Wavelet transforms provide simultaneous time–frequency localisation through a mathematically rigorous multi-resolution framework, yet their classical formulations fix filter coefficients independently of any learning objective. This paper makes three original contributions. First, we provide a unified mathematical treatment of learnable wavelet decomposition, establishing precise conditions under which trainable filters retain or forfeit perfect reconstruction guarantees. Second, we derive a gradient-based layer importance metric that quantifies which frequency bands drive model decisions, and demonstrate its application to physiological signal classification with reproducible experimental details. Third, we show that the multi-resolution signal decomposition principle underlying wavelets can serve as a structural prior for governing equation discovery in complex network dynamics, creating an explicit bridge between classical wavelet theory and modern neural symbolic regression. Worked examples on the ECGFiveDays benchmark and SIS epidemic dynamics illustrate the unified framework.
Educational Data Mining (EDM) is used to extract the important information from educational data. EDM identifies the trends from educational data to enhance the student academic performance. EDM uses the machine learning conceptsto recognize the learning, to improve teaching and to optimize the educational systems. Mental health issues are prevalent among students. Depression has significant obstacle for performing the long-term learning in educational system. Student dropout prediction is an important event for educational institutions and policymakers around world. Early student academic performance prediction is an essential research topic in educational data mining. Different deep learning and artificial intelligence methods are introduced to forecast the student academic performance. However, the existing prediction techniques failed to handle the student mental health and their mood changes. In order to address the existing issues, different artificial intelligence and deep learning methods is introduced for student academic performance classification based on mental health.
Employee motivation plays a critical role in improving individual performance and achieving organizational success. Organizations that effectively motivate their employees are more likely to experience higher productivity, stronger commitment, improved work quality, and sustainable competitive advantage. This study examines the impact of employee motivation on organizational performance by exploring how motivational practices influence employees' contribution to organizational goals. The research considers both intrinsic and extrinsic motivational factors, including recognition, career development opportunities, financial rewards, job security, supportive leadership, and a positive work environment. An empirical research design was adopted using a structured questionnaire to collect primary data from employees working in different organizations. The collected data were analysed using appropriate statistical techniques to evaluate the relationship between employee motivation and organizational performance. The findings indicate that higher levels of employee motivation are associated with improved organizational performance through enhanced productivity, greater employee commitment, better teamwork, and increased work efficiency. The study further reveals that organizations investing in effective motivational strategies are better positioned to achieve operational excellence and retain a committed workforce. The findings emphasize that employee motivation should be considered a strategic organizational priority rather than a short-term managerial practice. The study offers practical insights for managers and human resource professionals in developing motivation strategies that support employee performance while contributing to long-term organizational growth and sustainability.
Performance appraisal remains one of the most consequential, and most contested, tools of human resource management, shaping how organisations recognise contribution, allocate rewards, and develop talent (DeNisi & Murphy, 2017). This study examines the effectiveness of the performance appraisal system at R L Fine Chem Pvt. Ltd., a Bengaluru-headquartered manufacturer of active pharmaceutical ingredients (APIs) and psychotropic substances operating since the mid-1980s, with manufacturing facilities in Gauribidanur (Karnataka) and Hindupur (Andhra Pradesh) that hold USFDA, PMDA, and WHO-GMP approvals. Despite the organisation's regulatory maturity, limited independent research exists on how its workforce perceives the fairness, transparency, and developmental value of its internal appraisal process, a gap that mirrors a broader shortage of appraisal research in Indian process-manufacturing and API firms relative to banking, IT, and retail (Kumari et al., 2021; Verma & Sharma, 2016). Adopting a descriptive, quantitative research design, the study surveyed 124 employees selected through stratified random sampling across departments, using a structured questionnaire built on a five-point Likert scale and validated for reliability using Cronbach's alpha. The instrument captured employee understanding of appraisal criteria, perceived fairness and transparency, feedback quality, and the link between appraisal outcomes and motivation, promotion, and training decisions. Statistical analysis included descriptive statistics, reliability analysis, Pearson correlation, multiple regression, one-way ANOVA, and chi-square tests of association, with results reported against five formally stated hypotheses. The paper presents the complete research instrument, the collected data, and the statistical analysis results. The findings clarify the relationship between appraisal effectiveness and employee satisfaction and performance, and offer recommendations on appraisal transparency, rater training, and the integration of appraisal outcomes with career development pathways at R L Fine Chem Pvt. Ltd.
