Browse Items (14421 total)
Sort by:
-
Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.
Maintaining a consistent supply of power is essential for the well-being of the economy, the public, and one's own health. The generation of energy, as well as its distribution, monitoring, and management, are all undergoing fundamental changes as a result of the implementation of a smart grid (SG), which is authorised to include communication technology and sensors into power systems. There are a lot of problems that need to be fixed before the interoperability of the smart grid can be determined. The integration of renewable energy sources and smart grid technology market size and energy management is a sustainable solution to the problem of energy demand management. The importance work quickly toward the development of an efficient Energy Management Model (EMM) that integrates smart grids and renewable energy sources. When it comes to the modelling of complex and non-linear data, machine learning (ML), Internet of Things (IoT) approaches often perform better than statistical models. So, utilizing a machine learning approach for the EMM is a good option since it simplifies the EMM by generating a single trained model to anticipate its performance characteristics across all conditions. This may be accomplished via the use of an EMM created using an ML method. It was recommended that a certain flexibility sample be used as a control mechanism for incursion into the smart grid. The outcomes of the experiment indicate that the demand-side management (DSM) device is more resistant to infiltration and is enough to lower the energy usage of the smart grid. 2024, Ismail Saritas. All rights reserved. -
Rice Yield Forecasting in West Bengal Using Hybrid Model
Agriculture in India is the primary source of revenue, yet farmers still face challenges. The primary goal of agricultural development is to produce a high crop yield. The Datasets collected for the study of real-world time series include a blend of linear and nonlinear patterns. A mixture of linear and non - linear models, rather than a single linear or non - linear model, gives a more precise forecasting models for time series data. The ARIMA and ANN prediction models are combined in this paper to create a Hybrid model. This model is used to predict rice yield for all 18 West Bengal districts during the Kharif season, based on 20years of information(20002019) collected from various sources such as India Meteorological Department, Area, and production Statistics, DAV from NASA, etc. The hybrid model aims to enhance efficiency indicators such as MSE, MAE, and MAPE, demonstrating excellent performance for rice yield prediction in all the districts of West Bengal. In the future, it can be applied to other crops that can support farmers in their farming. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multiple Approaches in Retail Analytics to Augment Revenues
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Capturing customers spirit in the digital era
The chapter discusses the concepts of customer engagement in the digital era, more specifically focusing on emotional branding, convergence of physical and digital touchpoints for marketing, and the elements of technology. It explains in detail the processes of how brands are able to emotionally connect with their target audience, how they are able to use the data collected in a legal manner, and how they can provide a more engaging and personalized experience. By assessing consumer behavior patterns and trends in technology, the chapter provides useful insights on how brands can go after the soul of their consumers in a more and more online world. 2025, IGI Global Scientific Publishing. -
A STUDY ON CONJUGACY GRAPHS
In this paper, we introduce the notion of an equivalence graph based on equivalence relation defined on a group. Furthermore, restricting ourselves to conjugacy relation, a special type of equivalence graph called a conjugacy graph is also defined. In addition, a graph theoretical expression for the class equation is established followed by related results. 2025, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved. -
ON L(2, 1)-ORDER SUM SIGNED GRAPH OF A FINITE GROUP
In this paper, we have constructed a color-induced signed graph of an algebraic graph, called the L(2, 1)-order sum signed graph of a group. Based on the nature of the group, the L(2, 1)-span of the order sum graph is obtained and the structural aspects of thus obtained L(2, 1)-order sum signed graph such as planarity, chordality, etc. have been investigated. We have also defined an automorphism which turns out to be the only possible automorphism on the graph and have investigated the structural aspects of the graph such as edge transitivity and vertex transitivity. Further, a line-signed graph of L(2, 1)-order sum signed graph, which is a line graph with a signing protocol defined for the edges, has also been introduced. We have also explored the regularity of the line-signed graph. 2025 Sciendo. All rights reserved. -
Fraud detection in the era of AI: Harnessing technology for a safer digital economy
Fraudulent activities have increased along with the new prospects of the digital economy's quick growth for both consumers and enterprises. Conventional techniques of fraud detection are insufficient to keep up with these ever-evolving fraudulent strategies. In this sense, machine learning (ML) and artificial intelligence (AI) have become potent instruments to prevent and detect fraud and guarantee the safety of online transactions. This study examines the function of AI and ML and shows how these technologies can spot irregularities and intricate patterns that would be challenging to find with conventional methods. The study includes various methods of AI-based fraud detection and analyses important ethical issues related to these practices. Furthermore, the study looks at developing technology and trends that will probably influence fraud detection in the future. In conclusion, the revolutionary potential of AI and ML in building a safer digital economy is analysed. 2024, IGI Global. All rights reserved. -
From bean to brain: Coffee, gray matter, and neuroprotection in neurological disorders spectrum
Coffee is a popular drink enjoyed around the world, and scientists are very interested in studying how it affects the human brain. This chapter looks at lots of different studies to understand how drinking coffee might change the brain and help protect it from neurodegenerative disorders especially like schizophrenia. With the help of available literature a link between the coffee mechanism and neurodegenerative disorders is established in this chapter. Researchers have found that drinking coffee can change the size of certain parts of the brain that control things like thinking and mood. Scientists also study how coffee's ingredients, especially caffeine, can change how the brain works. They think these changes could help protect the brain from diseases. This chapter focuses on how coffee might affect people with schizophrenia as hallucination is caused during and after excess consumption of caffeine. There's still a lot we don't know, but researchers are learning more by studying how different people's brains respond to coffee over time. Overall, this chapter shows that studying coffee and the brain could lead to new ways to help people with brain disorders. This study also draws ideas for future research and ways to help people stay healthy. 2024 Elsevier B.V. -
