International Journal of Technology, Leadership and Sciences
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SECURE MANAGEMENT OF ENCRYPTED CLOUD DATA USING ROLE-BASED ACCESS CONTROL
For the sake of the confidentiality, availability, and integrity of sensitive information in today's digital environment, secure storage and limited file access are essential. This project proposes the development of a secure file storage system using advanced encryption and role-based access control (RBAC). This solution will provide data protection and limited access. Access to certain resources inside the system is limited to authorized users with defined roles, such Admin, Manager, or Employee, therefore ensuring the safe upload, storage, and retrieval of data online. To safeguard data at rest and avert illegal access without the requisite decryption key, we encrypt all files uploaded to the system using the Advanced Encryption Standard (AES-256). To augment data security, the system communicates information using encrypted HTTPS connections. Role-based access follows the concept of least privilege by granting users just the rights need for their responsibilities. An administrative interface facilitates comprehensive monitoring and control of system use, allowing for the administration of user accounts, role assignments, and audit logs. Timestamps are assigned to all activity, such as logins, file uploads, downloads, and sharing, to improve accountability and traceability. Organizations aiming to protect sensitive data while ensuring operational flexibility and compliance with security standards can employ this system, which combines strong encryption with exact access control, to meet the increasing demand for secure file management solutions in corporate and personal settings.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
SECURE LAND RECORD MANAGEMENT THROUGH ARTIFICIAL INTELLIGENCE AND DECENTRALIZED BLOCKCHAIN ARCHITECTURE
The rapid digitalization of land registration processes has heightened apprehensions over data security, transparency, and fraud detection. Centralized databases used by traditional land registration systems are susceptible to data tampering, illegal access, cyberattacks, and fraudulent activities. To rectify these shortcomings, the developers of this initiative present GREENLAND, an intelligent and secure land registration system using Blockchain, IPFS, ML, and XAI. The proposed system guarantees immutability, transparency, and decentralized governance by securely archiving user data and land transaction records on the Ethereum blockchain via the use of smart contracts. To safeguard vast land-related records and minimize storage requirements, IPFS archives them with their associated hash values documented on the blockchain. A variety of machine learning models are created and assessed on a dataset of real transactions to identify fraud. The models include Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM. LightGBM outperforms its competitors with an accuracy of 99.35%. Utilizing SHAP and LIME to clarify fraud predictions bolsters confidence and improves interpretability for human understanding. A scalable, safe, transparent, and contemporary system for digital land registration and governance is built via the combination of Blockchain, IPFS, machine learning, and explainable artificial intelligence.
REAL-TIME MALWARE DETECTION FOR IOT SYSTEMS USING DEEP LEARNING TECHNIQUES
Security vulnerabilities, especially malware attacks on resource-constrained systems, have arisen due to the development of Internet of Things (IoT) devices. The dynamic characteristics of contemporary threats render conventional malware detection methods sometimes inadequate for ensuring full protection. This research proposes a solution for virus detection in IoT devices using deep learning techniques.The suggested method employs advanced neural network models to independently identify intricate patterns and features from data on network traffic and device activities. The model attains high precision in identifying both established and novel malware variants by the use of architectures like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). The framework is designed to be versatile and adaptive, geared for efficient operation within the constrained processing capabilities of IoT devices.The proposed approach exhibits markedly improved detection accuracy, precision, and a reduced false positive rate compared to traditional machine learning methods, as shown by the experimental results. Moreover, the technology can identify attacks in real-time, hence enhancing the overall security of IoT ecosystems.
PREDICTIVE AGRICULTURAL ANALYTICS FOR CROP HEALTH AND PEST MANAGEMENT USING HYPERSPECTRAL IMAGING TECHNIQUES
AgriVision AI is a sophisticated agricultural platform that employs artificial intelligence, hyperspectral imaging (HSI), and sensor-based analytics to improve crop yield and sustainability. The technology enables accurate monitoring of crop health, soil conditions, and insect problems, aiding contemporary farmers in making educated decisions. The methodology is methodically organized and includes operations such as data loading, preprocessing, model training, hyperspectral analysis, soil suggestion, and plant disease prediction, among others.The technology utilizes Convolutional Neural Networks (CNN) and transfer learning to identify plant illnesses from leaf images. XGBoost facilitates soil-based crop recommendations by using nutrient and climatic attributes. Three-dimensional convolutional neural network models are used for hyperspectral land cover classification. A consolidated dashboard offers explainable AI features that provide confidence evaluations, clarify risks, and suggest remedies.AgriVision AI utilizes a combination of image analysis, soil intelligence, and spectral-spatial data processing to provide a holistic AI-driven solution for precision agriculture and food security, enabling early pest detection, optimizing fertilizer application, reducing chemical waste, and fostering sustainable agricultural practices.
