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FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection
The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method. 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 2024. -
Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features
By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. 2023 IEEE. -
An Optimal Load Balancing Framework for Fog-Assisted Smart Grid Applications
The growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. This paper introduces a unique hybrid framework on a highly virtualized platform and proposes three potential load balancing algorithms: throttled, Round Robin, and a novel Equilibrium Optimizer with Simulated Annealing (EO-SA). The article provides a comprehensive investigation on several load balancing techniques for obtaining optimized services in a smart grid environment thereby focusing on better utilization of network resources and reduction of costs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Impact of User-Generated Content on Business Performance:Strategic Insights for Hospitality in the Social Media Era
This chapter focuses on how user-generated content (UGC) has a significant effect on the business of the hospitality industry. The chapter delves into the extent to which online reviews, social media posts, and other consumer-generated content influence brand perception, customer trust, and, eventually, the financial results. By utilizing theories such as signalling theory, network effects, and social influence, the chapter sees UGC as not only feedback but also a strategic marketing tool. The chapter talks about research results, case studies from around the world and India, and also mentions Al and sentiment analysis as tools for reputation monitoring and management. Lastly, it considers ethical and governance issues and provides a set of practical strategies for hospitality firms to use UGC for innovation, customer engagement, and achieving a sustainable competitive advantage. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Clitoria ternatea flower extract assisted synthesis of Pluronic F127 and l-histidine coated SrO2 as a multimodality nanocomposite for anti-cancer, anti-oxidant, and antimicrobial activities
Hepatocellular carcinoma (HepG2) is a highly aggressive liver cancer with poor prognosis, limited treatment options, and high mortality rates, making it a serious global health concern that demands urgent development of more effective and safer therapeutic approaches. In this context, the present study focuses on the green synthesis of SrO2 nanoparticles using Clitoria ternatea flower extract, followed by surface modification with Pluronic F127 (PF127) and L-histidine (LH), to fabricate SrO2-PF127-LH nanocomposites aimed at evaluating their potential anticancer efficacy against the HepG2 cell line. Various analytical techniques were used to characterize the nanocomposite, and then their anticancer activity against HePG2 liver cancer cells, antioxidant properties, and antimicrobial activity against the bacteria mentioned above were evaluated. XRD revealed the crystalline nature of SrO2 with atetragonal phase. FTIR spectrum confirmed the SrO stretching band at 573cm?1 for SrO2-PF127-LH nanocomposite. UVvisible analysis revealed the band gap energies of 4.13eV for SrO2 and 4.07eV forSrO2PF127LH nanocomposite. The surface defects including oxygen vacancies of SrO2-PF127-LH nanocomposite were investigated using PL analysis. The SrO2PF127LH nanocomposite exhibited excellent antibacterial and antioxidant activities when compared to SrO2 nanoparticles alone. In addition, the SrO2PF127LH nanocomposite had enhanced anticancer activity against liver cancer (HePG2) cell lines. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Navigating the Nexus: An Integrated Framework for Energy- Water- Food Security in the MENA Region
The Middle East and North Africa (MENA) region confronts a severe and escalating challenge to its resource security, rooted in the complex interdependencies of the Energy- Water- Food (EWF) nexus. This paper presents a comprehensive analysis of the long- term impacts of alternative policy pathways on EWF security and associated socio- economic outcomes. Utilizing a specifically designed Input- Output (IO) modelling framework incorporating flexible production functions and price- induced factor substitution, this study projects outcomes to the year 2050 for ten key MENA countries under five distinct scenarios: a Business- as- Usual (BAU) trajectory, three sector- specific scenarios focused on Energy (ESC), Water (WSC), and Food (FSC), and an integrated EWF Nexus- oriented Scenario (NSC). The analysis reveals that the BAU path, which extrapolates current policies and consumption patterns, leads to a precipitous decline in water and food security, rendering current development models fundamentally unsustainable. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Onion peel derived carbon nanoparticles incorporated polysulfone membranes: enhanced dye removal from water
