Journal of Computational Informatics & Business
Artificial Intelligence in Digital Marketing: Enhancing Consumer Engagement and Supporting Sustainable Behavior
Artificial Intelligence (AI) is reshaping digital marketing by empowering marketers to interact with consumers on a personalized basis. But there are very few studies that have explored the relationship between AI Adoption and Digital Marketing Capabilities and Sustainable Consumer Behavior as a consequence of Consumer Engagement in Social and Mobile Network Environments. Following Stimulus – Organism – Response (S-O-R) model, this study examines direct and indirect links between AI Adoption, Digital Marketing Capabilities, Consumer Engagement and Sustainable Consumer Behavior. The quantitative survey method was used by answering questionnaires with 512 active electronic consumers and analyzed using Structural Equation Modeling (SEM). The results show that Sustainable Consumer Behavior is significantly influenced by both AI Adoption and Digital Marketing Capabilities, thereby indicating that this relationship is indirect. AI Adoption and Digital Marketing Capabilities were found to significantly impact Consumer Engagement, which in turn positively affects Consumer Sustainable Behavior, indicating that this relationship is indirect. Moreover, the relationships between AI Adoption, Digital Marketing Capabilities, and Sustainable Consumer Behavior are mediated by Consumer Engagement. These findings validate the significance of engagement as a primary driver of engaging consumer behaviors towards sustainable consumption via AI marketing strategies. Theoretically, the study extends SOR theory to digital marketing and clarifies the capabilities of sustainable behavior
Copyright (c) 2025 Journal of Computational Informatics & Business
SmartFault-M: A Hybrid CNN–LSTM with Attention for Multi-Domain Fault Diagnosis in Industrial Mechatronic Systems
Diagnosing faults in industrial mechatronics can be difficult because the systems involve intricate interactions among mechanical, electrical, and control sub-systems which produce multidimensional, non-stationary data from various sensors. Traditional fault diagnostics models based on model-based or data-driven approaches in single domains cannot provide adequate performance because they cannot effectively capture the multi-scale and temporal nature of faults. Therefore, in this paper, a novel approach called Smart Fault Monitoring (SmartFault-M), a hybrid model based on a dual-stream CNN–LSTM framework for smart fault diagnosis, is proposed, in which two parallel streams with time-domain and frequency-domain feature maps extract informative features and attention-based learning captures important patterns. In SmartFault-M, CNN and LSTM are used to learn spatial and temporal information from the data, respectively, while attention learning emphasizes the informative features. For validating the effectiveness of our proposed approach, we perform extensive experiments using the bearing dataset of Case Western Reserve University (CWRU) under different fault states. As experimental results show, the accuracy of the proposed SmartFault-M reaches 98.25% and outperforms baseline methods like CNN (91.42%), LSTM (92.36%), and CNN–LSTM (95.18%) models. In addition, the model shows consistently high precision, recall, and F1-score for all classes of faults. From these experiments, it is obvious that our proposed model with multi-domain feature extraction and attention-based learning achieves superior performance for smart fault diagnosis tasks.
