International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) is peer-reviewed, online international journal published Bimonthly by Shauryam Research Institute. IJSRCSEIT is a highly-selective journal, covering topics that appeal to a broad readership of various branches of Computer Science, Engineering, Information Technology and related fields. The Journal has many benefits all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRCSEIT Articles section have been subjected to a rigorous review process. Email addresses for submitting articles are ijsrcseit@gmail.com, editor@ijsrcseit.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Computer Science, Engineering and Information Technology Frequency : Bimonthly E-ISSN : 2456-3307 Publisher : Shauryam Research Institute Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2016 Subject : Computer Science, Engineering and Information Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRCSEIT ) Email Id : editor@ijsrcseit.com, ijsrcseit@gmail.com Mobile No. : +1 (404) 860-9920 (WhatsApp support only) Website : https://ijsrcseit.com
Cybersecurity Compliance as a Source of Competitive Advantage in Technology Markets
Cybersecurity compliance is increasingly viewed not merely as a regulatory obligation but as a strategic resource capable of generating competitive advantage in technology-driven markets. As digital platforms, cloud services, and data-intensive business models expand, firms operate under heightened scrutiny from regulators, customers, and partners regarding data protection, privacy, and operational resilience. This study examines how adherence to cybersecurity compliance frameworks can be leveraged to enhance market positioning, customer trust, and long-term firm performance. Drawing on strategic management and information systems literature, the paper conceptualizes cybersecurity compliance as an intangible organizational capability that signals reliability, reduces information asymmetry, and lowers perceived transaction risk. Rather than treating compliance as a cost center, leading technology firms integrate standards such as ISO/IEC 27001, NIST-aligned controls, and sector-specific regulations into product design, governance structures, and innovation processes. The analysis highlights three key mechanisms through which compliance creates competitive advantage: trust differentiation, operational efficiency, and market access. First, demonstrable compliance strengthens brand credibility and customer confidence, particularly in markets where data breaches and service disruptions are prevalent. Second, standardized security controls streamline internal processes, improve incident response, and reduce the likelihood and impact of cyber incidents, thereby lowering long-term operational costs. Third, compliance enables entry into regulated or high-assurance markets, facilitating partnerships, cross-border operations, and procurement opportunities that are inaccessible to non-compliant competitors. The paper also discusses dynamic considerations, emphasizing that competitive advantage arises not from static certification alone but from continuous compliance, monitoring, and adaptation to evolving threat landscapes. By framing cybersecurity compliance as a strategic investment rather than a defensive necessity, the study contributes to debates on digital trust, governance, and competitive strategy in technology markets. The findings offer practical insights for managers seeking to align cybersecurity governance with business objectives and for policymakers aiming to design compliance regimes that enhance, rather than hinder, innovation and competitiveness. Overall, the research underscores that proactive, well-governed cybersecurity compliance supports sustainable value creation by aligning risk management with innovation, strengthening stakeholder relationships across supply chains, improving strategic transparency, enhancing investor confidence, and reinforcing organizational resilience in fast-moving, trust-sensitive global technology ecosystems and competitive digital markets worldwide today.
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
How Do Credit Card Fraud Detection Models Make Decisions? An Explainable Machine Learning Analysis
Credit card fraud detection is an important application of machine learning, where incorrect predictions can lead to financial losses and reduced customer trust. Although many fraud detection models achieve high accuracy, their decision-making process is often unclear, which limits their use in real financial systems. This paper presents an experiment-based study of machine learning models for credit card fraud detection with a focus on explainability. Logistic Regression, Random Forest, and Gradient Boosting models are evaluated using a real-world credit card transaction dataset with extreme class imbalance. Model performance is assessed using precision, recall, F1-score, ROC-AUC, and confusion matrix analysis, with special emphasis on fraud recall. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied during training. To understand model decision-making, SHAP-based global and local explanations are generated for the best-performing model. Experimental results show that Random Forest provides the most balanced performance, achieving high fraud recall while controlling false positives. The explainability analysis shows that fraud predictions are driven by a small number of influential features. The study highlights the importance of evaluating both performance and explainability when deploying fraud detection systems in financial environments.
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
AI and Machine Learning in Cybersecurity: Applications, Challenges and Future Directions
The types of cyber threats are becoming more and more complicated, so the classical security method which rely on signature is less applicable. Artificial Intelligence (AI) and Machine Learning (ML) are a few of technology giant for the world of cybersecurity, allowing the formulation of an effective plan to proactively fight these threats. The aim of this paper is to offer a comprehensive summary on AI/ML in cybersecurity. In this paper, we will examine the current AI/ML technology applied to cybersecurity. This is paper address a few of the dominant challenges in AI/ML, such as adversarial threats, data privacy .1 Introduction The rapid growth and increasing complexity of computing systems have led to significant advances and adaptation in artificial intelligence (AI) and machine learning (M L). This study also attempts to synthesize the current trends in the field by conducting a thorough survey of the literature review. Finally, the future directions in the field include Explainability, AI for security adaptation, and the need for integration with AI. This study highpoint AI/ML is not the silver bullet for the field of cybersecurity, it is imperative that strategic use of AI/ML is made in the design of next-generation cybersecurity systems.
