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Redefining Online Commerce With AI-Powered Shopping Agents
The importance of artificial intelligence has rapidly changed the world of online commerce and turned digital shopping into a predictive and personalized practice. One of the most significant changes is the application of AI to create shopping agent intelligent machines helping consumers at every stage of the buying process by suggesting purchases, comparing products, and automating the process. This chapter discusses how AI agents of shopping are transforming e-commerce through redefining consumer behaviour, and making new types of online interaction possible. Based on a systematic literature review of the scholarly literature and industry practices, the chapter discusses the most important applications, including personalized recommendations, dynamic pricing, AR-driven virtual try-ons, and autonomous purchasing systems. The research comes up with the conclusion that, AI shopping agents are no longer considered an optional addition, but a strategic collaborator in contemporary commerce, although their implementation should be supported by moral values, transparency, and human supervision. Copyright 2026, IGI Global Scientific Publishing. -
Redefining Organizational Sustainability Through Revamping Digital Capital
[No abstract available] -
Redefining Photography in the Era of Artificial Intelligence
[No abstract available] -
Redefining traditional education using augmented reality and virtual reality
[No abstract available] -
Redesigning brand strategies in the phygital era
Phygital Era is changing the brand strategies. Blending conventional marketing methods with digital innovations as per the growing expectations of consumers. This chapter analyses the impact of Digital technologies such as artificial intelligence, augmented reality, virtual reality, and mixed reality which blend seamless brand and consumer relations while emphasizing the need for online & offline integration to further increase engagement and loyalty. The Case studies of Nike, Sephora, IKEA, and McDonald's, showcase how brands harness technology for a better customer experience. It also focuses on the challenges of Phygital branding. Lastly, it observes the role digital advancements have played in changing consumer psychology, purchase behavior, and constructive brand loyalty with attention to issues such as data privacy and digital fatigue. Finally, it highlights the future trends of hyperpersonalization and metaverse that are set to boom in the era of Phygital branding. 2025, IGI Global Scientific Publishing. -
Redox-active tetra-amino cobalt phthalocyanine electrocatalyst for sustainable electrochemical synthesis of 2-(pyridin-4-yl)-1H-benzo[d]imidazole
An electrocatalyst bearing a cobalt phthalocyanine derivative was developed by modifying carbon fibre paper electrode with polythiophene-3-acetic acid (pTAA) and further immobilizing tetra-amino cobalt phthalocyanine (TACoPc). The electrode was topographically and electrochemically characterized to validate its surface modification and functional suitability. This energy efficient electrocatalyst (CFP-TAA-TACoPc) was explored, for the sustainable synthesis of 2-(pyridin-4-yl)-1 H -benzo[ d ]imidazole at 1.35 V, with 87.6 1.267 % product yield at ambient conditions. o -phenylenediamine and pyridine-4-carboxaldehyde were used as starting materials, with ethanol as solvent, and lithium perchlorate as supporting electrolyte, using a three-electrode system, in a single compartment cell. The benzimidazole derivative was observed to crystallize out after completion of the reaction, negating the need for any further purification and was characterized using 1HNMR and GCMS. This work highlights the potential of electrochemical strategies as a sustainable and efficient alternative to conventional methods for heterocyclic synthesis. 2026 Elsevier B.V. -
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
Reducing approximation error with rapid convergence rate for non-negative matrix factorization (NMF)
Non-Negative Matrix Factorization (NMF) is utilized in many important applications. This paper presents development of an efficient low rank approximate NMF algorithm for feature extraction related to text mining and spectral data analysis. NMF can be used for clustering. NMF factorizes a positive matrix A to two positive matrices W and H matrices where A = WH. The proposal uses k-means clustering algorithm to determine the centroid of each cluster and assigns the centroid coordinates of each cluster as one column for W matrix. The initial choice of W matrix is positive. The H matrix is determined with gradient descent algorithm based on thin QR optimization. The performance comparison of the proposed NMF algorithm is illustrated with results. The accurate choice of initial positive W matrix reduces approximation error and the use of thin QR algorithm in combination with gradient descent approach provides rapid convergence rate for NMF. The proposed algorithm is implemented with the randomly generated matrix in MATLAB environment. The number of significant singular values of the generated matrix is selected as the number of clusters. The error and convergence rate comparison of the proposed algorithm with the current algorithms are demonstrated in this research. The accurate measurement of execution time for individual program is not possible in MATLAB. The average time execution over 200 iterations is therefore calculated with an increasing iteration count of the proposed algorithm and the comparative results are presented. 2021 by authors, all rights reserved. -
Reducing Delay and Network Load through Adaptive Threshold-Based Rate Control in IoT Systems