The Transparency Paradox: Rebuilding Trust in AI Shopping Agents Through Explainability
AI shopping agent systems that search, compare, negotiate, and purchase on a consumers behalf promise to reduce friction in digital commerce, but they also intensify long-standing concerns about price discrimination, manipulative choice architecture, and opaque data use. This chapter argues that the resulting transparency paradox is not merely a communication problem: the same AI capabilities that enable personalization can also conceal and optimize exploitation. Building on research in trust, explainable artificial intelligence, humanAI interaction, and consumer protection, the chapter develops a socio-technical approach to rebuilding trust in agentic commerce. It synthesizes regulatory and standards developments and proposes design and governance mechanisms layered explanations, contestable outcomes, auditability, and choice-integrity controls intended to make AI shopping agents intelligible and accountable across jurisdictions and market contexts. Copyright 2026, IGI Global Scientific Publishing. -
Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems
This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities. This book: Discusses load balancing and demand response using nature-inspired optimization techniques Presents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithms Covers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniques Explains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniques Illustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniques Nature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
The role of technology in advancing psychoneuroimmunology research
This chapter explores the transformative impact of technology on Psychoneuroimmunology (PNI), emphasizing advancements in neuroimaging, genomics, and proteomics. Techniques like functional MRI (fMRI) and Positron Emission Tomography (PET) have revolutionized our understanding of brain activity and neuroinflammation. Next-generation sequencing (NGS) and proteomic profiling have unveiled genetic and protein biomarkers linked to stress and immune responses. Wearable technology and mobile health apps now enable continuous monitoring and personalized stress management. Big data analytics and machine learning enhance pattern identification and outcome prediction. Ethical considerations, including data privacy and equity, are discussed alongside emerging technologies like AI and nanotechnology. Overall, the chapter highlights how these innovations are reshaping PNI research and improving treatment for stress-related disorders. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Overcoming barriers: Challenges and opportunitiesin multi-stakeholder collaboration for sustainable supply chain
Multi- stakeholder collaboration is increasingly recognized as a critical component of sustainable development, particularly in complex supply chains. A supply chain is a simple tool that sustainably excels in every TBL component. This Chapter emphasizes the adoption of sustainability in supply chains and its results which are influenced by two key elements. The first crucial component is the managerial orientation toward sustainability which is how managers and decision- makers see sustainability and how it relates to their incentives to carry out sustainability activities. National and international development organizations have started integrated multi- stakeholder projects supporting creative solutions to poverty and environmental degradation. Among the noteworthy projects are those that promote sustainable livelihoods market- driven human development and the use of ecosystem services to reduce poverty. Difficulties in communicating because of language, priority, expertise gaps, power imbalances, and divergent interests. 2025, IGI Global Scientific Publishing. All rights reserved. -
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE. -
Cluster analysis for european neonatal jaundice
The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means. 2018, Springer International Publishing AG. -
Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC. -
Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network
Social networking uses internet-based platforms to facilitate users to make connections with others and share various forms of content, including text, images, videos, and links. Social networking services are mainly used for non-social interpersonal communication. Many approaches have been developed for hate speech detection, but they still face significant challenges, particularly in classifying text into multiple labels accurately and in a timely manner. For accurate hate speech detection in social networks, a Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier (CRCOCDBC) model is developed. The objective of the developed CRCOCDBC is to detect multi-class hate speech with minimal time and error rate. Comparative analysis shows improved performance in terms of minimum error and higher authentication accuracy and precision than other well-known methods. 2026 Seventh Sense Research Group. -
Unveiling the root causes of diabetes using explainable AI
Diabetes is a non-communicable wide spread disease across the world. To investigate the risky factors that are associated with diabetes, and to start early and customized treatment, researchers are fascinated to explore existing machine learning or deep learning models and to develop more reliable algorithms. The advancement in technology and the increase in world population is an enriching source to prompt and explore the factors that decide a person to be diabetic. Several algorithms and approaches are in place to address these factors but are lacking in emphasizing with more interpretable features which convinces patent to trust the medicine, treatment, and have meaningful conversation with the physicians and artificially intelligent systems. To encourage the participation of people with diabetes for customized treatment and considering societal needs, this chapter explores the possibility of Explainable Artificial Intelligence (XAI) in diabetes detection and figuring out the significant features that dominate diabetes. 2025 Elsevier Inc. All rights reserved. -
Unveiling mental health nuances of male Indian classical dancers.