OPTIMIZED FEATURE SELECTION AND MSVM-BASED FRAMEWORK FOR AUTOMATED FAKE NEWS DETECTION
A significant issue in the contemporary interconnected world is the fast dissemination of disinformation and fraudulent material across many internet platforms. Disinformation may sway public perception, hinder democratic functions, and instigate pervasive uncertainty or anxiety. This paper presents an enhanced Multi-class Support Vector Machine (MSVM) classification method, using feature-based improvements to detect fraudulent news, therefore tackling the recognized difficulty. The system thoroughly comprehends incoming material by examining news articles for lexical patterns, syntactic structures, semantic representations, and contextual cues. To improve model efficacy and reduce computing complexity, these properties are refined by sophisticated selection methods. The MSVM classifier is taught to recognize not just binary outcomes but also several categories, including satire, partly true, and deceptive information, hence facilitating more nuanced and precise classifications. This hybrid method enhances detection precision, scalability, and flexibility across many data sources and languages. The system offers strong safeguards against the spread of disinformation via the use of machine learning and optimal feature engineering. It may be used in real-time contexts such as social media, factchecking tools, and news monitoring systems. The suggested solution improves intelligent misinformation detection systems and addresses the shortcomings of current models.
MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE APPLICATIONS IN OBSESSIVE-COMPULSIVE DISORDER: A COMPREHENSIVE REVIEW
Obsessive-Compulsive Disorder (OCD) is characterized by persistent and often debilitating intrusive thoughts and compulsive behaviors. Response rates vary significantly across people, and the trial-and-error approach in prescribing remains a substantial therapeutic challenge, despite the prevalent use of pharmaceutical treatments, particularly SSRIs. Advancements in machine learning (ML) have shown innovative opportunities for improving and personalizing OCD therapy. This research examines pharmacological repurposing, the characterization of adverse effects, and predictive modeling of treatment responses related to OCD medications. Supervised learning systems have shown potential in identifying biomarkers and clinical variables that predict individual responses to SSRIs and adjunctive drugs. Unsupervised learning techniques have facilitated the identification of OCD subgroups that may have varying responses to pharmacological treatments. Data heterogeneity, limited sample sizes, and the need for interpretability persist as obstacles, notwithstanding recent advancements. Machine learning in clinical decision-making may improve precision psychiatry for obsessive-compulsive disorder (OCD), perhaps resulting in safer, more effective, and expedited treatment methods.
INTELLIGENT PET RECOGNITION AND FEEDING SYSTEM USING IMAGE PROCESSING TECHNIQUES
The increasing integration of AI and ML into daily life has created new opportunities for sophisticated pet care solutions. This project presents an automated and customizable pet feeding system using image processing technology. The device employs image recognition algorithms and a camera module to recognize certain dogs and dispense the appropriate amount of food based on their individual nutritional requirements. Particularly in households with several dogs, this prevents food theft or excessive consumption. The system utilizes image processing techniques and machine learning classification models, such as convolutional neural networks, for accurate pet identification. The feeding mechanism engages to mechanically dispense food upon the recognitionand verification of a pet. This contactless approach to pet care enhances hygiene, efficiency, and functionality. We record feeding schedules and behaviors, together with any acquired images, to track pet feeding times. Integrating the system with a web application may improve accessibility. This will enable users to remotely modify feeding schedules, get notifications, and track feeding records. The ideal approach for pet health management and efficient feeding is an intelligent, automated, and tailored system.
