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AI-Based Risk Profiling of Online Human Trafficking Victims: A Multimodal Framework for Proactive Detection
Human trafficking is still a major worldwide crime that is made easier by digital channels, including social media, job advertisements, and websites that offer escort services. This study offers a thorough examination of artificial intelligence (AI) approaches to human trafficking detection and prevention, with a focus on identifying the most susceptible groups. From authorship attribution and geolocation extraction to social network analysis and multimodal detection, we analyze around 20 scholarly papers that include text, photos, audio, and network data. Lack of victim-focused profiling, data scarcity, bias in AI, and ethical deployment issues are some of the main research needs. In response, we provide a conceptual AI system that uses publicly available signals to evaluate individual vulnerability by combining network analysis, natural language processing, and weakly supervised learning. This work emphasizes the importance of ethically grounded AI systems to assist NGOs, law enforcement, and policymakers proactively identify at-risk individuals and prevent exploitation before it occurs. 2025 IEEE. -
Message from IEEE InC4 2024 General Chair
[No abstract available] -
Outcome Evaluation of Child Sponsorship Programme of A Non-Governmental Organization
Child sponsorship programme is a vital tool for the integral development of the children at risk. Family based child sponsorship programme is one of the best services for the marginalized children which ensure their education while also respecting the rights of the children. The current study attempts to evaluate the outcome of child sponsorship programme of a non-Governmental organization newlinethrough a mixed method. Quasi-experimental post-test only design is the methodology used to conduct the study. The study evaluated the programme with regard to Self-esteem, Achievement motivation and family functioning of the sponsored children. The data was collected from 80 individuals for the quantitative study; using 3 standardized scales. Thematic analysis of qualitative data collected by interviewing 5 pairs of beneficiaries of the child sponsorship programme. The data was analysed using SPSS and R. The findings that there is a significant difference in terms of self-esteem and achievement motivation between the two groups of children. With regard to family newlinefunctioning conflict is much lesser among sponsored children (M=20.75) while compared to non-sponsored children (M=43.80). In terms of parenting and intimacy, the sponsored children are having higher score. Also, it was found out that self-esteem significantly mediated the impact of family functioning on achievement motivation of the individual(plt0.05). It is noticed that the effect of family functioning on achievement motivation was 0.504 and the direct effect was found to be 0.333. Selfesteem was found to strengthen the impact of family functioning on achievement motivation.Academic excellence improves the employability of respondents. Employment of newlinethose who received sponsorship can provide financial stability to the family. Therefore, this evaluation study confirmed the phenomenal effect of child sponsorship newlinein realizing inclusivity goals, as well as facilitate the personal, familial, economic, and social growth of sponsored children. -
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Low temparature synthesis of non-toxic monoclinic yttrium oxide quantum dots for display and biomedical applications /
Patent Number: 202141053609, Applicant: Soorya G Nath.
Monoclinic Y203 quantum dots were synthesized at low temperature using urea as the fuel. The sample preparation was done using simple laboratory hot air oven and the synthesis temperature was maintained at 90°C throughout the experiment. Prepared samples were characterized using x ray diffraction (XRD), Raman spectroscopy, high resolution transmission electron microscopy (HRTEM), UV- Vis absorption spectroscopy and photoluminescence (PL) Studies. -
Smart saviour system for women /
Patent Number: 202041038520, Applicant: Dr, E A Mary Anita.