The tractor industry plays an important role in improving agricultural productivity by providing efficient and modern farming solutions. Among the leading tractor manufacturers in India, Mahindra Tractors has established a strong reputation for quality, durability, fuel efficiency, and technological advancement. Customer satisfaction has become a critical factor in determining the long-term success of tractor manufacturers because satisfied customers are more likely to remain loyal, recommend the brand to others, and continue purchasing the company's products. The present study examines customer satisfaction towards Mahindra Tractors. The research focuses on evaluating customer opinions regarding tractor performance, fuel efficiency, durability, pricing, after-sales service, spare parts availability, dealer support, and overall brand image. A descriptive research design was adopted for the study. Primary data were collected from 100 Mahindra tractor customers using a structured questionnaire, while secondary data were collected from books, journals, company reports, and reliable online sources. The collected data were analysed using percentage analysis and appropriate statistical techniques to identify the major factors influencing customer satisfaction. The study found that product quality, engine performance, after-sales service, and dealer support significantly influence customer satisfaction. The research concludes with practical recommendations to improve customer experience, strengthen customer loyalty, and enhance Mahindra's competitive position in the agricultural machinery market.
Financial performance analysis plays a vital role in assessing the operational efficiency, profitability, liquidity, and long-term financial stability of manufacturing enterprises. In today's highly competitive business environment, manufacturing organizations face increasing challenges arising from fluctuating raw material costs, technological advancements, changing customer preferences, and intense market competition. These challenges necessitate systematic financial evaluation to support strategic decision-making and ensure sustainable organizational growth. The present study examines the financial performance and profitability of a selected manufacturing enterprise over a period of three financial years (2022–23 to 2024–25). The study employs descriptive research methodology and utilizes both primary and secondary data collected from financial statements, company records, and relevant literature. Various financial tools including ratio analysis, comparative analysis, trend analysis, and working capital analysis are used to evaluate the financial health of the enterprise. The analysis indicates that the enterprise maintained a satisfactory liquidity position throughout the study period, as reflected by stable current and quick ratios. Profitability indicators demonstrate consistent growth in earnings, while solvency ratios reveal a gradual reduction in dependence on external borrowings and an improvement in shareholders' funds. Working capital management also showed positive improvement, enabling smooth operational activities and efficient utilization of resources. Overall, the findings suggest that effective financial planning, prudent cost management, and systematic financial monitoring contributed significantly to the enterprise's financial stability and profitability. The study concludes that continuous financial performance evaluation provides valuable information for management, investors, creditors, and other stakeholders in making informed financial decisions. The findings also highlight the importance of maintaining an appropriate balance between liquidity, profitability, and solvency to achieve long-term sustainability and competitive advantage in the manufacturing sector.
Digital marketing has become an essential component of modern business strategy, enabling organizations to reach customers effectively through online platforms and digital communication channels. In the highly competitive courier and logistics industry, companies are increasingly adopting digital marketing strategies to enhance brand visibility, improve customer engagement, and strengthen their market position. This study examines the digital marketing strategies adopted by DTDC Courier and Logistics Ltd. and evaluates their impact on customer awareness, engagement, and satisfaction. The study employs a descriptive and analytical research design based on a quantitative approach. Primary data were collected from 100 respondents through a structured questionnaire, while secondary data were obtained from company reports, websites, journals, books, and other relevant sources. Statistical tools such as percentage analysis, descriptive statistics, reliability analysis, correlation analysis, and regression analysis were used to analyze the collected data. The findings reveal that DTDC effectively utilizes digital marketing channels such as social media platforms, online advertisements, email marketing, mobile applications, and website services to connect with customers. The results indicate a strong positive relationship between digital marketing strategies and customer engagement. Correlation and regression analyses confirm that digital marketing initiatives significantly influence customer awareness, satisfaction, and preference for DTDC services. The study also highlights the importance of maintaining an active digital presence to enhance customer experience and improve business performance. The study concludes that digital marketing strategies play a vital role in strengthening DTDC's customer relationships and competitive advantage in the courier and logistics sector. The findings may help DTDC and similar organizations improve their digital marketing practices and develop more effective customer engagement strategies in the future.
