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Energy efficient heterogeneous clustering scheme using improved golden eagle optimization algorithmfor WSN-based IoT
In the Internet of Things, Wireless Sensor Networks (WSNs) are networks of interconnected sensors that wirelessly collect and transmit information about the environment. Using IoT sensors, IoT applications can remotely monitor and control physical environments. Clustering in WSNs involves organizing sensor nodes into groups called clusters with one or more CHs for efficient data integration, communication and management, improving network performance and resource utilization. In WSNs, achieving energy efficiency is critical to extend network lifetime and ensure stable operation. An important aspect contributing to energy optimization is the selection of CHs. However, the lack of an efficient cluster head selection mechanism remains a significant challenge. Therefore, this study introduces an optimized multivariate cluster head selection method that leverages the Improved Golden Eagle Optimization Algorithm (IGEOA). With this approach, the selection of CHs is optimized, combining multiple objective functions designed for energy efficiency. By using this algorithm, clusters are formed based on the selected CHs. In addition, a cluster maintenance phase is integrated to supervise the post-establishment clustering of the network, which ensures optimal cluster performance and resource utilization in WSN. Evaluation through simulation illustrates that the proposed method significantly improves both performance and energy efficiency in a WSN environment. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Trusted explainable AI based implementation for detection of neurodegenerative disorders (ND)
The potential of explainable artificial intelligence (XAI) in detection of neurodegenerative disorders (ND) holds great promise in the field of healthcare. These diseases interfere with the daily functioning and independence of a person. The current studies lack in highlighting the aspect of explainability in their predictions and the various algorithms cannot provide any plausible explanations for their predictions making it difficult for medical professionals to place trust in their findings. Thus, the proposed framework aims to bridge this gap by exploring the development of a trustworthy framework for XAI-based ND detection, focusing on key aspects that can significantly impact its effectiveness and acceptance. The framework makes use of Trust-based SHAP (SHapley Additive exPlanations) values in classification. By computing trust values, the framework ensures more reliable predictions and increases interpretability, instilling confidence in clinicians and patients. The results show that with the inclusion of the trust-driven framework, the accuracy of the algorithm increased from 93.33% in the normal circumstances to 98.21%, highlighting the efficacy of the framework as compared to the other works. This shows that a trustworthy framework for XAI-driven ND detection can reshape care by enabling early detection, personalized treatment plans and enhancing decision-making process. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Computing isogeny on Edwards curves for quantum safe cryptography
In recent years, cryptographic research has seen a surge of interest in post-quantum cryptography driven by the potential threat that quantum computers pose to traditional public-key cryptosystems. Isogeny-based cryptography is a promising method in post-quantum cryptography, relying on the computational challenge of calculating isogenies, which are specific mappings between elliptic curves. The efficiency of isogeny computations is vital for real-world cryptographic applications. However, computing isogenies, especially with large parameters, can be very resource intensive. To overcome this challenge, we purpose an efficient method for computing odd-degree isogenies on certain form of an elliptic curves by employing an auxiliary coordinate. Our work appears to bridge the gap in computational efficiency for odd-degree isogenies, especially in terms of reducing the complexity of the isogeny computations when compared to traditional affine and projective methods. The derived formula is more efficient than affine and projective cases. We also analyse the algebraic complexity of these calculations and compare them to alternative formulae. Additionally, we evaluate the runtimes for isogeny computation across different prime numbers and compare them with other elliptic curve model to check the performance. At last, we suggest potential avenues for future work. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A novel deep learning based multimedia video retrieval framework using may fly optimization