ENHANCING RAILWAY SECURITY WITH DEEP LEARNING-BASED PERSON RE-IDENTIFICATION AND TRACKING SYSTEMS
The difficulties in recognizing and monitoring suspicious persons via several surveillance cameras provide a considerable concern for public safety in densely populated transportation systems, such as the Indian Railways. This work introduces a deep learning-based Person Re-Identification (Re-ID) system designed to enhance railway security via the automated identification and recognition of suspects in surveillance video. The suggested system utilizes Convolutional Neural Networks (CNNs) to extract strong characteristics related to body posture and face recognition, hence enhancing identification effectiveness, in contrast to standard methods that depend on manually produced features, which may be susceptible to errors.The system process entails creating a database of questionable individuals, employing convolutional neural network (CNN) models for feature extraction, training machine learning classifiers like Support Vector Machine (SVM) and Random Forest, and ultimately implementing a real-time video surveillance system for railway security personnel. Administrators may promptly react after the assessment of alerts derived from CCTV video. The system provides monitoring modules for both administrative and staff levels, developed using Python and MySQL. The suggested method significantly improves public safety in Indian Railways via automated and scalable intelligent monitoring, as seen by the Random Forest classifier's better accuracy and less false positives.
DEEP FEATURE-BASED IMAGE FORGERY DETECTION USING THE RIFD-NET FRAMEWORK
The authentication of image originality has gained prominence owing to the proliferation of social media and digital photography. Journalism, legal evidence, and cybersecurity are particularly vulnerable to image forgery, which is defined as the alteration or modification of digital photographs. Conventional forgery detection methods sometimes fail against complex editing strategies, exhibiting deficiencies in both resilience and accuracy. In addressing these issues, the authors provide RIFD-NET, a deep learning architecture proficient in properly identifying various forms of picture forgery. RIFD-NET utilizes a multi-branch convolutional neural network design to detect subtle forging artifacts, using feature extraction from both spatial and frequency domains. Attention methods are used to improve detection effectiveness under challenging settings like as compression, noise, and scaling by concentrating on modified areas. The suggested paradigm may successfully generalize several types of forgeries, including copy-move, splicing, and object removal attacks. RIFD-NET surpasses current leading forgery detection systems in accuracy, precision, recall, and robustness, as shown by experimental assessments on benchmark datasets. Empirical evidence from ablation experiments confirms the effectiveness of attention and hybrid feature extraction modules. Moreover, RIFD-NET demonstrates computational efficiency appropriate for real-time applications and indicates considerable generalizability for novel forging strategies. The suggested architecture provides a strong, scalable, and dependable method for verifying digital media authenticity and conducting picture forensic analysis.
PREDICTIVE CYBER ATTACK DETECTION IN CLOUD COMPUTING USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
The scalability, adaptability, and cost-effectiveness of cloud computing have become it an indispensable element of contemporary digital infrastructure. The system's decentralized architecture renders it susceptible to data breaches, insider threats, zero-day vulnerabilities, distributed Denial of Service (DDoS) assaults, and several other cyber threats. Traditional intrusion detection systems struggle to identify novel and new threats in real time. This study introduces a novel approach for identifying cyber assaults in cloud systems via the use of machine learning techniques. The approach utilizes a hybrid deep learning architecture that integrates CNN and LSTM networks. The proposed method effectively identifies harmful activities by scrutinizing extensive network data, hence uncovering previously unrecognized attack patterns. Incorporating Explainable Artificial Intelligence (XAI) methodologies to elucidate model decisions improves transparency and fosters confidence. The proposed method provides a comprehensive and perceptive resolution to the issue of cloud computing security, as shown by experimental data demonstrating improved performance in recall, accuracy, precision, and false positive rate.
BLOCKCHAIN-DRIVEN FRAMEWORK FOR SECURING ELECTRONIC HEALTH RECORDS
Academic research indicates an urgent need for safe, tamper-proof storage of sensitive medical information due to the rapid digitalization of healthcare data. Traditional systems are susceptible to both internal and external assaults because of their dependence on centralized servers. SEC-HEALTH implements a system for the secure storage of electronic health records (EHRs) by combining the immutable, distributed ledger technology of blockchain with the InterPlanetary File System (IPFS).This system use Solidity smart contracts to archive patient data and transaction records on the Ethereum blockchain. Comprehensive EHR files are preserved on IPFS and may be accessed via their blockchain hash addresses. The architecture guarantees data integrity, transparency, and safe access independent of trusted third parties.User modules include appointment scheduling, prescription management, patient and physician authentication, and platform registration. The graphics illustrate a fully operational web interface created in Python, implemented smart contracts, and the integration of blockchain with IPFS. The approach is resilient and decentralized, providing an alternative to traditional centralized health data management systems.
