American Journal of AI Digital Transformation and Regenerative Pharmacist
FACIAL VERIFICATION OF FAMILIAL RELATIONSHIPS USING DEEP LEARNING AND SIAMESE NEURAL NETWORKS
Facial kinship verification is all about figuring out if two people are related by blood by looking at their faces. This task is different from traditional face recognition because it looks for small similarities that may exist between different people in a family. This work develops a deep learning-based method that uses a Siamese neural network to compare pairs of facial images and find out how similar they are in a learned feature space. The system processes input images through the same network branches and uses a distance metric to figure out how related they are, as well as a classification system based on a threshold. An uncertainty region is added to handle cases that are on the edge of being true or false. This makes the system more reliable.The system also uses explainable AI methods to give visual and numerical information about the prediction, such as heatmaps and region-wise analysis of facial features like the eyes, nose, and mouth. The model is tested on datasets like KinFaceW and Families in the Wild. It works well on structured data, but it shows problems when used in the real world. The findings suggest that integrating similarity learning with interpretability can yield a more transparent and effective approach for kinship verification.
Deep Learning, Explainable AI, Grad-CAM, Face Similarity, Siamese Neural Network, and Facial Kinship Verification
Mr. Prakash Nakirekanti¹, S. Vinayakarthik², G. M. Rohan Goud³, G. Sai Shashi Varan⁴
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
The Smart Timetable System is a web-based academic scheduling platform designed to automate and streamline timetable management for engineering colleges. The system manages multiple departments, academic years, and sections while ensuring efficient allocation of faculty, classrooms, and subjects. It incorporates a centralized administrative structure with role-based access control to coordinate college-wide scheduling activities. The platform includes an intelligent, constraint-based rescheduling mechanism that dynamically adjusts timetables during holidays or academic events without creating faculty or classroom conflicts. By reducing manual effort and improving scheduling accuracy, the system aims to provide a scalable and reliable solution for modern academic timetable management.
1Dr.D.Shanthi, 2A.Harika, 3Srujana.Balla, 4Sreeja Akula, 5D.Sathvika
AI-Powered Mock Interview System with Multi-Accent Support
The increasing competition in today’s job market has made interview preparation an essential skill for students and job seekers. Traditional mock interview methods often depend on human trainers, which can be timeconsuming, costly, and inconsistent. To address these challenges, this paper presents an AI-Powered Mock Interview System with Multi-Accent Support that provides an automated platform for practicing interview questions and receiving instant feedback. The system allows candidates to respond using either voice or text input. Spoken responses are converted into text using Automatic Speech Recognition (ASR), while Natural Language Processing (NLP) techniques are used to evaluate the semantic relevance, clarity, and completeness of the answers. The proposed system supports multi-accent speech recognition to improve fairness and usability for diverse users. It also separates content evaluation and delivery analysis to provide transparent scoring and actionable feedback. The system is implemented using Python, Streamlit, Whisper, SentenceTransformers, and spaCy. Experimental testing shows that the system can effectively simulate interview practice, provide instant feedback, and help users improve their communication skills and confidence. The proposed solution is costeffective, accessible, and suitable for students, job seekers, and educational institutions.
Mrs.Archana, Potharaju Bhoomika, Mulukalla Anusha, Guduru VimalaBhavana, Kande Shreecharani
A MACHINE LEARNING APPROACH FOR EARLY MENTAL HEALTH RISK DETECTION VIA SOCIAL MEDIA ANALYTICS
The rapid growth of social media platforms has generated vast amounts of user-generated content that can provide valuable insights into individuals’ mental health conditions. Early identification of mental health risks such as depression, anxiety, stress, and suicidal ideation is essential for timely intervention and preventive healthcare. This paper presents a machine learning-based framework for early mental health risk detection using social media analytics. The proposed system collects textual data from social media platforms and applies natural language processing (NLP) techniques for data preprocessing, sentiment analysis, feature extraction, and behavioral pattern identification. Various machine learning algorithms, including Support Vector Machine (SVM), Random Forest, Naïve Bayes, and Deep Learning models, are utilized to classify users based on mental health risk levels. The framework analyzes linguistic patterns, emotional expressions, posting frequency, and user engagement metrics to improve prediction accuracy. Experimental results demonstrate that the proposed approach achieves higher detection performance, improved accuracy, and faster prediction compared to traditional manual assessment methods. The system can assist healthcare professionals, counselors, and support organizations in identifying high-risk individuals at an early stage and enabling proactive mental health intervention. Furthermore, the integration of explainable AI techniques enhances the transparency and reliability of the prediction process, making the system more suitable for real-world mental healthcare applications.
Mental Health Detection, Social Media Analytics, Machine Learning, Deep Learning, Natural Language Processing, Sentiment Analysis, Early Risk Prediction, Artificial Intelligence, Behavioral Analysis, Explainable AI.
AI- BASED CROP RECOMMENDATION FOR FARMERS
Agriculture is essential to the economy, but farmers often face challenges in selecting the right crop due to unpredictable climate and limited soil knowledge. This AI-Based Crop Recommendation System uses machine learning (Random Forest Classifier) to analyze soil nutrients and weather conditions to suggest the most suitable crop. A Flask-based web application provides easy and secure access, along with an AI-powered agricultural chatbot for instant guidance. The system helps improve crop yield, reduce financial risk, and promote sustainable farming practices.
