All of the articles in this proceedings volume have been presented at the 1st Engineering Data Analytics and Management Conference (EAMCON) during 26-27 November, 2025 in Melaka, Malaysia. These articles have been peer reviewed by the members of the Technical Program Chair, Technical Program Committee...

This paper presents a systematic approach for designing the datapath and controller of an LCM and HCF computation unit using the Verilog hardware description language. The architecture of the unit is first outlined at the functional level, providing a structured basis for datapath development. The control...

Our work involves developing a spatial modeling approach to enhance health- care facility placement within rapidly urbanizing regions through identification of vulnerable zones where health care accessibility lags behind building construction growth. To measure healthcare services in identified zones,...

This case study presents the potential of integrating the concept of Artificial Intelligence (AI) and the Unified Payments Interfaces (UPI) to make government subsidy transfer in India more efficient and transparent. Our article addresses the Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) scheme, in which...

The global shift toward sustainability has increased pressure on organizations to provide accurate, transparent, and credible ESG (Environmental, Social and Governance) disclosures. This study investigates the strategic role of data analytics in strengthening ESG reporting and enhancing corporate sustainability....

Human pose estimation and image transformation are active areas of research in computer vision with far-reaching applications in animation, gaming, healthcare, and virtual reality. We present a user-friendly system integrating pose detection and AI-based image transformation by using recent deep learning...

Cancer prediction and classification are vital for early detection and personalized treatment. This study employs computational genomics and machine learning, integrating Random Forest and Gradient Boosting into an optimized ensemble model using lncRNA data. The model achieved an AUC of 0.84 in ten-fold...

Women entrepreneurs are significant in ensuring the economic growth is inclusive and sustainable. Nevertheless, they all mostly find it hard to access formal financial assistance due to stringent collateral conditions, ambiguous credit objectives, and discrimination at financial institutions. The conventional...

The employee engagement is an important aspect of stability of the workforce and organization. Traditional analytics usually use linear models which do not reflect non-linear correlation that affect retention. The present study is based on a neural network framework and provides an analysis of the engagement...

Human Resource Management is shifting towards the use of smart technologies to employee performance appraisal that will improve fairness and accuracy in the digital era. The conventional performance appraisal systems have been criticized since they are subjective, infrequent, and poor in effectively...

The increasing of climate change bring carbon credit schemes as crucial Instruments for mitigating greenhouse gas emissions and enabling sustainable finance. This paper investigates to what extent such institutions foster business climate action and green financial flows. Adopting a qualitative...

Agriculture sustains a large portion of India’s rural economy, where protecting crop health is essential for stable productivity. This study introduces a deep learning framework based on Convolutional Neural Networks (CNNs) for the automatic recognition of apple leaf diseases across four categories:...

Oil spills have long posed serious threats to marine ecosystems and the coastal economies that rely on them. While early identification is essential for limiting damage, detecting spills under constantly changing ocean conditions remains difficult. Synthetic Aperture Radar (SAR) imagery is especially...

Cart abandonment is a significant issue in the e-commerce industry since 70 percent of online shopping carts are abandoned even though the industry has improved the checkout systems and online payments and mobile services. The study of cart abandonment behavior is critical as it demonstrates not only...

The current study deals with the perishable food supply chain management system with a novel hierarchical AI system that combines the use of the Transformer-based demand forecasting and Proximal Policy Optimization (PPO) reinforcement learning. The predictive engine creates probabilistic store-SKU-day...

The use of deep learning-based CBIR models, particularly those incorporating convolutional nets (CNNs), meanwhile addressed some problems by learning hierarchical features automatically. However, most models now available fail to be interpretable and transparent. This limits their application in sensitive...

The exponential growth of big data has created unprecedented opportunities for data-driven insights while simultaneously raising critical privacy concerns. This paper presents a novel framework combining Context-Aware Generalization (CAG) with Risk-Aware Generation (RAG) for privacy-preserving data publishing...

The steady rise of mobile apps in finance and healthcare has brought more attention to the risks of cybersecurity breaches. While automated systems and algorithmic models are often used to monitor threats, surprisingly little work has examined what users themselves are saying about security in public...

Rise of artificial intelligence chatbots has changed the interaction pattern between customers and brands. Chatbots provide various benefits to firms such as operational efficiency, personalized recommendations, customized experience and scalable engagement. But the critical risks that are often overlooked...

The synthesis of Agentic AI, Retrieval-Augmented Generation (RAG), and Knowledge Graphs, is a paradigm shift in e-commerce systems of enterprises. The current paper suggests KG-RAG-Agent, a new framework that is based on the combination of autonomous AI agents with graph-enhanced retrieval methods and...

