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An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET
The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs. 2025 The Authors. Published by Elsevier B.V. -
Nurturing Adult Socio-Emotional Skills and Engagement: The Transformative Power of Mentoring Program
There has been a growing interest in understanding the ways education could integrate socio-emotional learning (SEL) skills in their curriculum. This chapter explores by considering mentoring approach as a channel to foster SEL skills that would be beneficial to both adult learners and educators alike. The chapter emphasizes on the key SEL skills and also focuses on the need for higher institutions to promote adult SEL, not only for faculties but also for adult learners. Two main types of mentoring have been addressed, viz the traditional mentoring versus alternative mentoring approach. The chapter also discusses about incorporating the train-the-trainer model for mentoring. In essence, this SEL-based adult mentoring ensures that both mentees and mentors benefit. The mentees have gained self-awareness, responsible decision-making skills, relationship skills and emotional intelligence through this mentoring approach, while the mentors have acquired a sense of accomplishment and fulfillment that promotes their emotional intelligence and decision-making skills. 2026 by IGI Global Scientific Publishing. All rights reserved. -
EEG Emotion Recognition Using PSO-Based Feature Selection and Convolutional Neural Networks
EEG signals have become a promising source for emotion recognition due to their ability to capture the brain's electrical activity connected with different emotional conditions. In this work, a novel approach is proposed that integrates Particle Swarm Optimization (PSO)-based feature selection with Convolutional Neural Networks (CNNs) for improved EEG emotion classification. The method with the preprocessing of a notch filter to eliminate noise and enhance the quality of the EEG signals. Key features, including Magnitude Squared Coherence Estimate (MSCE) and Power Spectral Density (PSD), are extracted to capture essential frequency-domain information. PSO is employed to optimize the selection of features, reducing dimensionality while preserving the most relevant and informative attributes for emotion recognition. The optimized feature was subsequently passed to a CNN classifier, which improves the model's capability to accurately differentiate between different emotional states. This study is implemented using Python software to analyze emotion, and the effectiveness of the proposed approach is assessed using the EEG Brainwave dataset. Experimental results demonstrate that the proposed approach delivers an accuracy of 92.6% and a precision of 91%, highlighting its effectiveness in real-time, high-precision emotion recognition from EEG data. 2025 IEEE. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida. -
Polycystic ovary syndrome: An exploration of unmarried women's knowledge and attitudes
Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age and a chief cause of subfertility attributed to ovulation. Besides, lack of knowledge about PCOS, its treatment, and lifestyle changes influence the prognosis. The present qualitative inquiry investigates the knowledge and attitudes of unmarried women towards the syndrome, associated treatment, and necessary lifestyle changes in the fight against the same. A total of 15 participants with PCOS were selected using purposive sampling (n from southern parts of India viz. Kerala and Tamil Nadu states. The telephonic interviews were conducted in late November and early December 2020. He conventional content analysis emerged with six major themes. The themes capsulated women's knowledge, causes, complications and risk factors, treatment of PCOS their perceived importance of health promotive behaviours such as physical activity, sleep patterns, and perceived support from society. The importance of diet, exercise and a healthy lifestyle were additional relevant factors stressed by the respondents. Although the medicines helped participants attain regular menstrual cycles, they also had side effects reported in the discussion. Few respondents reported that they lacked the necessary awareness of PCOS when diagnosed at a younger age. The study enhances the understanding of PCOS from a qualitative approach that has cultural relevance apart from pertinent clinical and lifestyle implications. 2022 The Author(s) -
Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Relation between electricity consumption and economic growth in Karnataka, India: An aggregate and sector-wise analysis