1 Mrs. K. Helini, 2 T. Bindu sri, 3 K. Akshitha, 4 K. Sarika, 5 L. Laxmi bhavani
Software Solutions to Identify Users Behind Social Media Based Drug Trafficking
With the fast evolution of social media websites, criminal behavior like drug trafficking can now be performed discreetly using those platforms. Manual detection of such activities becomes challenging owing to huge amounts of unstructured data being generated every single day. This research paper introduces a Drug Trafficking Detection System which automatically analyses tweets for detecting suspicious activities. It utilizes some simple text pre-processing techniques, along with keyword search and risk assessment mechanism, in order to analyze individual tweets. Using a specific risk score generated for each tweet, the tweets are categorized into three types - Low Risk, Medium Risk, and High Risk. This system will generate alerts for risky tweets that police can act upon immediately. The system minimizes human labor, increases accuracy, and makes detection quick. The system is easy to use, inexpensive, and scalable; therefore, it is applicable in real-life settings. Despite its reliance on the current model where detection is based on keywords, this system can be improved through time to include sophisticated machine learning models and real-time data analysis. All in all, this system serves as an efficient method of monitoring social media to fight drug abuse
1 Dr. D. Shanthi, 2O. Sanyogitha, 3B. Akhila, 4A. Anushka
AI-POWERED CROP YIELD PREDICTION AND OPTIMIZATION
Agriculture faces increasing challenges due to climate variability, plant diseases, soil degradation, and inefficient resource utilization, necessitating intelligent data-driven solutions for sustainable food production. This work presents an AI-powered agricultural prediction framework that integrates plant disease detection, crop recommendation, yield prediction, rainfall/climate analysis, and soil moisture estimation using machine learning, deep learning, ensemble learning, and explainable AI techniques. For plant disease prediction, deep convolutional neural networks including ResNet50, VGG16, Xception, NASNet, and a hybrid ensemble model are employed with extensive image augmentation. Crop recommendation is addressed using Random Forest, Decision Tree, KNN, XGBoost, AdaBoost, and a Voting Classifier. Yield prediction, rainfall/climate analysis, and soil moisture estimation are modeled using both classification and regression approaches with the same set of ensemble-based algorithms. Explainable AI techniques such as SHAP, LIME, and GradCAM are utilized to enhance transparency and interpretability of predictions. Experimental evaluation demonstrates that the Voting Classifier achieves superior performance for crop recommendation with an accuracy of 98.6%. Yield prediction shows the best performance using a voting-based ensemble regressor with an R² score of 91.3%. Soil moisture prediction attains high accuracy using a Voting ensemble model with 97.7% performance. Plant disease detection achieves 99.8% accuracy using a hybrid deep learning ensemble, while rainfall analysis is effectively handled using a Voting Classifier. The integrated framework supports precise decision-making for crop management, irrigation scheduling, and disease control.
1 Dr. D. Shanthi, 2 A.Ruthika, 3 D.Trishitha, 4 Y.Sai Priya, 5 S.Hindu
NEXT-GENERATION ALUMNI MANAGEMENT SYSTEM USING ARTIFICIAL INTELLIGENCE
Maintaining strong connections with alumni is essential for mentorship, career guidance, and professional networking. However, traditional alumni management methods often result in scattered data, outdated information, and limited interaction between alumni and students. This paper proposes a Next-Generation Alumni Management System that provides a centralized digital platform for managing alumni information and improving communication within the academic community. The system integrates features such as secure user authentication, centralized alumni profiles, AI-based mentor–mentee matching, and job opportunity sharing. LinkedIn synchronization helps maintain updated professional information of alumni automatically. The platform is developed using modern web technologies to ensure scalability and efficient data management. By enabling structured interaction between alumni and students, the system enhances mentorship opportunities and career support. The proposed system ultimately improves alumni engagement and strengthens institutional networking.
Mrs. A.Rajini Devi1 , D.Sreeja2 , R.Sai Navya3 , D.Akshitha4 , B.Anjali5
FARMER EQUIPMENT RENTAL AND PRODUCT RESALE SYSTEM
This project presents the design of a Farmer Equipment Rental and Product Resale System aimed at enhancing accessibility, affordability, and efficiency in rural agriculture. The platform enables farmers to rent or lend agricultural equipment during off-seasons, ensuring optimal resource utilization and promoting collaborative farming. In addition to equipment rentals, the system features a product resale module where essential items such as fertilizers, seeds, and tools can be traded at affordable prices. Advanced functionalities include a Gmail notification system that automatically alerts farmers when new products are added, eliminating the need to frequently visit the website., improving usability for regional farmers. Furthermore, assists users in locating nearby equipment, while secure payment integration ensures smooth financial transactions. Overall, this system fosters cost-effective, sustainable, and technology-driven farming practices, empowering small and marginal farmers across India
Farmer Equipment Rental System, Product Resale Platform, Agricultural Machinery Sharing Gmail Notification System, Location-Based Equipment Search, Secure Online Payments
1 R. Krishna Naik, 2 R. Rishika, 3 CH. Swarna, 4 P. Mythri, 5 M. Rajeshwari
ATM USING EYE CONTACT LENS (IRIS RECOGNISATION)
In the present digital era, banking systems play a very important role in everyday life. Automated Teller Machines (ATMs) are widely used for cash withdrawal, balance enquiry, fund transfer, and other banking services. Traditional ATM systems mainly depend on ATM cards and Personal Identification Numbers (PINs) for authentication. Although this method is simple, it suffers from many security issues such as card loss, card theft, PIN guessing, skimming attacks, and unauthorized access. To overcome these problems, biometric authentication systems are introduced in ATM technology. Biometrics uses unique biological characteristics of humans for identification and verification. Among different biometric techniques, iris (eye contact lens) recognition is considered highly secure and reliable. An ATM system that uses eye contact lens (iris recognition) provides a multi-level security system, which ensures that only the genuine user can access the bank account. This approach eliminates the need for carrying ATM cards and significantly reduces fraud and identity theft.