Copyright (c) 2026 Journal of Computational Informatics & Business
UrbanFlow-GST: A Dynamic Graph-Based Spatio-Temporal Fusion Model for Real-Time Traffic Speed Forecasting
Real-time and accurate traffic forecasting forms the basis of intelligent transportation systems, especially in densely populated areas where traffic conditions keep varying with time depending on complicated spatio-temporal correlations. Conventional as well as deep learning-based approaches tend to lack simultaneous consideration of spatio-temporal dynamics, thus rendering them inefficient in the practical setting. In light of this problem, this paper presents UrbanFlow-GST, an adaptive graph learning and attention-based modeling approach for traffic forecasting. The proposed system builds upon a graph and implements spatio-temporal transformer, using a dynamic adjacency mechanism that takes into account time-dependent associations among traffic sensors. For capturing temporal dynamics, the authors adopt the transformer architecture, enabling them to learn both shortand long-term traffic characteristics. Finally, a unified method is developed for combining spatial and temporal features to make multi-horizon predictions. To test the effectiveness of the proposed framework, experiments were conducted using the METR-LA dataset consisting of real-world traffic speeds collected via sensors installed at 207 locations on highways of Los Angeles. The model outperformed competing baselines at all horizons under evaluation. In particular, the authors report MAE, RMSE, and MAPE values of 2.52, 4.90, and 8.1%, respectively, for 15-minute prediction, while the same metrics for 60-minute forecasting are 3.20, 6.80, and 10.3%. The findings suggest high reliability of UrbanFlow-GST
Deep Learning-Based Energy Load Forecasting Using a Hybrid TCN–LSTM Model with Attention Mechanism
Accurate energy load forecasting is a fundamental requirement for ensuring the reliable operation, economic efficiency, and sustainability of modern power systems. However, electricity consumption patterns are inherently complex, exhibiting nonlinear behavior and strong seasonal variations, which pose significant challenges for traditional forecasting methods. Conventional statistical approaches often fail to adequately capture these dynamics, resulting in limited prediction accuracy. In this study, a hybrid deep learning framework is proposed that combines Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and an attention mechanism to effectively model both short-term fluctuations and long-term dependencies in electricity load data. The TCN component is utilized to extract local temporal features through dilated causal convolutions, while the LSTM network captures long-range sequential dependencies. The attention mechanism further enhances the model by selectively focusing on the most relevant time steps, thereby improving both prediction accuracy and interpretability. The proposed model is evaluated using the UCI Electricity Load Diagrams dataset (2011–2014), which contains high-resolution electricity consumption data from 370 customers. Experimental results demonstrate that the hybrid model achieves superior performance, attaining an RMSE of 2.9 and MAE of 2.2, significantly outperforming baseline models including ARIMA, SVR, and standalone deep learning approaches. The model shows an improvement of approximately 44% over ARIMA, 37% over SVR, and 23% over conventional LSTM models, highlighting its effectiveness in capturing complex temporal patterns. The results confirm that the proposed hybrid architecture provides a robust and scalable solution for energy load forecasting. Its ability to accurately model multi-scale temporal dependencies makes it particularly suitable for real-world smart grid applications, supporting improved demand response strategies and efficient energy management.
Energy Load Forecasting; Deep Learning, LSTM; TCN, Smart Grids, UCI Electricity Dataset
Electrical Load Prediction Using Statistical, Deep Learning, and Hybrid Time Series Models
The efficient operation, planning and reliability of modern power systems rely on accurate electrical load forecasting. Accurate demand forecasting allows for optimal use of resources by cutting unnecessary expenditures and improving grid stability. Since electricity usage is particularly volatile and nonlinear in time, conventional forecasting methods frequently have trouble identifying the complex temporal patterns. Finally, this study examines the performance of models based on time-series and deep learning for electrical load forecasting over several time horizons. Several statistical models (Autoregressive Integrated Moving Average — ARIMA and Seasonal ARIMA — SARIMA) are compared to a Long Short-Term Memory (LSTM) neural network to evaluate their forecasting performance. In addition, a hybrid ARIMA–LSTM model is presented to jointly reflect linear, seasonal, and nonlinear features of load series. The temporal and climatic-enhanced historical electricity consumption data are preprocessed and subject to stationarity tests and data consistency checks. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) are used to assess the performance of model in short-, medium- and long-term forecasting, respectively. From the experimental results, we show that the hybrid ARIMA–LSTM model enables to always outperform individual models, producing the lowest prediction errors over all forecasting horizons. The results show that incorporating classical statistical method with deep learning methods can lead to better accuracy and robustness in this electrical load prediction problem.