Karnataka is a highly progressive and rapidly growing state in India, with huge potential for industrial growth, however, it grapples with power deficits and other problems in electricity sector, which make it a good case study for Indian electricity sector. Given the importance of electricity in the urbanisation and growth process, the paper analyses the electricity consumption trend in Karnataka, examine its causality with economic growth at aggregate and sectoral levels using Granger causality test, and forecast the future electricity consumption applying Holt-Winters smoothening (no seasonality) technique. The general trend reflects higher consumption by the agricultural consumers, compared to the revenue-generating 'Industries' and 'Commercial' categories, mainly due to the policy of de-metering and providing 'free' power to agricultural consumers since late 1980s. The Granger causality tests reveal that there is no causality relation (neutrality hypothesis) between electricity consumption and economic growth in Karnataka, for total, agricultural and industrial consumption. This basically stems from the inaccurate measurements of agricultural consumption, higher dependence on captive generation, and poor quality grid supply. Finally, electricity consumption is predicted to be around 69,347 GW h by 2019?20. Future policies should focus on universal metering, reducing cross-subsidization, supplying good quality and reliable power to all sectors, and economical planning of resource-mix to achieve adequate, productive and efficient electricity consumption. 2020 Elsevier Inc. -
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After-Sale Service Failures and Their Influence on Customer Behaviour with Reference to Home Appliances
There are continuous technological advancements, and home appliance manufacturers have developed innovative products that make customer's life effortless. The increase in the purchasing power of the customers made the industry more competitive and put an extra burden on the manufacturers to adopt new technologies that help customers solve their problems and fulfil their needs. Firms face problems and challenges in the form of after-sale service failures. After-sale services are an integral part of home appliance products, and the companies can not avoid these while serving the customers. Although the after-service structure is rich in empirical studies on different service sectors like information technology, after-sale service failure, and consumer behaviour modelling in the home appliance have not been adequately investigated in Indian services. Previous researches have relied on understanding the services and their relation to either satisfaction or loyalty. Thus, they have been unable to disentangle the phenomenon of unfavourable reactions after an after-sale service failure from satisfaction and dissatisfaction. After-sale service is an essential component of customer behavioural outcomes. Therefore, businesses need to understand how after-sale service failures influence customer behaviour. Despite service superiority's importance, the home appliance industry lacks industry-specific, widely recognized instruments for after-sale service assessment. The primary goal of this study is to find major after-sale service failures and look at how these after-sale service failures affect customers, leading to unfavourable behavioural reactions. The study used a quantitative approach to understand the issue comprehensively. This research incorporated various after-sale service failure areas discussed and analyzed by previous research. It also discussed service theories and models (Expectancy Disconfirmation Paradigm, Justice Theory, Attribution Theory) related to failures and behaviours. However, this research focuses mainly on how these service failure areas lead to customer behavioural outcomes. Firstly, to know the major after-sale service failure areas, this study prepared the questionnaire based on the literature available on after-sale service failures and customers' reviews and their experience with the after-sale service of the home appliance companies. Data is collected from customers who have experienced after- sale service failures and their subsequent behaviour. The study analyzed the reasons for after-sale service failures, the types of failures that customers encounter, and the impact of these failures on customer behaviour, including their negative word of mouth, switching behaviour, willingness to recommend the brand etc. The findings of this study provided valuable insights into how businesses can improve their after-sale service and retain their customers. The study found seven major after-sale service failures that significantly impact customer behaviours. Unreasonable charges and policy clarity issues are the most significant service failures affecting customers, leading to negative behaviours. These findings show that different types of service failure elicit different reactions. The present study is one of the few empirical studies examining the links between service failures and actual behaviours in consumer durable after-sale service failures. -
English to Hindi Translation System Using Hybrid Techniques