1.Mrs. A. Rajini Devi, 2.S. Bhavana, 3.K. Shreethisahasra, 4.S. Bhavya Sri, 5.S. Shravani
A Study on Role of Financial Advisor in Finance and Retail Investment at HDFC Life
Financial advisors occupy a critical intermediary position in the life insurance and retail investment ecosystem, translating complex financial products into comprehensible, need-aligned solutions for individual and household investors. HDFC Life Insurance Company Limited, one of India's leading private life insurers with a gross written premium exceeding Rs. 26,000 crore and over 64 million policies in force, relies on a network of more than 2.5 lakh licensed financial advisors as its primary distribution engine. This study examines the role of financial advisors in facilitating retail investment decisions at HDFC Life, analysing their contribution to product suitability assessment, customer financial planning, premium persistency, and investor education. Primary data was gathered through structured questionnaires administered to 100 respondents comprising HDFC Life financial advisors (60) and retail policyholders (40) across Hyderabad. Secondary data was sourced from HDFC Life Annual Reports, IRDAI publications, AMFI research, and academic literature on financial advisory and retail investment behaviour. Findings confirm that advisor-intermediated policy purchases demonstrate 23% higher persistency ratios than direct digital channel purchases, and that financially literate advisors generate 38% higher average premium per policy than minimally qualified peers. Key challenges include advisor attrition, digital disruption of traditional advisory roles, and regulatory compliance burdens under IRDAI's amended guidelines. Recommendations include investing in advisor digital enablement, structured financial planning certification programmes, and performance-linked advisory compensation frameworks.
Financial advisor, HDFC Life, retail investment, life insurance distribution, policy persistency, financial planning, IRDAI, investment advisory, insurance intermediary, financial literacy.
Soma Varun Kumar¹, Arva Prashanth², Mudholkar Shailesh Prashantrao³, Pranay Pettem⁴, Ms. A. Jyothsna⁵
A STUDY ON RISK AVERSION STRATEGIES FOLLOWED BY ICICI BANK DURING INVESTMENT DECISIONS
Risk aversion in investment decision-making represents one of the most strategically consequential dimensions of commercial bank management, directly influencing portfolio composition, asset allocation, provisioning adequacy, and ultimately the resilience of the institution during economic downturns. ICICI Bank Limited, India's largest private sector bank, has developed a sophisticated multi-layered risk aversion framework encompassing credit risk mitigation, market risk hedging, liquidity risk management, and operational risk controls that collectively govern its investment and lending decisions. This study examines the specific risk aversion strategies employed by ICICI Bank in its investment decisions over the period FY 2018–19 to FY 2022–23, analysing the effectiveness of these strategies through observable improvements in key risk metrics. The research draws on secondary data from ICICI Bank Annual Reports, RBI regulatory disclosures, and Basel III compliance reports to document credit risk management tools including AI-driven credit scoring, collateral management, loan diversification, and provision coverage; market risk controls including Value-at-Risk frameworks, interest rate swaps, and Asset-Liability Management Committee oversight; and investment portfolio risk governance including HTM/AFS/HFT classification strategy, sectoral exposure limits, and concentration risk caps. Findings confirm that ICICI Bank's risk aversion framework has delivered measurable outcomes—gross NPA ratio declining from 6.70% to 2.48%, credit cost falling from 2.58% to 0.42%, and CRAR remaining above 18%—over the study period, validating the effectiveness of its risk aversion architecture.
risk aversion, investment decisions, ICICI Bank, credit risk, market risk, Value-at-Risk, NPA, provisioning, Asset-Liability Management, Basel III, portfolio risk, hedging strategies.
Kongandla Srinu¹, Mohd Omer², Maku Siddu³, Kompalli Gayathri⁴ Ms. P. Amulya⁵
A STUDY ON FINANCIAL PERFORMANCE ANALYSIS OF ICICI BANK
The banking sector serves as the backbone of any economy, and the financial performance of commercial banks directly influences credit availability, monetary transmission, and overall economic growth. ICICI Bank Limited, India's largest private sector bank by total assets, presents a compelling case study in financial performance evolution, having navigated the dual challenges of asset quality stress and digital transformation over the past decade. This study conducts a comprehensive financial performance analysis of ICICI Bank over the five-year period FY 2018–19 to FY 2022–23, employing a multidimensional analytical framework encompassing profitability ratios, liquidity ratios, asset quality indicators, capital adequacy assessment, and efficiency metrics. Secondary data sourced from ICICI Bank Annual Reports, RBI regulatory returns, and NSE disclosures forms the empirical basis of the analysis. Key findings reveal a sustained improvement trajectory across all performance dimensions: net interest margin expanded from 3.17% in FY19 to 4.09% in FY23; gross NPA ratio declined sharply from 6.70% to 2.48% over the same period, reflecting disciplined credit risk management; return on assets improved from 0.39% to 2.35%; and capital adequacy remained comfortably above regulatory minimums throughout. The study concludes that ICICI Bank's financial performance improvement is attributable to a convergence of strategic credit portfolio repositioning, digital banking-driven cost efficiency gains,and Macroeconomic tailwinds from India's post-pandemic economic recovery.
financial performance analysis, ICICI Bank, profitability ratios, net interest margin, NPA, capital adequacy, liquidity, banking sector, ratio analysis, asset quality.
