International Journal of Sustainable Intelligent Technologies
International Journal of Sustainable Intelligent Technologies The International Journal of Sustainable Intelligent Technologies is a peer-reviewed, open-access scholarly journal dedicated to advancing research at the intersection of sustainability and intelligent technological innovation. The journal serves as a global platform for researchers, academicians, industry professionals, and practitioners to publish high-quality work that addresses modern challenges through sustainable and intelligent solutions. IJSIT focuses on promoting interdisciplinary research that integrates smart technologies, green engineering, environmental sustainability, digital transformation, and data-driven innovation. By encouraging both theoretical and applied research, the journal supports the development of technologies that enhance efficiency, reduce environmental impact, and contribute to a sustainable future. The journal maintains strict academic standards, ensuring that all submissions undergo a rigorous peer-review process. IJSIT welcomes contributions from around the world and aims to facilitate knowledge exchange among academia, industry, and policy makers. Aim of the Journal The aim of the International Journal of Sustainable Intelligent Technologies is to: Promote high-quality research in sustainable and intelligent technological development. Encourage interdisciplinary collaboration among engineers, computer scientists, environmental researchers, and industry practitioners. Support innovative approaches that contribute to sustainable growth and global technological advancement. Provide a platform for disseminating impactful research that addresses real-world issues through smart and eco-friendly solutions. Bridge the gap between emerging intelligent technologies and sustainability-driven applications. Scope of the Journal IJSIT publishes original research articles, review papers, case studies, and technical reports in (but not limited to) the following areas: Sustainable Technologies Green computing and energy-efficient systems Renewable energy technologies Sustainable manufacturing and smart industry (Industry 4.0) Eco-friendly materials and sustainable engineering practices Intelligent Systems & Automation Artificial Intelligence, Machine Learning, and Deep Learning Intelligent automation and robotics Smart sensors, Internet of Things (IoT), and cyber-physical systems Autonomous systems and intelligent decision-making Smart Infrastructure & Environments Smart cities and urban sustainability Intelligent transportation systems Environmental monitoring technologies Smart grids and energy management systems Data-Driven Innovations Big data analytics for sustainability Predictive modelling and optimization Edge and cloud computing applications Digital twins and simulation technologies Emerging Technologies Blockchain for sustainable development Human–computer interaction for sustainable applications Bio-inspired and nature-based intelligent solutions Sustainable cybersecurity and risk management Journal Details Title International Journal of Sustainable Intelligent Technologies Frequency of Publication Quarterly (January, April, July, October) ISSN 3139-1249 Starting Year 2025 Subject Area Multidisciplinary Subjects Language English Publication Format Online Copyright International Journal of Sustainable Intelligent Technologies Publisher Name D3 Publishers Publisher Address #3-6-7, 2nd Floor, Near Metro Station, Opp: Dmart, LB Nagar, Hyderabad, Telangana – 500074, India. Editor-in-Chief Dr. Kishor Kumar Gajula Phone Number +91 9030161092 Email ID editor@ijsit.org | editorijsitjournal@gmail.com Journal Website https://ijsit.org/ Publisher Website https://d3solutionsindia.com/ Publisher Information Journal Name International Journal of Sustainable Intelligent Technologies Published by D3 Publishers Address #3-6-7, 2nd Floor, Near Metro Station, Opp: Dmart, LB Nagar, Hyderabad, Telangana – 500074, India. Phone +91 9030161092 Email editor@ijsit.org, info@d3solutionsindia.com Publisher Website https://d3solutionsindia.com/ Call for Papers The International Journal of Sustainable Intelligent Technologies invites high-quality original research articles, review papers, and case studies from researchers, academicians, and industry experts across the globe. Manuscripts must present original, unpublished work not under consideration elsewhere. All submissions will undergo a rigorous double-blind peer-review process. Accepted papers will be published online in the upcoming issue. Authors can submit their manuscripts through the journal’s online submission system (once enabled) or by contacting the editorial office via email: editor@ijsit.org.