Some of the major challenges in managing IoT networks, which are normally resource-constrained, come from restricted bandwidth, processing power, and energy supplies. Traditional random transmission usually leads to network overload, increased packet delays, and inefficient use of resources. This paper reviews smart rate control mechanisms designed for IoT networks that have limited resources. We analyze and contrast random baseline transmission against threshold-based adaptive control methods by way of extensive simulation runs under realistic network scenarios via the Contiki-Cooja framework. Our experimental results have shown that threshold-based rate control can achieve as much as 31% reduction in average packet delay and 62% reduction in network load when compared with traditional random transmission techniques. Threshold-based rate control represents a deployable and practical solution that properly balances the tradeoff between performance enhancement and computational ease and thus is a good match for real-world IoT deployments over actual resource-constrained networks. Hybrid machine learning, multiobjective optimization, federated learning, and context-aware mechanisms might be potential avenues of future research toward enhancing the performance of IoT systems. 2025 IEEE. -
Reducing Systemic Bias in Behavioral Targeting Using Explainable AI: The HARMONIA Complex Systems Approach
Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered latent behavioral signals that affect the relevance of ads based on users' online behavior. The analysis identified four interrelated behavioral dynamics: ad fatigue attenuation, diurnal engagement oscillations, device-driven preference divergence, and category-affinity dominance. These dynamics served as the foundational architectural principles for HARMONIA's design. The method uses gradient boosted prediction models and a multilayer explainability stack that includes SHAP for global interpretability, LIME for local surrogate approximation, and counterfactual reasoning for causal transparency. Quantitative evaluation indicates that HARMONIA maintains relevance accuracy (approximately 1.2% CTR), achieves a 31% enhancement in transparency metrics, and a 27% improvement in user-trust indices, while concurrently reducing systemic entropy by nearly one-third. This research redefines personalization to be self-explanatory and ethically sound AI by incorporating explainability as a regulatory mechanism in the adaptive ecosystem of complex digital advertising. This system takes explainable computational marketing from an idea to a full-scale implementation. 2026 Binghamton University Libraries. All rights reserved. -
Reducing the workload: AI as a support tool for human content moderators
Content moderation on digital platforms has evolved into a critical challenge due to the volume and complexity of user- generated content. This chapter explores the transformative role of Artificial Intelligence (AI) in addressing this issue, highlighting its capacity for scalability, efficiency, and consistent enforcement of policies. However, AI's limitations-such as interpreting context, navigating cultural nuances, and avoiding bias-underscore the necessity of human involvement. The hybrid AI- human approach emerges as the most effective model, leveraging AI's data- processing capabilities to alleviate workload while relying on moderators for nuanced judgments and ethical oversight. Through case studies of major platforms like Meta, Reddit, and YouTube, this chapter demonstrates the potential and challenges of integrating AI into moderation. It advocates for transparency, ethical AI practices, and research to refine hybrid models, aiming to balance efficiency, fairness, and trust. This chapter contributes to the discourse on sustainable, ethical frameworks in the digital age. 2025, IGI Global Scientific Publishing. All rights reserved. -
Reduction of a Tri-Modal Lorenz Model of Ferrofluid Convection to a CubicQuintic GinzburgLandau Equation Using the Center Manifold Theorem
The differential geometric method of the center manifold theorem is applied to the magnetic-Lorenz model of ferrofluid convection in an electrically non-conducting ferrofluid. The analytically intractable tri-modal nonlinear autonomous system (magnetic-Lorenz model) is reduced to an analytically tractable uni-modal cubicquintic GinzburgLandau equation. The inadequacy of the cubic GinzburgLandau equation and the need for the cubicquintic one is shown in the paper. The heat transport is quantified using the solution of the cubicquintic equation and the effect of ferrofluid parameters on it is demonstrated. The stable and unstable regions in the conductive regime and the conductive-convective regime is depicted using a bifurcation diagram. The noticeable discrepancy between the results of the two models is highlighted and the quintic non-linearity effects are delineated. 2021, Foundation for Scientific Research and Technological Innovation. -
Reel Therapy: Taking the Cinematic Route to Suicide Intervention
Suicide has been recognised as a significant public health concern by the United Nations, making effective interventions crucial to prevent loss of life and the ripple effect it has on the family and friends of the individual. Conventional therapeutic approaches tend to focus on talk therapy as a way to provide assistance to the individual in distress but emerging research evidences point towards the impact created by the use of auditory and visual stimuli. Reel therapy also known as movie therapy is one such intervention that aims at using films as a therapeutic tool. Studies suggest that movies can act as a powerful tool for creating insight and reducing stigma associated with seeking help when required. The chapter examines the theoretical foundation of reel therapy, reviewing associated concepts such as catharsis and symbolic representation in films. It explores how films can reduce emotions and provide a platform for the individuals to process their ideations and feelings related to suicide. Reel therapy exhibits potential to become a valuable addition to the ongoing efforts towards prevention of suicide, especially for populations that do not respond to the traditional intervention approaches. Through the chapter the authors attempt at emphasising the value of incorporating the universally loved phenomena of movies into suicide intervention as a complementary therapeutic tool, by bringing forth the unique strengths of reel therapy in facilitating emotional expression and assisting recovery. The chapter aims to contribute to growing literature on novel approaches to suicide intervention. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reel Therapy: Taking the Cinematic Route to Suicide Intervention