Businesses are confronting a data deluge in volume and complexity that is bearing on traditional data engineering and decision making models to the beyond. The Quantum-Enhanced Data Engineering Agent Framework (QED-AF) resource offered in this paper is a new framework that combines the power of autonomous...

Accurate segmentation of brain tissues (White Matter, Gray Matter, and Cerebrospinal Fluid) is critical for neurological diagnosis. This paper presents a novel Hybrid Agentic Vision Trans-former Framework with Hummingbird-Optimized Patchwise M-Net (HAVT-HOM) that synergistically combines: (1) patch-wise...

AutoGrader+ is artificial intelligence technology that evaluates PDF answer sheets that contain written descriptive answers. AI techniques such as TF-IDF with Cosine Similarity and other NLP techniques such as Sentence-BERT are employed to process the text, extract the answers, and compare them against...

Poetry and music share deep emotional connections, yet their computational alignment has remained largely unexplored. Existing music–text retrieval methods typically focus on English prompts or largescale audio–caption datasets, leaving low-resource languages and creative genres such as poetry understudied....

The swift evolution of computational intelligence (CI) technologies, including AI, ML, NLP, and data analytics, has reshaped human resource management. These tools automate routine tasks and bring strategic transformation across HR functions. This literature review synthesizes recent research on AI integration...

Social networking plays important part of our everyday life. Social media sites are widely used for community development, post sharing, and communication. Growing use of social media may lead to online harassment. The use of the Internet to harass someone is known as unintentional cyberbullying. It...

Image captioning bridges visual natural language generation, letting systems craft textual descriptions of pictures. This ability fuels an array of uses—technology, content retrieval and human-computer interaction, among them. Progress has been propelled by datasets such as Flickr8k, Flickr30k and Visual Genome,...

The growing population of the world is increasing tension of our agriculture system. The Tension is forcing us to provide more and more food. But the resources are becoming fearful. Urbanisation is growing so fast that the land worth growing crop is being occupied by the cities and water problem is becoming...

Medical image captioning is an emerging area that integrates computer vision techniques and natural language processing to automatically generate relevant descriptive text for medical images. This holds significant promise for improving clinical documentation, diagnostic accuracy, and decision support....

Yoga is widely used for improving flexibility, strength, balance, and mental well-being; however, incorrect posture execution can reduce efficacy and increase injury risk. A lightweight, interpretable pose recognition and feedback system is presented that combines MediaPipe Pose landmark extraction with...

The rapid integration of artificial intelligence technologies in marketing communication has created a dynamic research landscape requiring systematic examination to understand emerging trends and theoretical developments. Despite growing scholarly interest, a comprehensive bibliometric assessment mapping...

This study provides a concise bibliometric analysis of 1,785 Scopus-indexed publications (2023 onwards) exploring the convergence of artificial intelligence and undergraduate management education. Employing Boolean queries across 23 AI and 21 education terms, the dataset was filtered for English-language...

The gold industry is highly dynamic, with gold price fluctuations affecting both buyers and sellers. Traditional valuation methods rely on expert opinions and historical data but often lack efficiency and objectivity. Recently, machine learning (ML) has gained prominence as a robust tool for analyzing...

Microservice architectures have brought agility to an unprecedented level at scale, and have at the same time created a fundamentally new attack surface, making traditional security models irrelevant. The present paper presents the concept of the Agentic Context-Aware Security Framework (ACASF), a new...

The multiplied growth of financial information together with the often complex regulatory requirement has only worsened the challenge involved in maintaining information quality, integrity and coherence across dissimilar banking frameworks. In this manuscript, a detailed roadmap on data quality governance...

The complexity of healthcare project and program management has never been as high as it is with the multi-dimensional regulatory constraints, asynchronous orchestration of stakeholders, stochastic resource allocation and dynamically changing clinical demands. Conventional deterministic project management...

Conventional search systems used in enterprise settings often find it difficult to overcome the semantic gap between intention and document content on one hand within their information seek, hence making the process of information seeking to be inefficient and productivity low. At the same time, the...

Life expectancy forecasting is an important instrument for evidence-based health policy and socio-economic planning since it reflects the intricate interaction between population health, environmental conditions, and availability of health care. Reliable forecasts allow governments and health authorities...

In the meantime, the standard approaches predominantly focus on extracting spatial features from pre-trained convolutional neural network (CNN) architectures, including VGG16, InceptionV3, and DenseNet121, while rarely considering the sequential dependencies intrinsic to histopathological patterns. This...

A Fusion Framework power-driven by intelligent technologies, designed for personalized and smart learning with multimedia capabilities aims to overcome the limitations of conventional one-size-fits-all educational models. It uses Audiotext Net for precise speech recognition, a next-generation intent...