Kannada Speakers Speech Emotion Recognition Using Deep Learning Technique: Anger, Fear, Sad and Neutral state
In this paper, we have experimenting to identifying the Kannada emotional speech signals recognition using deep learning technique. Speech Emotion Recognition (SER) is a hot research topic in the field of HCI (Human Computer Interaction). To experiment we have created our own Kannada Emotional Speech corpus recording in different android mobile phone, with sampling rate 48 kbps and mono channel and speakers are from the Karnataka and Tamil Nadu boarder region, whose accents is a mixer of Kannada, Tamil and Telugu languages. The emotions are anger, fear, sad and neutral is considered. Totally we have 440 Kannada emotional speech signals. To use this speech signal data in our experiment, we have first down sampled all speech signals into 16 kbps, mono channel and stored as wav files. Then we pass the each sample signals into the pre-emphasize phase, then framing and windowing is done, then we extracted features from each voice frame signal like MFCC, Chroma and ‘Mel’ and then passed to CNN (Convolution Neural Network) to train and testing the voice signals. The CNN is capable to handle small amount of data for training, and with smaller number of parameters. The average Kannada emotion recognition accuracy rate of 93.77% is achieved. All computations are done using Python programming language and used Python libraries like Librosa, Keras, pyaudio, soundfile, sklearn.
Design and Development of an AI-Powered Online Doctor Consultation and Booking System
The Healthcare Management Platform is a web-based E-Health Center created under the MERN stack (MongoDB, Express.js, React.js, Node.js) to promote the accessibility and efficiency of healthcare. This paper is a quantitative study aiming to measure the system performance in relation to the user engagement metrics, the rate of successful booking of appointments, the accuracy of chatbots response, and the ability of the system to handle loads. The platform facilitates the interaction between patients and doctors by offering a simple system of booking appointments, a chatbot to analyze the symptoms, and a role-based dashboard to doctors, patients, and administrators. In pilot usability test which included 200 users, the system was found to have a reduction in the time spent in scheduling an appointment by 73 percent over the traditional phone-based systems. When the chatbot was tested against medical databases, the accuracy rate of the symptom-based recommendation was 87%. Moreover, the average response time of the server was 320ms when the simulated load of 5,000 users became active simultaneously, which indicates the scalability of the platform. It was also revealed that the research recorded a 42% growth in patient engagement in terms of repeated platform use and number of interactions per session. Security tests established that it is 99.8 percent effective against the general cyber criminal attacks, assuring data privateness. Moreover, 91 percent of respondent users had a positive user experience, which mentioned simplicity of use, simplified information and reduced time of waiting. These findings indicate the potential of the platform in ensuring access to healthcare and enhance efficiency with a digital transformation. The further development of the work will be aimed at AI-based diagnostics, the integration of telemedicine, and the safety of patient data on a blockchain platform to expand the capabilities of the platform. The results highlight the importance of healthcare solutions based on technologies in enhancing patient outcomes and efficiency in the functioning of a modern healthcare ecosystem.
AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems
The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. However, this paradigm shift has also introduced complex security challenges, including heterogeneous device vulnerabilities, dynamic network topologies, limited computational resources, and increased exposure to cyberattacks at the edge. This review paper examines AI-driven adaptive security frameworks as a promising approach for securing decentralized edge computing environments within IoT ecosystems. It systematically analyzes how artificial intelligence techniques—such as machine learning, deep learning, federated learning, and reinforcement learning—enable real-time threat detection, anomaly identification, intrusion prevention, and automated security policy adaptation at the network edge. The review highlights architectural models that integrate AI with edge and fog computing to achieve scalable, low-latency, and privacy-preserving security mechanisms while reducing reliance on centralized cloud infrastructures. Furthermore, it evaluates existing frameworks, recent case studies, and emerging trends, emphasizing challenges related to data privacy, model robustness, explainability, energy efficiency, and interoperability. By synthesizing current research and identifying open research gaps, this paper provides a comprehensive foundation for developing resilient, intelligent, and adaptive security solutions that can support the evolving requirements of decentralized IoT edge computing ecosystems.
A Comprehensive Survey of Distributed Consensus Algorithms for Wireless Sensor Networks
Distributed consensus represents a vital approach in Wireless Sensor Networks. By sharing local data, it allows a group of resource-constrained sensor nodes to collaborate and agree on a common value or decision. Consensus methods improve scalability, reliability, and energy efficiency by eliminating the need for centralized data processing systems. This research provides an in-depth review of distributed consensus approaches in WSNs. We examine traditional average and binary consensus algorithms, cluster-based and topology-aware methods, energy efficient solutions, and secure consensus protocols. Architectural frameworks, performance metrics, and an extensive comparative analysis are also presented. Based on convergence, communication costs, fault tolerance and application, a summary table contrasts important algorithms. The survey highlights significant areas where research is lacking and proposes future pathways, underlining the necessity of creating flexible, secure, and effective consensus solutions for next-generation wireless sensor networks (WSNs).