All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
ENHANCING EV INVERTER PERFORMANCE THROUGH INTEGRATED POWER AND COMMUNICATION ARCHITECTURES
This research aims to enhance the efficiency, dependability, and real-time control of modern electric vehicle (EV) systems by optimizing the performance of EV inverters through the integration of power and communication architectures. The suggested approach integrates embedded communication protocols with modern power electronic configurations to ensure flawless coordination across motor drives, battery management systems, and inverter modules. The technology advances switching processes, eliminates harmonic distortion, and improves thermal management through high-speed data exchange and sophisticated control algorithms. The integration facilitates adaptive functioning under diverse load conditions and predictive diagnostics, leading to enhanced energy efficiency and stable voltage output. The results of the modeling and experiment validate that the proposed architecture markedly enhances overall inverter efficiency, dynamic response, and system scalability, rendering it a feasible choice for next-generation electric car applications.
Copyright (c) 2026 International Journal of Sustainable Intelligent Technologies https://creativecommons.org/licenses/by-nc-nd/4.0
AN OPTIMIZED POWER FACTOR CORRECTION FRAMEWORK FOR INDUSTRIAL MOTOR DRIVES
The research suggests an optimal power factor correction framework for industrial motor drives that will improve energy efficiency, reduce reactive power losses, and adhere to contemporary power quality standards. By integrating intelligent compensation methods, such as active and passive filtering, with advanced management algorithms, the proposed system dynamically modifies the power factor in response to changing load conditions. Real-time monitoring and adaptive algorithms are integrated into the system, which enables it to effectively reduce harmonics and improve voltage stability, thereby enhancing overall performance. The impact of the proposed strategy on the reduction of energy consumption and operational expenses in industrial environments is also evaluated in this study. The enhanced framework is a viable option for the upcoming generation of industrial motor drive applications, as simulation and analytical results suggest that it maintains system reliability and efficiency while achieving a substantial power factor of approximately unity.
PERFORMANCE ANALYSIS OF A SIMPLIFIED MULTILEVEL CONVERTER FOR OFF-GRID SOLAR SYSTEMS
This investigation evaluates the functionality of a simplified multilevel converter for off-grid solar systems, with the objective of delivering a dependable and efficient DC-AC power conversion with a reduced level of circuit complexity. The converter that is recommended utilizes a switch methodology that is more efficient and utilizes fewer components to enhance voltage levels while simultaneously decreasing total harmonic distortion (THD) and switching losses. The system's efficiency, voltage gain, and output waveform quality are evaluated in various solar irradiation and load scenarios. The converter is suitable for independent and remote solar applications, as it generates high-quality power and functions consistently and efficiently without the need for large transformers, as indicated by the simulation findings. The research suggests that the simplified multilevel converter is a cost-effective and efficient alternative for the generation of sustainable off-grid power.
ENHANCING HIGH-VOLTAGE DC–AC CONVERSION THROUGH MODULAR SWITCHED CAPACITOR ARCHITECTURES
This research aims to enhance the efficiency of high-voltage DC-AC conversion through the use of modular switched-capacitor designs. Compared to conventional converter topologies, this method provides a small, scalable, and effective substitute. By methodically stacking several switching capacitor modules, the suggested solution offers a significant voltage gain and higher power density while reducing dependency on massive magnetic components. The architecture creates customizable voltage levels, reduces switching losses, and improves overall efficiency through the use of superior control algorithms. Its versatility, which provides flaw tolerance and simple scalability, makes it beneficial for grid-connected applications, electric vehicles, and renewable energy systems. The study shows how modular switching capacitor converters can enhance the efficiency, dependability, and cost-effectiveness of high-voltage DC–AC power conversion systems.
OPTIMIZATION OF ENERGY TRANSFER IN MULTI-SOURCE EV BATTERIES THROUGH FLEXIBLE CONVERTER TECHNOLOGY
This research is focused on the utilization of flexible converter technology to improve energy transfer in EV battery systems that utilize multiple sources. In order to enhance the longevity and performance of systems that incorporate a variety of energy sources, such as batteries, supercapacitors, and renewable inputs, it is imperative that power management be both effective and efficient. Flexible converters are essential for reducing energy losses and enhancing overall efficiency because they can effectively and adaptively regulate power flow in response to changes in load and operating conditions. In order to ensure that energy is distributed among sources as efficiently as possible, the investigation implements sophisticated control strategies, including real-time monitoring and intelligent switching mechanisms. The proposed method expedites the development of environmentally friendly, high-performance electric vehicle (EV) power systems, while concurrently enhancing system reliability and reducing the strain on individual energy storage components.