ATTENTION-AUGMENTED DEEP LEARNING FOR ACCURATE PROPERTY PRICE PREDICTION AND MARKET ANALYSIS
rdfEstimating the value of property values is complicated by several aspects, including property photos, numeric features, and verbal descriptions. Conventional models often depend on single-modality data to elucidate intricate interactions among several parameters, so limiting their efficacy. This paper presents a deep learning model that incorporates a self-attention mechanism to analyze heterogeneous data for predicting future housing prices. The system utilizes specialized encoders to amalgamate structured data, image data, and textual information. These encoders include MLP for tabular data, CNN for pictures, and transformer-based models for textual information. A widespread latent representation is formed by synthesizing various attributes to facilitate thorough learning. Themodel adeptly captures complex interdependencies and improves prediction accuracy using a joint self-attention mechanism that dynamically assigns priority weights to input inside and across modalities. For an astute and dependable strategy to evaluate real estate prices, try the suggested multimodal approach. It exceeds both conventional and single-modality models in terms of accuracy, robustness, and interpretability.
AN EXPLAINABLE AI FRAMEWORK FOR NETWORK INTRUSION DETECTION USING MULTI-LAYER PERCEPTRONS
Intrusion detection systems (IDS) are critical for protecting network infrastructure from nefarious activity. The growing use of machine learning models, especially Multi-Layer Perceptrons (MLPs) in intrusion detection systems (IDS), highlights the essential need of transparency and interpretability. This work seeks to improve the interpretability of MLP-based IDS models by investigating the incorporation of two Explainable Artificial Intelligence (XAI) methodologies: LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). We evaluate LIME and SHAP using conventional IDS datasets to ascertain their effectiveness in clarifying model predictions in an understandable way for individuals. To improve the reliability and accountability of AI-driven security systems, our findings suggest that both techniques provide significant insights into model decision-making. The results illustrate the practical use of explainable artificial intelligence (XAI) approaches to improve the visibility and interpretability of black-box neural network models for cybersecurity experts.
AN AI-ENABLED DIAGNOSTIC SYSTEM FOR DIABETIC EYE DISEASE DETECTION USING DEEP NEURAL NETWORKS
Timely detection of diabetic retinopathy and other diabetic ocular conditions is crucial, since they significantly contribute to worldwide visual impairment. This research introduces DeepDiabetic, a method using deep neural networks to independently identify diabetic eye problems in retinal pictures. DeepDiabetic employs sophisticated convolutional neural network (CNN) architectures to rapidly and accurately categorize and identify disease stages via the analysis of fundus pictures. The study findings demonstrate that the system proficiently differentiates diabetic eye diseases, possibly expediting patient help and enhancing overall outcomes. The suggested method provides a scalable, non-invasive, and economical tool to aid ophthalmologists in clinical decision-making and the implementation of comprehensive diabetic eye screening programs.
A MACHINE LEARNING APPROACH TO STOCK MARKET PREDICTION BASED ON MULTI-SOURCE INFORMATION
Multiple interrelated elements hinder precise stock market predictions. Conventional machine learning models may insufficiently represent the intricacies of financial markets since they rely on data from a single source and presume exact instance-level classification. Our research presents an innovative method for stock market forecasting called Multi-Source Multiple Instance Learning (MS-MIL). This methodology allows the model to integrate information from many sources, including historical stock prices, financial news, social media sentiment, and macroeconomic indicators, all organized into aggregated data instances (bags). The model adeptly tackles restricted supervision and intrinsic uncertainty in financial data by consolidating each source into separate sets of examples and using a multiple instance learning approach. Our MS-MIL system employs sophisticated feature extraction and attention techniques to combine numerical and textual data for the training of discriminative representations. The experimental results demonstrate that the suggested strategy exceeds traditional learning models in forecasting stock movements and market trends. This technique exhibits promise as a tool for analysts and investors to make informed judgments owing to its superior resilience, flexibility, and interpretability.
A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION
Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to provide safe, transparent, and privacy-conscious fraud detection. The solution utilizes federated learning and differential privacy methods to train machine learning models without revealing raw user data, while using blockchain's decentralized framework to guarantee data immutability and reliability. A dynamic incentive framework using smart contracts further motivates users to provide detection-ready, high-quality data. The suggested method promotes cooperation among entities, protects user data security, and achieves enhanced fraud detection accuracy via the integration of privacy-preserving computing and decentralized trust. Experimental assessments on both simulated and actual financial datasets illustrate the system's precision, robustness, and scalability in detecting intricate and evolving fraud patterns.