Development of Decision Support System for Duck and Chicken Culinary Businesses Based On Good Manufacturing Practices Using Data Mining
This study presents the development of a Decision Support System (DSS) for duck and chicken culinary businesses through the integration of Good Manufacturing Practices (GMP) and data-driven analysis using K-Means clustering and Association Rule Mining. The system processes production, nutritional, economic, and sensory data collected from processed poultry products to support business decision-making. K-Means clustering successfully segmented 32 samples into four meaningful product groups based on meat attributes, cooking characteristics, cost levels, and quality evaluations. The clustering results distinguished fast-cooking, low-cost chicken products from slow-cooked, high-cost duck dishes, revealing clearly differentiated culinary market segments. Association rules further identified strong correlations among product type, fat level, production cost, and cooking duration, providing valuable knowledge for identifying premium or mainstream culinary categories. When combined, the clustering and rule-based insights enhance GMP implementation by supporting standardization, quality control, and traceability throughout food preparation processes. The resulting DSS enables producers to optimize resource usage, refine menu strategies, and strengthen product consistency while aligning with food safety and hygiene standards. Overall, this research demonstrates that integrating GMP with AI-based analytics provides a practical and scalable approach for improving governance, operational efficiency, and competitiveness in small and medium-scale duck and chicken culinary enterprises.
Advances in Hybrid Machine Learning and Physics-Based Models for Enhanced Reservoir Simulation
Reservoir simulation is critical in optimizing resource extraction and ensuring the efficient management of subsurface reservoirs. While rooted in robust physical principles, traditional modeling approaches often face limitations in handling complex reservoir dynamics, computational intensity, and data sparsity. Recent advances in hybrid methodologies combining machine learning (ML) and physics-based models offer transformative solutions to these challenges. This paper explores the evolution and integration of hybrid approaches in reservoir simulation, highlighting key developments such as physics-informed neural networks, surrogate modeling, and adaptive workflows. The benefits of these models, including enhanced predictive accuracy, computational efficiency, and adaptability, are examined alongside the challenges of implementation, such as computational demands and data quality constraints. Practical implications for the industry are discussed, particularly in improving decision-making and optimizing production strategies. The paper concludes with recommendations for further research and industry adoption, emphasizing the need for interdisciplinary collaboration, advancements in explainable artificial intelligence, and the development of standardized frameworks. These hybrid approaches mark a significant milestone in the evolution of reservoir simulation, offering innovative pathways for sustainable and efficient resource management.
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Software Testing Techniques and Strategies
In the Software Development Life Cycle, software testing is the most important stage. The report states that the tested software undergoes several stages, including test preparation, test execution, testing analysis, problem reporting and tracking, and bug fixing. Many studies have been conducted in the past to optimize the testing process in general to enhance software quality as soon as possible. Software testing is the process of running a program to find software defects (errors or other issues). Testing can analyse the software product to evaluate its characteristics and identify any differences between the recommended and actual conditions. Software testing helps decrease errors and reduces software costs. This paper gives the overview of various software testing methods.
Sustainable Healthcare Infrastructure Performance Metrics for Long Term Asset Management and Value Creation
Sustainable healthcare infrastructure requires performance metrics that extend beyond initial capital delivery to support long term asset management, service continuity, and public value creation. This study examines the development and application of sustainability oriented performance metrics for healthcare facilities across their operational life cycle. It integrates insights from infrastructure asset management, health systems planning, and sustainability science to propose a structured metric framework that aligns technical performance, financial stewardship, and social outcomes. The analysis emphasizes the limitations of traditional project focused indicators that prioritize cost, schedule, and short-term efficiency while neglecting operational resilience and lifecycle impacts. Key metric domains are identified, including asset condition and reliability, energy and resource efficiency, environmental emissions, patient safety, service accessibility, and workforce wellbeing. Financial metrics addressing lifecycle cost, maintenance optimization, and risk adjusted value preservation are examined alongside clinical and community outcome indicators. The study highlights the role of digital technologies in enabling continuous performance measurement. Building management systems, asset information models, and data analytics platforms are discussed as tools for real time monitoring, predictive maintenance, and evidence-based decision making. By linking operational data with governance and reporting structures, these tools support transparency, accountability, and informed reinvestment strategies. Sustainability metrics are positioned as decision support instruments rather than static reporting requirements. The framework emphasizes feedback loops that allow infrastructure managers to adapt asset strategies in response to changing demand, climate risks, regulatory expectations, and technological advances. Particular attention is given to aligning performance metrics with long term health system goals, including equity, resilience, and carbon reduction commitments. Overall, the abstract argues that sustainable healthcare infrastructure performance metrics are essential for transforming assets from cost centers into strategic enablers of health system value. By embedding lifecycle oriented, multidimensional metrics into asset management practice, healthcare organizations can improve reliability, optimize resource use, and protect public investment over time. The proposed approach provides practical guidance for policymakers, facility managers, and investors seeking to enhance long term asset performance, fiscal sustainability, and societal value creation within complex and resource constrained healthcare environments globally, across diverse institutional contexts and evolving demographic, economic, and epidemiological conditions worldwide sustainably.