ONLINE SHARE TRADING PLATFORMS AND INVESTOR BEHAVIOR AT ANGEL ONE
The investment landscape has been transformed by online share trading platforms, which provide investors with unparalleled access to financial markets and enable rapid transactions. The objective of this investigation is to evaluate Angel One's position in the broader context of the impact of internet trading platforms on investor behavior. Market data, usability, execution speed, and analytical tools comprise the evaluated platform factors that influence investment decisions. The research elucidates the decision-making processes, risk propensity, and trading behaviors of investors. The results suggest that Angel One's digital platform and minimal transaction costs have contributed to an increase in investor engagement and involvement. Additionally, increased accessibility encourages overconfidence and impetuous trading. Investors' responses to online trading are contingent upon their demographics and investment experience. The essay underscores the importance of platform-based strategies in the prevention of risk and the education of investors. The investigation has improved our comprehension of investor behavior and online trading technologies.
A ZERO VOLTAGE SWITCHING METHOD FOR BIDIRECTIONAL DCDC CONVERTERS
This paper discusses a two-way zero-voltage switching (ZVS) technique for DC-DC converters. By reducing switching losses, this approach enhances the efficacy of contemporary power electricity systems. Bidirectional converters facilitate the transmission and reception of power for renewable energy systems, electric vehicles, and energy storage devices. Equipment malfunction, electrical interference, and energy loss are the consequences of conventional abrupt switching. The primary focus of the investigation is on soft-switching zero-voltage switching (ZVS) technology, which reduces component stress and losses without the use of voltage. The Zero Voltage Switching (ZVS) converter's structure, function, and control of electricity transmission in both directions are the subject of this article. The system's dependability, efficacy, and capacity to withstand thermal stress may be enhanced by the proposed approach, as evidenced by the results of simulations and experiments.
Zero-Voltage Switching (ZVS), Bidirectional DC–DC Converter, Soft Switching, Power Electronics, Switching Loss Reduction, Energy Efficiency, Resonant Circuits,
HYBRID DATA DRIVEN WEATHER PREDICTION USING NEAR SURFACE MEASUREMENTS AND ATMOSPHERIC NUMERICAL MODELS
The purpose of this research is to present a hybrid data-driven weather prediction framework that blends atmospheric numerical weather prediction (NWP) models with near-surface observational datasets in order to improve the accuracy and reliability of forecasts. Common problems with traditional numerical models include inaccurate parameterization, poor spatial resolution, and a lack of localized meteorological data. The proposed solution applies state-of-the-art machine learning algorithms to integrate numerical atmospheric model outputs with real-time near-surface meteorological data (such as humidity, temperature, wind speed, and pressure). In order to more accurately portray intricate atmospheric patterns and localized weather variations, the hybrid system combines the strengths of data-driven learning with simulations based on physical principles. This system is able to adapt to new environmental conditions, enhance short-term forecasting, and decrease prediction errors by integrating historical data with model outputs. If you want reliable, practical, and widely applicable weather predictions, the proposed hybrid approach is a great bet. The reason behind this is that numerical atmospheric models, when combined with near-surface measurements, greatly improve prediction capacity, according to experiments.
Hybrid Weather Prediction, Data-Driven Modeling, Near-Surface Measurements, Numerical Weather Prediction (NWP).