INTELLIGENT AND HYBRID AI-BASED FAULT DETECTION FRAMEWORK FOR REAL-TIME MONITORING IN INVERTERDOMINATED MICROGRIDS
The evolution of microgrids toward renewable energy integration has led to increased deployment of inverterdominated and low-inertia systems. Traditional fault detection mechanisms, tailored for conventional grids, struggle under these new dynamics. This paper proposes an intelligent and hybrid AI-based framework incorporating Wavelet Transform and Convolutional Neural Networks (WT-CNN) alongside Support Vector Machine–Fuzzy Logic (SVM-FL) models. The framework aims to achieve high-speed, adaptive, and accurate fault detection and classification in real time. Implemented using MATLAB/Simulink and tested under varying operating conditions, the model demonstrates superior performance in detection latency, classification accuracy, and robustness against system disturbances.
A STUDY ON AI IN RESUME SCREENING AND RECRUITMENT BENEFITS AT INFOSYS
Artificial intelligence is revolutionizing the workforce by streamlining and complicating the employment process. HR workers can save a lot of time by not having to go through as many applications because AI can efficiently evaluate and find the most eligible individuals. When compared to people, it is often more accurate in determining an applicant's knowledge, abilities, and fit for the position. In order to ensure that individuals from diverse backgrounds have equal opportunities, AI works to reduce bias. AI can locate competent candidates in a matter of minutes, greatly reducing the workload of recruiters. You may learn a lot about a candidate's potential and cultural fit with a company using predictive technology. This effectiveness shortens the hiring process and saves money by making better use of repetitive administrative tasks. All levels of management can benefit from having access to transparent, data-driven insights when making hiring decisions. Companies and candidates alike are finding the hiring process to be more engaging, transparent, and expedited thanks to AI.
A STUDY ON CORPORATE SOCIAL RESPONSIBILITY AS INVESTOR ATTRACTION WITH REFERENCE TO DABUR INDIA
Corporate social responsibility (CSR) and its impact on investor interest is the focus of this research. It zeroes down on Dabur India Ltd., a famous Indian fast-moving consumer goods company known for its dedication to eco-friendly operations. A company's reputation, risk profile, and long-term financial value are all affected by its CSR initiatives, especially since investors increasingly consider ESG factors when making investment decisions. To examine the effects of socially responsible company practices on stakeholder trust, investor confidence, and investment flows, this case research uses Dabur's CSR strategy, disclosures, and success as a case research. The research highlights the growing importance of corporate social responsibility as a competitive differentiator and market attractiveness factor for Indian companies.
ENHANCING CUSTOMER RETENTION THROUGH AI-ENABLED CRM SOLUTIONS A MARKETING ANALYTICS PAPER OF GENPACT
This paper delves at the ways in which Genpact, an AI-driven CRM platform, may maximize client retention through the application of advanced marketing analytics. This paper looks at how customer relationship management systems can use artificial intelligence, machine learning, and predictive analytics to better understand customer tastes and habits. Businesses can instantly analyze massive volumes of structured and unstructured customer data with the help of AI-powered CRM solutions. Customer journey visualization, sentiment suggestions, and sentiment analysis all contribute to higher engagement, according to the paper. By identifying potentially vulnerable customers, predictive models pave the way for proactive retention strategies. The importance of making decisions based on data in building long-term relationships with customers is emphasized by the research. The use of chatbots and intelligent procedures automates client contacts, which improves service quality and response speed. The ability of AI to conduct segmentation analyses and deliver tailored marketing is the focus of this research. If demand projections are accurate, customer satisfaction goes up. The findings show that customer relationship management systems with AI capabilities allow for consistent and personalized interactions with clients across different platforms. In order to improve marketing strategies, the paper highlights the significance of insights produced by analytics.
AI CHATBOTS FOR HR SERVICE DELIVERY AT HCL TECHNOLOGIES
The way businesses handle employee contacts and optimize HR procedures has changed as a result of the incorporation of AI chatbots into HR service delivery. AI-powered chatbots have been used by HCL Technologies to improve HR productivity by instantly responding to employee inquiries about payroll, leave administration, hiring, and policy clarifications. These chatbots guarantee precise and consistent information distribution throughout the company, lessen manual labor, and speed up response times. HCL's AI chatbots leverage machine learning and natural language processing to adapt to user behavior and provide context-aware and customized solutions. Additionally, the solution increases overall productivity and employee happiness by allowing HR personnel to concentrate on strategic goals rather than tedious activities. This paper outlines the advantages, difficulties, and potential applications of AI chatbots in HCL Technologies' HR service delivery.