Integrating Healing Centered Design Principles into Diagnostic and Laboratory Facility Planning
Integrating healing-centered design principles into diagnostic and laboratory facility planning has gained increasing attention as healthcare systems recognize the influence of physical environments on patient experience, staff wellbeing, and clinical performance. Diagnostic and laboratory spaces are traditionally engineered for efficiency, biosafety, and regulatory compliance, often overlooking psychosocial factors that shape stress, trust, and cognitive functioning. This paper explores how healing-centered design can be systematically embedded within the planning and delivery of diagnostic and laboratory facilities without compromising technical rigor or safety standards. Drawing on interdisciplinary literature spanning healthcare architecture, environmental psychology, laboratory design, and health services research, the study synthesizes evidence on spatial configuration, lighting, acoustics, biophilic elements, wayfinding, and material choices. The analysis demonstrates that environments designed to reduce anxiety, enhance comfort, and support dignity can improve patient cooperation, diagnostic accuracy, and staff productivity while mitigating fatigue and error. Particular emphasis is placed on laboratory workflows, specimen collection areas, waiting zones, and staff workspaces where stress and cognitive load are highest. The paper further examines regulatory and operational constraints, highlighting strategies for aligning healing-centered features with infection prevention, biosafety, and quality assurance requirements. Case-informed insights illustrate how modular layouts, natural light integration, noise control, and culturally responsive design can be adapted to diverse health system contexts, including resource-constrained settings. A conceptual planning framework is proposed that integrates healing-centered objectives into early needs assessment, functional programming, stakeholder engagement, and post-occupancy evaluation. By reframing diagnostic and laboratory facilities as therapeutic environments rather than purely technical spaces, the study provides practical guidance for planners, architects, laboratory managers, and policymakers. The findings support a shift toward human-centered infrastructure that enhances diagnostic effectiveness, workforce resilience, and patient trust, while maintaining compliance, efficiency, and long-term operational sustainability. Such integration contributes to more compassionate, equitable, and high-performing diagnostic services capable of meeting growing clinical demand and evolving expectations for quality, safety, and holistic care delivery. Future research should evaluate outcomes using mixed methods, longitudinal metrics, and equity indicators, ensuring design decisions are evidence informed, scalable, and culturally grounded, while supporting regulatory compliance, technological adaptability, continuous learning, and measurable improvements in diagnostic quality and patient-centered system performance.
Decentralized Produced Water Treatment Systems for Remote Energy Assets, a Conceptual Design Framework
Produced water (PW) is the largest by-product of oil and gas operations and presents significant management challenges for remote energy assets, where centralized treatment infrastructure is often impractical due to logistical, energy, and environmental constraints. This review proposes a conceptual design framework for decentralized PW treatment systems tailored to remote onshore and offshore operations. The framework emphasizes modular, scalable, and adaptable treatment units capable of addressing the variable physicochemical composition of PW, including high salinity, hydrocarbons, heavy metals, and naturally occurring radioactive materials (NORM). Treatment trains integrate physicochemical processes such as gravity separation, flotation, adsorption, membrane filtration, and advanced oxidation with bio-based polishing systems including biofilm reactors, constructed wetlands, and algal-bacterial consortia. This hybrid approach ensures robust contaminant removal, operational flexibility, and resilience under fluctuating flow rates and water quality typical of remote operations. The framework also considers off-grid energy solutions, leveraging renewable sources such as solar and wind to reduce operational carbon footprints, and incorporates resource recovery pathways, including nutrient extraction, biomass utilization, and salt recovery, supporting circular water management principles. Digital monitoring, automation, and AI-driven process optimization are highlighted as key enablers for real-time control, predictive maintenance, and adaptive operation, reducing risks associated with system failure in isolated locations. Environmental and safety considerations, including minimization of land use, chemical inputs, and residual waste management, are integrated into the design to ensure sustainable operation. Overall, the proposed conceptual framework demonstrates that decentralized PW treatment systems can provide a flexible, energy-efficient, and environmentally responsible solution for water management in remote energy assets. By combining modular engineering, hybrid treatment strategies, and digital intelligence, these systems enable fit-for-purpose water reuse, reduce dependence on freshwater resources, and enhance operational resilience, supporting sustainable and circular water management practices in water-stressed and isolated operational settings.