CASH MANAGEMENT STRATEGIES IN BANKING SECTOR AT ICICI BANK
This study looks into the currency management tactics used by ICICI Bank, an Indian bank, with a focus on how they optimize liquidity, improve operational efficiency, and reduce risk. It checks the efficiency of the bank's everyday operations by looking at its controls over the flow of money in and out. Several tech-driven solutions are highlighted in the paper. These include digital payment platforms, automated currency forecasting tools, and fundamental banking systems. Solutions for managing liquidity that are both profitable and compliant with regulations are given top priority. The methods adopted by ICICI Bank to control excess funds and cut down on idle cash are also covered in the study. It compares and contrasts the effects of good financial management on happy customers and steady business. Liquidity and currency reserve requirements are examined in relation to RBI rules. Concerns include operational risks and variations in cash demand. Centralized currency management improves visibility and command, according to the study. Improving cash efficiency is another focus of treasury operations, which are discussed in the study. Financial success is enhanced by efficient cash management, according to the results.
Cash Management, Liquidity Optimization, Risk Control, ICICI Bank, Banking Sector, Cash Flow Management, Core Banking Systems, Digital Payment Platforms
B2B BRANDING STRATEGIES WITH REFERENCE TO TATA STEEL
In order to demonstrate how a strong corporate identity can create long-lasting partnerships, technical excellence, sustainability, and customer perception, this study looks at Tata Steel's business-to-business (B2B) branding strategy. The goal is to be competitive in the industrial market. By exhibiting honesty, dependability, transparency, and creativity, the company promotes client-focused solutions, strategic communication, and collaboration in addition to price and product offerings. By implementing digital transformation, value-added services, and sustainability, Tata Steel has gained international partners and institutional clients. Unlike other steel producers, Tata Steel is driven by its brand mission and operational excellence. According to the study, industrial businesses can improve their brand value by managing customer connections, communicating consistently, and fulfilling their social and environmental responsibilities. Long-term, this will weaken B2B clients' loyalty.
B2B Branding, Corporate Identity, Industrial Marketing, Customer Relationship Management (CRM), Brand Trust, Sustainability, Digital Transformation
HRM APPROACHES FOR AGILITY AT ICICI PRUDENTIAL AMC
The financial industry is constantly evolving, and ICICI Prudential AMC must remain agile in order to achieve long-term success. This abstract investigates HRM methodologies with the objective of enhancing organizational agility in the workplace. The objective of strategic talent acquisition is to recruit high-potential, adaptable employees. Continuous learning and digital upskilling enable the workforce to adjust to market and regulatory changes. Agile performance management facilitates the alignment of personal objectives with organizational priorities. Leadership development fosters empowerment and prompt decision-making. Employees who are engaged are more inclined to collaborate and generate novel concepts. Efficiency and adaptability are enhanced by workplace flexibility. HR analytics enable workforce planning that is data-driven. An agile culture that is robust promotes customer focus and resilience.
ICICI Prudential Asset Management Company, Human Resource Management (HRM), Organizational Agility, Talent Acquisition, Digital Upskilling, Agile Performance Management
DIGITAL MARKETING STRATEGIES FOR CUSTOMER RETENTION IN A COMPETITIVE MARKET WITH REFERENCE TO AIRTEL
Airtel recognizes that digital marketing alone is inadequate for establishing enduring relationships with customers. The company may be able to customize its offerings to foster more meaningful exchanges by conducting data analysis to gain a more comprehensive understanding of its customers' preferences. The objective of personalized email marketing, social media engagement, and customer loyalty programs is to establish enduring relationships with consumers. To ensure that products and services are both beneficial and satisfying for the average consumer, it is necessary to take into account customer feedback. The findings indicate that Airtel's digital strategy substantially enhances customer retention rates by emphasizing their requirements. In order to remain competitive and cultivate loyal consumers, Airtel must frequently evaluate and improve its marketing strategies. It is imperative that the organization prioritize the requirements of its consumers and adapt to their evolving requirements.
Airtel, digital marketing, customer engagement, personalization, consumer data analytics, customer retention, loyalty programs, email marketing,
AUTOMATED VIOLENCE RECOGNITION IN SMART CITIES USING ADVANCED DEEP LEARNING TECHNIQUES
An automated framework for violence recognition is introduced in this research. It employs state-of-the-art deep learning algorithms to improve public safety in smart cities using analytics derived from real-time video surveillance. In order to successfully detect violent incidents including riots, physical assaults, and unexpected crowd hostility, the suggested method extracts spatial and temporal information from surveillance footage and merges Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Using attention techniques for improved feature representation and transfer learning with pre-trained architectures, the model successfully differentiates violent events from random human interactions in a variety of illumination, occlusion, and crowd-density scenarios. The computational efficiency is maintained while achieving great accuracy, precision, and recall, as demonstrated experimentally on benchmark datasets. This makes it an ideal candidate for smart city infrastructure that is situated on the edge. This research improves IUSS by developing a proactive, scalable, and automated method for detecting violent incidents; this allows for quicker responses and better crime prevention measures.