Suicide has been recognised as a significant public health concern by the United Nations, making effective interventions crucial to prevent loss of life and the ripple effect it has on the family and friends of the individual. Conventional therapeutic approaches tend to focus on talk therapy as a way to provide assistance to the individual in distress but emerging research evidences point towards the impact created by the use of auditory and visual stimuli. Reel therapy also known as movie therapy is one such intervention that aims at using films as a therapeutic tool. Studies suggest that movies can act as a powerful tool for creating insight and reducing stigma associated with seeking help when required. The chapter examines the theoretical foundation of reel therapy, reviewing associated concepts such as catharsis and symbolic representation in films. It explores how films can reduce emotions and provide a platform for the individuals to process their ideations and feelings related to suicide. Reel therapy exhibits potential to become a valuable addition to the ongoing efforts towards prevention of suicide, especially for populations that do not respond to the traditional intervention approaches. Through the chapter the authors attempt at emphasising the value of incorporating the universally loved phenomena of movies into suicide intervention as a complementary therapeutic tool, by bringing forth the unique strengths of reel therapy in facilitating emotional expression and assisting recovery. The chapter aims to contribute to growing literature on novel approaches to suicide intervention. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reengineering the Rural Economy by Leveraging the Use of Financial Services: A Lynchpin
Financial services form the bedrock of the economy, intricately woven into its fabric, exerting a profound influence on its dynamics and growth trajectory. Financial services play a momentous role in the complex web of global economies, delicately interlacing the strands of trade, capital accumulation, and risk mitigation. These services serve as an essential hub, linking people, companies, and governments in a complex web of exchanges and interactions, from the busy streets of big metropolises to the serene settings of rural villages. The chapter emphasizes the importance of financial services in promoting rural businesses by uplifting their stream of earnings by making use of emerging technologies. It also focuses on the challenges and opportunities faced by the rural population in obtaining financial assistance. The chapter also discusses the modern financial service schemes introduced so far for rural development and the suggestions for future implementation. The ways and means to mitigate shortcomings of the development of the rural business due to negligible awareness and restricted accessibility to financial services are also highlighted in the study. 2026 Jenny Stanford Publishing Pte. Ltd. All rights reserved. -
Reflection of Public Policy on Environment
International Journal of Research in Social Sciences Vol.3, Issue 1, pp.184-196. ISSN No. 2249-2496 -
Reflections on the issues and determinants associated with women's career progression in hospitality industry at Bengaluru /
Social Sciences International Research Journal, Vol.2, Special Issue, ISSN: 2935-0544. -
Reflective thinking in school: a systematic review
Everything around us changes rapidly and to adapt to these constantly changing conditions and to understand the meaning of our life in the society in which we live, we must reflectively and consciously think about our actions in each given scenario. A school is a miniature form of society where learners are exposed to situations where they need to find solutions for every problem faced. No faultless solution and conclusions can be arrived at without a carefully employed reflective thinking process. In this context, the present study reviewed 19 intervention studies on reflective thinking in schools published between 2010 and 2021 and presents a brief summary. Various theories on reflective thinking, approach of educationists on reflective thinking of students and the relation between reflective thinking and students academic performance, are extensively analyzed. The findings of the study reveal that there are a few generally accepted theories of reflective thinking; reflection is a useful learning strategy and reflective thinking is an essential characteristic of academic excellence. This study recommends future research with a wider scope to accommodate more theoretical perspectives and wide-ranging databases. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Reflective writing skills among pre service teachers: a scoping review
Reflection is a soul-searching process. It is an innate ability to delve down the memory lane to judge a reaction to a particular situation as right or wrong as a response. The positive reactions are reinforced and the ineffective negative ones are relinquished. Developing reflective skills among preservice teachers include regular reflective practice sessions. They have to painstakingly record all their reflections after the delivery of each lesson as part of their curriculum along with other reflective practice opportunities. This effort should lead to evolution of professional practitioner in the long run. Although, there are factors affecting its development, preservice teachers seem to do it more monotonously without much reflective learning. Their reflective writing skills are way behind the expected level. This study adopts the research design outline advocated by Arksey and OMalley. The study appraised the research studies conducted from 2015 to 2024 as a part of scoping review. The study throws light on the various aspects related to the teacher-trainees reflective writing skills. Future studies may focus on empirical validation of the reflective writing skills among preservice teachers. 2025 Institute of Advanced Engineering and Science. All rights reserved.