase transformerless inverter designed to control two solar photovoltaic (PV) arrays operating under different atmospheric conditions such as unequal irradiance and temperature. In conventional PV systems, mismatch between arrays due to partial shading or varying environmental conditions reduces overall power extraction and system efficiency. To overcome this limitation, the proposed system integrates two series-connected PV subarrays with independent maximum power point tracking (MPPT) control, allowing each array to operate at its own optimal power point regardless of differing conditions. The transformerless inverter topology reduces system cost, size, and power losses while improving conversion efficiency. A dedicated control strategy ensures proper grid synchronization, sinusoidal current injection, low harmonic distortion, and stable power delivery to the utility grid. By enabling independent power optimization for both PV arrays within a single inverter stage, the proposed system significantly enhances energy harvesting, reduces mismatch losses, and provides an efficient and reliable solution for residential and distributed solar power applications under non-uniform atmospheric conditions.
1 Mr.L.Bichu Naik,2 V. Uma Maheshwara Rao, 3 Samala Gayathri, 4 Lavudya Srinath, 5 K Sreenu, 6Gurrapu Sheshanth
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Design And Analysis Of Multilevel Current Charging For Electric Vehicles
The design and analysis of multilevel current charging for electric vehicles focuses on developing an efficient and flexible charging system that can optimize battery performance while ensuring fast and safe energy transfer. The proposed system utilizes a multilevel current control strategy to regulate charging in different stages, improving power quality, reducing stress on the battery, and enhancing overall charging efficiency. By implementing advanced power electronic converters and control algorithms, the system minimizes harmonic distortion and switching losses while maintaining stable voltage and current profiles. The analysis demonstrates that multilevel charging significantly improves thermal management, extends battery life, and supports compatibility with various battery types used in electric vehicles. Overall, the study highlights that multilevel current charging is a promising solution for next-generation EV charging infrastructure, offering improved efficiency, reliability, and scalability.
Electric Vehicles, Multilevel Charging, Battery Management System, Power Electronics, Charging Efficiency, Current Control, Fast Charging, Harmonic Reduction, DC-DC Converter, Energy Optimization.
1Mr.K.Prasad,2Kommawar Manikanta, 3Sapavath Rana Pratap, 4Selvam Himapriya, 5 Jillela Murari
Power Quality Enhancement And Power Flow Analysis Of a Pv Integrated Upqc System In a Distribution Network
The increasing penetration of renewable energy sources, particularly photovoltaic (PV) systems, into distribution networks has introduced significant challenges related to power quality and reliable power flow management. This study presents a comprehensive approach for power quality enhancement and power flow analysis of a PV-integrated Unified Power Quality Conditioner (UPQC) in a distribution network. The proposed system combines the functionalities of series and shunt active power filters with a PV generation unit to mitigate common power quality issues such as voltage sag, swell, harmonics, and reactive power imbalance.The PV-integrated UPQC not only compensates for disturbances in the supply but also injects active power generated from the PV system into the grid, thereby improving overall system efficiency. Advanced control strategies are implemented to ensure stable operation, effective harmonic mitigation, and seamless coordination between the PV source and UPQC components. The system is analyzed under various operating conditions to evaluate its performance in terms of voltage regulation, harmonic distortion reduction, and power flow optimization. Simulation results demonstrate that the proposed approach significantly enhances power quality and ensures efficient energy utilization in modern distribution networks with renewable energy integration.
Photovoltaic (PV) System, Unified Power Quality Conditioner (UPQC), Power Quality Enhancement, Distribution Network, Harmonic Mitigation, Voltage Sag and Swell, Reactive Power Compensation, Power Flow Analysis, Renewable Energy Integration, Active Power Filters.