Dashannagari Sai Jaya Vardhan¹, Polkam Sandeep², Gidda Rajini³, Asam V Varun Kumar⁴ Mr. K.V. Chiranjeevi⁵
Machine Learning Applications in E-Commerce at Flipkart
Machine learning (ML) has emerged as the technological backbone of modern e-commerce, enabling platforms to deliver hyper-personalised shopping experiences, optimise dynamic pricing, detect fraudulent transactions, forecast demand with precision, and automate supply chain decisions at scale. Flipkart, India's largest domestic e-commerce marketplace with over 500 million registered users and more than 150 million product listings, has deployed machine learning across virtually every dimension of its platform operations since its acquisition by Walmart in 2018. This study examines the application of machine learning algorithms at Flipkart across six functional domains: product recommendation systems, dynamic pricing engines, demand forecasting, fraud detection, customer sentiment analysis, and logistics optimisation. Primary data was gathered through structured interviews with 20 technology and data science professionals associated with Flipkart's Bengaluru and Hyderabad operations. Secondary data was sourced from Flipkart's published technology blogs, academic literature on e-commerce ML applications, and industry research from McKinsey and Gartner. Findings confirm that Flipkart's collaborative filtering recommendation engine accounts for 35% of total platform revenue, that ML-driven dynamic pricing processes over 5 million price adjustments daily, and that the fraud detection system achieves 97.3% accuracy with a false positive rate below 0.8%. Challenges include data privacy compliance under India's Digital Personal Data Protection Act (DPDPA) 2023, coldstart problem in recommendations for new users, and model explainability requirements for pricing decisions. Recommendations focus on federated learning for privacy-preserving personalisation, explainable AI frameworks for pricing transparency, and reinforcement learning for real-time inventory optimisation.
Machine learning, ecommerce, Flipkart, recommendation system, dynamic pricing, demand forecasting, fraud detection, sentiment analysis, collaborative filtering, logistics optimisation.
Athe Pramusha¹, Boini Venkatesh², Pendem Rakesh³, Mr. Shaik Abdul Zameel⁴
A STUDY ON FINANCIAL INSTITUTIONS OF FINANCING SMALL SCALE INDUSTRIES AT AXIS BANK
Small scale industries (SSIs) form the backbone of India's industrial economy, contributing approximately 30% of GDP, 45% of manufacturing output, and employing over 110 million people across diverse sectors including textiles, food processing, engineering goods, chemicals, and handicrafts. Despite their economic significance, SSIs face chronic financing constraints arising from limited collateral, inadequate credit histories, weak financial documentation, and the risk aversion of formal financial institutions toward small borrower segments. Axis Bank, India's third largest private sector bank by assets, has emerged as a significant financier of small scale industries through specialised MSME credit products, government scheme linkages, supply chain financing, and digital lending platforms. This study examines the role of financial institutions, with particular focus on Axis Bank, in financing small scale industries, analysing product offerings, credit appraisal methodology, loan performance, financial inclusion impact, and operational challenges. Primary data was collected through structured questionnaires administered to 110 respondents comprising Axis Bank MSME credit officers, SSI entrepreneurs, and industry association representatives. Secondary data was sourced from Axis Bank Annual Reports (2021– 2024), RBI MSME credit reports, Ministry of MSME publications, SIDBI financing data, and academic literature on SSI financing in India. Findings reveal that Axis Bank has expanded its MSME loan book from ₹78,420 crore to ₹1,12,650 crore over the study period, with 68% of new SSI customers acquired through government scheme linkages, collateral-free CGTSME guarantee coverage, and digital onboarding platforms reducing loan processing time from 21 days to 7 days. Recommendations address collateral framework modernisation, digital credit scoring adoption, and government scheme disbursement acceleration for enhancing SSI financing effectiveness.
Small scale industries, MSME financing, Axis Bank, CGTMSE, MUDRA, working capital, term loan, SSI credit, financial inclusion, collateral-free lending.
Mandoji Pranathi1 , Ambati Anil2 , Marapaka Prashanth3 , Moluguri Poojitha4 , Dr. B. Narmada5
GLOBAL FINANCIAL MARKETS AND THEIR IMPACT ON BUSINESS AT TATA MOTORS
Global financial markets constitute the circulatory system of the modern economy, channelling capital across borders, pricing risk at international scale, and transmitting macroeconomic shocks with unprecedented speed and amplitude. Formultinational corporations operating across multiple currency zones, product segments, and capital structures, the volatility of global equity, debt, currency, and commodity markets represents a fundamental source of strategic uncertainty and financial risk. Tata Motors Limited, India's largest automobile manufacturer and the parent company of Jaguar Land Rover (JLR), operates at the intersection of global financial market dynamics through its exposure to foreign exchange rate fluctuations, international commodity prices, cross-border debt financing, and global equity investor sentiment. This study examines themultidimensional impact of global financial market variables—including currency exchange rates (INR/GBP/USD), commodity prices (steel, aluminium, palladium), international interest rate cycles, and global equity market indices—on Tata Motors' revenue, profitability, capital structure, and strategic investment decisions over the period FY 2018–19 to FY 2022–23. Drawing on secondary data from Tata Motors Annual Reports, NSE/BSE filings, Bloomberg financial market datasets, and RBI publications, the study employs correlation analysis, trend analysis, and ratio-based financial assessment to quantify financial market impact. Findings reveal that JLR's GBP-denominated revenue exposure and Tata Motors' steel procurement cost structure are the two most significant financial market transmission channels, with GBP/INR depreciation and commodity price inflation collectively explaining a substantial proportion of margin compression observed in FY2019–20 and FY2021–22. The study offers strategic recommendations for enhanced currency hedging, commodity risk management, and capital structure optimisation.