The invention is directed to an loT based system for alerting the group of predetermined volunteers and officials located near the site of crime occurring to the end user. The system includes a manual activated push button switch for utilizing at times of distress occurring to the women which activation generates the alert signals to be transmitted wirelessly through the WiFi module to the central processing server. -
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Cardiac Endothelial Impairment in the Danio Rerio Due to Change in the Circadian Rhythm
Light is one of the environmental factors which regulates the circadian rhythm in humans and animals. Circadian rhythm is a light and dark cycle which controls awakeness and sleepiness. Circadian rhythm regulates all the physiological functions. Artificial light at night disrupts the circadian rhythm in the population. Mostly developing and developed country population is very much prone to the disturbance in the circadian rhythm as shift work becomes very common. In this study we have disturbed the circadian rhythm of the Danio rerio by continuously exposing them to bright light and disturbing their resting period by creating surface waves for 96 h. At regular intervals, triplicates were meticulously extracted from control and experimental tanks, their hearts tenderly dissected and preserved in formaldehyde for subsequent analysis. Through the lens of a microtome, the intricate architecture of cardiac tissue unveiled a disquieting narrative, after 48 h and 72 h show trabeculae and necrosis in the inner layer of the ventricles and lumens were seen in the bulbus arteriosus. These findings not only mirror the cardiac consequences observed in humans experiencing circadian disruptions but also underscore the potential of zebrafish as valuable models for investigating pharmacological interventions aimed at mitigating such cardiovascular consequences. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Utilizing brain-computer interfaces for personalized marketing strategies
Through the provision of direct insights into the preferences and emotional responses of consumers, the objective of this research paper is to evaluate the potential for Brain-Computer Interfaces (BCIs) to bring about a change in the approaches of personalized marketing. Brain-computer interfaces, also known as BCIs, are devices that allow for a link to be made between the brain and external equipment. This allows marketers to have access to real-time neurological data that is truly unequalled. In turn, this makes it possible for marketers to develop marketing strategies that are extremely focused on the population that they are trying to reach. The objective of this project is to examine how brain-computer interfaces (BCIs) can be leveraged to evaluate the responses of consumers to advertisements, product designs, and brand messages. To refine their plans, this would make it possible for marketers to make use of subconscious reactions rather than the conventional survey methods. Some significant challenges are also discussed, including the difficulty of decoding brain signals. 2025, IGI Global Scientific Publishing. -
Digitization assisted circular economy: a business strategy to attain sustainability in global supply chains
Contemporary businesses that aim for sustainable global supply chains (GSCs) through circular economy (CE) models has been a crucial topic of research and practice for addressing the climate change risks and resource scarcity. This study identifies and prioritizes the critical success factors (CSFs) for digitalization assisted CE for sustainability in GSCs using a modified multi criteria decision making technique, Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL). Analysing the cause-and-effect relationships among 14 identified CSFs for digitization assisted CE in GSCs offers actionable insights for organizations and policymakers seeking to enhance sustainability efforts by navigating the complexities of integrating digitization and CE practices. The findings from the study reveal that process improvement and optimization through digitization and human centric sustainable operations towards CE as the highest ranked CSFs. The results suggest that managers shall invest in digital integration, prioritize transparency, and foster collaboration to create resilient and sustainable supply chain ecosystems and this will serve as the initial step towards the future digital transformations. This study provides a strategic roadmap for managers and policymakers aiming to integrate CE principles through digital transformation. 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 2026. -
Exploring the linkage between digital transformation, green innovation, and carbon neutrality: Implications for business sustainability
Achieving business sustainability progressively depends on alignment of digital transformation, green innovation and carbon neutrality initiatives. This study aims to identify and prioritize the key factors to achieve green innovation and carbon neutrality. Based on the Technology-Organization-Environment (TOE) framework, the Grey Ordinal Priority Approach (G-OPA) is applied to evaluate the relative importance of factors under uncertainty using insights from 14 industry experts. The results highlight 16 critical factors, with "Digital orientation", "Innovation capability"and "Optimizing energy consumption structure"as three most influential factors that businesses must address to effectively integrate digital transformation strategies with sustainability initiatives. The study contributes a structured understanding of how digital transformation drives sustainability and provides practical guidance for managers and policymakers pursuing carbon-neutral strategies. 2026 Ashish Dwivedi et al. -
