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Hybrid Machine Learning Approach for Gait Type Classification Using Pose-Based Feature Extraction
Gait analysis is essential for the diagnosis of neuromuscular and musculoskeletal disorders. Traditional methods are vulnerable and lead to inconsistency as they rely on subjective assessments. An angle-based approach which uses advanced machine learning techniques have been used address this. Extracted joint angle measurements have been extracted from the video data using computer vision methods. The characteristics used in this research were used to train a hybrid model of a Random Forest classifier and a Fuzzy C-Means clustering algorithm. Random Forest model was used as it is stable and capable of dealing with intricate nonlinear relationships and Fuzzy C-Means was used as it can manage ambiguity in the data as well as overlapping class distributions. The results showed that the Random Forest classifier has a classification accuracy of 94.62%, which is better than the other models in distinguishing between normal and abnormal gait patterns. Fuzzy C-Means also shows high accuracy is capable of clustering various forms of gait and extracting detailed features in gait dynamics. Results suggest that integrating joint angle analysis with machine learning methods provides a credible tool for gait analysis, which can aid clinicians in the early detection and treatment of gait related disorders. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Use of AI in Selected Financial Institutions: A Double-Edged Sword Effect
In the contemporary era, our nation is progressing towards increased integration of advanced technologies, and these innovations are influencing various facets of our daily activities. Notably, artificial intelligence (AI) has become pervasive across all sectors and functions within organizations. Its impact is particularly evident in financial institutions where AI plays a crucial role in activities such as accounting transactions and fraud detection. The relationship between AI and the concept of a 'Double Edged Sword' is explored in this paper. The primary objective is toexamine and assess the utilization of AI in diverse financial institutions and its connection to the 'Double Edged Sword' phenomenon. Additionally, the paper delves into the pros and cons following the implementation of AI in the accounting industry, incorporating insights from selected Chartered Accountants regarding their perspectives on AI application. 2025 IEEE. -
SUPPLY CHAIN ENTRAINMENT AND ORGANIZATIONAL PERFORMANCE: A STUDY IN CONTEXT OF MANUFACTURING SECTOR; [SYNCHRONIZACJA ?A?CUCHA DOSTAW A WYDAJNO?? ORGANIZACYJNA: BADANIE W KONTEK?CIE SEKTORA PRODUKCYJNEGO]
The manufacturing industry is growing more complex and dynamic, demanding a deeper insight into the factors that promote synchronization and boost productivity. In this context, the emerging concept of Supply Chain Entrainment (SCE), which promotes the synchronisation and alignment of processes, activities, and flow across the supply chain, can lead to sustainable growth. This study investigates the impact of SCE on Organizational Performance (OP) within manufacturing organizations. Specifically, it examines how synchronizing measures between supply chain partners influence performance outcomes. This study employs partial least squares structural equation modeling to analyse the effects of SCE facilitators on supplier collaboration, information exchange, and process integration. Additionally, the moderating role of technology adoption on the SCE-OP interrelationship has been studied, acknowledging its crucial influence in today's rapidly evolving digital landscape. The results support a positive effect of supplier collaboration, information sharing, and process integration on SCE and underscore that these are essential factors in accomplishing a harmonized and efficient supply chain. Furthermore, the study provides a direct and meaningful relationship between SCE and OP. This highlights the strategic importance of a supply chain that has been well-entrained in the overall success of the organization. This focus on technology adoption enhances the study's relevance and offers valuable insights for managers operating in the current business environment. The findings from the study contribute valuable knowledge to academicians and industry practitioners, deepening our understanding of manufacturing supply chain dynamics and effective management strategies. 2025, Czestochowa University of Technology. All rights reserved. -
Contributing Factors for Building a Flexible Supply Chain in the Digital Age: Studying Their Impact on SDGs
The rapid advancement in digital technologies has required supply chains to adapt to more flexible and resilient frameworks. This study explores the potential contributing factors to developing a flexible supply chain in the digital age and evaluates their impact on the United Nations Sustainable Development Goals (SDGs). The study employs the fuzzy Delphi method and the fuzzy bestworst method to systematically identify and prioritise the potential contributing factors from a literature survey and expert insights. The fuzzy Delphi method is utilised to attain a consensus among experts on relevant contributing factors, while the fuzzy bestworst method assesses the relative importance among factors and ranks them based on their contributions to supply chain flexibility. The findings emphasise the importance of digital integration, data analytics, and agile methodologies to foster a responsive supply chain. Additionally, the study highlights the positive association between enhanced supply chain flexibility and attaining several SDGs. The study presents a comprehensive framework for supply chain flexibility, integrating supplier diversity, technology, and risk management. Furthermore, it suggests that sustainability, human capital, and risk management are key to building flexible, adaptable supply chains. The studys findings emphasise the need for investment in digital technologies, agility, and collaboration. This study provides a comprehensive framework for policymakers and business leaders, aiming to align sustainable development objectives with supply chain strategies in the digital era. The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2025. -