The ongoing discharge of hazardous dyes from industrial processes has intensified global water pollution, posing serious threats to aquatic ecosystems and human health. Addressing this challenge, our study explores the potential of bio-based carbon nanomaterials (CNM), synthesized from onion peel biowaste and designated as ON11, as effective agents in dye removal. These CNMs were incorporated into a mixed matrix membrane (MMM), using polysulfone (PSU) as the membrane substrate, to enhance dye adsorption. The CNM synthesis was achieved through a simple, eco-friendly process. We examined their impact on adsorption efficiency by introducing ON11 nanoparticles at varying concentrations into the PSU membrane (ON11@PSU). This CNM-embedded membrane structure offers a solution to challenges associated with the large-scale application of nanomaterials, particularly by minimizing leaching into water and improving durability. The ON11 and ON11@PSU membranes were characterized using various techniques, including SEM, Raman spectroscopy, XRD, optical profilometer, and FTIR, to confirm their behavior, morphology, and structural integrity. The surface area of ON11 was 423.26 m2 g?1, with BJH average pore diameter of 4.5 nm and BET pore volume of 0.26 cm3 g?1. ON11 nanoparticles were adsorptive in nature, and their utility in membrane adsorption is explored. The influence of parameters, including contact time, dye concentration, membrane thickness, pH, and adsorbent dosage, was systematically evaluated to optimize the dye adsorption efficiency of the ON11@PSU membrane pad. It was observed that the thickness of the 60 ?m membrane (Sa = 2.170 ?m and Sq = 2.75 ?m) showed higher removal efficiency for all the selected dyes than the other thicknesses at the native pH itself. The MMM demonstrated its effectiveness as an adsorbent membrane, achieving maximum removal efficiencies of approximately 98% for MG dye, 92% for RhB dye, and 67% for MB dye. The negative zeta potential of adsorptive membranes enabled the electrostatic attraction of positively charged dyes, enhancing adsorption capacity. The findings contribute to developing sustainable and effective membrane utility as adsorbents, opening avenues for the effective use of agricultural waste products in environmental remediation applications. 2025 The Royal Society of Chemistry. -
Efficient Photocatalytic Degradation of Methylene Blue From Aqueous Solution Using Hybrid Biomass-Derived Nanostructured Carbon-TiO2 Photocatalyst
Industrial dye usage results in substantial wastewater discharge, posing environmental and health hazards. Hence, developing efficient, sustainable, and cost-effective treatment technologies is crucial. Photocatalysis using TiO? has emerged as a promising approach for dye degradation. This study explores the photocatalytic removal of methylene blue (MB), a model dye pollutant, using a composite of biomass-derived carbon nanoparticles (CNPs) and nanosized TiO? under UV light. The CNPs were synthesized via one-step pyrolysis from waste coffee leaves, offering a sustainable carbon source. The resulting CNPs (CL-10) and the TiO?-CNP composite (PC@CL-10) were thoroughly characterized using advanced techniques. Incorporating carbon significantly reduces the band gap of TiO? from ?3.2eV to 2.90eV, enhancing photocatalytic activity. Degradation studies under varying catalyst doses, dye concentrations, and pH levels demonstrate effective MB removal under UV irradiation. Photocatalytic experiments revealed up to 99% degradation of MB under UV light, while tests conducted in the dark showed negligible activity, confirming the light-dependent efficiency. Kinetic analysis indicated that intra-particle diffusion (IPD) governs the dye degradation process. Moreover, recyclability tests over seven cycles showed consistent performance with minimal decline, highlighting the catalyst's stability and reusability. These findings suggest that PC@CL-10 is a highly effective, low-cost photocatalyst with strong potential for large-scale wastewater treatment applications. 2025 The Author(s). Chemistry A European Journal published by Wiley-VCH GmbH. -
Green Investment and Circular Finance: Pioneering Financial Strategies for a Sustainable Future
Circular finance is transforming global investment by integrating financial decisionmaking with sustainability, beyond the conventional linear models of exploitation and waste. This chapter discusses how financial products green bonds, impact investing, ESG funds, and sustainability- linked loans-are propelling the transition toward regenerative economic systems. It emphasizes the contributions of financial institutions in supporting circular economy practices through innovative lending, risk management, and sustainable investment funds. Technologies such as blockchain, AI, and big data increase transparency. Nonetheless, obstacles such as short- term profit orientation, regulatory loopholes, and missing standardized metrics stifle progress. Realizing circular finance's full potential will depend on policymakers, investors, and companies working together to integrate circular principles into financial systems and create a more resilient, sustainable economy. 2026, IGI Global Scientific Publishing. All rights reserved. -