FUTURE OF VIRTUAL BANKING MODELS AT KOTAK MAHINDRA BANK
This research assesses the influence of digital transformation on traditional banking practices and considers the future of virtual banking models, with a focus on Kotak Mahindra Bank. It looks into the bank's innovative use of technology to provide financial services that are both user-friendly and customer-centric. The research shows how combining blockchain, AI, and advanced analytics may improve operational security and efficacy. Furthermore, the analysis identifies emerging trends including API-based ecosystems and open banking. The essay discusses cybersecurity issues, the importance of regulatory compliance, and a lack of technological understanding. It is underlined that virtual banking has the ability to reduce expenses while also reaching people who do not have access to traditional financial services. Businesses use market research and customer feedback to forecast how consumers will use a product. There are strategic guidelines for expanding digital services while remaining ecologically friendly. Overall, the research demonstrates the importance of Kotak Mahindra Bank in shaping the future of internet banking.
WORKPLACE WELLNESS AND EMPLOYEE SATISFACTION AT LANCO
This research looks into the relationship between employee happiness and workplace wellness programs at LANCO, a well-known organization that operates in a competitive, fast-paced business environment. It analyzes the impact of mental, emotional, and physical well-being initiatives on worker motivation, morale, and overall workplace satisfaction. The inquiry looks into key aspects of wellbeing, such as stress management, organizational support systems, work-life balance, and health and safety protocols. It analyzes the effect of workplace wellness initiatives on employee dedication and output, as well as their emotional reactions to them. According to the research, people may experience job satisfaction when they work in an environment that promotes a positive work culture and supportive leadership. It also looks into how much work can harm employees' health. The research clearly demonstrates a link between successful wellness efforts and lower absenteeism and turnover. Research shows that employees who feel appreciated and supported are more devoted and engaged. Furthermore, the importance of wellness awareness programs and communication is frequently discussed. Furthermore, the data shows that firms that prioritize health improve productivity and workplace culture.
SIMULATION-BASED TRAINING FOR DECISION AT MAKING SKILLS AT INFOSYS
Simulation-based training has proven to be an effective method for developing decision-making skills in fastpaced, technologically advanced contexts. Infosys is increasingly incorporating simulation-based learning frameworks into its professional development activities. These frameworks provide employees with safe, authentic work environments that closely mirror actual business settings. This strategy enables students to improve their critical thinking skills, examine their options, and negotiate challenging situations when they are unsure about their next moves. Simulation-based training helps students shift from theoretical knowledge to practical application by incorporating experiential learning, data-driven feedback, and practice that builds on itself. The research found that structured simulation modules improve participants' ability to make strategic decisions, interact successfully, and think creatively. The findings show that simulation-based training improves organizational performance significantly by enhancing decision-making consistency, precision, and confidence. According to the research, simulation-driven learning ecosystems can be a scalable and effective framework for improving the decision-making capabilities of Infosys' current workforce.
DIGITAL TRANSFORMATION IN B2B MARKETING AT WIPRO
This research examines the influence of digital transformation on B2B marketing, with a focus on Wipro. It underlines how innovative digital technologies are transforming the enterprise services sector, changing client interaction, marketing strategies, and value creation. The research investigates Wipro's use of data analytics, artificial intelligence, automation, cloud platforms, and digital ecosystems to provide personalized, insightdriven, and relationship-focused B2B marketing. It emphasizes the shift from transaction-focused traditional marketing to collaborative digital experiences that improve customer journeys, better manage prospects, and build stronger, long-term client connections. The research highlights the competitive advantage, scalability, and adaptability that Wipro's digital marketing tactics provide in worldwide B2B markets. It also addresses issues related to data integration, organizational change, and talent development.