A Comprehensive Review of Sign Language Translation for Inclusive Education Systems
Sign Language Translation (SLT) has emerged as a critical technological enabler for inclusive education systems, aiming to bridge communication gaps between hearing-impaired learners and mainstream educational environments. Recent advancements in computer vision, deep learning, and artificial intelligence have significantly improved the accuracy, robustness, and real-time feasibility of sign language recognition and translation systems. These systems increasingly support isolated signs, continuous sign language, multilingual alphabets, and sentence-level translation, making them suitable for classroom integration, e-learning platforms, and assistive educational tools. This review paper presents a comprehensive analysis of recent research on sign language translation, with a particular focus on methods applicable to inclusive education systems. The paper systematically examines state-of-the-art techniques, including YOLO-based detection, CNN–LSTM hybrids, graph convolutional networks, attention mechanisms, and optimization-driven feature reduction approaches. Key research findings, challenges, and limitations are critically discussed, highlighting gaps related to scalability, dataset diversity, real-time deployment, and educational usability. By synthesizing current trends and insights, this review aims to guide researchers and educators toward the development of more effective, accessible, and learner-centered sign language translation systems for inclusive education.
A Comprehensive Review of Methods, Frameworks, and Domains for Metadata Extraction from Scientific Texts
Metadata extraction from scientific texts plays a crucial role in enabling efficient organization, retrieval, and analysis of scholarly knowledge. With the exponential growth of scientific publications across disciplines, manual metadata annotation has become infeasible, motivating the development of automated and semi-automated extraction techniques. This review paper presents a comprehensive analysis of recent advances in metadata and structured information extraction from scientific documents. It explores traditional rule-based methods, machine learning approaches, deep learning architectures, and emerging large language model-based frameworks. The paper also examines domain-specific applications, including systematic reviews, digital libraries, scientific repositories, and open journal systems. By synthesizing findings from recent literature, this study highlights key research trends, strengths, and limitations of existing methods. Furthermore, it identifies major challenges such as document heterogeneity, semantic ambiguity, evaluation complexity, and human–machine collaboration. The review aims to provide researchers with a structured understanding of current methodologies and open research directions, thereby supporting the development of robust, scalable, and high-precision metadata extraction systems for scientific knowledge management.
An In-Depth Survey of Software Testing Methodologies
Software testing is an essential process to ensure the correctness, completeness, security, and quality of a product. It helps verify and validate whether the product meets customer requirements and functions as expected. Testing identifies and fixes bugs or defects to improve software quality and reliability. This paper explores the principles, methods, and different types of software testing. It highlights key strategies like verification (ensuring the product is built right) and validation (ensuring the product meets the intended needs). We also look at different testing stages, like unit testing, integration testing, and user acceptance testing are also considered. Testing techniques like white-box testing (focusing on the internal structure of the software) and black-box testing (focusing on outputs and user requirements) are discussed. Different test methods, such as boundary value analysis and decision tables, are also introduced to help find defects early and efficiently. The paper emphasizes the importance of early testing, systematic planning, and a structured approach to uncover defects and ensure the product's reliability. Ultimately, the goal of software testing is to create a defect-free product that fulfills customer expectations and performs well under various conditions.
Breast Cancer Image Classification Using Multi-Modal Datasets: A Comprehensive Review
Breast cancer is one of the most frequently diagnosed malignancies worldwide and remains a leading cause of cancer-related mortality among women. Early and accurate detection significantly improves survival rates and treatment outcomes. Recent advances in artificial intelligence, particularly deep learning, have transformed breast cancer diagnosis through automated image classification. This review presents a comprehensive analysis of breast cancer image classification techniques across multiple dataset modalities, including histopathological images, mammography scans, cytopathology images, and multi-resolution datasets. Traditional machine learning methods and modern deep learning architectures are critically examined with respect to feature extraction, dataset characteristics, and classification performance. Special attention is given to transfer learning, hybrid models, class imbalance handling, and magnification variability. The review also highlights key research findings, limitations, and open challenges hindering clinical adoption. By synthesizing recent state-of-the-art studies, this paper provides valuable insights into current trends and future directions for developing robust, accurate, and clinically deployable breast cancer diagnostic systems.
A Comprehensive Review of Hotel Recommendation Systems Using Reviews and AI
Hotel recommendation systems have become a vital component of modern tourism platforms due to the exponential growth of online hotel reviews and user-generated content. These systems assist travelers in making informed booking decisions by analyzing customer preferences, sentiments, and contextual factors. Recent advances in artificial intelligence, machine learning, and deep learning have significantly improved recommendation accuracy, personalization, and scalability. This review paper presents a comprehensive analysis of state-of-the-art hotel recommendation approaches published between 2024 and 2025, with particular emphasis on sentiment analysis, hybrid recommender models, deep learning architecture, and explainable AI techniques. The study systematically examines methodological trends, advantages, and limitations across existing works, highlighting how textual reviews, metadata, and contextual information are integrated into recommendation pipelines. Furthermore, this review identifies key research findings and challenges related to data sparsity, ambiguity in natural language, fairness, explainability, and ethical considerations. By synthesizing recent research contributions, this paper aims to provide a structured understanding of current progress and future research directions in hotel recommendation systems, offering valuable insights for researchers and practitioners working in intelligent tourism and hospitality analytics.
Multi-Class Deep Learning Approaches for Automated Liver Disease Diagnosis: A Review
Liver diseases constitute a major global health burden, ranging from hepatitis and fatty liver to fibrosis, cirrhosis, and hepatocellular carcinoma. Early and accurate detection of disease stages is critical for effective clinical intervention, yet conventional diagnostic approaches often rely on invasive biopsies, expert-driven imaging interpretation, or isolated biomarker thresholds. Recent advances in deep learning, particularly multi-class deep neural networks, have enabled automated, scalable, and data-driven liver disease detection using heterogeneous clinical data. This review presents a comprehensive analysis of multi-class deep neural network–based methods for liver disease detection, integrating laboratory biomarkers, ultrasound, CT, MRI, and multi-modal clinical data. The study synthesizes recent progress in neural architectures, feature fusion strategies, explainable AI techniques, and weakly supervised learning frameworks. Emphasis is placed on how multi-class formulations improve disease stratification compared to binary classifiers and enhance clinical decision support. Furthermore, this review identifies key research findings, methodological strengths, and persistent challenges such as data imbalance, interpretability, and real-world deployment. By consolidating current evidence, the paper aims to guide researchers toward robust, interpretable, and clinically viable deep learning solutions for liver disease detection.
Artificial Intelligence-Based Rice Variety Classification : A Comprehensive Review
Rice variety classification plays a crucial role in ensuring food quality, agricultural productivity, seed purity, and market value. Traditional classification methods rely heavily on expert knowledge and manual inspection, which are time-consuming, subjective, and prone to error. With the rapid advancement of artificial intelligence (AI), computer vision, and deep learning techniques, automated rice variety classification has gained significant research attention. Recent studies have explored convolutional neural networks, vision transformers, transfer learning, and ensemble learning models to improve classification accuracy using grain-level, leaf-level, and field-level images. These approaches leverage feature extraction, attention mechanisms, and data augmentation to handle intra-class similarity and inter-class variation among rice varieties. This review provides a systematic overview of state-of-the-art AI-based rice variety classification techniques, highlighting methodologies, datasets, advantages, and limitations reported in recent literature. Furthermore, it analyzes research findings, identifies key challenges such as dataset scarcity, environmental variability, and model generalization, and outlines future research directions. The study aims to serve as a comprehensive reference for researchers and practitioners seeking to develop robust, scalable, and interpretable rice variety classification systems using modern AI techniques.
DeepFake Detection Through Deep Learning: A Comprehensive Review
Deepfake technology, driven by advances in deep learning and generative models such as Generative Adversarial Networks (GANs), has emerged as a significant threat to digital media authenticity. The increasing realism of manipulated images, videos, and audio has raised serious concerns in areas including social media, journalism, digital forensics, national security, and public trust. This review paper presents a comprehensive analysis of deep-fake detection techniques based on deep learning methodologies. The paper systematically explores convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, ensemble learning, multimodal frameworks, and lightweight real-time detection models. A detailed literature study highlights recent state-of-the-art approaches, performance metrics, advantages, and limitations. Comparative analyses are provided to evaluate detection accuracy, computational complexity, robustness, and generalization capability. Furthermore, existing research gaps and challenges such as dataset bias, generalization to unseen manipulations, real-time constraints, and adversarial robustness are discussed. The paper concludes by outlining future research directions emphasizing multimodal learning, explainable AI, domain adaptation, and ethical and regulatory considerations. This review aims to serve as a valuable reference for researchers and practitioners working in the field of deepfake detection.
Deep Learning–Based Soybean Leaf Disease Classification : A Comprehensive Review
Soybean is one of the most economically important crops worldwide, yet its productivity is severely affected by a wide range of foliar diseases. Traditional disease diagnosis relies on expert visual inspection, which is time-consuming, subjective, and impractical for large-scale monitoring. In recent years, computer vision and artificial intelligence have emerged as promising tools for automated soybean leaf disease classification. This review presents a comprehensive analysis of state-of-the-art image-based soybean leaf disease classification techniques, with particular emphasis on deep learning, transfer learning, federated learning, and transformer-based models. The paper systematically examines preprocessing strategies, feature extraction methods, classification architectures, and evaluation protocols used in recent studies. Furthermore, comparative insights are drawn across convolutional neural networks, lightweight models, ensemble approaches, and vision transformers. The review also highlights key research findings, existing challenges, and unresolved limitations such as dataset imbalance, real-field variability, explainability, and deployment constraints. By synthesizing recent advances and identifying open research directions, this review aims to guide researchers toward more robust, scalable, and intelligent soybean disease diagnosis systems for sustainable agriculture.