Analysis of Biometric Systems for Secure Human Recognition
In the realm of contemporary computing, the recognition of humans has emerged as a crucial element, finding utility in both mundane daily tasks and sophisticated operations across diverse IT applications. The process of identifying individuals often involves harnessing their distinctive biological, chemical, and behavioral attributes. To achieve this, a biometric system, functioning as a computer-based automated mechanism, is employed to authenticate and confirm alter the perspective or framing of users by exploiting their biological characteristics. In present-day applications, an existing entity exists notable emphasis on the biological aspects of individuals as the primary means of identification. While utilizing the chemical traits of humans for identification yields greater accuracy and reliability, practical implementation proves to be challenging. This article presents the execution or outcome of automatic human recognition systems derived from diverse sources or perspectives parameters such as user psychology, ease of use, security, reliability, and market share. The results suggest that these systems offer authentication and recognition capabilities, but it is noteworthy that the security of these systems at the template level poses a significant challenge for designers. 2025 Author(s) -
AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks
With massive DDoS attacks and Sybil attacks targeting national digital frameworks, financial institutions, and vital infrastructures, India is seeing an unparalleled increase in cyber threats. These attacks reveal significant vulnerabilities in national cybersecurity by jeopardizing system availability and integrity. Sybil attacks use numerous falsified identities to get unauthorized control over trust-based systems, while DDoS attacks flood networks with illegal traffic, making services inaccessible. This study investigates advanced machine learning (ML)-based identification and prevention strategies, including support vector machine (SVM), random forest (RF), decision tree (DT), and logistic regression (LR). To identify attack patterns, the methodology entails gathering actual network traffic data, preprocessing it to extract key information, and then using these models. To identify the best strategy, a comparison study is carried out depending on various parameters such as accuracy, precision, recall, and computing efficiency. The research suggests that random forest outperforms other ML algorithms in detecting Sybil attacks and DDoS attacks, achieving the maximum stability and accuracy. Nevertheless, the classification method is improved by merging decision trees and logistic regression, which further increases detection accuracy. In order to actively fight changing cyber threats, our findings highlight how important it is to include machine learning-driven security frameworks into India's cybersecurity infrastructure. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
The Synergistic Effect of Sustainable Business Practices on Corporate Performance
This book explores the intricate relationship between sustainable business practices and economic performance. By examining how companies can achieve financial benefits through the integration of environmental and social responsibility into their core strategies, this book seeks to bridge the gap between environmental and economic concerns. It demonstrates that sustainability is not merely a moral imperative but a strategic economic driver in the modern business landscape. The book provides valuable insights into how companies can align their economic goals with environmental and social responsibilities, leading to long-term financial success. Readers will also benefit from the practical applications and case studies that demonstrate the tangible financial benefits of sustainable business practices. Additionally, the book serves as a resource for implementing sustainability strategies within organizations, making it an indispensable guide for business leaders, policymakers, and academics seeking to navigate the complexities of modern economic and environmental challenges. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Internet of Things and Indian Banking Industry: Applications and Challenges
Technological advancements have changed the way people think about how they operate. Various new approaches to work and processes have been identified after adopting various technological changes. This paper discusses various changes adopted by the finance and banking sector with the help of various successful changes in the banking industry. The banking sector, where human-to-human interaction was the core of operating technology, has made it machine-to-machine. Various significant and recent changes have been discussed, followed by some challenges faced by this industry even after successfully adopting technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Valorization of lignin to produce nanofibers of industrial importance