Enablers of Circular Practices in Fast Fashion Supply Chains: a Study Towards Sustainable Fashion Development
The fast fashion industry confronts substantial sustainability issues because of its high resource consumption and waste output. The literature reveals that past studies have less focused on circular practices in fast fashion supply chain. This study aims to identify and analyse the potential enablers of circularity within fast fashion supply chains, promoting sustainable production and consumption practices. Through a comprehensive literature review and expert consultations with 12 domain experts from academia, apparel manufacturing, and sustainability practices, sixteen enablers of circularity were identified. To understand the interrelationships and hierarchical structure among the identified enablers, Grey DEMATEL method was employed. The results from the study reveal that sound purchasing policies, reverse logistics and adoption of eco-friendly and recyclable packaging act as the most influential causal enablers, while selection of fibres and consumer awareness and acceptance of recycled, refurbished clothing emerge as key effect enablers. By mapping these interrelationships, the study offers actionable insights for fashion retailers, policymakers, and sustainability practitioners to strengthen circular strategies. The findings contribute to advancing circular economy theory in the fashion sector and provide a practical framework for accelerating the transition towards sustainable and circular business models in fast fashion. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Advancing Healthcare Decision Support: Leveraging Fuzzy DEMATEL for Delivering Quality Care
Healthcare Decision Support Systems (DSS) play a pivotal contribution in modern healthcare, aiding in informed decision-making and the distribution of high-quality care. To optimize the systems, it is critical to recognize and prioritize the enablers that provide to their successful establishment and operation. This study presents a comprehensive analysis of 10 key enablers essential for the development and deployment of healthcare DSS for quality care. Utilizing Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), a powerful methodology for discovering complex interdependencies among factors, we systematically evaluate the relationships among these enablers. The enablers, ranging from data integration and clinical collaboration to privacy safeguards and continuous improvement mechanisms, are scrutinized through the lens of Fuzzy DEMATEL, which accommodates the inherent uncertainties and ambiguities within healthcare data. The findings from the study shed light on the strength and direction of the relationships among the enablers, unveiling critical factors that exert substantial influence and those that are most susceptible to external changes. By applying Fuzzy DEMATEL, this study backs to a deeper understanding of the multifaceted nature of healthcare DSS development, offering insights to guide decision-makers, healthcare practitioners, and system developers in their pursuit of improved DSS that enhance the quality of healthcare delivery. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Studying the contribution of digital economy on entrepreneurship and innovative systems: pathway to sustainable development
Purpose This study aims to investigate the impact of digital economy on fostering entrepreneurship and innovative systems. Through comprehensive analysis, the study identifies potential contributors shaping the digital economys involvement to entrepreneurship and innovation systems. Design/methodology/approach The study uses a modified total interpretive structural modeling approach which delves into the complex interrelations between digital economy, entrepreneurial activities, innovation ecosystems and sustainable development objectives. In addition, the authors explain the hierarchical structure and mutual influences among various contributors, highlighting critical pathways and feedback loops. Findings The findings from the study illustrate the contributors of digital economy on entrepreneurship and innovative systems such as supporting regulatory framework, information democratization and outreach and awareness at the bottom level. Furthermore, the study assesses the implications of these findings, including economic growth, job creation, enhanced competitiveness and societal well-being. Moreover, it explores the potential for addressing environmental challenges and fostering inclusive development through digital innovation. Originality/value By providing a nuanced understanding of the dynamics between digital economy, entrepreneurship, innovation and sustainable development, this study offers actionable insights for policymakers, businesses and stakeholders seeking to leverage digital technologies for long-term socioeconomic progress. 2026 Emerald Publishing Limited -
Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains
This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers,business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization. The Author(s) 2025. -
NET ZERO TRANSITION TOWARDS DECARBONIZATION IN CONTEXT OF ENERGY SECTOR