This study explores the lives of male Indian classical dancers, highlighting the duality of dance as a sanctuary and a stressor. As male Indian classical dancers negotiate and redefine norms of masculinity, the study calls for recognition of diverse masculine identities within traditionally feminized spaces. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.; This research explores the mental health nuances of male Indian classical dancers (MICDs), through a lens of redefining masculinity, focusing on their perceived quality of life, psychosocial challenges, and coping strategies. This study follows an interpretive phenomenological approach to follow the lived experiences of MICDs. The participants are male, fluent in English, and pursuing Indian classical dance styles professionally, like Kathak, Bharatanatyam, Chhau, etc. Six participants were recruited for personal, semistructured, in-depth interviews, whereas, a focus group discussion with four participants was conducted to explore the stigma. The data were analyzed using interpretive phenomenological analysis, revealing themes of (a) identity fragmentation and negotiation in gendered social contexts, (b) gendered experiences, (c) emotional distress and psychological challenges, (d) coping mechanisms and resilience, and (e) stigmatization and social integration dynamics. MICDs grapple with identity formation, navigating a paradox of self-perception, artistic identity, and societal expectation. They reported experiencing emasculation, compromising artistic expression, and struggling with gender norms and gendered training constraints. They have faced name-calling, bullying, taunting, slandering, and discrimination leading to psychological challenges and distress. However, the paradox continues as male dancers use adaptive coping strategies despite the adversities that intertwine self-perception, societal pressures, and their passion for dance. These findings provide a strong foundation for making changes in the dance community for acceptance of male dancers, policy making for better job opportunities for male dancers, and mental health services to be provided to help them deal with distress. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Exploring the nexus of climate change and vector-borne disease transmission
Climate change is a critical global challenge that significantly impacts the redistribution of malaria endemicity worldwide. While efforts have been made to model malaria transmission using climatic factors, relying solely on these factors can lead to discrepancies and ineffective decision-making. To address this, we used the VECTRI modela dynamic framework developed by the International Center for Theoretical Physics (ICTP) that integrates both climatic and entomological factorsto map malaria risk for India and project its potential future under the SSP370 warming scenario. Our findings indicate that the length of malaria transmission is expected to increase across India by the end of this century. The shift of malaria endemicity to further north and also into highland areas could increase the at-risk population due to lower immunity in these regions. Therefore, integrated climate and entomological modeling is essential for effectively anticipating malaria transmission risks and enhancing public health responses. 2025 -
A stakeholder theory approach to analysing strategies for improving pandemic vaccine supply chain performance
This study aims to formulate strategies that impact the vaccine supply chain (VSC). This study measures the VSC performance using the proposed strategy concerning stakeholders theory. From the literature review and experts consent, the strategies are classified into six broad strategies as-VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory. A questionnaire is developed for surveying healthcare organisations and hospitals. All six proposed hypotheses got accepted. The developed model satisfies all the model fit parameters. Strategies like VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory have positively impacted vaccine supply chain performance. This research will be helpful for healthcare professionals and organisations for the faster delivery of the vaccine. This research will also help policymakers in improving the performance of VSC. This study is also the first to use the stakeholder theory approach for measuring VSC performance. Copyright 2024 Inderscience Enterprises Ltd.