Lignin nanofibers (LNFs) have emerged as promising materials for various environmental applications due to their unique properties, abundance, and sustainability. This review examines recent advances in LNF synthesis and their environmental applications, lignin types are discussed in relation to nanofiber production. Synthesis techniques are evaluated, with electrospinning emerging as a versatile method for producing LNFs with diameters typically in the nanometer range. The intrinsic properties including molecular weight, polydispersity, and glass transition temperature, significantly influence nanofiber formation and performance. Environmental applications of LNFs are extensively reviewed, highlighting their potential in adsorption of pollutants, air filtration, energy storage devices, and as catalyst supports. Despite significant progress, challenges remain in large-scale production, consistency of properties, and economic viability. This review provides a comprehensive overview of the current state of LNFs technology, addressing both opportunities and challenges in leveraging this sustainable material for environmental solutions. 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
3D Printed Skin Graft Scaffolds as Potential Alternative for the Cellulitis-Induced Skin Damages
Cellulitis is a bacterial infection starting from damaging the skin and soft tissue of the body and eventually leading to affect the immune and circulatory system. Demarked erythema, warmth, edema, and tenderness are symptoms of cellulitis affected skin. Unfortunately, due to the symptoms mimicry more than 30% of patients admitted to the hospitals are misdiagnosed as cellulitis. The existing treatment methods for the cellulitis include antibiotics and treatments associated to symptoms. Three-dimensional printing (3D printing) is emerging and innovating technology for the skin and other medical treatments. The combination of antibiotics with hydrogel and their integration with 3D printing technology can be a potential alternative to traditional dressing and solution-based approach. Current review article looks into the feasibility of 3D printing technology to manage the cellulitis-induced skin damages based on existing reports on 3D printed hydrogels for other skin problems. Review article provides insights into the cellulitis-induced skin damage, biomaterials and hydrogels in other skin damages, infections, wounds and scope of integrating 3D printing to treat to treat cellulitis. Review article projects the feasibility of 3D printed hydrogels, biomaterials, dressings, and artificial skin patches as possible solution to cellulitis-based skin damages. Furthermore, this review highlights the safety and regulatory challenges in employing 3D printing technologies for the cellulitis treatment. Current review article is first report proposing the possibility of 3D printing as alternative treatment for the cellulitis-based skin damages. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Empathetic Technology: Integrating Emotional Intelligence Into Assistive Devices for Aging Adults and Individuals With Disabilities
This chapter explores the emerging field of empathetic technology and its application in assistive devices for ageing adults and individuals with disabilities. It examines the integration of emotional intelligence into technological solutions, aiming to address functional needs and emotional and psychological well-being. The chapter delves into the foundations of emotional intelligence in technology, current applications, and future possibilities of empathetic assistive devices. It discusses key design principles, implementation strategies, and the challenges faced in developing these technologies. The text also covers methods for impact assessment and evaluation, with a strong emphasis on user-centred approaches, reassuring the audience about the thoroughness of the research. Mathematical models for quantifying emotional states, device performance, and user well-being are presented. Ethical considerations, including privacy concerns and cultural sensitivities, are addressed. The chapter focuses on the quality of life for ageing adults and individuals with disabilities. 2025 by IGI Global Scientific Publishing. -
Quantum-inspired algorithms for cognitive computing: Enhancing cloud-based problem-solving