The practice of influencer marketing has evolved in a very dynamic manner in contemporary digital branding, which has changed the contemporary interaction process with products and services. Unlike the traditional advertising process, which evokes distrust in consumers, the impact of influence-driven...

Accurate cost estimation in engineering projects is vital for effective planning and risk analysis. Traditional analytical models yield high precision but compromise on interpretability, hence limiting managerial confidence. In this paper, a reproducible, Python-based explainable-AI (XAI) workflow integrating...

Efficient prediction of institutional food demand is an essen- tial tool in enhancing the reduction of waste, improving procurement and sustainability for production kitchens including university campus dining services, military bases and public welfare organizations. Such day level forecasting cannot...

With the advent of automated grading and feedback systems, application of artificial intelligence (AI) has revolutionized the evaluation practices in higher education. The educators’ efficiency can be enhanced by the AI feedbacks, the detailed feedback giving can be relieved from the educator’s shoulder,...

In order to sustain predetermined competencies, the international tendency towards the Outcome-Based Education (OBE), people need to transform radically the curriculum design, its implementation, and assessment ensuring that the graduates have acquired the predetermined competence levels. Despite the...

In the domain of online advertising, particularly within the Pay-per-Click (PPC) framework, identifying fraudulent entities remains a critical challenge for the data mining research community. The inherent class imbalance—where fraudulent publishers represent a minor fraction compared to legitimate ones...

Detecting fraudulent clicks in digital advertising remains a complex issue due to the disproportionate distribution of class labels within user-click datasets. The quantity of legitimate publishers usually surpasses that of fraudulent, resulting in classification models exhibiting bias towards the predominant...

Even with the major progress in diagnostics and treatment, tuberculosis (TB) is one of the major causes of mortality related to infections in the world. Despite a low sensitivity and inter-observer variability, microscopy of sputum smears remains the most frequently used diagnostic technique in resource-constrained...

This work examines the crucial problem of task scheduling in cloud computing, where cost-effectiveness and service quality depend on good resource allocation. Although cloud computing offers scalable and adaptable on-demand services, work scheduling is challenging due to these environments’ dynamic nature....

This paper contains a detailed description of Agentic (artificial intelligence) AI) and its possible implementation in the personal career guidance system. The changes occurring in the labor markets along the digital spectrum necessitate the new, adaptive, context-sensitive development models that go...

Eco-friendly energy sources (Renewable sources) are being adopted more extensively because of higher power bills and concerns over the environmental impact of fossil fuels. Photovoltaic (PV) panel arrays are the primary means of capturing solar energy. Even one panel in an array loses a significant amount...

Polycystic Ovary Syndrome (PCOS) is a multifactorial endocrine disorder, which often goes undiagnosed due to its heterogenous presentation and lack of early detection tools. This study explores a machine learning based approach for early PCOS prediction using a publicly available clinical dataset from...

Road accidents have been found to be the major cause of preventable deaths all over the world whereby speed of emergency response is the most crucial aspect of survival. The possibility of survival depends significantly on the delays in sending aid, which are usually caused by the incapacitation of the...

This research focuses on children affected by ADHD, aiming to decode patterns using clinical, behavioral, and fMRI data. The team used ML and DL models such as multilayer perceptron, XGBoost, and LightGBM and then took the best instances of each model to then ensemble them into a hybrid ML model. This...

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that tends to be unnoticed, particularly in adults, mostly women. The lack of diagnosis is due to the fact that the adult symptomatology remains unnoticed or is misconstrued, and adults with ADHD usually present...

Hepatitis B is a potentially life-threatening liver infection caused by the Hepatitis B Virus (HBV). Despite the availability of vaccines, the disease remains a global health challenge. This research applies data-driven approaches using Machine Learning (ML) models to analyze patient data and uncover...

This paper delve deeper and discuss the temporal trend pattern of trends in rice yield in the district level in India between 1966 and 2000. Rice is the main national staple grain and one of the most important elements in food security, nutritional sufficiency, and food subsistence in rural areas. It...

Airbnb has transformed the world travel and accommodation market by offering flexible and low-cost short-term rentals. Yet, comprehending the intricate price and demand patterns and customer behavior remains a significant challenge among hosts, tourists, and investors. In this study, an Exploratory Data...

Enterprise data management has advanced to a level of complexity that traditional Extract, Transform, and Load (ETL) approaches can no longer cope with. Data pipelines are often manually documented, consuming extensive engineering time and frequently lagging months behind actual deployments, creating...

Sleep is essential for preserving general health, emotional stability, and cognitive function. However, because conventional diagnostic techniques are not widely available, the prevalence of undiagnosed sleep disorders continues to be high. Making Use of the Sleep-EDF We created a unique convolutional...