Recruitment and selection are strategic human resource activities which directly influence the organization’s ability to attract competent employees and to achieve sustainable performance. Furthermore, as the demand for skilled workers increases, it is important to apply suitable and effective methods of recruitment and selection, as well as the process itself. This research will be aimed at examining the efficiency of recruitment and selection practices in the company. The objectives of this research are to find out the methods of attracting workers, the factors influencing the selection process and employees opinions on recruitment and selection. This study will adopt the descriptive research design which involes the collection and analysis of primary and secondary data. The first one refers to the information collected by the researcher. The second one means data discovered by the researcher. To achieve the objectives a questionnaire will be distributed among the employees to measure their satisfaction, experiences and opinions on the subject. Also, journal articles and books will be reviewed to identify relevant information and support the research. The collected data will be analyzed using statistical methods, which will ensure the validity of thr results and the ability to make appropriate conclusions. The information gathered is expected to provide useful suggestions and recommendations regarding recruitment and selection of the company.
Marketing innovation has become one of the most important factors influencing business growth in today's competitive irrigation equipment industry. Companies that adopt innovative marketing approaches are better positioned to reach customers, build brand awareness, and improve customer satisfaction. This study examines the role of marketing innovation in enhancing the growth and performance of Kushi Irrigation. It focuses on how modern marketing practices such as digital marketing, promotional campaigns, customer relationship management, product positioning, and after-sales service contribute to business development. A descriptive research design was adopted for this study. Primary data were collected from customers through a structured questionnaire, while secondary data were gathered from company records, books, journals, and published reports. The collected information was analysed using percentage analysis and appropriate statistical techniques to understand customer opinions and evaluate the effectiveness of the company's marketing initiatives. The findings indicate that innovative marketing strategies have positively influenced customer awareness, customer satisfaction, sales growth, and the overall market performance of Kushi Irrigation. The study also identifies opportunities for strengthening digital marketing activities, expanding promotional efforts, and improving customer engagement to sustain long-term growth. The study concludes that marketing innovation is an essential driver of business success in the irrigation equipment industry. Continuous investment in customer-focused marketing practices and modern communication channels will help Kushi Irrigation strengthen its competitive position and achieve sustainable growth.
Promotional strategies play a vital role in enhancing brand awareness, attracting potential customers, and influencing consumer purchase decisions in today's highly competitive apparel industry. With the increasing adoption of digital marketing, social media platforms, sales promotions, advertising, and public relations activities, organizations continuously seek innovative promotional techniques to strengthen their market position and improve customer engagement. The effectiveness of these promotional strategies significantly determines customer perception, purchase intention, and overall business performance. This study examines the effectiveness of promotional strategies adopted by Venkateshwara Clothing Company and evaluates their impact on consumer buying behaviour and organizational performance. The research aims to identify the promotional tools preferred by consumers, assess their influence on purchasing decisions, and measure the overall effectiveness of the company's promotional activities. A descriptive research design was employed for the study. Primary data were collected from 100 respondents using a structured questionnaire, while secondary data were gathered from books, research journals, company reports, websites, and other reliable sources. The collected data were analysed using percentage analysis, frequency distribution, correlation analysis, and the Chi-square test to identify the relationship between promotional strategies and consumer purchase behaviour. The findings indicate that digital marketing, social media promotions, advertising, and sales promotion activities have a significant positive influence on customer awareness and purchase decisions. The study also reveals that attractive promotional campaigns enhance customer satisfaction, strengthen brand loyalty, and contribute to increased sales performance. Based on the findings, the study recommends that Venkateshwara Clothing Company should further strengthen its digital marketing initiatives, improve customer engagement through personalized promotional campaigns, and adopt innovative promotional strategies to maintain a sustainable competitive advantage. The study contributes to the existing literature on marketing promotion strategies and provides practical insights for managers, researchers, and marketing professionals in developing more effective promotional programmes.
In today's competitive market, customer perception, service quality, and customer satisfaction have become critical determinants of business success and long-term customer loyalty. This study aims to examine the relationship between customer perception, service quality, and customer satisfaction in the pre-owned bike industry. The research explores the influence of factors like product quality, pricing, dealership reputation, employee behavior, transparency, financing options, and after-sales services on customer satisfaction. The pre-owned bike industry has experienced remarkable growth due to consumer demand for reasonably priced and dependable transportation. A structured questionnaire was used to gather primary data from consumers of used bike dealerships using a descriptive study approach. Books, journals, research articles, and reliable internet sources were the sources of secondary data. Appropriate statistical methods and percentage analysis were used to examine the gathered data. The results show that favorable customer perception and excellent service greatly increase customer loyalty and satisfaction. According to the study's findings, dealerships can attain sustainable growth and a competitive edge by enhancing service quality, upholding transparency, and concentrating on client needs.