The convergence of quantum-inspired algorithms and cloud-based frameworks represents a transformative shift in computational capabilities tailored to the human-centric goals of Industry 5.0. Unlike Industry 4.0, which focused on automation and digitization, Industry 5.0 emphasizes intelligent systems that complement human decision-making. Quantum-inspired algorithms, derived from the principles of superposition and entanglement, offer superior capabilities in optimization and pattern recognition without requiring quantum hardware. When integrated with scalable and distributed cloud computing infrastructures, these algorithms enable high-performance cognitive computing, tackling previously intractable problems across domains. This chapter explores the theoretical foundation and practical implementation of such systems, including quantum-inspired neural networks (QiNNs), quantum-inspired immune algorithms (QiIAs), and quantum-inspired particle swarm optimization (QPSO). These models exhibit enhanced accuracy and efficiency in applications like pattern recognition, anomaly detection, and multiobjective optimization. Real-world case studies in finance, cybersecurity, healthcare, and smart grid management highlight their impact on risk modeling, threat mitigation, and decision support systems. The chapter further proposes a cloud integration framework, addressing challenges in scalability, performance, and security. Implementation strategies and architectural designs are discussed with a focus on dynamic resource management, real-time analytics, and secure deployment. The synthesis of these technologies marks a significant advancement toward achieving adaptive, intelligent, and secure computational ecosystems, aligned with the values and vision of Industry 5.0. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Artificial intelligence based system and method for management, recommendation, mapping of skill /
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702). -
The competencycontrol paradox in parenting adolescents: Evidence from an Indian mixed-methods study
Background: Adolescence is a critical developmental period during which parenting practices interact with temperament and sociocultural context to shape mental health and adaptation. Most parenting models are derived from Western settings, with limited evidence from India. Methods: This simultaneous mixed methods study drew on cross sectional data from the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, including adolescents aged 1217 years (parent report n?=?931; child report n?=?836). Exploratory factor analysis was conducted on parent and child versions of the Alabama Parenting Questionnaire. Qualitative data were obtained through in-depth interviews with 31 adolescents and their parents and analysed using thematic analysis. Findings were integrated at the interpretation stage. Findings: The original APQ structure did not replicate. Parent reports yielded three dimensionsInvolvement/Positive Parenting, Poor Monitoring, and Corporal Punishmentwhile child reports yielded five, distinguishing fathers and mothers involvement. Inconsistent disciplining did not emerge as a distinct construct. Qualitative findings indicated high involvement and behavioural and psychological control, largely driven by academic goals. Adolescents experienced these practices as both supportive and restrictive, with parental openness shaping communication. Contextual pressures, including resource constraints and urban stressors, contributed to a competencycontrol paradox. Interpretations: Parenting of adolescents in India must be understood within its relational and sociocultural ecology. While involvement and control function as primary supports, excessive control may constrain broader competency development. Integrating parent and adolescent perspectives is essential for culturally grounded research and intervention. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Bridging tradition and sustainability through a behavioural model for the adoption of green wedding practices
The study aims to develop a behavioural model which indicates the intentions of unmarried people for adoption of green marriages. The study aims to explore the factors which affects the green marriage intentions, and also developing a green marriage intention matrix for categorizing the green marriages. The study is based on the primary data collected from a sample of 480 unmarried people restricting from age groups of 2040 only. Researchers have used Exploratory factor analysis to explore the factors, multiple regression analysis to develop the green marriage intentions model, and correlation analysis. ANOVA method was used to measure the impact of age, gender, and education on environmental attitude and green marriage intentions. It was found from the study that environmental attitude, social influence, perceived barriers, and perceived benefits are the four major factors which affects the green marriage intentions of the people. Further, the green marriage intentions matrix showed four categories of the people based on the environmental attitude and social influence namely; Influential green marriage, Casual green marriage, fashionably Green Marriage, and Eco-conscious green marriage. The study also included a detailed strategic plan with proposed actions to handle the barriers and promoting the green marriage practices along with environmental stewardship. The Author(s) 2025.