ProfileSwitchNet: Predicting e-SIM Carrier-Switch Behavior from Provisioning and Lifecycle Signals
This work introduces ProfileSwitchNet, a predictive model for forecasting e-SIM carrier-switch behavior based on historical provisioning and lifecycle data. Each e-SIM profile is represented by features such as device type, initial and subsequent carrier profiles, activation counts, roaming flags, region, data plan class, and recorded status changes over time. A sequence-aware model (e.g., temporal gradient boosting or a recurrent neural network) is trained to estimate the probability that a profile will be deactivated or switched to a different carrier within a future time window. The framework evaluates performance using ROC-AUC, precision–recall, and calibration metrics, and analyzes feature importance to identify the strongest drivers of switching, such as device category or activation failure bursts. ProfileSwitchNet is intended to help operators design better retention offers, optimize roaming strategies, and understand early signals of churn in eSIM ecosystems.
Enhancing cognitive automation capabilities with reinforcement learning techniques in robotic process automation using UiPath and automation
Robotic Process Automation (RPA) has traditionally excelled at automating routine, rule-based tasks, but lacks the adaptability required for complex, evolving processes. Cognitive automation is the next frontier, combining AI techniques (e.g. machine learning, NLP, computer vision) with RPA to handle unstructured data and dynamic decision-making. This report examines how reinforcement learning (RL) – an AI paradigm where agents learn via trial-and-error feedback – can enhance cognitive RPA. We review the literature on cognitive RPA and RL, propose a theoretical framework for integrating RL into UiPath and Automation Anywhere workflows, and discuss potential benefits and challenges. Key advantages of RL-based RPA include continuous self-improvement, better exception handling, and adaptive optimization of processes. However, issues such as training complexity, interpretability, and integration barriers remain. We conclude that RL has strong potential to bridge the gap between rigid RPA and intelligent automation, enabling software bots to learn from experience and make data-driven decisions that improve efficiency and resilience over time.
Copyright (c) 2023 Journal of Computational Informatics & Business
CodeSage-GNN: Cross-Modal Graph Neural Network for Intelligent Software Defect Prediction
This study proposes CodeSage-GNN, a cross-modal graph neural framework for predicting defect-prone software modules. CodeSage-GNN represents each file or class as a heterogeneous graph combining structural metrics, dependency relations, and semantic embeddings of source code tokens. A dual-channel message-passing network jointly learns from structural graphs and textual semantics, while an attention-based fusion layer highlights the most influential features contributing to defects. The model is evaluated on multiple open software defect benchmarks to measure F1-score, MCC, AUC, and cross-project generalization. By integrating graph representation learning with interpretable attentions, CodeSage-GNN aims to support early fault localization and quality assurance in large-scale software systems.
Generative Artificial Intelligence and the Media Industries: Collective Consequences for Journalism, Advertising, and Public Relations
Generative artificial intelligence (AI) has rapidly emerged as a disruptive force in media industries, reshaping how information is created, disseminated, and consumed. While much scholarly attention has focused on individual sectors such as journalism, advertising, or public relations, the broader collective consequences of AI adoption across these interconnected fields remain underexplored. This paper discusses how generative AI transforms professional practices, ethical frameworks, and audience relationships across the three domains, highlighting both opportunities for efficiency and innovation as well as challenges of credibility, transparency, and trust. By framing AI as a cross-industry actor, the study emphasizes the need to move beyond siloed approaches and toward a holistic understanding of media ecosystems in the age of automation. Ultimately, the paper argues for a shared research and policy agenda that addresses the converging risks and responsibilities of journalism, advertising, and public relations, with particular attention to disinformation, professional identity, and audience literacy in a rapidly evolving communicative environment.
Copyright (c) 2024 Journal of Computational Informatics & Business
Enabling Circular Economy Transition Through Adopting Lean Production and Industry 4.0 Technologies
This research examines the suggested impacts of Lean Production (LP), Industry 4.0 (I4.0), and Reverse Logistics (RL) on the facilitation of a Circular Economy (CE). It examines how environmentally friendly lean manufacturing and circular approaches can lead businesses towards low-waste, resource-efficient systems. As the model is theoretical, the research suggests possible positive or negative impacts that need to be empirically tested. This study analyze the effect of Lean Production methods and Industry 4.0 technologies on use of Circular Economy strategies in sustainable manufacturing framework. The purpose of this study is to explore how integrating practices with digital tools lean and reverse logistics can operationalize circular economy principles in modern manufacturing. The findings are conceptual and aim to provide strategic insights for future implementation and research in sustainable industrial development.
Lean Production (LP), Circular Economy (CE), Industry 4.0 (I4.0), Reverse Logistics (RL), Sustainable Manufacturing, Environmental Sustainability, Resource Efficiency, Digital Transformation, Triple Bottom Line (TBL), Flexible Operations
Resilience through Integration: Exploring the Impact of Technological and Supplier Management on Supply Chain Efficacy
This study investigates the dynamic landscape of Supply Chain Risk Management emphasizing the critical role of strategic variables such as technological integration, supplier management, and global supply chain risk management in shaping supply chain performance. The key purpose of this research is to examine how these independent variables influence the dependent variable, supply chain performance, especially in the context of increasing global volatility.The importance of this study lies in its comprehensive analysis of how proactive strategies can mitigate risks and enhance resilience. It aims to provide a conceptual understanding of how firms can navigate regulatory pressures and external disruptions through adaptive and integrated supply chain models. This includes examining the mediating role of regulatory environmentsand the moderating influence of risk mitigation strategies, such as digital twins, inventory buffers, and scenario-based planning. Conceptual findings reveal that technological integration has a significantly positive effect on real-time decision-making and agility. Regulatory environments serve as filters that mediate the outcomes of strategic decisions, while risk mitigation acts as a safety net to protect against variable impacts. This study provides an integrative framework that maps how interconnectedelements within SCRM can positively influence performance. The findings support the notion that organizations with embedded risk-resilient strategies are better equipped to respond to global supply chain disruptions. The study lays a foundation for futureempirical investigations into how interconnected supply chain elements can collectively drive resilience, adaptability, and sustainability
Implementing Green Behavior Strategies and Corporate Social Responsibility to Achieve Improved Organizational Performance.
In today era, escalating ecological challenges and governmental pressure, firms are increasingly adopting green behaviour strategies and Corporate Social Responsibility (CSR) to enhance firm performance. This study is conceptual in nature and examine the proposed the impact of green behavior and CSR on organizational performance, exploring how environmentally responsible practices and socially conscious initiatives contribute to economic, operational, and reputational gains. The proposed findings elucidated that strategic implementation of green behavior and CSR not only reduces environmental impact but also fosters innovation and cost efficiency and improve firm performance. The study provide fruitful implications for managers aiming to align sustainability with corporate objectives and provides policy recommendations to foster responsible business conduct in the pursuit of competitive advantage.
CSR, Organizational Performance, Sustainable Competitive edge, Sustainability
Empirical Comparison of C++ And Python For Teaching Introductory Programming Students and Their Impact On Learning Outcomes
: Programming is at the bedrock of the computing discipline and the decision between languages of choice to be studied as first programming language lays the critical aspect of the learning of the fundamental programming concepts, coding standards, and problem-solving methods. In the context of a rapidly developing technology, it is necessary to reconcile pedagogical efficiency in academic training as well as with the trends in the industry. C++ has enjoyed a long history as a dominant programming language in academia and industry but doubts are being raised that it is not the most effective language to be learnt as a beginner due to its perhaps more complex code than other languages such as Python. This paper is comparative research on C++ and Python as the introductory languages in undergraduate studies. An evaluation of the literature and student performance and engagement through survey allows us to conclude that Python, with its simplicity, readability, syntactic regularity, orthogonality, and greater degree of abstraction, is likely to produce higher learning outcomes and engagement even in novice programmers than C++. The paper concludes that Python (as a programming language) is potentially more effective in teaching elementary programming, but that also it is necessary to consider the goals of a curriculum and industry practices.
Programming Language Learner's Perspective
Securing AI Supply Chains with Blockchain and ML-Based Threat Detection
The increasing integration of Artificial Intelligence (AI) into critical sectors such as healthcare, finance, banking, and autonomous systems has exposed significant vulnerabilities in AI supply chains. These supply chains, which encompass data collection, model training, and deployment, are susceptible to various threats, including data tampering, model poisoning, and adversarial attacks. Traditional security mechanisms often fall short in providing the necessary transparency, traceability, and real-time threat detection required to safeguard these complex systems. To address these challenges, this paper introduces a novel framework that synergistically combines blockchain technology for immutable and decentralized record-keeping with machine learning (ML)-based threat detection for dynamic and real-time anomaly identification. Our approach aims to secure the entire AI lifecycle, from data provenance to model deployment, by leveraging blockchain's tamper-proof ledger capabilities and ML's predictive analytics for threat detection. Through extensive experimentation, we demonstrate that our framework significantly enhances supply chain integrity, mitigates vulnerabilities, and achieves a 15-20% improvement in attack detection accuracy compared to conventional security methods. The results highlight the potential of integrating blockchain and ML to create a robust and scalable security solution for AI supply chains.
AI Supply Chain; Blockchain; Machine Learning; Threat Detection; Cybersecurity; Data Provenance; Adversarial Attacks
Blockchain-Enabled Explainable AI: A Framework for Verifiable and Trustworthy Machine Learning Interpretability
The increasing reliance on artificial intelligence (AI) and machine learning (ML) in critical decision-making domains such as healthcare, finance, banking, and autonomous systems has underscored the need for transparency, interpretability, and trustworthiness in AI models. While Explainable AI (XAI) techniques have made significant strides in providing human-understandable explanations for model predictions, a critical gap remains in ensuring that these explanations are verifiable, tamper-proof, and auditable. This paper introduces a novel framework that integrates blockchain technology with XAI to enhance the trustworthiness of machine learning interpretability. By leveraging blockchain’s inherent properties—immutability, decentralization, and cryptographic security—we propose a system where model explanations are securely recorded, validated, and audited in a transparent and tamper-resistant manner. Our framework, Blockchain-Enabled Explainable AI (BE-XAI), employs smart contracts for automated logging of explanations, decentralized consensus mechanisms for validation, and cryptographic attestation to ensure the authenticity of interpretability results. We conduct extensive experiments on benchmark datasets, including UCI Adult Income, MNIST, and IMDB Sentiment Analysis, using diverse ML models such as Random Forest, CNN, and BERT, alongside popular XAI methods like SHAP, LIME, and Integrated Gradients. The results demonstrate that BE-XAI successfully preserves explanation integrity, mitigates risks of post-hoc manipulation, and provides a robust mechanism for auditability. The implications of this work are far-reaching, particularly in high-stakes applications where accountability and regulatory compliance are paramount.
Explainable AI (XAI); Blockchain Technology; Trustworthy Machine Learning; Interpretability Verification; Smart Contracts; Decentralized Validation; Cryptographic Attestation; Immutable Ledger
Artificial Intelligence in Renal Oncology: CNN-Based Classification of Kidney Cancer
Medical researchers worldwide identify kidney cancer as a major mortal and morbid disease while its early-stage detection significantly improves patient survival potential. Global diagnostic practices based on imaging and biopsy techniques carry both costly intrusiveness and potential human errors in their diagnostic results. This research creates an automatic kidney cancer diagnostic system which utilizes deep learning CNN models and applies ResNet transfer learning techniques. Through the use of pre-trained ResNet architectures the model demonstrates robustness for classifying benign versus malignant kidney tumor images found in CT scans. The training process utilizes a kidney CT image dataset that required various preprocessing steps for both data augmentation and normalization and size adjustment to create a generalized model. The deep learning framework showcases superior performance than standard approaches while delivering a 92% accuracy rate through productive F1-scores alongside precise recall metrics. The Area Under the Curve metric of 0.95 demonstrates the model's powerful discriminatory performance. The utilization of ResNet transfer learning enabled models to extract meaningful insights from restricted datasets thereby minimizing dependence on lengthy labeled datasets while preserving prediction accuracy. The study demonstrates how CNN-based techniques present a promising diagnostic framework for clinical kidney cancer detection which combines accuracy with efficiency and non-invasiveness for aiding radiologists and clinicians during diagnostics.