Automated Violence Recognition, Smart Cities, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Video Surveillance
CASH CONVERSION CYCLE AND PROFITABILITY IN BANKING WITH REFERENCE TO BANDHAN BANK
The Cash Conversion Cycle (CCC) affects Bandhan Bank's financial performance. This rating is significant since it shows the bank's currency management ability. This ensures effective corporate operations and continuous cash flow. Secondary financial data from the past five years is used to identify inventory management, invoice payment, and cash collection trends. These factors also affect crucial success measures like ROE and ROA. This paper examines cash cycle components and profit margins to show that good cash cycle management can boost financial success. The findings show that Bandhan Bank can improve working capital and cash flow management to boost profitability.
Bandhan Bank, Cash Conversion Cycle (CCC), Working Capital Management, Financial Performance, Profitability Analysis, Return on Equity (ROE)
WORKFORCE ENGAGEMENT WITH REFERENCE TO HERITAGE FOODS
Heritage Foods' efficiency and staff dedication depend on employee involvement. The company prioritizes open communication, continual training, performance-based remuneration, and teamwork, which boosts employee happiness and reduces turnover. Heritage Foods prioritizes employee wellness and leadership involvement to create a healthy workplace. Technology-driven HR systems, thorough feedback mechanisms, and better cross-functional collaboration may benefit even if they diminish involvement. The results show that Heritage Foods needs passionate employees to succeed. A motivated staff boosts output, long-term growth, and customer satisfaction.
Heritage Foods, Employee Engagement, Workforce Productivity, Job Satisfaction, Employee Retention, Human Resource Management (HRM)
REGIONAL RURAL BANKING AND FINANCIAL PERFORMANCE WITH REFERENCE TO IDBI FEDERAL LIFE INSURANCE
Regional rural banks offer loans and other financial services to communities without formal banks. This improves rural development and financial access. Banks and insurance companies like IDBI Federal Life Insurance are intertwined. They struggle to build trust by participating in the community, selling specialized insurance products, and reaching neglected areas. Technology can speed up claims and premium processing, improve efficiency, educate more people about money, and reach more remote people. Additionally, we can reduce risk and improve market share. This symbiotic relationship shows the importance of smart partnerships that generate revenue and help people. Insurance companies thrive in rural locations thanks to financial networks, and insurance products keep rural economies safe.
Regional Rural Banks (RRBs), Financial Inclusion, Rural Development, IDBI Federal Life Insurance, Bancassurance, Insurance Penetration, Rural Outreach
EARLY IDENTIFICATION OF AT RISK STUDENTS USING MACHINE LEARNING FOR GRADUATION PREDICTION
This research employs machine learning to detect at-risk students in order to enhance graduation projections and institutional decisions. The analysis of academic achievement, attendance, demographics, behavior, and engagement data predicts early withdrawal and delayed graduation. Decision trees, logistic regression, random forests, and support vector machines are used to assess the accuracy and prediction of the model in order to identify the optimal fast intervention strategy. In order to retain and graduate students, educational institutions should invest resources in specialized academic aid, counseling, and mentorship after an early assessment of their needs. Traditional student monitoring methods can be repurposed to provide data-driven initiatives that improve learning and student performance.
Student Success Prediction, Higher Education, Machine Learning, Educational Data Mining, Predictive Modeling, Academic Performance, Student Retention, Intelligent Systems.