global financial markets, Tata Motors, Jaguar Land Rover, foreign exchange risk, commodity prices, interest rate risk, capital markets, currency hedging, automobile industry, financial market impact.
Neerati Nikhil¹, Polepaka Narender², Pendela Vedithanjali³, Vanaparthi Likitha⁴ Ms. R. Swapna⁵
A STUDY ON ETHICAL AI AND DATA PRIVACY IN WORKPLACE PLANNING
Artificial intelligence is rapidly transforming workplace planning processes including talent acquisition, workforce scheduling, performance evaluation, succession planning, and employee monitoring, generating vast quantities of personal and behavioural data that raise fundamental questions of ethical governance and data privacy. As HR functions increasingly adopt AI-driven tools— predictive hiring algorithms, sentiment analysis platforms, productivity tracking software, and automated appraisal systems—organisations face complex ethical obligations to employees whose professional lives, career trajectories, and psychological wellbeing are shaped by algorithmic decisions they often cannot see, understand, or contest. This study examines ethical AI adoption and data privacy practices in workplace planning across Indian organisations, analysing algorithmic bias risks, employee consent frameworks, transparency obligations, data minimisation practices, and regulatory compliance with India's Digital Personal Data Protection Act 2023. Primary data was collected through structured questionnaires administered to 115 respondents comprising HR professionals, IT compliance officers, and employees subject to AI-based workplace planning systems. Secondary data was sourced from NASSCOM AI governance reports, MEITY Digital Personal Data Protection guidelines, IEEE Ethically Aligned Design standards, and academic literature on algorithmic management and workplace privacy. Findings indicate that 73% of surveyed organisations deploy AI in at least one HR planning function, but only 31% have implemented formal AI ethics governance frameworks and 28% provide employees with meaningful explanation of algorithmic decisions affecting them. Recommendations address ethical AI framework development, algorithmic transparency obligations, employee data rights implementation, and regulatory compliance strategy under DPDPA 2023.
Ethical AI, data privacy, workplace planning, algorithmic bias, DPDPA 2023, employee monitoring, HR analytics, algorithmic transparency, consent management, responsible AI.
Are Karthik1 , Gopinath V2 , Chinnambati Thilak3 , Peddi Reddy Preethi Reddy4 , Ms. S. Ramya5
EFFECTS OF RBI GUIDELINES FOR THE DEVELOPMENT OF BANKING SECTOR
The Reserve Bank of India (RBI), as the apex monetary authority and banking regulator, plays a central role in shaping the development, stability, and efficiency of India's banking sector through its regulatory guidelines, monetary policy frameworks, prudential norms, and supervisory directives. RBI guidelines covering capital adequacy, asset quality management, liquidity standards, credit risk frameworks, priority sector lending, digital banking regulation, and customer protection have collectively transformed India's banking landscape over the past three decades. This study examines the effects of key RBI guidelines on banking sector development, analysing their impact on bank financial health, credit growth, NPA management, financial inclusion, digital banking adoption, and systemic stability. Primary data was collected through structured questionnaires administered to 110 respondents comprising banking professionals, financial analysts, and banking sector researchers. Secondary data was sourced from RBI Annual Reports (2021–2024), RBI Financial Stability Reports, Indian Banking Association publications, and academic literature on banking regulation. Findings indicate that Basel III capital adequacy implementation has strengthened bank resilience, SARFAESI and IBC frameworks have improved NPA recovery rates by 34%, priority sector lending mandates have expanded financial inclusion to 490 million Jan Dhan account holders, and RBI's digital lending guidelines have regulated the FinTech credit ecosystem. Key challenges include balancing regulatory compliance costs against credit growth imperatives and managing systemic risks from rapid digital banking expansion. Recommendations address regulatory framework simplification, risk-based supervision enhancement, and collaborative FinTech regulatory sandbox expansion.
Burra Shivani1 , Sardar Nandini2 , Niduram Mamatha3 , Mettu Nishitha Yadav4 , Dr. Y. Azith5
RESUME SHORTLISTING AND RANKING WITH TRANSFORM
The recruitment process often involves reviewing a large number of resumes, which is time-consuming and prone to human bias. This project proposes an automated resume shortlisting and ranking system using Transformer-based deep learning models, such as BERT, to efficiently evaluate candidate resumes. The system extracts relevant features from resumes, including skills, education, experience, and certifications, and matches them against job descriptions to assess suitability. By leveraging contextual embeddings and semantic analysis, the Transformer model can understand the nuances in candidate profiles and provide an accurate ranking. Experimental results demonstrate that this approach reduces manual effort, minimizes bias, and improves the efficiency and fairness of candidate selection. The system can be applied in various human resource management platforms to streamline recruitment and enhance decision-making
Venkata Ratnam Ganji¹, Chigatapu Kavya², Pasupuleti Navya Lakshimi³, Veeramallu Rupa Maheswari⁴,Veerabathina Syam Kumar⁵
NutriVision: Calorie Estimation of Food Using Deep Learning
With the increasing awareness of health and nutrition, accurate calorie estimation has become essential for maintaining a balanced diet. Traditional manual tracking methods are often time-consuming and prone to human error. This project, NUTRIVISION, proposes a deep learning–based approach to automatically estimate food calories from images. The system uses computer vision techniques and convolutional neural networks (CNNs) to identify food items, analyze portion sizes, and predict calorie values based on trained datasets. By leveraging image preprocessing, feature extraction, and classification models, the framework provides real-time nutritional insights with improved accuracy and efficiency. The proposed solution aims to assist users in monitoring daily intake, supporting healthier lifestyle decisions, and enabling smart dietary management through an intelligent and user-friendly platform
Mrs.G.VIJAYA SRI1 , DOKALA USHA SRI2 , BHATTU SUSHMAVIJAYA SRI3 , GORLA GUNA VEERA NAGA GAYATRI4 , AVULA LEELA PAVAN SAI5
GRAPH NEURAL NETWORKS FOR SOCIAL NETWORK ANALYSIS IN INDIA: DETECTING FAKE PROFILES & BOTNETS
The rapid growth of social networks in India has facilitated unprecedented connectivity but has also amplified risks associated with fake profiles, botnets, and misinformation propagation. Traditional detection methods often rely on heuristic or feature-based techniques, which struggle with scalability and evolving malicious behaviors. This study explores the application of Graph Neural Networks (GNNs) for social network analysis, leveraging the inherent graph structure of online communities to detect anomalous accounts and coordinated bot activity. By modeling users as nodes and their interactions as edges, GNNs capture both local and global relational patterns, enabling the identification of suspicious profiles with high accuracy. The proposed approach incorporates advanced graph convolution and attention mechanisms to enhance representation learning while mitigating the influence of noisy or incomplete data. Experimental evaluation on Indian social media datasets demonstrates that GNN-based detection outperforms traditional machine learning classifiers in terms of precision, recall, and robustness against sophisticated botnet strategies.
G.VENKATA RATNAM¹, KADALI DIVYA SRI², CHALAMALAPALLI ASHA LATHA³,BAVISETTI GEETHA PRIYA⁴,DEVARAPALLI MANOHAR⁵
DEEP FAKE AUDIO DETECTION USING DEEP LEARNING
The rapid advancement of artificial intelligence has enabled the creation of highly realistic synthetic speech, commonly known as deep fake audio, which poses significant risks to security, privacy, and digital trust. Deep fake audio can be used for misinformation, identity fraud, and unauthorized voice cloning, making reliable detection methods essential. This research proposes a deep learning-based framework for detecting fake audio by analyzing subtle acoustic patterns and inconsistencies that are difficult for humans to identify. The proposed system utilizes advanced neural network architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), or Transformer-based models to learn discriminative features from audio spectrograms and raw waveform signals. Preprocessing techniques including noise reduction, feature extraction (MFCC, Mel-spectrogram), and normalization are applied to enhance model performance. The model is trained and evaluated on benchmark datasets containing both real and synthetic speech samples. Experimental results demonstrate that deep learning models can effectively differentiate between genuine and manipulated audio with high accuracy, precision, and recall. The system shows strong potential for real-time applications in cybersecurity, digital forensics, and social media monitoring. This work highlights the importance of combining robust feature engineering with advanced deep learning architectures to combat the growing threat of AI-generated audio manipulation.
Mrs.K.TEJASWI1 ,MIRIYALKAR BRIDULA2 ,MUDILI DEVI ALEKHYA3 ,KOMATIPALLI VIJAY KUMAR4 ,SONTI SASI KALA5
A NOVEL HYBRID MODEL TO PREDICT THE DISSOLVED OXYGEN OF THE WATER IN AQUACULTURE
Dissolved oxygen (DO) is a critical parameter in aquaculture, directly affecting the health, growth, and survival of aquatic organisms. Accurate prediction of DO levels can help optimize water quality management, prevent fish mortality, and improve productivity. Traditional methods for measuring DO are often time-consuming, costly, or prone to errors. This study proposes a novel hybrid predictive model that integrates machine learning techniques with statistical modeling to forecast DO levels in aquaculture systems. The model combines Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) neural networks to capture both linear and nonlinear dynamics in water quality data, including temperature, pH, turbidity, and ammonia concentration. Experimental results demonstrate that the proposed hybrid approach outperforms conventional standalone models in terms of accuracy, robustness, and computational efficiency. The findings suggest that this predictive framework can serve as an effective tool for real-time monitoring and management of water quality in aquaculture environments, enabling proactive interventions and sustainable aquaculture practices.