Implementing Smart Automation Solutions in Medium-Scale Industries Using SCADA Systems
This study examines the strategic deployment of intelligent automation systems in medium-scale industrial environments, focusing on the integration of Supervisory Control and Data Acquisition (SCADA) architecture as a catalyst for operational transformation. The research sought to investigate how smart automation technologies, supported by Artificial Intelligence (AI), Machine Learning (ML), and the Industrial Internet of Things (IIoT), enhance real-time process control, efficiency, and sustainability. Using a conceptual and analytical approach grounded in contemporary industrial frameworks, the study synthesized multidisciplinary insights from automation engineering, data analytics, and systems management to develop a comprehensive understanding of how SCADA-based automation advances industrial performance. Findings reveal that the convergence of SCADA with AI-driven analytics and IoT infrastructures fosters intelligent decision-making, predictive maintenance, and efficient resource utilization across production networks. The study further establishes that while automation fosters transparency and productivity, medium-scale industries often face implementation challenges, including financial constraints, cybersecurity vulnerabilities, and insufficient technical expertise. The incorporation of robust cybersecurity protocols and adaptive governance mechanisms was found to be essential for ensuring secure and sustainable system integration. The paper concludes that SCADA systems form the foundation of digital transformation in medium-scale industries, enabling operational resilience and competitive advantage. It emphasizes the necessity of a phased, scalable approach to automation, one that harmonizes technological innovation with workforce readiness, data ethics, and sustainable practices. The study recommends policy-driven support, infrastructure investment, and cross-sector collaboration to promote equitable access to automation technologies and long-term industrial modernization. Through this synthesis, the research underscores the evolution of medium-scale industries toward intelligent, self-regulating, and ethically governed operational ecosystems.
Bridging Technical Expertise and Social Impact: The Role of Engineers in Workforce Development
This study critically explores the evolving function of engineers as pivotal actors in fostering workforce innovation, leadership development, and social transformation within technology-driven economies. The purpose of the research was to examine how engineering expertise, when combined with social awareness and ethical intelligence, can bridge the gap between technical advancement and human capital development. Employing a comprehensive literature-based analytical approach, the study synthesized scholarly contributions from multidisciplinary sources to conceptualize the engineer’s expanding role in enhancing organizational capability, driving workforce resilience, and promoting inclusive growth. The findings revealed that the integration of digital transformation, artificial intelligence, and data-driven systems has fundamentally redefined the nature of engineering work, requiring a balanced blend of technical mastery and interpersonal competence. Engineers are increasingly positioned as agents of systemic change, facilitating adaptive learning cultures and strategic innovation across industries. The study further demonstrated that engineering leadership, underpinned by emotional intelligence and ethical foresight, is instrumental in cultivating sustainable and equitable workforce ecosystems. Moreover, technology emerged not only as a tool for operational efficiency but also as a catalyst for resilience, creativity, and lifelong learning within the modern workforce. The study concludes that meaningful progress in workforce development depends on an interdisciplinary understanding of engineering that prioritizes ethics, collaboration, and societal impact alongside technical rigor. It recommends that academic institutions, policymakers, and industry leaders collaborate to redesign educational frameworks, leadership programs, and technological policies that prepare engineers to thrive in increasingly complex socio-technical environments. The findings underscore the imperative of embedding human-centered innovation within engineering practice to ensure a balanced pursuit of technological excellence and social sustainability.
Survival Study on Plant Growth Monitoring and Irrigation Management in Smart Farming
Agriculture is considered as an essential sector for performing efficient food production. The fast growth of digital technology has transformed in every sector. Smart Farming employs the Internet of Things (IoT) technology to automate and optimize the agricultural operation. IoT devices are employed for efficient data communication between farming equipment and cloud-based platforms for remote plant growth monitoring and irrigation control. In smart farming, plant growth monitoring and intelligent irrigation perform efficient prediction with IoT technologies. Many researchers carried out their research on efficient plant growth monitoring and irrigation management in smart farming. But, the accuracy level was not enhanced and time complexity was not minimized by existing prediction methods. Therefore, machine learning and artificial intelligence based methods are reviewed in this article for plant growth monitoring and irrigation management.
Smart Cloud-Based Meeting summarization using an Artificial Neural Network
The rapid adoption of video conference systems has created a demand to automate the meeting summary process, aiming to boost productivity and reduce memory consumption. This paper presents a meeting summarization framework based on an Artificial Neural Network (ANN) that can develop concise and rich context summaries for real-time or recorded meetings. The collection of data is carried out with the help of audio, video, and text reports from a public dataset and live meetings of based cloud database. Preprocessing involves converting speech into text, filtering out noise, performing speaker diarization, and removing irrelevant content store in the cloud of the network. The Natural Language Processing (NLP) technique of feature extraction employs TF-IDF, word embeddings, and other topic modelling to capture semantics in the cloud of the network in performance. An ANN is used to classify and prioritize important discussion points, decisions, and action items in the cloud. The measures of performance, being ROUGE score 83%, precision 77%, recall 77%, and F1-score 85%, are used. The experimental evidence indicates that the ANN model offers high summarization quality and relevance, allowing for efficient review of meetings and avoiding the need to spend time writing notes.