Developing a video retrieval framework in multimedia management is a main challenge due to the massive growth of video content on the internet. A major drawback of video retrieval is its long search response time and low accuracy. To tackle these issues, this paper introduces a novel deep learning-based Multimedia video retrieval system (DL-MVR) to minimize the search response time with high accuracy. The collected video is initially converted into key frames and pre-processed with contrast adaptive histogram equalization to remove noise artifacts thereby improving image quality. After pre-processing, the images are fed to Efficient Net to extract patch features. Finally, to retrieve the similar video, matching is done using may fly optimization (MFO), that compares the query frame features to the video database. Several performance metrics are analysed to measure the effectiveness of the proposed strategy in terms of accuracy and response time. Experimental results indicate that the proposed system has a search response time of 0.71s, which is lower than existing methods. The proposed DL-MVR method achieves 99.26% of accuracy. The proposed method improves the overall accuracy by 9.32%, 22.04%, and 19.40% which is better than CNN-AlexNet (convolutional neural network), Pyramid regional graph network and CBVR respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
AI-driven deep learning framework for energy-efficient optimization in IoT-enabled wireless networks
Artificial intelligence (AI) and Internet of Things (IoT)-enabled wireless sensor networks (WSNs) have revolutionized industries by providing automation, real-time monitoring, and analytics that are predictive. WSNs still face significant obstacles such data security, network flexibility, and energy limitations in spite of these developments. In order to optimize energy use in Internet of Things (IoT)-based WSNs, this study introduces a novel Reinforcement Learning-based Energy-Efficient Communication Protocol (RL-EECP) to optimize the lifetime of networks and guarantee effective data transmission. The suggested protocol integrates sleep scheduling, reinforcement learning, and data fusion techniques. Also, an adaptive prioritization approach is introduced that assesses nodes according to the surroundings, significance, and energy consumption. Experiments show that RL- EECP performs better than existing studies in extending node lifetime and preserving excellent network performance. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments
This study proposes a hybrid machine learning framework to predict supply chain resilience by integrating principal component analysis, K-Means clustering, and ensemble learning models. The approach captures firm-level heterogeneity, enabling context-specific resilience prediction and interpretability using SHAP values. The findings demonstrate that ensemble models, particularly XGBoost, outperform traditional regression models, and reveal distinct resilience drivers across operational clusters. The framework offers actionable insights for improving resilience strategies and contributes a scalable, explainable approach for data-driven supply chain risk management. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting
In this work, we propose a Graph?Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not?very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to?learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as?well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop?yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for?short-term and long-term forecasts. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM
Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didnt differentiate the Logical Flaws (LFs) and anomalies in WAs, thereby increasing the misclassification rate. Hence, in this paper, a novel black-box testing framework that incorporates an advanced technique called Gated Transformer Memorized transferred Recurrent Mishswish unit (GTMRM) is proposed for distinguishing between LFs and other vulnerabilities, thus enhancing the reliability of WAs. Initially, the user registration is carried out, followed by Hash-based Message Authentication Code Hash-based Message Authentication Code (HMAC) creation. Afterward, the registered users log into the application to request a Uniform Resource Locator (URL) for access. In the meantime, to authenticate the user, the HMAC verification is performed. Once the authentication is successful, the user is granted for accessing the functionalities. Thereafter, the black-box-centric LF and anomaly identification is done; here, the raw dataset is initially pre-processed. Subsequently, concerning a similar domain, the pre-processed data is clustered. Next, the features are extracted, followed by feature selection. Then, from the grouped data, the graph is constructed. The pattern labelling is carried out centered on the graph features. Lastly, the Logical Flaws (LF), anomaly, and legitimate access are proficiently classified by the proposed GTMRM. A compensation measure is applied in the case of a LF. After that, the data is securely stored in the cloud server with an accuracy of 99.14%. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN
Timely and precise pediatric Brain Tumor (BT) classification is challenging in the prevailing studies owing to the lack of growth rate calculation. Therefore, this paper proposes a growth rate-aware intelligent BT classification using child Magnetic Resonance Imaging (MRI) based on Arcsin Deep Convolutional Neural Network (Arc-Deep-CNN). Initially, the child's MRI is collected and then pre-processed for angle correction, resolution improvement, skull removal, and edge sharpening to improve the image quality. Meanwhile, the binary image dilation is done in the postpre-processing for accurate tumor location identification using the Central Limit Theorem-based Battle Royale Optimization Algorithm (CLT-BROA). From the pre-processed images, the wavelet features are extracted to improve the detection rate. Based on the tumor-identified images, pre-processed images, and extracted features, a robust Shuffled Group Convolutional layer added U-Net (SGC-U-Net) significantly segments the normal brain, benign, core, and malignant tumors affected brain. Then, the 3D tumor reconstruction is done by performing splitting, feature extraction, and growth rate calculation. Finally, a novel Arc-Deep-CNN proficiently classifies the BT into Medulloblastoma, Glioma, and Meningioma tumors with respect to the growth rate. The proposed Arc-Deep-CNN achieved maximum accuracy and minimum training time of 98.77% and 52136ms, respectively, showing impressive performance in pediatric BT classification. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Segmentation of overlapping leukemic cells in histopathological images using HSV- based watershed transformation
Accurate segmentation of white blood cells (WBCs) is essential for computer-aided diagnosis, as overlapping and densely clustered cells often present significant challenges. This work introduces a hybrid framework for segmentation that proposes a fusion of hue and saturation in the Hue Saturation Value (HSV) domain. Gaussian smoothing, Otsu thresholding, and Morphological refinement is employed to enhance cell contrast and eliminate noise. A marker-based watershed algorithm is subsequently applied for accurate separation of overlapping WBCs. Evaluation on the ALL-IDB2 dataset confirms the methods capability through achieving a Dice Similarity Coefficient(DSC) of 0.8929 and an Intersection over Union (IoU) of 0.8099 to produce well-defined cellular boundaries. The novelty of this study lies in the integrated hue-saturation fusion and marker-based watershed strategy, offering improved boundary localization and reliable segmentation of overlapping WBCs. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures
We introduce FEDGE: FEDerated Learning at the EDGE, a framework designed for efficient AI deployment in resource-constrained satellite constellations. FEDGE integrates federated learning with edge computing to address communication overhead and latency challenges in distributed space environments. The framework features a novel edge-enhanced ground station protocol that dynamically schedules model aggregation based on satellite-provided metadata, combined with local stochastic gradient descent training at satellite edge devices and gradient compression via quantization. Experimental validation on MNIST and EuroSAT datasets demonstrates the practical viability of the approach. On MNIST, FEDGE achieved 94.33% training accuracy with 0.21 loss and 90.05% test accuracy with 0.24 loss. On EuroSAT, the framework reached 93.47% training accuracy with 0.18 loss and 91.51% test accuracy with 0.21 loss. Gradient quantization reduces data exchange by up to 14 with approximately 4% impact on test loss. These results validate FEDGE as a communication-efficient solution for decentralized AI deployment in satellite systems, enabling autonomous spacecraft intelligence and addressing the unique constraints of space-based computing platforms. The Author(s) 2025. -
Flow of nanofluid past a stretching cylinder subject to Thompson and Troian slip in the presence of gyrotactic microorganisms
Incorporating the Thompson and Troian slip condition, this work studies the bioconvective flow of a nanofluid past a vertically stretching cylinder. The Thompson and Troian slip deals with the molecular scale interactions at the solidfluid interface, which plays a pivotal role in the fluid flow analysis. This study helps in understanding the behaviours of fluid flow in the presence of non-linear slip past a vertically stretching cylinder. The corresponding partial differential equations (PDEs) for momentum, energy, concentration of nanoparticles, and concentration of microbes are developed using Buongiornos model. A suitable similarity transformation is then applied to these PDEs, converting them into a set of ordinary differential equations (ODEs). The RungeKuttaFehlberg (RKF-45) method is utilized to calculate the numerical solution of the resulting ODE problem. The results demonstrate that the interaction of slip conditions, viscous dissipation, heat source, and bioconvection causes complex flow patterns and heat transfer characteristics. These observations are extremely relevant for applications including better oil recovery procedures, biomedical engineering, and microfluidic devices where exact control over nanofluid behaviour is necessary. Some of the major observations of the study include the enhancement of the temperature in the nanofluid for higher Eckert numbers, control of fluid flow through an external magnetic field, and Peclet number significantly decreased the motile density in the nanofluid. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning
Jute is a vital agricultural commodity contributing significantly to the GDP of countries like Bangladesh, India, Myanmar, and China. However, because of its inaccuracy and slowness, its vulnerability to pest infestations-which are often handled by manual inspections-poses serious cost concerns. This study suggests a unique method for early and accurate pest identification that combines contrastive and supervised learning. Contrastive learning enhances feature representation by distinguishing between positive and negative samples, ensuring that instances within the same class are closely grouped while maintaining separation between different classes. It reduces false negatives by classifying some samples as negative and others with the same label as positive. Supervised learning enables precise pest identification by aligning features with distinctive characteristics of each class. Metrics including precision, recall, F1 score, ROC curve, and confusion matrix are used to assess the hybrid models performance; the findings show notable accuracy gains over conventional techniques. This scalable and dependable solution lowers losses caused by pests and provides a sustainable method of growing jute using cutting-edge advanced machine-learning techniques. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Animal-Assisted Therapy for the Promotion of Social Competence: a Conceptual Framework
Developmental disorders have a substantial effect on the social competence of children affecting their overall psychosocial functioning. Social competence entails the process of being socially mature by establishing stable and adaptive patterns of social behavior. Animal-assisted therapy, as an alternative treatment modality, has offered some new prospects for improving social cognition. This conceptual paper, thus, attempts to throw light on how animal-assisted therapy can help improve social competence. The paper draws its knowledge from the existing theories and empirical work done to propose a conceptual framework that can enhance social competence by incorporating therapy animals. It can be concluded that animal-assisted therapy has found to improve different dimensions crucial for development of social competence. This further suggests the dire need to explore the effectiveness of human-animal interactions by utilizing it for improving social competence. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. -
Exploring White Knight Syndrome in an Indian Setting: A Grounded Theory Approach
White knights are individuals who enter into romantic relationships with damaged and vulnerable partners, hoping that love will transform their partners behaviour or life. Existing literature on white knight is limited to a handful of studies, primarily based on Western population. The present research aimed at developing a substantive theory on white knight syndrome in an Indian setting. The study follows a qualitative paradigm and the research design is grounded theory approach to be specific. Participants for the study were screened using Lamias white knight checklist. Data has been collected from eighteen young adults aged 1825years through semi-structured interviews. The data was analyzed using Strauss and Corbin grounded theory analysis. The study identified six phasespre-relationship phase, needs exploration phase, shining white knight phase, drained white knight phase, golden realization phase, and finally delayed breakup. Along with the phases, the study identified factors, characteristics, and types of white knight. The study has implications in the clinical and counselling field in identifying and understanding white knight tendencies. Additionally, the theory is applicable in the Indian setting highlighting the intricate interaction between culture, norms, roles, and the recent social factors. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Sexual Relationship Decision Making Based on Entertainment Media: A Qualitative Perspective Among Young Couples
As important as physical, mental, or social health is sexual health. Teenage pregnancy, STDs/STIs, and unsafe abortions are just a few of the population health issues that can arise from the absence of adequate sex education for young people. The purpose of this study is to investigate the process of sexual decision-making as influenced by media intervention among couples. Entertainment education (EE) is an approach that uses storytelling to influence large-scale behaviour change. EE has been used as a potent tool to educate, enlighten, and influence society and individual behaviour change worldwide. Through entertainment education, people have been taught about themes like HIV, family planning, pregnancy and child health, violence against women, and other subjects. Web series or movies that are accessible on the online subscription service, Netflix was taken into consideration for this study. Although there is a great deal of research on adolescent sexuality, studies of sexual decision-making have traditionally been gendered, meaning that men and women have been examined separately. This study is designed for a qualitative investigation using a phenomenological approach. Thematic analysis was employed to analyse semi-structured interviews of couples in a heterosexual romantic relationship. The findings will reveal the influence of entertainment education on young couples choices in their intimate relationships. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Understanding Peer Victimisation and Related Coping Strategies Among Young Adults