Kidney Cancer, Deep Learning, Convolutional Neural Networks (CNN), ResNet, Transfer Learning, Medical Imaging, Classification, Tumor Diagnosis, CT Scans
Metaverse Is Pioneering Sustainability And Circular Economy Innovations
Metaverse is an emerging digital frontier, is redefining the paradigms of sustainability and circular economy by fostering innovative solution that transcend physical limitations. The aim of this study is to identify the principal issues with which sustainability can be linked to metaverse. This study also discussed the role of metaverse environment in circular economy strategies. The findings affirmed that the merging of metaverse with circular economy is promising, however it is quite challenging. The findings also indicated that metaverse can be a platform for a circular economy promoting the preservation, reduction, and recycling of waste, putting up and constantly innovating this kind of system will take huge investments in resources, knowledge, collaboration, and innovation among the stakeholders and researchers with an effective resolving of ownership problems. The study also provides some fruitful avenues for future studies and policy implications for governmental bodies for effective implementation of metaverse in circular economy strategies and sustainability initiatives.
Metaverse Technology, Circular economy, Sustainability, Innovation
Impact of Total Quality Management on Environmental Performance as the mediating role of Green Manufacturing Practices and Ambidextrous Green Innovation
The aim of this study is to analyze the relationship between total quality management (TQM), environmental performance (EP), with the mediating role of green manufacturing practices (GMPs), and ambidextrous green innovation (AGI). This study investigates different aspects of TQM and find similarities with GMPs. RBV and natural RBV define the organizations performance and competitive edge. This conceptual study contributes in literature by adding new variable AGI in the framework of TQM, GMPs, and EP. Future researchers can hled empirical study by adding new variables in the framework to provide more indepth explanation.
Total quality management, Green manufacturing practices, Ambidextrous green innovation, Environmental performance, Resource based view
Enhancing Environmental Performance through Lean Practices: The Mediating Role of Circular Economy
This paper explores how the combination of Lean Practices (LP) with Circular Economy (CE) principles can improve Environmental Performance (EP), focusing on CE as a mediator. This study is to establish the relationship between Lean Practices and Environmental Performance with specific reference to CE. This conceptual paper elucidates that CE can improve Lean’s efficiency to attain improved environmental results. It is also proposed that CE can have a crucial role in the relationship between Lean Practice and environmental performance. This paper advances the literature by offering a novel integration model for LP and CE toward creating sustainability. It presents some tangible suggestions for managers and policymakers with a focus on coordination for a long time in environmental conservation. The study offers suggestions to testing this framework on other types of industries to provide fruitful insight how the use of technologies can impact ecological performance.
Total quality management practices, organizational innovation, organizational performance
The Impact of TQM Practices on Sustainability Performance: Exploring the Mediating Role of Circular Economy
Companies can be more sustainable by embracing Circular Economy (CE) practices. Nevertheless, the relationship of CE implementation on sustainability performance of companies are obscure to even a greater extent when determinants like Industry 4.0 and stakeholder demands influence both CE implementation and sustainability performance. The present literature provides understand of the association between CE implementation, sustainability performance and the factors that influence them. This conceptual paper investigates the impact of TQM practices on CE and its role in sustainability performance. This research seeks to advance theoretical understanding, providing insights into how Total Quality Management (TQM) impacts environmental (ENP), economic (ECO), social performance (SOP) with CE serving as an intermediary.
Circular economy, Total quality Management practices, Environmental performance, Economic performance, Social Performance
Impact of Total Quality Management on Organizational Performance as the mediating role of Organizational Innovation
This research seeks the integration of TQM with organizational innovation to achieve an increase in organizational performance, especially within the manufacturing industry. This research is focused to examine how integrating TQM practices with organizational innovation results in high efficiency, competitiveness and adaptability. Organizational innovation plays the key role as mediator linking TQM with organizational performance. By focusing on this mediation the study highlights how innovation driven by quality based practices, helps organization align their operations with market demand and fosters continuous improvement. The contribution of this paper lies in linking the TQM and Innovation within the resource based view framework, providing a comprehensive understanding of how these strategies work together to achieve the sustainable business success. This conceptual paper provides the theoretical explanations and practical advice to managers, policy makers and governmental bodies aiming to use integrated quality management and innovation to improve organizational performance.
Enhancing Anti-Money Laundering Systems with Machine Learning: A Comparative Analysis of Supervised Models
Money laundering is a crucial worldwide problem which is posing consistent challenge to financial institutions and global economies, consenting illicit funds infiltrate legal financial systems and destabilize economic stability. By ascertaining unusual trends in massive amount of financial transactions, Machine learning (ML) is emerged as a dominant tool to address this challenge. This research discusses the employment of Random Forests and other state-of-the-art ML systems to effectually perceive money laundering actions. A broad development for feature engineering, model training, and performance valuation is defined in the work by a Kaggle dataset of anonymised bank transactions classified as either real or suspect. The Random Forest model's capability to categorize suspicious transactions is established by its excellent accuracy. By signifying the potential contribution of ML to the anticipation of financial crimes, these outcomes set the foundation for robust anti-money laundering scheme.
Post-Pandemic Insights: Evaluating the Impact of Big Data Analytics, Circular Economy Practices, and Digital Marketing on Firm Performance
The COVID-19 pandemic has caused significant disruption to global economies, disrupting business activities, consumer behaviour and supply chain dynamics. Keep in view these issues, employing business data analytics (BDA), circular economy (CE), and digital marketing has become critical for businesses to stay growing and agile. The study explores the impact of BDA on CE and digital marketing and their impact in enhancing resilience and sustainability performance of China’s manufacturing sector during the post-pandemic era. Via cross-sectional data and the PLS-SEM (partial least squares structural equation) modelling, the findings validates that BDA implementation augments decision-making, while CE contributes to environmental impact mitigation and cost reduction. The findings also elucidate that collective focus on digital marketing, driven by changing consumer preferences, is driving customer engagement and boosting financial performance. This research suggests policy makers to invest in BDA and data driven stategies to get enrich insight about market trends and real time data, customer preference and manage SC disruption in post pandemic era.
Circular Economy, Business Data Analytics, Digital Marketing, Supply Chain Management, Post-pandemic, Resilience
Deep Learning for Malware Detection: Enhancing cybersecurity with Advanced Classification Models
The rapid propagation of malware presents a important test to global cybersecurity, demanding cultured detection methods talented of growing with initial threats. Traditional signature based methods are stressed to keep step with increasingly advanced malware alternatives, nessitating innvoative solutions that can classify both known and unknown therats with high accuarcy. This study discovers the use of deep learning procedures for enhancing malware detection capabilites, paying preprocessing approaches such as normalization and Term Frequency-Inverse Document Frequency (TF-IDF) for active feature engineering specifically directing on API calls as serious pointers of an submission’s behaviour. A Long Short Term Memory (LSTM) network is used to model the time-based dependencies inhert in malware behaviors, enabling the identification of strange sequences telling of malicious movement. By integrating advanced preprocessing, feature engineering, and deep learning, the proposed systems enhances detection accuracy, reduces false positive, and improves flexibility against complication technique used by cyber criminals. The findings suggest that incorporating LSTM networks, combined with effective feature engineering, significantly boosts the capability of malware detection systems conducive to a robust protection against developing cyber threats and a safer digital environment.