1Mr.K.Prasad,2Dongiri Rajeshwari,3Kusuma Akshay,4Kollam Yaswanth5Gattupalli Sarath Kumar
The Student Skill Swap System (SkillSwap) is a web-based platform designed to facilitate peer-topeer skill exchange among students. In educational institutions, students possess diverse skills but often lack a structured way to share and learn from each other. This system allows users to register, create profiles, list skills they can offer, and specify skills they want to learn. The platform uses a smart matching mechanism to connect students with complementary skills. It also includes features such as messaging, swap requests, and a rating system to ensure trust and smooth communication. The system promotes collaborative learning, reduces dependency on paid services, and enhances student interaction. Overall, SkillSwap provides an efficient and user-friendly solution for skill development within student communities.
1Mrs.A.Eenaja, 2C.Navya Sri, 3G.Yamini, 4C.Jyothika, 5G.Meghana
CARE CONNECT: A REAL-TIME CIVIC HELP & EMERGENCY RESPONSE NETWORK
CareConnect is a real-time civic help and emergency response platform designed to support people during urgent situations. It allows users to quickly report emergencies like accidents, medical issues, or public safety problems using a mobile or web application with live location sharing. The system sends instant alerts to nearby volunteers, hospitals, or authorities so that help can reach faster. Users can also report civic problems such as damaged roads, garbage issues, or streetlight failures, helping improve communication between citizens and local government. CareConnect uses technology, location tracking, and community support to make cities safer and more responsive. The main goal of this system is to reduce response time during emergencies and create a strong network where people can help each other in real time.
1Mrs.K. Helini, 2O. Gowthami,3M. Bhargavi,4K. Vaishnavi,5K. Harini
ONLINE EXAMINATION SYSTEM WITH PROCTORING
A real-time online examination system with proctoring revolutionizes secure remote assessments by delivering questions instantly via Web Sockets, ensuring synchronized timers and answersubmissions across global participants. It integrates live webcam monitoring, driven facial recognition, and gaze tracking to detect anomalies like multiple faces or screen distractions, while browser locking prevents tab-switching. Administrators access realtime dashboards for progressanalytics and instant alerts. Built on Node.js, React, and Socket.io, with TensorFlow.js for on-device, the system supports randomized question banks to curb cheating. This project showcasesfull-stack expertise for IT students, though it demands attention to GDPR privacy and low-latencystreaming challenges. Deployable on AWS or Heroku, it boosts employability in edtech.
1Mrs.K. Sandhya Rani, 2S.Sankeerthi, 3S. Haasya, 4V. Chethana
OPTIMIZED DOMAIN-SPECIFIC KNOWLEDGE RETRIEVAL USING HYBRID RAG ARCHITECTURE
Efficient retrieval of domain-specific knowledge is essential for improving the accuracy and reliability of intelligent systems in specialized fields such as healthcare, finance, and legal analytics. Traditional information retrieval methods often fail to capture semantic context, while large language models (LLMs) may produce responses lacking factual grounding. To address these limitations, this paper proposes an optimized domain-specific knowledge retrieval framework using a Hybrid Retrieval-Augmented Generation (RAG) architecture. The proposed system integrates dense vector retrieval with sparse keyword-based retrieval to combine semantic understanding with exact term matching. Domain-specific embedding models are used to generate meaningful vector representations, enabling efficient similarity search in a vector database. A lexical retrieval component complements this process by improving precision for specialized terminology. The retrieved results are further refined using a re-ranking mechanism to ensure contextual relevance before being passed to a generative model. The generation module produces accurate, context-aware responses grounded in retrieved knowledge, reducing hallucination and enhancing reliability. Experimental results show improved retrieval accuracy and response quality compared to traditional methods. The proposed hybrid RAG framework is scalable, efficient, and well-suited for real-world applications requiring precise domain-specific knowledge access.
Retrieval-Augmented Generation (RAG), Hybrid Retrieval, Domain-Specific Knowledge, Dense Retrieval, Sparse Retrieval, Large Language Models, Semantic Search.
Yamjala Sai Vamshidar Reddy, Rayala Koushik, Songa Akhil, Mrs. S. Usha Manjari