Peer victimisation is a term used to describe the process of an individual being bullied by other peers, physically as well as socially, which affects the victims personality, emotional functioning, behaviour, and well-being. In response, the victims adopt several coping strategies to deal with the situation. However, the experience makes an indelible mark on those who have been victims of the process. This current study aims to understand the perspectives of young adults who have experienced peer victimisation and also explore the coping strategies used by them. The study adopts a qualitative research design, based on which data was collected from young adults (n = 10) using semi-structured interviews. After a process of screening, participants were chosen using a purposive sampling method. The data collected was analysed using Interpretative Phenomenological Analysis (IPA), through which various super-ordinate themes emerged. The significant themes focus on the nature of peers, experiences of peer victimisation, risk factors leading to peer victimisation, consequences of peer victimisation, coping mechanisms, and strategies to prevent peer victimisation. The findings highlight the understanding of the repercussions of peer victimisation and emphasise the need to adopt innovative strategies, implement pre-emptive interventions, and mandate decisive measures to mitigate instances of peer victimisation. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Untold Stories from the Slums: A Qualitative Exploration of the Lives of Female Informal Waste Workers
The present study investigates the nuanced experiences of urban informal waste workers, shedding light on the intricate realities shaping their daily lives. Employing purposive sampling, in-depth interviews were conducted, specifically targeting female participants aged eighteen and above, engaged in informal waste work for a minimum duration of one year. A total of ten in-depth interviews were meticulously executed, with recordings subsequently translated and transcribed for thorough analysis. Utilizing Braun and Clarks thematic analysis method, a comprehensive examination of the data yielded a hierarchical structure comprising codes, sub-themes, and overarching themes. The central themes identified encapsulate the multifaceted challenges encountered by urban informal waste workers, including Occupational Hardships and Vulnerability, Economic Struggles and Diminished Quality of Life, Commitment to Family, Emotional and Psychosocial Challenges, Appreciation and Acknowledgment of Support Received, Decreased Willingness to seek help, and Aspirations and Hopes for the future. By amplifying the voices of these marginalized workers, this study advocates for the implementation of inclusive policies and interventions tailored to address their diverse needs within the urban milieu. Through its findings, the study aims to cultivate a more compassionate and supportive environment that not only recognizes the invaluable contributions of urban informal waste workers but also strives to enhance their overall well-being and socio-economic standing. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Can Tradition and Ambition Coexist? Unpacking Career and Collective Identity Integration Among Indian Emerging Adults
Developing a meaningful identity requires integrating lived experiences by coordinating the past, present and future identities, integrating multiple personally meaningful identity domains, or aligning ones identities with ones culture to form a coherent sense of self. However, there is a dearth of studies on identity integration across multiple identity domains in the Indian sociocultural context. Even in the West, identity integration is studied using survey methods predominantly in minority populations and on identity domains like ethnicity/religion and religion/sexuality. However, this focus risks overlooking the complexity and nuance of identity experiences, which are often deeply shaped by personally salient and central domains such as career and family. Thus, this paper expands the understanding of identity integration across career and collective identity, considering its relevance among Indian emerging adults using a qualitative approach. Ten emerging adults (1825 years) were purposively selected, and their interviews were analysed using inductive thematic analysis. The narrative accounts revealed career and collective identity integration as a bidirectional phenomenon where family dynamics influence career identities, which, in turn, influence family relations and the reshaping of their worth and communication dynamics. A unique configuration of struggling to balance between family and career emerged as emerging adults negotiated between their desire to stay with family and career pursuits. The impact of financial independence on career identity and parental relationships emerged as another significant aspect. The results discuss theoretical and practical implications for identity research in light of urban Indias unique sociocultural context. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.