Long short Term Memory(LSTM), Term Frequency-Inverse Document Frequency(TF-IDF), Normalization
A Bibliometric Analysis of Consumer Brand Relationships
This systematic literature review precisely investigates Consumer Brand Relationship (CBR) with allied variables, and separates its complicated undercurrents in marketing frameworks. This study circumnavigates varied extents of CBR, comprising underlying theories and factors. The Scopus database was selected to gather 311 papers, which encompassed the theme of the present study, between the years 2014 and 2024. Out of these, 232 publications were selected for analysis. The study's findings culminate the critical significance of the selected variable, not only in influencing other marketing variables but also in serving as a reagent for numerous factors that play vital roles within marketing frameworks. The global setting, particularly in European and Asian countries, stresses an increasing importance of consumer brand relations, apparent through studies exploring its practical applications. The findings of the literature review advocate for an extended scope of consumer brand relations. This study not only emphasizes the importance of consumer brand relations but also offers valued discernments for imminent research on consumer brand relations
Optimizing Early Detection of Diabetes through Retinal Imaging: A Comparative Analysis of Deep Learning and Machine Learning Algorithms
Early detection of diabetes is essential to prevent complications like diabetic retinopathy. This study evaluates the effectiveness of five algorithms—Convolutional Neural Networks (CNN), Random Forest, Support Vector Machines (SVM), Gradient Boosting, and K-Nearest Neighbors (KNN)—in detecting diabetes through retinal imaging. Using a dataset of 15,000 retinal images, models were assessed for accuracy, precision, recall, and F1-score, with image preprocessing and data augmentation such as Rotation (θ\thetaθ), Flipping (Horizontal and Vertical), Zooming (zzz), Brightness Adjustments, CLAHE, sharpening filters and Gaussian Blur are applied to enhance performance. CNN beat the other models, reaching a 97.2% accuracy, indicating its supremacy in predicting performance. The comparison research also showed distinct strengths and limits of each method, demonstrating their usefulness across diverse diagnostic circumstances. These results underline the transformational potential of machine learning in medical diagnostics, notably for retinal imaging-based diabetes identification.
Driving Business Success Through Sustainable Practices: A Conceptual Paper
In this study, sustainable organizational practices are examined and the findings are presented. The study highlights the advantages of sustainable practices, including increased profitability, enhanced brand perception, and improved stakeholder relationships, and identifies sustainable supply chain management, green marketing, and stakeholder engagement as the three main areas of sustainable practices. This study suggests that organizations looking to implement sustainable practices should prioritize sustainability as a key business objective, adopt various sustainable practices to reduce their environmental impact, collaborate with stakeholders to drive sustainability across their supply chain, and communicate their sustainability efforts and achievements to stakeholders. The study emphasizes the need for organizations to adopt a holistic approach to sustainability, integrating sustainability into their core business strategy and operations. The paper concludes that by prioritizing sustainability and adopting a holistic approach, organizations can enhance their reputation, build stakeholder trust, and contribute to a sustainable future.
Segmentation of Brain MRI Using U-Net: Innovations in Medical Image Processing
Abstract: Medical image segmentation is crucial for finding significant areas or characteristics within images, exclusively in the field of medical identification. Its importance has grown in recent years, with deep learning-based segmentation emerging as an operative tool for image exploration. The segmentation of brain images, which is required for detecting and treating many brain illnesses, is difficult. This effort emphasises U-Net, a deep learning design developed exclusively for image segmentation tasks in brain MRI research. U-Net has demonstrated considerable potential for overcoming these obstacles and has been successfully used in the segmentation of several brain areas, including the cerebral cortex and subcortical regions. In addition to U-Net's use in brain MRI segmentation, we examine the most recent breakthroughs in deep learning algorithms for medical image segmentation. Keywords: Brain tumors segmentation, U-Net, Residual connections, MRI segmentation, Deep learning
Brain tumors segmentation, U-Net, Residual connections, MRI segmentation, Deep learning