The study provides an identification and analysis of potential enablers that facilitate transition towards net zero in the energy sector through Multi Criteria Decision-Making (MCDM) framework. The identified enablers and causal relationships between them in terms of decarbonization initiatives are studied using the DEMATEL method and combining trapezoidal fuzzy numbers (TFNs). The research design involves an overarching review of thirteen potential enablers to net zero transition within the energy sector, in order of their impact and causality. Top-ranked enablers that would have the greatest impact in achieving the energy transition were carbon pricing mechanisms, waste-to-energy conversion, decentralized energy systems and circular procurement policies. The research indicates that the enablers show causal pathways that are interconnected and can take place as both causes and effects in the decarbonization framework. Application of DEMATEL method using TFNs increases the strength of causal relationship derivation. The study adds to the literature on enabling net zero transition in energy and highlights the importance of a conceptual approach involving a combination of policy, technology and principles of the circular economy. Such lessons can guide policy makers, industry players and academics in planning and speeding up the process to sustainable energy systems and world climate targets. 2026 Sciendo. All rights reserved. -
Prophesying Credit Card Frauds Using Predictive and Deep Transfer Learning: A Comprehensive Experimental Perspective
Credit card fraud has become a major issue in the online financial environment, requiring the implementation of smart and automated tools for real-time detection of frauds. Machine Learning (ML) has been an important asset in this area because of its capability to discover underlying patterns, learn new fraud methods, and offer scalable solutions. This study investigates the usage of different classical machine learning and deep transfer learning based on predictive models for credit card fraud detection with a focus on their comparative performance on six important parameters: time elapsed, accuracy, precision, recall, TNR and F1 score. The investigation makes use of a PCA transformed benchmark dataset with a total of 2,84,807 credit card transactions to train models. In depth experimentation is performed using five classical ML models named Random Forest, Logistic Regression, Linear SVM; Non-Linear SVM; XGBoost and four classical Deep Learned models named MLP, Shallow ANN, ID CNN, and LSTM. To enhance experimental validity, prediction capability of four GNN based CNN models such as Boosting-GNN, Jump-Attentive GNN, GNN and PC-GNN are also tested. Deep learning based neural network models are analysed using seven different activation functions and each model is fit using 10 epochs of batch size 512. Testing results point out that overall best performance in classical ML models is shown by Non-Linear SVM with best recall score depicted by ANN on RBG kernel and GPU. In the ensemble category, Random Forest model exhibits overall best performance with best recall for XGBoost. Precision, accuracy and F1 score of random forest and XG boost are highest. Results have shown that in case of Random Forest the accuracy, precision, recall and F1score are 99.9%, 97.7%, 81.9% and 89.12% respectively whereas for XG boost the values for accuracy, precision and F1 score are 99.96%, 92.63%, 83.81% and 88% respectively. Deep Learned models showed high accuracies, however they were significantly utilized computational resources in respect to elapsed time. The study provides a roadmap to financial institutions for efficient model selection while deciding on implementing automated and trustworthy fraud detection systems and helps shape the dynamic world of intelligent financial security solutions to reduce financial losses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Optimizing financial fraud detection models using genetic algorithms
In the contemporary financial environment, financial deception is a persistent challenge that results in significant economic losses annually. Using machine learning models to detect fraud has become an essential instrument for financial institutions to mitigate these risks. Nevertheless, the optimization of these models to achieve a balance between efficiency and accuracy continues to be a significant obstacle. In this chapter, the application of Genetic Algorithm (GA) as a potent optimization technique for improving financial fraud detection models is examined. Inspired by natural selection, GAs provide a unique way to addressing complicated optimization problems by iteratively improving a population of solutions. The chapter commences by providing a brief summary of financial detection and the limitations associated with conventional approaches. It then explores the fundamental concepts of GAs, including selection, crossover, mutation, and fitness evaluation, to provide a comprehensive understanding of how GAs may be used to improve fraud detection systems. In an exhaustive methodological section, we explore the actual use of GAs to optimize different model parameters, such as feature selection and hyperparameter tweaking. The analysis shows that GA-optimized models outperform standard approaches in terms of detection accuracy, false-positive rate, and computing efficiency. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Integrating SMOTE and Heterogeneous Ensemble Methods for Online FraudDetection