RECOGNITION PROGRAMS AND JOB FULFILLMENT AT FEDERAL BANK
The research on Federal Bank's Recognition Programs and job Fulfillment examines the relationship between the organization's overall job contentment and employee recognition programs. Recognition programs are critical for improving the working environment, inspiring employees to put up more effort, and raising output. Structured recognition systems, such as performance awards, appreciation gatherings, and incentive programs, are critical for Federal Bank to keep its staff happy and engaged. According to the report, acknowledging achievement and hard work improves employee engagement, reduces turnover, and boosts dedication to the firm. Employee research reveals that when employees receive continuous praise, they feel a better feeling of fulfillment and belonging at work. The research also shows that transparent and equal recognition procedures promote collaboration and ensure that all stakeholders' aims are aligned with those of the bank. Finally, it is established that Federal Bank needs well-designed recognition programs to ensure long-term performance and staff happiness.
FINANCIAL LITERACY AND BANKING HABITS AT IDFC FIRST BANK
An individual's level of financial literacy has a major impact on their financial behavior and decisions. The purpose of this project is to determine the level of financial literacy among consumers and how it effects their banking activities, with a focus on IDFC FIRST Bank. The poll assesses key factors such as familiarity with banking products, awareness of digital banking, understanding of savings and investment possibilities, and smart financial practices. Structured questionnaires were used to collect information from a wide group of IDFC FIRST Bank customers. The statistics show a link between financial literacy and responsible banking practices, such as making sound investment selections, repaying loans on time, using digital banking services, and saving consistently. The findings show that people with a higher level of financial literacy are better at managing their finances, using banking services more effectively, and protecting their overall financial well-being. The research underlines the need of ongoing customer education initiatives for financial institutions in cultivating a financially responsible society, increasing customer happiness, and promoting educated financial decisions.
DEBT RECOVERY TRIBUNALS AND STRENGTHENING BANKING AT SBI LIFE INSURANCE
This article examines the potential of Debt Recovery Tribunals (DRTs) to strengthen the banking industry, using SBI Life Insurance as a case study. The smooth operation of lending institutions, as well as the preservation of economic stability, rely heavily on debt collection. DRTs were meant to improve the financial stability of banks and other financial institutions, reduce the number of non-performing assets (NPAs), and provide a quick and easy way to recover bad loans. The study looks into how successful debt collection tactics can help SBI Life Insurance improve asset management, increase liquidity, and develop customer trust, all of which can contribute to the company's long-term growth. This paper examines legislative frameworks, operational efficacy, and recovery results to demonstrate the crucial role of DRTs in strengthening the financial ecosystem and increasing confidence in the banking and insurance sectors.
DEBT FINANCING ALTERNATIVES FOR INFRASTRUCTURE GROWTH AT LARSEN AND TOUBRO LIMITED
This research looks into the debt financing options available to Larsen & Toubro Limited (L&T) in order to speed up infrastructure development in India and other regions. In light of the growing financial requirements for large projects, the research assesses both traditional and hybrid loan instruments. External commercial borrowings (ECBs), infrastructure debt funds, corporate bonds, project financing loans, syndicated bank facilities, and green/social bonds fall under this category. Furthermore, it studies structured solutions such as asset-backed project financing, receivables securitization, and mezzanine financing. Using a risk-return analysis, the abstract shows how each instrument affects L&T's balance-sheet leverage, cashflow matching, cost of capital, covenant exposure, and repayment tenor. The research also considers regulatory and market constraints, such as the depth of the capital market, banks' willingness to lend, investor preferences, and interest rate and currency risk. The strategic importance of public-private partnership (PPP) frameworks in the transfer of project risk is also discussed. According to the paper, the most effective funding strategy for L&T would include a mix of long-term, low-cost institutional debt (such as bonds and infrastructure debt funds) for critical revenue-generating assets, project/syndicated financing for greenfield projects, and targeted mezzanine capital to protect equity appreciation. Finally, the paper recommends implementing governance techniques, risk-mitigation strategies (such as ring-fencing SPVs and hedging), and investor communication tactics to lower financing costs and promote the acquisition of sustainability financing.
CAREER DEVELOPMENT INITIATIVES AND STRATEGIC ALIGNMENT AT MAHINDRA & MAHINDRA
The goal of this investigation is to find out how well Mahindra & Mahindra's professional growth programs match up with the company's long-term goals. It shows how succession planning, well-organized training programs, and chances to help others learn can all help employees stay engaged, improve their skills, and stay with the company. According to the study, it's important to make sure that professional development plans are in line with the long-term goals of the organization. This is because matching a person's growth path with the goals of the organization improves both their own performance and the results of the business. Mahindra & Mahindra needs to align its job development programs on purpose if it wants to keep its employees competitive and improve its overall performance, according to the results.