AI-Based Enhancing Supply Chain Management Using PSO with K-Nearest Neighbour Approach in Biotech
Recent developments in biotechnology rely heavily on new technologies such as Artificial Intelligence (AI). Over the past few years, AI has played a significant role in driving developments in the biotechnology industry. Effective Supply Chain Management (SCM) to build long-term partnerships requires rigorous standards and decision-making processes that have a significant impact on overall results. To resolve this problem, we propose the Particle Swarm Optimization-K-Nearest Neighbors (PSO-KNN) algorithm in biotech to improve AI-based supply chain management. Furthermore, the Z-score Normalized Outlier Detection (ZSNOD) model is utilized to pre-process the data by eliminating missing or random values. After that, the optimal feature can be selected using the Pearson Covariance-Chi-Square Test (PC2ST) model during the feature selection process.Finally, AI-based supply chain management in biotechnology enhances efficiency by utilizing the PSO-KNN algorithm to classify data and minimize errors. Moreover, the proposed method can enhance supply chain management in biotech by increasing test accuracy to 96.28% through performance evaluation, including precision, recall, time complexity, error rate, and overall accuracy.
AI-Powered Based Fraud Detection Using Graph Neural Network for Mobile Payment System
Artificial Intelligence (AI) enables machines to perform tasks that require human-like intelligence, including the prediction and detection of anomalies.The GRAPH Neural Networks (GNNs) can be successfully used in mobile payment systems to model user-user, device-user, merchant-user, and transaction relationships and detect fraud based on the pattern of relationships. Nevertheless, traditional approaches to AI face the challenges of massive imbalanced data, changing feature usefulness, multi-entity interactions, and real-time detection. To address these obstacles, we combine the best methods in our workflow. The proposed method, Edge-Level Real-Time Graph Neural Network Risk Scoring (ELRLVN2RS), allocates real-time risk ratings on transactions during the streaming mode, which allows the industry to provide high-risk activity alerts immediately. Additionally, synthetic fraud examples produced by SMOTE-GAN seem realistic and balance the PaySim data set, and they improve the acquisition of rare patterns of fraud. Furthermore, Meta-Learning of Feature Selection is a dynamic feature selection model that selects the most predictive features, adapting to the evolving fraud patterns as well as removing noise. Finally, Heterogeneous Graph Neural Networks (HGNNs) learn both local and global patterns of many entities, and this is because they capture the intricate interactions between them. The presented method AI-GNN framework that offers mobile payment fraud detection based on adaptive, heterogeneous, and real-time risks.
Heterogeneous Graph Neural Networks (HGNN)
AI Automation of B2B Event Management Using Predictive Workflows and Entity Matching
There are many times consuming and complicated tasks that have to be done in business-to-Business (B2B) event management like attendee targeting, lead distribution and scheduling of the vendors. The given paper introduces the idea of an AI-based automation platform that can ease these activities with the help of predictive modelling and matching of entities. It involves a predictive workflow that is based on random forest to predict the interest and engagement of the attendees based on features name, email, company, role, past_ events_ attended, opened _ email, clicked link, registered and attended. Simultaneously, an entity matching component that applies NLP and fuzzy logic automatically links and eliminates duplicates between database contacts, which removes the necessity of manual cleansing. Data preprocessing consists of Label Encoding the categorical variable and normalizing the numerical through Standard Scaler. Real-world B2B event data evaluation indicated extremely high accuracies of 92%, precision of 0.89, recall of 0.90 and F1-score of 0.91. The automation is a big boost to better planning, matchmaking, predication, and lead conversion. The next iteration will have a real time feedback loop and multi-language entity recognition so as to be able to scale the global B2B events.
A Review of Artificial Intelligence Techniques for Rice Leaf Disease Detection and Classification
Rice leaf diseases significantly threaten global rice production, directly affecting crop yield, quality, and food security. Early and accurate detection of these diseases is therefore essential for effective crop management and sustainable agriculture. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated, efficient, and scalable solutions for rice leaf disease detection and classification. This review provides a comprehensive analysis of existing AI-based techniques applied to rice leaf disease diagnosis, covering traditional image processing methods, classical ML classifiers, and state-of-the-art DL architectures such as convolutional neural networks, transfer learning models, and hybrid frameworks. Publicly available datasets, data augmentation strategies, and evaluation metrics commonly used in the literature are also discussed. Furthermore, the review critically examines key challenges, including limited labeled data, class imbalance, environmental variability, and poor generalization under real-field conditions. Special attention is given to recent trends involving explainable AI, lightweight models for edge deployment, and multi-disease classification systems. By identifying research gaps and limitations in current approaches, this review highlights future directions toward robust, interpretable, and real-time AI-driven solutions for rice leaf disease detection. The insights presented aim to support researchers and practitioners in developing reliable decision-support systems for precision agriculture and sustainable rice production.