DISTRIBUTION STRATEGY OF AN ORGANIZATION WITH REFERENCE TO AMUL MILK DIARY
Amul's performance is influenced by its distribution strategy, which ensures that its products are consistently accessible, fresh, and available in a variety of regions. Amul establishes a system to connect with consumers in both urban and rural areas by means of online platforms, wholesale agreements, and direct retail stores. Regional centers and cold storage facilities are critical components of the supply chain that facilitate the efficient distribution of dairy products from production to the end user. By integrating traditional and digital channels to adapt to evolving consumer demands, it is feasible to enhance market penetration and efficiency. An efficient logistics design can result in long-term growth, consumer satisfaction, and brand loyalty, as demonstrated by Amul's distribution network.
Amul, Distribution Strategy, Supply Chain Management, Logistics Efficiency, Cold Chain Management, Market Penetration, Retail Network,
ENHANCING TRUST IN OBESITY PREDICTION USING EXPLAINABLE STACKING MODELS
The accuracy and reliability of obesity prediction are enhanced by the explainable stacking-based ensemble learning approach employed in this study, which integrates machine learning models. For the prevention of obesity, a global health concern, it is necessary to conduct an early and precise risk assessment. Decision trees, SVMs, and logistic regression comprise fundamental learners. Meta-learners enhance their performance by integrating predictions. Ensemble models are elucidated by Explainable AI (XAI). The decision patterns, feature contributions, and model outputs are all described by these methods. Interpretability and predictive power are enhanced by specifying BMI, nutrition, exercise, and lifestyle. The explainable layering model outperforms single classifiers and enhances the confidence and utility of clinical and public health. Experimental results support this assertion.
Obesity Risk Prediction, Ensemble Learning, Interpretable Machine Learning, LIME Explanations, Transparent AI, Healthcare Analytics,
PRIVACY PRESERVING FEDERATED LEARNING WITH INTEGRATED MALICIOUS CLIENT IDENTIFICATION
A federated learning architecture that protects users' privacy and incorporates a malicious client detection system is proposed in this research. The goal of the design is to improve the safety and reliability of collaborative model training in remote settings. Data privacy is protected with federated learning (FL), which allows several clients to train a shared global model without revealing raw data. However, FL is still susceptible to model manipulation, poisoning assaults, and unreliable participation. In order to address these issues, a robust client evaluation module is integrated with safe aggregation and differential privacy techniques. This module uses statistical deviation analysis and adaptive trust score to detect odd updates. The method improves the model's stability, convergence speed, and overall performance by identifying and resolving malicious or faulty client contributions prior to the global model aggregation. Experimental validation proves that the integrated strategy achieves a balance between privacy protection, attack resistance, and model correctness. Its security features make it an ideal choice for decentralized smart system, healthcare, and financial applications.
Federated Learning (FL), Privacy Preservation, Malicious Client Detection, Secure Aggregation, Differential Privacy, Model Poisoning Attacks, Anomaly Detection
STRENGTHENING FINANCIAL CYBERSECURITY THROUGH MACHINE LEARNING BASED THREAT ANALYSIS
In light of the increasing frequency and sophistication of cyberattacks on financial institutions, this initiative seeks to enhance cybersecurity measures by employing threat analysis based on machine learning. The rapid digitization of financial services, internet transactions, and payment systems has rendered financial systems increasingly susceptible to fraud, malware, phishing, and unauthorized access. Machine learning algorithms analyze extensive volumes of transactional and network data to identify anomalies and potential vulnerabilities. This is a sophisticated method for identifying and managing potential risks. Algorithms facilitating the rapid identification of suspicious activity and the monitoring of real-time events encompass random forests, neural networks, support vector machines, and anomaly detection models. Machine learning-based cybersecurity frameworks employ adaptive learning and predictive analytics to enhance threat intelligence, accelerate response times, and fortify the resilience of financial institutions. Cybersecurity systems and cognitive threat analysis collaborate to mitigate risks preemptively, enhance data protection, and ensure the security of online financial transactions.
Financial Cybersecurity, Machine Learning, Threat Detection, Fraud Detection, Anomaly Detection, Digital Banking Security.