MR.T.ROHINIKUMAR ,MORAM MEGHANA SAICHANDRIKA ,VADADI VIJAYA DURGA , NUNNA MOUNIKA KUMARI , KUNCHARLA RUPESH
Adaptive AI-Driven Hybrid Movie Recommendation System with Real-Time Personalization
This paper proposes an Adaptive AI-Driven Hybrid Movie Recommendation System designed to provide highly personalized and dynamic movie suggestions by leveraging real-time user interactions and advanced machine learning techniques. The system integrates multiple recommendation approaches, including contentbased filtering and collaborative filtering, within a hybrid framework to overcome common limitations such as cold-start issues and data sparsity. User data—such as ratings, browsing history, and interaction patterns—is continuously collected and processed through a structured pipeline. Key features like genre, cast, director, and metadata are extracted and transformed into numerical vectors, enabling similarity computation using techniques such as cosine similarity and matrix factorization. The proposed system dynamically updates recommendations in real time, adapting to evolving user preferences and improving accuracy and relevance over time. The architecture comprises a well-structured dataset, feature extraction module, machine learning models, and an interactive user interface to ensure a seamless user experience. Experimental insights demonstrate enhanced recommendation accuracy, diversity, and scalability compared to traditional static systems. This research highlights the practical implementation of Artificial Intelligence in building adaptive recommender systems and its potential applicability across various domains such as e-commerce, music streaming, and online learning platforms.
BLOCKCHAIN-BASED FRAMEWORK FOR SECURE AND EFFICIENT FINANCIAL TRANSACTIONS
The increasing digitization of the banking sector has brought about significant advancements in customer service and transaction efficiency. However, this evolution has also introduced a range of security challenges, particularly concerning data privacy, transaction integrity, and vulnerability to cyber threats. Traditional e-banking systems, though efficient, are often susceptible to security risks such as phishing, malware attacks, and data breaches, which can compromise sensitive financial information and undermine customer trust. In response to these challenges, blockchain technology has emerged as a revolutionary solution that provides enhanced security, transparency, and decentralization, making it highly suitable for secure banking applications. This project, Secure Banking Using Blockchain, aims to explore the potential of blockchain technology in mitigating the security risks associated with online banking systems. By leveraging blockchain’s decentralized architecture, cryptographic principles, and consensus mechanisms, this research seeks to provide a more secure and transparent banking environment. The study begins by reviewing the current state of traditional e-banking systems, identifying their vulnerabilities, and analyzing the security methods currently implemented by banks. Surveys and archival data are utilized to understand customer and banker perspectives on adopting blockchain-based solutions, with security emerging as the core concern for both groups. The research further explores various categories of threats specific to both traditional and blockchain banking systems, such as phishing, hacking, Distributed Denial of Service (DDoS) attacks, smart contract vulnerabilities, and 51% attacks. A comparative study of existing security protocols in traditional systems (e.g., SSL, HTTPS) and blockchain mechanisms (e.g., encryption, digital signatures, consensus algorithms like Proof of Work and Proof of Stake) is conducted. The focus is on how blockchain’s immutable ledger, decentralized control, and cryptographic methods offer significant advantages in mitigating these threats. Two blockchain-based encryption algorithms are presented as part of the project. The first algorithm, NPN (Prime Numbers and Pseudo-Random Number Generator), combines cryptographic techniques to enhance the security of banking transactions by ensuring unpredictability and randomness in encryption keys. The second algorithm, LIE architecture, leverages a multi-round encryption process based on the Fiestel network, utilizing a set of discrete cryptographic subkeys and permutation matrices to provide robust data security. The LIE algorithm also applies the blockchain principles of confusion and diffusion to maximize the difficulty of data manipulation and enhance transaction integrity. Additionally, the research highlights the mathematical underpinnings of these algorithms, such as XOR operations, multiplication, and cryptographic hashing, which are critical to achieving essential properties like avalanche effects, statistical independence, and completeness. Both algorithms are designed to meet the stringent security demands of modern banking systems, ensuring that sensitive data remains secure during transactions. To validate the performance and effectiveness of the proposed algorithms, a comparative analysis is conducted with existing blockchain encryption methods. The time and space complexity of the designed algorithms are optimized and evaluated to ensure they are both efficient and scalable for real-world banking applications. The project concludes with recommendations for implementing blockchain-based security in modern banking systems, focusing on preventive measures to protect data from hacking, corruption, or unauthorized alteration. By proposing innovative cryptographic algorithms and optimizing their performance, this project contributes to the growing body of research on blockchain security in the financial sector and provides a practical framework for enhancing the security of online banking transactions.
DR. D. BHADRU, A. SAI KUMAR, S. CHANDANA, P. SIRI HAKSHITHA, PINJA ADITHYA
NEURAL NETWORK-BASED INSIDER THREAT DETECTION FOR ENHANCED CYBERSECURITY
Insider threats have become one of the most critical challenges in modern cybersecurity, as malicious or negligent insiders can exploit their authorized access to compromise sensitive organizational data and systems. Traditional security mechanisms such as rule-based monitoring and signature-based detection are often ineffective in identifying insider attacks because these threats originate from trusted users and may follow normal usage patterns. To address this challenge, advanced artificial intelligence techniques, particularly neural networks, have been explored for detecting anomalous behaviors and potential insider threats in real time.This study presents a Neural Network-Based Insider Threat Detection System designed to enhance cybersecurity by analyzing user behavior patterns within organizational networks. The proposed system utilizes machine learning models to monitor user activities such as login patterns, file access behavior, system usage, and network interactions. By learning normal user behavior, the neural network can identify deviations that may indicate malicious insider activities, including data theft, unauthorized access, or privilege misuse.The system employs data preprocessing, feature extraction, and neural network-based classification to accurately detect suspicious activities. Experimental evaluation demonstrates that the proposed approach improves detection accuracy and reduces false alarms compared to traditional security mechanisms. This intelligent system provides organizations with a proactive solution for protecting sensitive information and strengthening cybersecurity infrastructure.