Wireless Sensor Networks (WSNs) have emerged as a foundational technology for the realization of the Internet of Things (IoT), facilitating autonomous monitoring across industrial, medical, and environmental landscapes. Despite their proliferation, the operational longevity of WSNs is acutely bottlenecked by the finite energy reserves of individual sensor nodes. This paper provides a systematic, multi-dimensional review of WSN architectures, protocol stacks, and energy conservation paradigms. We synthesize literature spanning 2014–2022 to critically analyze the trade-offs between network throughput and power consumption. The review distinguishes itself by providing a granular comparison of hardware operational components and a robust performance mapping of routing protocols (LEACH, PEGASIS, TEEN). Our findings highlight a significant research shift from basic connectivity toward intelligent, self-sustaining networks. The paper concludes with an extensive roadmap of 20 future research directions to guide scholars toward unresolved challenges in energy harvesting and AI-driven network management.
With the surge of large language models (LLMs) and multi-agent AI, a paradigm shift in data engineering practice has started. Enterprise data is growing at an exponential rate, with the number of data schemas increasing. Operational overhead and maintenance effort for conventional Extract Transform Load (ETL) methods, which involve manually-authored scripts, poorly-forged dependency graphs and reactive maintenance, are vastly exorbitant. This paper introduces the multi-agent AI system, the Autonomous Data Pipeline Orchestration (ADPO) framework, where specialised agents can autonomously generate, deploy, monitor, self-heal and govern different kinds of data pipelines with minimal human oversight. The ADPO architecture consists of a large language model (LLM) backend of GPT-4 class language models, a langchain based ReAct agent, Apache Airflow 2.8 for workflow's scheduling, Kubernetes for elastic container orchestration, and Delta Lake to store the state of machines in an ACID (atomicity, consistency, isolation, durability) compliant way. Empirical testing has shown that, against 150 real world pipeline scenarios, ADPO decreases pipeline generation time by 78.6% (from 36.4s to 7.8s), decreases mean time to repair (MTTR) by 88.1% (from 41.2 min to 4.9 min), and improves the data quality composite score from 63.7% to 91.6% as compared to manual baselines. ADPO continues to scale near-linearly up to 500 pipelines simultaneously and delivers 51,000 records per second for a 3.6× improvement over pipeline rule-based implementations. These findings make ADPO a leading proprietary solution for autonomous data engineering that have far-reaching impacts for enterprise reliability, compliance and the transformation of engineering people.
End-of-Line (EOL) calibration and verification processes for battery electric commercial vehicles (BEVs) have become increasingly complex as heavy-duty vehicle platforms transition from mechanically dominated architectures to software-defined and electronically coordinated propulsion systems. Manual EOL procedures used in conventional commercial vehicle manufacturing environments are difficult to scale for modern Class 8 BEV platforms because calibration activities now span multiple interdependent electronic control units (ECUs), including the Vehicle Control Unit (VCU), inverter, Battery Management System (BMS), Electric Vehicle Communication Controller (EVCC), DC-DC converter, and Electric Power Take-Off (E-PTO). Manual execution introduces risks associated with sequencing errors, inconsistent traceability, incomplete audit evidence, and configuration mismatches across vehicle variants. This paper presents a Continuous Integration and Continuous Delivery (CI/CD)-driven EOL calibration automation framework for heavy-duty battery electric commercial vehicles integrating Python-based orchestration with Jenkins pipeline management for structured execution, automated reporting, and audit-ready traceability. The framework supports calibration and verification of multiple ECUs through SAE J1939/CAN and Unified Diagnostic Services (UDS) communication over standard diagnostic interfaces. Structured JSON logging, Git-based evidence archiving, and CI/CD dashboard reporting are incorporated to provide production-level traceability and compliance support. Results from deployment within a representative Class 8 BEV EOL environment indicate approximately 20% reduction in manual calibration effort, improved configuration consistency, reduced error escape rates, and enhanced audit traceability compared with conventional manual workflows. The paper contributes the first openly described CI/CD-driven EOL calibration automation framework integrating Python orchestration, Jenkins pipeline management, J1939/CAN and UDS diagnostic communication, and structured audit traceability specifically for multi-ECU Class 8 BEV commercial vehicle production environments.