In the continuous evolving digital era, the escalation of online fraud demands a robust and efficient mechanism for its detection and prevention. In the recent years there has been a significant increase in the online bank transactions. The research delves into the integration of different machine learning algorithms and to enhance the models adaptability, Synthetic Minority Oversampling Technique (SMOTE) has been utilized. The approach addresses the challenges of data imbalance and also strengthens the overall detection performance. Through an extensive literature review the study highlights the limitations in the existing issues in online financial fraud. The proposed model employs a heterogeneous ensemble model consisting of K-Nearest Neighbors (KNN), Random Forest, and XGBoost. KNN functions as an anomaly detector, identifying irregularities in transactional data. Simultaneously, Random Forest assesses feature significance and detects intricate patterns, contributing to a comprehensive understanding of fraudulent activity. XGBoost, known for its computational efficiency, ensures real-time responsiveness by adapting to emerging fraud tactics. The system also introduces a soft voting mechanism that seamlessly integrates individual algorithm predictions, resulting in a robust and highly accurate ensemble fraud detection system. Validation on an authentic bank fraud dataset underscores the framework's prowess, showcasing superior fraud detection capabilities and a significant reduction in false positives. The purpose of adopting this approach is to enhance the financial security and safeguard the consumers assets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Introduction to optimization: Techniques and applications in engineering
A key idea in computer science, engineering, economics, and mathematics is optimization, which seeks to identify the optimal option from a range of workable options. An overview of optimization, its importance, and its many uses are given in this chapter. It highlights various forms of optimization, such as linear, nonlinear, convex, and combinatorial optimization, and examines the fundamental concepts of optimization, such as objective functions, constraints, and viable regions. In addition to contemporary strategies such as evolutionary algorithms, machine learning-based optimization, and metaheuristic techniques like genetic algorithms and simulated annealing, the chapter explores few traditional optimization techniques. Real-world applications in banking, logistics, AI, and industrial process optimization are also covered. This chapter offers insights into issue formulation, solution approaches, and efficiency concerns, with a focus on both theoretical underpinnings and real-world applications. It also presents important optimization tools and software that are frequently used in both industry and academics. By the end of this chapter, readers will have a basic understanding of optimization concepts that will allow them to use these ideas to effectively tackle challenging issues. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System
Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources. 2025 IEEE. -
Bridging Compliance and Sustainability: The Role of Integrated Reporting Under the Companies Act 2013 in Advancing the SDGs
The Companies Act 2013 has played a transformative role in reshaping corporate reporting in India by mandating greater transparency, accountability, and disclosure, especially in areas related to environmental, social, and governance (ESG) performance. These legal provisions have laid the groundwork for adopting Integrated Reporting (IR). While IR is not yet mandatory for all Indian companies, its principles align closely with the intent of Indian corporate law to promote responsible and sustainable business practices. The research highlights how Indian Corporate Laws can encourage more companies to adopt the integrated reporting framework, as it is more holistic as compared to the Business Responsibility and Sustainability Reporting (BRSR) and helps in the attainment of SDGs. This research offers meaningful insights for policymakers, regulators, corporate leaders, and investors on the potential of Integrated Reporting to serve as a bridge between compliance and sustainability, thus reinforcing India's commitment to global development goals and sustainable economic growth. 2026 by IGI Global Scientific Publishing. -
Beyond the Bottom Line: An Impact of CO2 Emissions and R&D on Corporate Sustainability in India
This paper evaluates the progress of Indian companies in adopting sustainable business practices, specifically focusing on carbon emissionsCarbon emission and research and development (R&D). It aligns with the United Nations Sustainable DevelopmentSustainable development Goals (SDGsSDGs), particularly SDG 9SDG 9, which aims to foster innovation, build resilient infrastructureInfrastructure, and promote sustainable industrialization. The study focuses on key targets, including CO2 emissions per unit of value-added, enhanced research and upgraded industrial technologyTechnology, and R&D expenditure. The research aims to assess company policies on carbon emissionsCarbon emission and analyze their investment in research related to this issue. The present research adopted a descriptive research design using secondary data from cement manufacturing companies to analyze the relationship between R&D spending and its impact on carbon emissionsCarbon emission from 2017 to 2022. A convenient sampling method was used to select the sample, and statistical methods such as correlation and regression analysis were applied to assess the relationship between the variables. The study has identified a twofold relationshipqualitative and quantitative between companies R&D spending and carbon emissionsCarbon emission within the framework of Sustainable DevelopmentSustainable development Goal 9. Focusing on cement manufacturing companies from 2017 to 2022, the findings show that increased R&D spending significantly reduces emissions by improving manufacturing processes through advanced technologyTechnology and machinery. However, the study is limited by the lack of detailed data on specific carbon-related R&D investments, making it challenging to gauge the exact impact. The precise proportion of R&D funds dedicated to reducing carbon emissionsCarbon emission remains unclear. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Impact of pharmacy industries growth on India economy during covid 19 /
Patent Number: 202241050891, Applicant: Deepha V.