Good communication is critical for overcoming cultural and linguistic divides in today's internationalized society. An essential communication component is the Translation of written materials, primarily academic papers, from one language into another. This abstract focuses on the research involved in translating academic publications from Hindi to English. Translating Hindi academic papers into English is naturally hard due to the significant linguistic and cultural differences between the two languages. The proposed work provided an analytical analysis of various models used in language translation, including the seq-to-seq model, MT5, and LSTM, with the help of BLEU score, Learning rate, and average loss. MT5 model outshines others in terms of an average loss of 4.75; meanwhile, LSTM has an average loss of 5.56, and the seq-to-seq model has an average loss of 6.09, implying weaker Translation. 2024 IEEE. -
Interface improvement and multiscale assessment of recycled concrete aggregates with epoxy resin polymer
Recycled concrete aggregate (RCA) exhibits challenges like weak bonding, high porosity, and inferior strength compared to natural aggregates. This study evaluates the effect of epoxy resin polymer treatment on RCA on enhancing compressive and split tensile strengths in concrete, replacing natural aggregates with untreated RCA (UTRAC) and treated RCA (ERTAC) at 25%, 50%, 75%, and 100% levels. The tests were conducted at 3, 7, and 28 days. UTRAC showed reductions of up to 26.32% in compressive strength and 35.38% in tensile strength at 100% replacement; ERTAC outperformed control concrete (CC) with gains of up to 26.32% in compressive strength (at 25%) and 122.73% in tensile strength (at 100%), identifying 25% as the optimum replacement ratio. SEM and XRD analyses confirmed improved particle packing, reduced porosity, and stronger interfacial transition zones (ITZ) in ERTAC. The Author(s) 2026. -
Female Political Representation and Economic Development in India: An Empirical Analysis
Recent years have seen an enhanced focus on women's roles in politics, with research increasingly showing that having a more significant gender representation in decision-making roles can significantly impact economic growth. This chapter delves into how women's political involvement, economic advancement, and gender equality have evolved in India over twenty years from 2000 to 2020, using a time series analysis. The study uses vector autoregression (VAR) analysis to examine how political representation of female, participation rate of labour force (LFPR), and health investment affect the Gender Development Index (GDI). The model diagnostics successfully demonstrated stationarity, non-serial correlation, and the lack of homoscedasticity. The analysis highlights that Female LFPR and GDI are positively related, whereas health expenditure and GDI are negative. Female labour market participation improves GDI, whereas females consistently receive less healthcare expenditure than males, leading to a negative relationship between health expenditure and GDI. Importantly, it is observed that labour market participation has a more substantial effect on GDI than political representation or health investments. This shows that greater female labour force participation is more critical in gender equality than increased political representation or healthcare spending. Highlighting the necessity for policies tailored to women, the chapter argues that these measures are critical for enhancing LFPR and boosting GDI and societal progress. The chapter contributes to the gender discourses in political participation and the empowerment of female, proposing a strategy to improve women's contribution to the labour market, leading higher GDI and, as a result, a more equitable society. 2026 selection and editorial matter, Hebatallah Adam and Abul Hasnat Monjurul Kabir; individual chapters, the contributors. All rights reserved. -
Investigation of the correlation between optical and ?-ray flux variations in the blazar Ton 599
The correlation between optical and ?-ray flux variations in blazars reveals a complex behaviour. In this study, we present our analysis of the connection between changes in optical and ?-ray emissions in the blazar Ton 599 over a span of approximately 15 yr, from 2008 August to 2023 March. Ton 599 reached its highest flux state across the entire electromagnetic spectrum during the second week of 2023 January. To investigate the connection between changes in optical and ?-ray flux, we have designated five specific time periods, labelled as epochs A, B, C, D, and E. During periods B, C, D, and E, the source exhibited optical flares, while it was in its quiescent state during period A. The ?-ray counterparts to these optical flares are present during periods B, C, and E; however, during period D, the ?-ray counterpart is either weak or absent. We conducted a broad-band spectral energy distribution (SED) fitting by employing a one-zone leptonic emission model for these epochs. The SED analysis unveiled that the optical-ultraviolet emission primarily emanated from the accretion disc in quiescent period A, whereas synchrotron radiation from the jet dominated during periods B, C, D, and E. Diverse correlated patterns in the variations of optical and ?-ray emissions, like correlated optical and ?-ray flares, could be accounted for by changes in factors such as the magnetic field, bulk Lorentz factor, and electron density. On the other hand, an orphan optical flare could result from increased magnetic field and bulk Lorentz factor. 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Study of correlation between optical flux and polarization variations in BL Lac objects