The article reviews the application of machine learning based on the automation of the enterprise document systems to be applied to scale up mortgages services by classification and retrieval of mortgage information from documents with a processing volume of more than a million pages in a day. The system can implement a cloud-native and microservice architecture to deliver 98 percent accuracy in classification and more than 85 percent in field extraction with document integration that can support over 700 types of documents. Rather, it will cut the amount of time devotable to the analysis of manuals by 60 percent and the level of compliance preparation by 40 percent. Multimodal models are high performance learned pipelines that can easily be distributed (using Redis and Kafka), scalable, and economical. Companied with the findings, it can be proposed that the pace, precision and scale are greatly improved with the automation of the mortgage document proceedings using AIs.
Network File System (NFS)–based storage has been a foundational component of enterprise infrastructure for decades due to its simplicity, shared access model, and POSIX-compliant semantics. However, as organizations modernize infrastructure and migrate workloads to cloud environments, traditional NFS architectures increasingly struggle to meet scalability, availability, and operational efficiency requirements. Object storage services such as Amazon Simple Storage Service (S3) offer high durability, elastic scalability, and managed operations, but differ fundamentally from file-based storage systems. This paper presents a comprehensive architecture for migrating on-premises NFS workloads to Amazon S3 and documents lessons learned from practical migration efforts. The proposed approach addresses architectural adaptation, data transfer mechanisms, application compatibility challenges, performance considerations, and operational tradeoffs. Through controlled experiments and observational analysis, this study evaluates the feasibility of replacing NFS-backed storage with S3-based object storage for selected enterprise workloads. The results highlight both the benefits and limitations of such migrations and provide guidance for organizations considering similar transitions.
The cloud-native and distributed systems of modernity create complex failures that can hardly be detected and recovered manually or through the rule of thumb. The current paper is a proposal of an Autonomous AI Self-Healing Distributed System based on Deep Reinforcement Learning (DRL). The structure integrates real time observability, artificial intelligence fault detection and a DRL based action engine to make autonomous choices and take autonomous action by selecting and executing the recovery actions. Controlled failure injection was used as a quantitative experimentation. The findings indicate that there are a great deal of improvement in Mean Time to Repair (MTTR), increased availability of the system, and there is also a low rate of false positive remediation when using the traditional ones. The results prove that DRL facilitates efficient, persistent, and self-reliant system resilience.
Although there is much opportunity for AI-driven products in emerging markets, common machine learning techniques struggle with shortages of data, poor infrastructure and diversity in behavior. In this paper, we discuss using strategies based on analytics, like few-shot learning, producing synthetic data and federated learning to deal with these constraints. Using observations and case studies, we present practical guides for building local and ethical AI systems that are also resilient. We use fairness-aware federated systems and context expertise to provide both AI professionals and product managers with useful techniques for ethical progress in areas where resources are limited and the impact is high.
The case study explores setting up GitHub Copilot, an AI code suggestion tool, in companies that develop and test enterprise data systems to boost their work efficiency. Through the review of literature published until 2020, the study discovers that software developer’s face some issues, including repeating certain actions, inefficient projects and a lack of smart-thinking environments. The study shows that GitHub Copilot helps meet software development challenges by offering real-time, suitable code suggestions for each task in the Software Development Lifecycle (SDLC). The discussion demonstrates that using the tool can lead to more automated work, fewer errors and quicker tests. At the end of the study, there are suggestions for further research, involving evaluating outcomes, adjusting for each area and considering social aspects of using AI in businesses.
This article is dedicated to the exploration of fuzzy eigenvalues and fuzzy eigenvectors within the context of a fuzzy metric space. To facilitate this discussion, we introduce a specific metric for this space. Furthermore, we provide comprehensive definitions for fuzzy eigenvalues and fuzzy eigenvectors, focusing on their application to fuzzy square matrices. In the course of our exploration, we establish a series of theorems pertaining to fuzzy eigenvalues and eigenvectors within a fuzzy metric space. To enhance understanding, we illustrate these theorems with practical examples.