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analyzing the Inter-relationships of Business Recovery Challenges in the Manufacturing Industry: Implications for Post-pandemic Supply Chain Resilience
The COVID-19 pandemic brought about a rapid change in the global business environment, leading to increased risks of supply and demand disruptions. As society and the industry continue to acclimate to the new normal, the contributions of the manufacturing industry are critical in the recovery process. However, the existing literature lacks a framework to analyze the manufacturing sectors challenges during the recovery to enhance supply chain resilience (SCR). To address this gap, this study develops a framework for business recovery, especially in the manufacturing sector. A broad literature examination and expert survey were conducted to identify the critical potential business recovery challenges. Further, the interplay of business recovery challenges was analyzed using mixed methodologies such as total interpretive structure model and the cross-impact matrix multiplication applied to classification (MICMAC) to foster a framework that can assist the manufacturing industry in improving SCR. The study found that challenges like lack of flexible policies for handling disruptions and lack of management support toward building resilience have the highest driving power impeding business recovery. Other challenges, such as lack of reconfiguring production lines, lack of product competencies to meet disturbances, and less adoption of robust technologies are also identified as major challenges. The implications of the study offer valuable insights into global manufacturing industries. It also has significant propositions for the Pacific region. The Pacific region faces unique challenges, including geographic isolation, resource dependency, diverse economies, climate vulnerabilities, and complex trade relationships. The suggested frameworks adaptability and applicability to these regional characteristics enable businesses and policymakers in the Pacific to better understand and address the specific dynamics of post-pandemic recovery, ultimately contributing to enhanced SCR tailored to the regions needs. The study enriches the existing SCR literature by analyzing inter-relationships between business recovery challenges in the manufacturing industrys post-pandemic context. The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2024. -
Examining the facilitators of I4.0 practices to attain stakeholders collaboration: a circular perspective
The fourth industrial revolution (I4.0) has changed the traditional business model, bringing various benefits, including increased efficiency and productivity in organizations. However, to attain success in I4.0 practices requires collaboration from various stakeholders. This study objectives to identify the facilitators of I4.0 practices that can lead to successful collaboration among stakeholders from a circular perspective. An extensive literature review is performed to identify 14 potential facilitators. Further, the study adopts a mixed methodology of Best-Worst Method (BWM) and Interpretive Structural Modeling (ISM) to analyze the interconnectedness among the identified facilitators. BWM method was used to determine the relative importance of the identified facilitators, while ISM technique was used to determine the relationships between the facilitators of I4.0 practices. The findings from the study reveal that to strengthen stakeholder collaboration, organizations need to focus more on training and capacity-building programs and create more opportunities for technology exchange. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Empowering Social Enterprises Through Financial Inclusion in Emerging Markets
Financial inclusion is also a main mover of economic and social development, par-ticularly in the developing economies that still have minimal access to finance. The chapter discusses how financial inclusion reinforces social innovation by cultivating social enterprises, narrowing inequalities, and facilitating sustainable development. This chapter takes an account of enablers, including government policy, financial education initiatives, shadow financing, and green finance, with a perspective of comprehending the role of facilitation of inclusive financial ecosystems. Relying on secondary data from institutions like the World Bank and DBIE, it analyzes how access to finance enhances the resilience of vulnerable groups and strengthens economic resilience. It also considers policy implications and future research to maximize the role of financial inclusion in social change. Its findings point to its ability to unleash social enterprise opportunities and a more balanced economic ecosystem in emerging markets. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The Shift Towards Sustainable Fashion: Redefining Shopping Practices
The chapter delves into the transformative shift in the fashion industry toward sustainable practices, examining its environmental, social, and economic impacts, as well as the roles of consumers, retailers, and policymakers in driving this change. It highlights how the demand for sustainable fashion has moved beyond a trend to become a guiding principle that is reshaping the industry's operations and consumer behaviors. The mission of this chapter is to provide a comprehensive study of sustainable fashion, exploring both the motivation behind and the mechanisms through which the industry is evolving, and inspiring readers to reflect on their own roles within this change. 2025 by IGI Global Scientific Publishing. All rights reserved. -
The Shift Towards Sustainable Fashion: Redefining Shopping Practices
The chapter delves into the transformative shift in the fashion industry toward sustainable practices, examining its environmental, social, and economic impacts, as well as the roles of consumers, retailers, and policymakers in driving this change. It highlights how the demand for sustainable fashion has moved beyond a trend to become a guiding principle that is reshaping the industry's operations and consumer behaviors. The mission of this chapter is to provide a comprehensive study of sustainable fashion, exploring both the motivation behind and the mechanisms through which the industry is evolving, and inspiring readers to reflect on their own roles within this change. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Pain Detection System In Real Time Healthcare Environment
The negative feeling of pain is often involuntarily expressed through facial expressions. Facial expression therefore is an important non-verbal cue to determine if a person is in pain. This property can be applied for diagnosis of pain especially among patients who are differently newlinechallenged and lack the ability of expressing their issue. In spite of the developments made so far, this field still lags behind in finding pain expressing faces in an uncontrolled environment through unprocessed newlinereal time images and videos. To bridge this gap, the study proposed a hybrid or fusion model that could adequately detect a face expressing pain. The model was executed with inputs taken from pre-recorded or stored newlinevideos and live streamed videos. It involved the combination of Patch Based Model (PBM), Constrained Local Model (CLM), and Active newlineAppearance Model (AAM) in concurrence with image algebra. This allowed the efficient pain identification from raw home-made stored newlinevideos and live stream even through a bad recording device and under poor illumination. The hybrid model was implemented in a frame-by-frame manner for feature extraction and pain detection. The feature extraction part was done in pixel-based and point-based representation. For point-based representation, a concept called image algebra was used. For classification, three approaches viz. histogram technique, Feed newlineForward Neural Network (FFNN), and Multilayer Back Propagation Neural Network (MLBPNN) were implemented and analyzed. The videos newlineof different subjects showed facial expressions of pain::face, not::pain face and neutral::face. A home-made dataset was produced for storing the videos which was later used as the input and the selected features were stored. This dataset served as the training set for the proposed model. Though the data was not highly sensitive it was sufficient to confer adequate information for detecting pain expression. -
Pain detection system in real time healthcare environment
The negative feeling of pain is often involuntarily expressed through facial expressions. Facial expression therefore is an important non-verbal cue to determine if a person is in pain. This property can be applied for diagnosis of pain especially among patients who are differently challenged and lack the ability of expressing their issue. In spite of the developments made so far, this field still lags behind in finding pain expressing faces in an uncontrolled environment through unprocessed real time images and videos. To bridge this gap, the study proposed a
hybrid or fusion model that could adequately detect a face expressing pain. The model was executed with inputs taken from pre-recorded or stored videos and live streamed videos. It involved the combination of Patch Based Model (PBM), Constrained Local Model (CLM), and Active Appearance Model (AAM) in concurrence with image algebra. -
Enhancing Time Series Forecasting in Low-Liquidity Markets Using Generative Adversarial Networks
Financial assets that are low liquidity are very difficult to forecast as they are sparsely traded, their volatility is not regular, and scarce historic evidence exists. This paper will explore the hypothesis of whether in this kind of limited environment, generative models can enhance the effectiveness of forecasting. A dual model framework is constructed which contrasts a normal Long Short Term Memory (LSTM) network with TimeGAN based synthetic data augmentation method in 60-day long-range forecasting of the TRY/USD exchange rate. The methodology consists in the training of an LSTM model on real historical sequences and the improvement with TimeGAN generated synthetic sequences with a maintained temporal structure. It has been shown that TimeGAN has a significant effect on the accuracy of the forecasts, the RMSE decreased to 0.0002 by approximately fifty percent, and the R2 grew to 0.9921 by approximately fifty percent. The results suggest that augmentation through GAN enhances generalization of models in thin and dynamic markets. The most important contributions include implementation of TimeGAN to low-liquidity FX forecasting, the assessment of the effects of synthetic data on forecast accuracy and the empirical benchmark of LSTM and TimeGAN in low-volume finance. 2025 IEEE.