Polarized radiation from blazars is one key piece of evidence for synchrotron radiation at low energy, which also shows variations. We present here our results on the correlation analysis between optical flux and polarization degree (PD) variations in a sample of 11 BL Lac objects using ?10 yr of data from the Steward Observatory. We carried out the analysis on long-term (?several months) as well as on short-term time-scales (?several days). On long-term time-scales, for about 85 per cent of the observing cycles, we found no correlation between optical flux and PD. On short-term time-scales, we found a total of 58 epochs with a significant correlation between optical flux and PD, where both positive and negative correlation were observed. In addition, we also found a significant correlation between optical flux and ?-ray flux variations on long-term time-scales in 11 per cent of the observing cycles. The observed PD variations in our study cannot be explained by changes in the power-law spectral index of the relativistic electrons in the jets. The shock-in-jet scenario is favoured for the correlation between optical flux and PD, whereas the anticorrelation can be explained by the presence of multizone emission regions. The varying correlated behaviour can also be explained by the enhanced optical flux caused by the newly developed radio knots in the jets and their magnetic field alignment with the large-scale jet magnetic field. 2022 The Author(s). -
Temporal correlation between the optical and ? -ray flux variations in the blazar 3C 454.3
Blazars show optical and ? -ray flux variations that are generally correlated, although there are exceptions. Here we present anomalous behaviour seen in the blazar 3C 454.3 based on an analysis of quasi-simultaneous data at optical, ultraviolet, X-ray, and ? -ray energies, spanning about 9 yr from 2008 August to 2017 February.We have identified four time intervals (epochs), A, B, D, and E, when the source showed large-amplitude optical flares. In epochs A and B the optical and ? -ray flares are correlated, while in D and E corresponding flares in ? -rays are weak or absent. In epoch B the degree of optical polarization strongly correlates with changes in optical flux during a short-duration optical flare superimposed on one of long duration. In epoch E the optical flux and degree of polarization are anticorrelated during both the rising and declining phases of the optical flare. We carried out broad-band spectral energy distribution (SED) modelling of the source for the flaring epochs A,B, D, and E, and a quiescent epoch, C. Our SED modelling indicates that optical flares with absent or weak corresponding ? -ray flares in epochs D and E could arise from changes in a combination of parameters, such as the bulk Lorentz factor, magnetic field, and electron energy density, or be due to changes in the location of the ? -ray-emitting regions. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
State-of-art Techniques for Classification of Breast Cancer: A Review
Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. -
Using machine learning architecture to optimize and model the treatment process for saline water level analysis
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. -
Sustainable Finance and Investor Dynamics in Emerging Markets
This chapter examines the role of sustainable finance in emerging markets, focusing on the integration of Environmental, Social, and Governance (ESG) principles to promote economic growth and social impact. It discusses how ESG investments address challenges like poverty, financial inclusion, and climate change, while exploring factors influencing investor behavior and barriers such as data transparency and greenwashing. The chapter compares ESG adoption in developed and developing economies, highlights sector-specific trends, and emphasizes the importance of public-private partnerships and multilateral collaborations. It also explores sustainable finance's potential for social innovation, supporting gender equality, inclusive development, and green technologies, and advocates for improved transparency and impact measurement to build investor confidence. 2026, IGI Global Scientific Publishing. All rights reserved. -
Demography-Based Hybrid Recommender System for Movie Recommendations
Recommender systems have been explored with different research techniques including content-based filtering and collaborative filtering. The main issue is with the cold start problem of how recommendations have to be suggested to a new user in the platform. There is a need for a system which has the ability to recommend items similar to the users demographic category by considering the collaborative interactions of similar categories of users. The proposed hybrid model solves the cold start problem using collaborative, demography, and content-based approaches. The base algorithm for the hybrid model SVDpp produced a root mean squared error (RMSE) of 0.92 on the test data. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.