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Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding
A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu's criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and SensenDice Similarity Index. The KruskalWallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality. 2021 Elsevier B.V. -
Hyperspectral multi-level image thresholding using qutrit genetic algorithm
Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and Sensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. 2021 Elsevier Ltd -
Photocatalytic nanomaterials: Applications for remediation of toxic polycyclic aromatic hydrocarbons and green management
Nanomaterials (NMs) have piqued the attention of scientists and researchers across many biomedical sciences due to their superior physical, chemical, and magnetic properties. The efficacy and efficiency of NMs depend on adapting to specific site conditions and soil composition. NMs have lately received much attention in the context of polycyclic aromatic hydrocarbons (PAHs) polluted soil remediation and water mitigation because of their unique properties resulting from their nanoscale sizes. The remediation of hazardous PAHs in water and soil is a hot research subject. Because the exposure of PAHs in water and soil results in pollution, which raises major human health concerns. The current review reports novel advancements in NMs that subsidize enhancement for degradation of PAHs. Challenges to the fabrication of high activity-based photocatalytic materials are also discussed. Furthermore, this review delivers exclusive and wide-ranging perspectives on the fabrication of nanomaterial-based photocatalytic systems. The knowledge of both soil remediation and water mitigation is also updated. 2022 -
Investigating the factors influencing health equity
This chapter determines various determinants of global health and wellbeing in relation to the Third Sustainable Development Goal of ensuring "Good Health and Well-being." It investigates the contribution of cultural, environmental, systemic, and socio-economic factors to health outcomes and the expected international cooperation towards closing the gaps in the health of nations. In the chapter, a qualitative methodology has been followed based on secondary data in the form of two peer-reviewed literature pieces. From there, the synthesis has been done to find an overview of global healthcare governance structures, their effectiveness, and major challenges.The study shows that while mechanisms of global health care governance are rated as being moderately effective, wide gaps in funding, transparency, and equitable access to services persist. Respondents also showed the need for increased international collaboration, especially on social determinants of health and strengthening health systems in low-and middle-income countries. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Message from IEEE InC4 2024 General Chair
[No abstract available] -
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. -
Energy Intelligence: The Smart Grid Perspective
Smart grids enable a two-way data-driven flow of electricity, allowing systematic communication along the distribution line. Smart grids utilize various power sources, automate the process of energy distribution and fault identification, facilitate better power usage, etc. Artificial Intelligence plays an important role in the management of power grids, making it even smarter. With the help of Artificial Intelligence and Internet of Things, smart grids can optimize the energy consumption, provide continuous feedback on usage, and monitor live usage statistics, thereby making the energy intelligent. Smart grids require specific hardware to continuously monitor and adapt to the requirements of the system. By enabling energy intelligence, we empower building-level and city-level optimizations that make use of green energy, thereby contributing more toward sustainable development. Thus, the multifaceted energy management system uses sustainable and renewable energy sources, combined with smart devices to provide a two-way communication system to optimize the end-to-end distribution of energy, beneficial to both suppliers and consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Security Aspects for Mutation Testing in Mobile Applications
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.