Impact of Pharmacy Industries growth on India Economy during COVID 19 Abstract Pharmacy is an industry that can continue to function without being affected by economic fluctuations. This industry is socially respected by people. Whether people have food to eat or not, everyone wants to preserve the health of the body. In particular, the demand for medicines is more than ever in today's era. -
Extended virtual reality based memory enhancement model for autistic children using linear regression
Extended Virtual Reality has expanded its wings to almost each and every sector enabling immersive experience in various fields and has found applications in gamification, learning, healthcare, etc. This technology has aided in providing solutions to various problems in different fields, and healthcare is the most prominent one among them. Children suffering from ASD which is a developmental disorder affecting the brain that impacts how a person perceives external responses, are finding it increasingly difficult to get treated as the treatment methods are tedious. There are very few methods which are regarded as standardized means of treating autistic children but there are a few common traits that can be found in children affected by ASD which can be grouped under three common categories. They are lack of communication skills, lack of basic mathematical knowledge and low levels of remembrance. With the help of Gamification, which provides therapy by means of games to those affected, the kids affected by ASD can be treated, powered by the concept of Extended Virtual Reality. In this paper, we have developed a model to provide autistic children a real world experience of playing games which will help them in enhancing their skills without any external interferences. Children who play these Extended Virtual Reality based games show gradual improvement, for which the results can be facilitated with the help of a Linear Regression model, helping us predict future response times. The proposed model results in enhancement of memory levels of the kids as a result of the game and classifies kids based on their enhancement in memory into high, medium and low. The mean absolute error of the linear regression model is found to be 0.0394. 2024, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
A Comparative Study of Unsupervised Models for Anomaly Detection in Maritime AIS Data
The integrity of global maritime trade is increasingly threatened by deceptive practices such as sanctions evasion and illicit trafficking, often facilitated by the manipulation of vessel tracking data from the Automatic Identification System (AIS). While AIS provides a rich source for monitoring vessel behavior, the vast scale of the data and the novelty of anomalous patterns necessitate advanced, automated detection methods. This paper presents a comprehensive benchmarking study of four dis-tinct unsupervised machine learning architectures for detecting anomalies in historical AIS vessel trajectories. The evaluated models include a Bidirectional GRU (Bi-GRU) autoencoder, a probabilistic GeoTrackNet with A Contrario detection, a two-level grid representation with Isolation Forest, and a multi-model approach combining spatial-thematic attributes with Isolation Forest. We provide detailed mathematical formulations, algorithmic descriptions, and rigorous comparative analysis of each approach, examining trade-offs between temporal modeling, spa-tial context awareness, feature engineering, and computational complexity. Our benchmarking results on 985,700 AIS messages indicate that spatially-aware models (GeoTrackNet, grid-based methods) demonstrate significantly higher sensitivity (6.76%-10.00% anomaly rates) than purely temporal models (0.20%), but at greater computational cost. This study provides practical guidance for model selection based on operational requirements and proposes future directions toward multimodal architectures integrating trajectory analysis with document-based verification. 2025 IEEE. -
An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense
Next-generation AI-enabled defense is essential to deter enemy drones, swarms, and autonomous vehicles in contested and deceptive environments, with modern battlefields becoming increasingly dependent on autonomous robot platforms. To discover and dissect and eliminate hostile autonomous threats in real-time, this study presents an integrated Adversarial-Resilient Swarm Defense AI Framework (AR-SDAI) with a Spatio-Temporal Transformer, a Multi-Agent Reinforcement Learning Countermeasure Module, and a Hybrid Graph Attention Network. To identify hidden or counterfeit threats and improve defense against enemy attacks, the system begins with applying a Transformer-based situational awareness model to merge multi-sensor battlefield data to fuse. Autonomous defense drones are then controlled by a multi-agent reinforcement learning framework to perform actions of dynamic electronic jamming and optimal interception maneuvers in a swarm environment. Finally, the system can find unusual patterns and create human-understandable counter-strategies for human-in-the-loop control with the help of a graph-based explainability layer that models the interactions of adversary swarms as dynamic graphs. Compared to traditional rule-based and CNN-RNN baselines using experiments on a simulated Red-Blue drone warfare test benchmark, the suggested AR-SDAI is better by 23% in threat detection accuracy, 31% in swarm interception success rate, and 19% in response latency. With its provision of robust, explainable, and flexible AI capability for next-generation robotic warfare settings, the paper in general enhances the state of autonomous defense operations. 2026 Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare.