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Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal
This chapter explores the implementations of deep learning algorithms along with remote sensing technologies for precise identification and categorization of plant diseases, focusing on enhancing accuracy and efficiency in agricultural practices. This research study intends to succeed in building a hybrid model for the classification and forecasting of diseased plants with high accuracy. Plant disease detection and classification is a critical field of study within agricultural science and technology. It involves identifying and categorizing diseases affecting plants to ensure timely and effective management practices. Early and accurate identification of plant diseases is crucial to minimize crop loss, maintain food security, and reduce the use of pesticides, which can have adverse environmental and health effects. In any country, both the yield and the quality of agricultural products are essential for the success of agriculture. Plant disease (i.e. abnormal growth or functionality) detection is tough work, which has prompted numerous investigators to apply image processing, machine learning (ML), computer vision, and big data analytics, etc., techniques, which make the challenging assignment easier. The proposed approach integrates the deep convolutional neural network ResNeXt50 with long short-term memory (LSTM) networks to tackle the dual tasks of plant leaf disease classification and segmentation. The ResNeXt50 backbone extracts intricate spatial features from plant leaf images, while the LSTM component models the temporal dynamics of disease progression. This hybrid model exploits the hierarchical feature representation of ResNeXt50 and the sequential learning capabilities of LSTM to enhance accuracy and contextual understanding of plant leaf diseases. The model's training accuracy was enhanced to a maximum of 99.74% and a validation accuracy of 95.44%, scoring 94% in F1, 96% in recall, and 96% in accuracy. Comparative analysis reveals that the ResNeXt50 + LSTM model outperforms other classifiers, including Inception V3, AlexNet, ResNet50, and VGG16, addressing overfitting and vanishing gradient issues. The model demonstrates superior performance in handling imbalanced data and excels in plant disease prediction, validated through various benchmarks and datasets. This study confirms the hybrid model's robustness and potential for practical application in plant pathology. 2025 by The Institute of Electrical and Electronics Engineers, Inc. -
Perspectives on the Intersection of Gender, Customary Laws and Land Rights in India
For centuries, tribal communities in India have maintained distinct social and cultural identities, often with communal land ownership practices that were inclusive of women. The struggle of tribal women in India for land rights is a poignant manifestation of their fight against intersecting forms of oppression rooted in patriarchy, traditional power structures, and historical marginalisation. Given the existing background, this article discusses the intersection of property rights and gender relations in India, making a case for independent property rights for tribal women. It analyses the role of customary laws of inheritance in a legal pluralistic India and its conflict with positive law. The article also focuses on the role of the Indian judiciary in remedying the systemic discrimination against tribal women in India. It analyses the approach of the Indian courts in maintaining a balance between the autonomy granted to the tribes by the Indian Constitution and ensuring justice to women who are victims of such self-governance. Jyoti Singh and Kajori Bhatnagar, 2024. -
Strategies and Consumer Psychology in Luxury Brand Marketing: A Literature Review
This chapter explores the intricate dynamics between consumer psychology and marketing strategies within the luxury brand sector. Luxury consumption is deeply rooted in both symbolic value and experiential satisfaction, transcending functional utility and embedding itself within consumers selfconcept and social identity. This review synthesizes recent research on the emotional, psychological, and cultural drivers behind luxury purchasing behaviors. It examines strategic approaches in luxury brand positioning, consumer perception, and the growing impact of digital and social media in shaping brand identity and loyalty. By highlighting evolving consumer motivations and emerging trends such as experiential luxury and sustainable branding, this chapter aims to provide a comprehensive overview of effective luxury marketing strategies, thereby offering insights into future opportunities and challenges in the field. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Prognosis of relocation disease in animals using aggregation method with optimization techniques
In the most cutting-edge setting, health data is processed by machine learning algorithms, which are used to forecast illnesses. Dementia, especially Alzheimer's disease (AD), is a leading cause of diminished quality of life in the elderly. Early diagnosis by medical professionals increases the likelihood of reducing the aggressiveness of the disease. In this study, we develop a new uncertainty-based clustering model to handle the centroid selection ambiguity and the issue of noisy instances and outliers that lower the efficiency of prediction models. This work employs an uncertainty-based optimization technique to handle the unknown pattern of AD patients, since it is relatively tough to handle unknown patterns with unsupervised learning algorithms. Converting the instances in the AD dataset to the membership value of the dependent variable allows for an accurate determination of whether they belong as AD patterns or non-AD patterns. This proposed study takes a migration-based optimization method to animal migration, where the best instances are chosen as centroids and fresh instances are evaluated for clustering; this minimizes outliers throughout the clustering process by using comparable patterns. To make sure, we check the fitness values of each instance; the ones with the highest values are called centroids. To control the unknowns when dealing with outliers, the fuzzy Euclidean distance is employed. By comparing it to current state-of-the-art clustering methods, the OASIS dataset simulation results show that the proposed uncertainty-based Animal Migration optimization method (UAMO) performs better. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
Deep CP-CXR: A Deep Learning Model for Classification of Covid-19 and Pneumonia Disease Using Chest X-Ray Images
The global spread of the Coronavirus has caused a disastrous effect, affecting millions of people and making it crucial to take action. Numerous experts have worked extensively to create viable vaccines in the fight against this infectious disease. The current study offers hope by suggesting a deep learning model, Deep CP-CXR, for determining patients with Covid-19 and pneumonia. Our study encompasses two significant investigations. First, we used images from chest X-rays for binary classification to distinguish Covid-19-diagnosed patients from normal patients. Second, using chest X-ray images, we expanded the study to include several groups, such as pneumonia, Covid-19, and normal instances. The results of our studies were extremely promising. The binary classification achieved a remarkable average accuracy of 100%, allowing for accurate classification between Covid-19 patients and normal cases. In addition, the multiple-category classification was able to distinguish between Covid-19, pneumonia, and normal individuals with a remarkable average accuracy of 98.57%. These astounding findings lead us to conclude that the Deep CP-CXR method weve suggested for classifying Covid-19 and pneumonia patients enables medical professionals to perform it accurately. Healthcare providers worldwide will benefit significantly from this development since it has the potential to enhance both the detection and treatment of these ailments. The proposed deep learning approach improves the speed and precision in classifying the disease with which doctors can diagnose and treat their patients effectively. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Effective detection of Covid-19 using Xception net architecture: A technical investigation using X-ray images
The disastrous era of COVID-19 has altered the perspectives of nearly all nations concerning the health and education sectors. Artificial intelligence is a pressing need that needs to be implemented thoroughly in the medical and educational fields. Imperatively, the diagnosis of Covid-19 has become crucial. In this study, we have designed a classification model based on Convolutional Neural Network (CNN) and transfer learning. The COVID-19 chest X-ray images have been considered for the proposed methodology and are classified as COVID-19 positive and normal cases. The proposed shallow CNN Model achieved an accuracy of 96%, which is computationally very effective as only three Convolutional blocks are required. Then, the Xception architecture-based model is experimented with. The accuracy and loss of the proposed model have been evaluated using Adam and SGD optimizer. With the Adam Optimizer, Xception Net achieved the best classification accuracy of 99.94%. The precision, recall, and f1-score of 100% are achieved. The proposed model has outperformed the previous studies in the same domain, which highlights the models state-of-the-art performance. Our study will be helpful for decision-makers and can help further minimize mortality and morbidity by effectively diagnosing the disease. The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
BC-MBINet: A Novel Architecture for Accurate Classification of Breast Cancer with Microscopic Biopsy Images Using Deep Convolutional Neural Networks
Breast cancer (BC) is the second most frequent malignancy, accounting for roughly 25% of all cases of cancer. BC is caused by genetic, epigenetic, and environmental factors, and their interaction too. The diagnosis of a BC is a critical step in the treatment process, and histopathological imaging is required to determine the type of illness. Identifying a disease is an important stage in the treatment process. However, this time-consuming task is exhausting, and people are prone to making mistakes that go unnoticed, making it difficult to determine the severity of the condition and this diagnosing step also relies on a pathologists expertise. In this paper, we have developed a novel BC with microscopic biopsy images network (BC-MBINet) model using deep convolutional neural networks. Feature extraction is handled by a sequence of convolutional layers, nonlinearity is handled using LeakyReLU activations, and learning is stabilized by batch normalization. A last Softmax layer is employed for binary classification into benign and malignant tumors, and dropout layers are included to decrease overfitting. The model achieves state-of-the-art accuracy and resilience in discriminating BC types by being trained on a publically available dataset of microscopic biopsy images. The proposed model is capable of classifying between the benign and malignant BC tumors with 99.04% accuracy. The model gives state-of-the-art results in its accuracy in classifying BC tumors into Benign or Malignant. 2025 World Scientific Publishing Company. -
DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability
Early diagnosis of retinal pathologies is critical for preventing irreversible blindness, particularly in rare conditions with limited labeled medical data. Traditional diagnostics employ a single imaging modality, limiting the identification of heterogeneous anomalies in the retina. DMRD-Net, a diagnostic system is presented that integrates spectral-domain optical coherence tomography with fundus photographs, utilizing two parallel branches of a neural network, that is EfficientNet-B0 encoders and few-shot episodic meta-learning module based on Prototypical Networks, that merge their outputs to enhance the precision of diagnosis. Supervised learning methodologies are employed to identify common retinal diseases, followed by the application of meta-learning technique, referred to as Prototypical Networks, to aggregate a limited set of data for the study of rare diseases. To support clinical confidence and improve transparency, explainable artificial intelligence is utilized to facilitate decision-making by models. It facilitated the evaluation of performance on both common and rare retinal disorders. The system achieved over 96% episodic accuracy in diagnosing rare conditions, including Macular Hole, Retinitis Pigmentosa, and Stargardt Disease, in Central Serous Chorioretinopathy. The overall classification accuracy for common diseases was 96.5%. Overall, DMRD-Net is a unified, data-efficient, and interpretable multimodal diagnostic system that works well for both common and rare retinal disorders. 2026 The Author(s) -
Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches
In an effort to alleviate traffic and improve urban mobility, intelligent transportation systems (ITS) relies heavily on forecasting traffic. In the paper, a comprehensive survey on spatial temporal predictive modelling techniques for forecasting traffic has been presented. The focus remains on advanced machine learning and deep learning that have been developed between 2017 and 2025. With the use of state of the art technologies to forecast both in real time scenarios (short-term) traffic prediction and long term forecasting, such as transformer based models, (RNN) recurrent neural networks, convolutional networks on grids and graphs, and (GNN) graph neural networks. Former approaches were examined for strengths and limitation to capture intricate temporal dynamics and spatial interdependencies. Through the above findings, a brand-new conceptual methodology that associates attention mechanisms and graph-based learning to increase prediction accuracy with computing efficiency has been proposed. The performance improvements of newer methods over the conventional methods are also shown through a comparison of the experimental findings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Test case reduction and SWOA optimization for distributed agile software development using regression testing
Regression testing is a well-established practice in software development, but its position and importance have shifted in recent years as agile approaches have grown in popularity, emphasizing the fundamental role of regression testing in preserving software quality. In previous techniques, the challenge to address is determining the number and size of clusters and optimization to stabilize the cost and efficacy of the strategy. To overcome all the existing drawbacks; this research study proposes test case reduction and Support-based Whale Optimization Algorithm (SWOA) for distributed agile software development using regression testing. The purpose of this research study is to look into regression testing strategies in agile development teams and to find out what they are optimum clustered test cases. The proposed strategy is divided into two stages: prioritization as well as selection. Prioritization and selection are carried out once the test instances have been retrieved and grouped. The test case clusters are sorted and prioritized in this stage to ensure that the most critical instances are chosen first. During this stage, the test case clusters undergo sorting and prioritization to guarantee that the most essential cases are selected initially. Second, the SWOA is used to choose test cases with a greater frequency of failure or coverage criterion. The results of the assessment metrics show that the proposed approach outperforms other current regression testing strategies substantially. Based on experimental findings, our proposed approach betters existing methods in terms of information performance. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
EASM: An efficient AttnSleep model for sleep Apnea detection from EEG signals
This paper addresses the crucial task of automatic sleep stage classification to assist sleep experts in diagnosing sleep disorders such as sleep apnea and insomnia. The proposed solution presents a novel attention-based deep learning model called, Efficient Attention-sleep Model (EASM), designed specifically for sleep apnea detection using EEG signals. EASM incorporates a streamlined architecture that includes a modified Muti-Resolution Convolutional Neural Network (MRCNN), Adaptive Feature Recalibration (AFR), and a simplified Temporal Context Encoder (TCE) module to reduce complexity. To mitigate overfitting, ridge regression is utilized, which incorporates a penalty term to enhance model generalization. Furthermore, the proposed EASM utilizes a class-balanced focal loss function to address data imbalance issues. The effectiveness of EASM is evaluated on two publicly available datasets, SLEEP EDF-20 and SLEEP EDF-78. Comparative analysis of EASM against state-of-the-art models demonstrates its superior performance in terms of accuracy, training time, and model complexity. Notably, the proposed model achieves a 50% reduction in training time and a 55.7% decrease in complexity compared to the Attnsleep model. The EASM achieves a classification accuracy of 85.8% with minimum loss when compared to the Attnsleep model. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Future Possibilities for Integrating AI with Nano-Carrier Technology
The convergence of artificial intelligence (AI) and nanotechnology presents exciting prospects for advancing drug delivery systems. This review explores the potential synergies between AI and nanocarrier technology to enhance drug delivery efficiency and therapeutic outcomes. We examine current developments in both fields and propose future directions for integrating AI algorithms with nanocarrier design, optimization, and personalized medicine approaches. AI can play a pivotal role in guiding the rational design of nanocarriers, optimizing drug loading and release kinetics, predicting in vivo behavior, and tailoring treatments to individual patient needs. Challenges such as regulatory hurdles, data privacy concerns, and the need for interdisciplinary collaboration are also discussed. Overall, the integration of AI with nanocarrier technology offers unprecedented opportunities to revolutionize drug delivery and improve patient care in the years to come. 2026 by Apple Academic Press, Inc. -
Building Trust and Fostering Collaborations Through Community Policing: Insights from India and the Global Perspective
A novel approach to law enforcement, community policing places a strong emphasis on policecommunity cooperation to maintain safety, settle disputes, and foster trust. Community policing has been especially effective in India in tackling complicated social issues including gender-based violence, communal conflicts, and the grievances of marginalised people. Programmes such as the Mohalla Committees in Maharashtra and the Janamaithri Suraksha Project in Kerala are prime examples of grassroots efforts that place an emphasis on cooperation and communication between the public and law enforcement agencies. Globally, community policing models from the United States, the United Kingdom, and Japan demonstrate how flexible it is in various sociocultural contexts. The Koban system in Japan and the Neighbourhood Policing model in the United Kingdom are renowned for promoting trust through proactive problem-solving, increased transparency, and the integration of law enforcement with local communities. With police officers acting as community allies in addition to executing the law, these approaches place an emphasis on reciprocal accountability. Nonetheless, issues such as a lack of resources, opposition to reform in police agencies, and possible politicisation still exist. Global success stories provide valuable insights into the significance of consistent training, incorporating under-represented perspectives, and utilising technology to improve community involvement. The potential of community policing to change antagonistic policecommunity dynamics into cooperative ones is examined in this research. The study highlights the importance of trust-building as a pillar of efficient law enforcement and inclusive governance by looking at Indian initiatives and learning from global experiences, opening the door to safer and more cohesive societies. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Accessibility, Diversity, and Inclusion in the Digitally Transformed University
This chapter, as per the authors, investigates the critical dimensions of accessibility, diversity, and inclusion in the context of digitally transformed higher education institutions. It presents a comprehensive review of theoretical frameworks such as universal design and social equity, followed by practical implementation models that highlight inclusive pedagogy, adaptive technologies, and administrational best practices. The chapter addresses persistent challenges including digital divides and ethical considerations of Al-driven systems, while also forecasting future trends in sustainable, culturally responsive smart campuses. Through a synthesis of global case studies and policy insights, the chapter offers actionable strategies for building equitable and inclusive digital learning environments that foster participation and success among diverse learner populations. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging Big Data Analytics for Sustainable and Ethical Supply Chains
This chapter explores the transformative role of Big Data Analytics in advancing sustainable and ethical supply chains. It examines how data- driven decision- making enables companies to enhance transparency, reduce environmental footprints, and uphold labour and human rights across multi- tier supply networks. The chapter addresses challenges in data integration, ethical AI use, and balancing cost with sustainability objectives, supported by real- world case studies from fashion, food, and electronics sectors. It highlights technological innovations such as digital twins, AI, and blockchain that empower circular economy practices and supply chain resilience. Offering critical insights and future directions, this chapter serves as a comprehensive guide for academia and practitioners committed to responsible supply chain management. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Study of a modified JohnsonCook model for 304 stainless steel incorporation with coupled strain, strain rate, and temperature effects
304 stainless steel (SS 304) with its superior high-temperature resistance has been highly sought after. To explore the rheological behavior of SS 304 at high-temperature conditions, isothermal hot compression tests were conducted using the Gleeble-3800 thermal simulation machine under temperatures of 8001200C, strain rates 0.011 to 11 s-1, and a strain of 60%. The JohnsonCook (JC) constitutive model was constructed and optimized based on the experimental data. By introducing coupled strain, strain rate, and temperature effects, a more precise constitutive equation was established. The result indicates that the optimized JC model predicts the rheological behavior of SS 304 more accurately, as evidenced by a correlation coefficient (Rco) value of 0.9884 and an average absolute relative error (AARE) of 8.452%, indicating high prediction accuracy. ABAQUS further verified the optimized model. This study has important theoretical value to the hot processing of SS 304 and helps to ensure accurate calculation of the stress response of the material at high-temperature conditions, which will aid in optimizing process parameters and optimizing the performance of the material. Novelty of the research is a modified JohnsonCook model incorporating coupled strain, strain rate, and temperature effects was developed and validated to accurately predict the high-temperature rheological behavior of SS 304, achieving high predictive accuracy (R = 0.9884, AARE = 8.452%). The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2025. -
A study on digital intelligence and influencer marketing for sustainable diversification of India's retail economy: A qualitative study
The purpose of the study is to investigate the role of Digital intelligence via influencer marketing to see its significance impact on India's retail economy. Throughout the decade digital technologies have been adopted into the social eco-system. Through mixed methodologies, the study has collected data from the Hyderabad region from multiple retail outlets to understand the influence of digital intelligence on consumers in interacting with retail outlets. The results of the study indicate that a significant presence of retail outlets online improved the performance of retail outlets. The customer's point of interaction and contact has been improved significantly. The findings of this study will provide valuable insights for the policymakers, retailers, and marketers to navigate in digital landscape and to be a part of sustainable economic growth in India's retail boom. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Role of Influencer Marketing in the Indian Retail Business Sector: A Qualitative Study
The enhancement of technology has increased social media usage among internet consumers. As the people from young to old generations people are addicted to social media and it turns out to be an opportunity for marketers to attract potential customers. A qualitative study with an in-depth interview and a structured questionnaire was conducted with the establishment managers and owners of the retail outlets who are endorsing their retail outlets with social media influencers. The study has established a framework "CARE" for social media Influencers (Content & Campaign, Awareness, Reach & review, engagement) identified as the key indicators in Influencer promotions. The paper provides an understanding on influencer marketing, endorsements & promotions at low cost while reviewing the product performance to users benefiting the social society. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Transforming Organizational Structure: Decision-Making Strategically Using HR Analytics
The study done in this paper examines the benefits of doing strategically decision making through HR analysis in changing the organizational structure. Through the usage of the data-driven insights, HR analytics has become a very helpful in- strumentfor the organisations as it helps them to maximize their human resources, improves the performance of their workers and promote the regular improvement in the culture. By collecting and using the data from various functions of the HR such as recruiting, training and performance management helps the business to take the necessary decisions that can help them to match the skills of their workforce with the organizational goals. It also suggests that HR data must be used in the overall decision making in the organization that at all the levels as the decision-making process must not only take place on the basis of the past performances but also analysing the future trends. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Bamboo Trade Dynamics: A Hybrid ARIMALSTM Forecasting Approach for Indias ExportImport Trends (20172025/26)
Bamboo plays a dual role in Indias economy, serving as both an ecological safeguard and a driver of rural livelihoods. This paper examines bamboo exportimport flows between 2017 and 2025/26 using official trade statistics. A hybrid ARIMALSTM forecasting model is implemented to capture both linear and nonlinear patterns. Results demonstrate rising exports, stable imports, and higher predictive accuracy compared to ARIMA alone, confirming bamboos growing role in sustainable trade. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.