EMOTIONAL INTELLIGENCE IN LEADERSHIP EFFECTIVENESS WITH REFERENCE TO AUROBINDO PHARMA
This paper examines the relationship between leadership success and emotional intelligence (EQ) at Aurobindo Pharma, one of the most prominent pharmaceutical companies in the world. The five fundamental competencies of emotional intelligence—self-awareness, self-regulation, motivation, empathy, and social skills—are used to assess organizational success and leadership. Participants in the paper were employed in competitive, fast-paced environments. Those whose leaders demonstrated emotionally intelligent leadership reported higher levels of cooperation, employee satisfaction, and decision-making. These findings underscore the significance of incorporating emotional intelligence development into Aurobindo Pharma's leadership training programs to enhance the company's overall performance.
Emotional Intelligence, Leadership Effectiveness, Self-Awareness, Self-Regulation, Motivation, Empathy, Social Skills, Organizational Performance
ENHANCING MODEL GENERALIZATION IN CROSS PROJECT SOFTWARE DEFECT PREDICTION UNDER IMBALANCED DATA
Strong machine learning frameworks that can detect problematic modules in a target project using data from many source projects, irrespective of data distribution or class balance, are necessary for cross-project software defect prediction (CPDP) using imbalanced data in order to improve model generalization. Over time, CPDP forecasts lose value due to numerous flawed cases. The majority is favored by several models. Data resampling (SMOTE and undersampling), cost-sensitive learning, transfer learning, domain adaptation, advanced techniques, and ensemble methods address this. It enhances model stability and minority class recognition. Distribution and sync alignment promote cross-field knowledge and minimize project discrepancies. This uses imbalance control and generalization to improve memory, F1-score, and AUC. Fault-prediction systems become more reliable and scalable in many software development scenarios.
Cross-Project Fault Prediction, Class Imbalance, Generalization, Software Quality and Machine Learning.
TALENT MANAGEMENT STRATEGIES FOR WORKFORCE AGILITY WITH REFERENCE TO YES BANK
Yes Bank is a model to emulate when it comes to modern banking and the utilization of people management to enhance staff flexibility. Employee objectives are aligned with the bank's objectives. The business's objectives must be in alignment with the capabilities of its employees. With the appropriate resources, employees can now adapt to new technological and governmental norms. Programs that encourage the acquisition of skills and lifelong learning are essential for the attainment of this objective. Performance management systems that are adaptable and leadership development programs that are flexible have fostered a culture of innovation, flexibility, and responsibility, thereby fostering the development of future leaders. Staff retention is one of the advantages that digital HRMSs can provide to organizations. Additionally, employees who participate in internal mobility programs and work rotations are afforded the opportunity to experiment with new responsibilities and roles. The staff of YES BANK will be able to promptly address the needs of the market and customers by implementing these strategies. The bank's long-term success is facilitated by the enhanced adaptability of its customers.
Employee Adaptability, People Management, Yes Bank, Skill Development, Continuous Learning, Leadership Development, Performance Management, Digital HRMS
COMPARATIVE ANALYSIS OF MARKETING STRATEGIES OF VODAFONE VS AIRTEL WITH REFERENCE TO VODAFONE IDEA
This paper compares and contrasts the competitive strategies of Vodafone Idea and Airtel in the Indian telecom market in order to acquire and retain customers. The investigation concentrates on the pricing, promotions, product designs, and distribution strategies of each organization. Airtel has established a reputation for quality network coverage, technology-driven services, and premium positioning. In contrast, Vodafone Idea has prioritized affordable value bundles, emotive advertising campaigns, and youth-oriented branding. Both brands are reliant on digital marketing, celebrity endorsements, and customer engagement platforms to enhance their presence, with consumer perception, brand loyalty, and service quality serving as the emerging critical success criteria. This paper contrasts customer satisfaction levels by analyzing the diverse strategies employed by each company to remain competitive, utilizing data from surveys and published reports. It also evaluates the influence of service innovations and pricing strategies on market share.