DR. P. DILEEP KUMAR REDDY , S. SINDHU PRIYA , M. VAMSHI KRISHNA , P. SHIVA PRASAD , G. SRIRAJ REDDY
AUTOMATED BUILDING DAMAGE ASSESSMENT USING FEATURECONCATENATED SIAMESE NEURAL NETWORKS
Building damage assessment is a crucial task in postdisaster management, enabling rapid response and resource allocation. Traditional methods relying on field surveys and manual remote sensing analysis are time-consuming, costly, and often impractical in disaster-stricken areas. To address these limitations, this research proposes an advanced approach utilizing a Feature-Concatenated Siamese Neural Network (FCSNN) for automated building damage classification. The model leverages pre-earthquake and post-earthquake data, implementing a novel concatenation mechanism that enriches feature extraction by aggregating low-, mid-, and high-level representations. This enhancement enables improved discrimination of structural changes, significantly boosting classification performance. Our study conducts three experimental scenarios: (1) binary classification of intact (G1) vs. collapsed (G5) buildings, (2) classification of non-collapsed (G1-G4) vs. collapsed (G5) buildings, and (3) multiclass classification based on the five EMS-98 building damage grades. The proposed FCSNN model achieves F1-scores of 79.47%, 54.09%, and 40.64% for the respective experiments, outperforming conventional Siamese Neural Networks (SNN) and state-of-the-art Support Vector Machine (SVM)-based methods. The model also attains accuracy scores of 87.24%, 95.28%, and 42.57% across the three scenarios, demonstrating its robustness in handling imbalanced datasets and complex damage patterns. The findings validate the effectiveness of feature concatenation in deep learning-based damage assessment, offering an efficient and scalable solution for post-disaster building evaluation. Future enhancements include the integration of voxelbased representations and alternative sub-network architectures to further refine classification accuracy. This study contributes to the advancement of AI-driven disaster response frameworks, facilitating faster and more reliable damage assessments in real-world scenarios.
DR. DABBU MURALI, N. ANUSHA, B. KAVYA, S. AKSHAY KRISHNA, P. SAI KRISHNA
ETHNO-MEDICINAL USES FOR COMMON DISEASES IN MULUGU DISTRICT, TELANGANA
The paper deals with ethno-medicinal use of 30 plant species in Mulugu District, Telangana, and med to focal people do The methods of preparation and doses of adunation of tribal herbalists are mentioned. Uses of these plants hats and who pared with literature on medicinal and economic plants of the common diseases.
ENERGY- AND LATENCY-AWARE OPTIMIZATION OF SERVERLESS PLATFORMS USING REINFORCEMENT LEARNING
Serverless computing has emerged as a promising cloud paradigm that enables on-demand execution of functions without requiring infrastructure management. Despite its scalability and cost-efficiency, serverless platforms often face challenges related to high latency, cold-start delays, and inefficient energy utilization, particularly under dynamic workloads. This paper proposes an Energy- and Latency-Aware Optimization Framework for serverless environments using Reinforcement Learning (RL). The proposed model formulates resource allocation and function scheduling as a sequential decision-making problem, where an intelligent RL agent dynamically adjusts resource provisioning, scaling policies, and workload placement to minimize response time and energy consumption simultaneously. A multi-objective reward function is designed to balance trade-offs between latency reduction and energy efficiency. Experimental evaluation under varying workload intensities demonstrates that the proposed approach significantly reduces average response time and power consumption compared to traditional auto-scaling mechanisms. The framework enhances operational efficiency while maintaining service-level agreements (SLAs), making it suitable for large-scale, energy-conscious cloud deployments
Serverless Computing; Reinforcement Learning; Energy Optimization; Latency Reduction; Resource Allocation; Auto-Scaling; Function-as-a-Service (FaaS); Cloud Performance Optimization; Multi-Objective Optimization
DEEP LEARNING-BASED CONTINUOUS AUTHENTICATION IN ZEROTRUST ENTERPRISE ENVIRONMENTS
The rapid expansion of remote work, cloud services, and distributed enterprise infrastructures has significantly increased cybersecurity risks, rendering traditional perimeter-based security models inadequate. Zero-Trust Architecture (ZTA) has emerged as a modern security paradigm that assumes no implicit trust and continuously verifies user identity and device integrity. However, conventional authentication mechanisms such as passwords and one-time verification methods are insufficient to ensure persistent security throughout a user session. This paper proposes a Deep Learning-Based Continuous Authentication Framework tailored for Zero-Trust enterprise environments. The system leverages behavioral biometrics, including keystroke dynamics, mouse movements, and user interaction patterns, to continuously verify user identity in real time. Advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are employed to model temporal and spatial behavioral patterns. The framework integrates risk-based scoring and adaptive access control policies to dynamically enforce authentication decisions. Experimental evaluation demonstrates improved detection accuracy, reduced false acceptance rates, and minimal user disruption compared to traditional authentication systems. The proposed solution strengthens enterprise security by enabling adaptive, non-intrusive, and real-time identity verification aligned with Zero-Trust principles
Zero-Trust Architecture; Continuous Authentication; Deep Learning; Behavioral Biometrics; Enterprise Security; LSTM; Convolutional Neural Networks; Adaptive Access Control; Cybersecurity; Risk-Based Authentication.