Vodafone Idea, Bharti Airtel, Indian Telecom Market, Competitive Strategies, Product Design, Pricing Strategies, Promotional Strategies, Distribution Channels,
SUCCESSION PLANNING FOR FUTURE LEADERSHIP WITH REFERENCE TO HOTEL SWETHA
The complexity of the succession planning process at Hotel Swetha may not be immediately apparent. The organization's future strategy is meticulously deliberated. The hotel has worked assiduously to establish a stellar reputation in the hospitality industry, where satisfied customers and efficient operations are of the utmost importance. Hotel Swetha facilitates the development of prospective leaders through training programs, coaching, and performance evaluations. This all-encompassing approach accomplishes the aforementioned objectives and more by preserving organizational stability, involving employees, and providing the hotel with a competitive advantage. Hotel Swetha acknowledges that proactive leadership development is essential for its long-term success and expansion, and it is dedicated to succession planning.
Succession Planning, Leadership Development, Talent Management, Employee Training, Performance Evaluation, Hospitality Industry, Employee Motivation
BLOCKCHAIN TRANSACTION ANALYSIS FOR MONEY LAUNDERING DETECTION USING MACHINE LEARNING
Money laundering in blockchain transactions is identified through the application of machine learning in this investigation. The complexity of transaction patterns and pseudonymous identities in cryptocurrencies and decentralized financial systems make it more difficult to detect money laundering. Blockchain transaction data is analyzed, money laundering trends are identified, and structural and behavioral aspects are identified using supervised and unsupervised machine learning algorithms in the propose method. Predictive effectiveness is enhanced by network-based data, which includes transaction frequency, wallet connectivity, clustering coefficients, and temporal transaction behavior. The framework enhances detection accuracy and minimizes false positives by employing classification algorithms, anomaly detection, and feature engineering. The study posits that blockchain analytics powered by machine learning can enhance AML safeguards, assist organizations in adhering to regulations, and facilitate the development of decentralized financial systems.
Cryptocurrency, Money Laundering Detection, Machine Learning, Blockchain Analysis, Anomaly Detection, Financial Crime, Transaction Monitoring
CAREER ADVANCEMENT POLICIES AND EMPLOYEE RETENTION WITH REFERENCE TO PUNJAB NATIONAL BANK
Employee retention at Punjab National Bank is affected by employment advancement schemes. Due to rising competition and changing industry norms, banks must have effective job development plans to retain staff and reduce attrition. PNB's career development programs—training, mentoring, promotions, and succession planning—are reviewed alongside employee satisfaction and long-term retention. These restrictions support the bank's growth plan, corporate goals, and employee goals. Structured career development programs have been shown to improve employee retention, work satisfaction, and supervisor loyalty. This drawing emphasizes the importance of investing in human capital to stay competitive in finance.
DECISION MAKING AND GLOBAL WORKFORCE MANAGEMENT WITH REFERENCE TO IDBI BANK
This global paper investigates IDBI Bank's decision-making and personnel management practices to show how intelligent business decisions can boost productivity in the tough financial sector. Data-driven insights, risk assessments, and stakeholder alignment help IDBI Bank make long-term choices. Inclusive policies, talent development, and adaptable leadership are needed to manage a diverse workforce. IDBI Bank integrates flexible work techniques with systematic decision-making to expand globally and motivate and strengthen employees in the ever-changing banking business.
EXPLAINABLE DEEP LEARNING FOR INTELLIGENT GEO DISASTER DETECTION AND RESILIENCE PLANNING
This research employs explainable deep learning to identify clever geo-disasters and plan for resilience. We need data-driven solutions to help identify disasters more quickly and manage them more effectively as the frequency and intensity of earthquakes, landslides, floods, and wildfires increase. In order to identify patterns associated with likely geo-disasters, deep learning algorithms can rapidly examine geographic data from satellite photos, remote sensing systems, environmental sensors, and records of previous disasters. Despite this, decision-makers struggle to comprehend their estimations due to the ambiguity of conventional deep learning models. Explainable deep learning employs techniques for attribute attribution, visualization, and attention to make model predictions more understandable. Explainable deep learning improves early warning systems, resilience planning, and crisis risk assessment by making prediction models more transparent and trustworthy. Lastly, geospatial analytics and explainable AI can assist researchers, governments, and emergency management organizations in making informed decisions that reduce risks, safeguard infrastructure, and improve communities.
Explainable Artificial Intelligence (XAI)