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Leveraging history to invoke nationalism: from the annals of history to social engineering of present and future in Hindi cinema
Nationalism calls for ones loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalists individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical eventsthe exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers opinion in favour of the dominant socio-political class. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Leveraging history to invoke nationalism: from the annals of history to social engineering of present and future in Hindi cinema
Nationalism calls for ones loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalists individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical eventsthe exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers opinion in favour of the dominant socio-political class. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Leveraging Hybrid Dual-Level Contextual Attention and Spiking Neural Networks for Effective Hepatic Malignancy Diagnosis
s Abstract Liver cancer remains the leading cause of cancer-related mortality, prompt-ing advanced diagnostic techniques for early detection and accurate classification in the health sector worldwide. Multifaceted deep learning methods have shown significant potential in medical imaging, but challenges exist in capturing intricate contextual information. In our research, we propose a novel hybrid framework that integrates Dual-Level Contextual Attention (DLCA) with Spiking Neural Networks (SNNs) to enhance the diagnosis of liver cancer. The proposed framework uses a DLCA mechanism that effectively extracts both local and global contextual features within the medical images and aids in precise lesion differentiation. The SNNs module supports computational efficiency and robust pattern recognition, enabling precise identification of subtle cancerous patterns by reducing redundant activations while preserving critical diagnostic information. Experimental evaluations on publically available datasets demonstrate the effectiveness of our work, showcasing its reliability in clinical applications. Moreover, the model offers a direction for future AI-assisted diagnostic tools in medical imaging and oncology. Grenze Scientific Society, 2025. -
Leveraging Java for Developing Privacy-Preserving and Cross-Platform Machine Learning Applications
The growing focus on data privacy and system interoperability has created a clear need for machine learning (ML) applications. These applications must be able to protect sensitive data while maintaining consistent performance in various computing environments. Java is considered as a strong choice due to its platform independence, strong static typing, and rich ecosystem of development tools and libraries. This study checks how Java supports privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption, instilling confidence in its role for secure ML development. It also reviews Java-based libraries, including Weka, Deeplearning4j, and Apache Spark, highlighting their role in building safe, scalable, and portable ML solutions. Through architectural analysis, benchmark-based evaluation, and comparisons with other programming languages, the study demonstrates the strengths of Java to deliver secure, scalable, and interoperable privacy-sensitive machine learning applications. 2025 IEEE. -
Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction
This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Machine Learning for Epidermal Ailment Detection
Skin disorders are common across the globe, often proving to be difficult to diagnose because of coexisting signs and symptoms. In this paper, we study the feasibility of using machine learning (ML) techniques for automatic skin disease detection. We look at the emerging patterns in fundamental studies within the scope of focus that deals with image processing for feature extraction and employing classification methods for disease detection. We focus on feature extraction and the classification of images. One of the major strengths is the ML-based approach with better access and usability and higher chances of them being detected at an early stage. In addition, we consider some of the drawbacks and problems of these methods, including biased data and lack of sufficient professional oversight. We also consider other aspects, whereby one of them is further analysis of the requirement in the case of the absence of the adequate data, standard models, and unambiguous explanations of the inner processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging machine learning models for intelligent hazard management
[No abstract available] -
Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms
Due to the rapid growth of e commerce, develops effective predictive models of online shopper behavior has become important. The goal of this study is to use dataset of online shopping sessions to predict purchase intentions based on session characteristics, user behavior and site metrics. This research aims to apply machine learning and deep learning models to predict online purchasing intentions to assist businesses to improve their strategies of maximizing conversion rates. Using a dataset having numerical and categorical features, features like page views, session duration, bounce rates etc., and the presence of some special days near the user session, we used. We evaluated nine models, including the traditional methods: Logistic Regression, Decision Tree, Naive Bayes, ensemble methods: Random Forest, Gradient Boosting, XGBoost, and more advanced ones like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Neural Networks. Then, key metrics including Accuracy, Precision, Recall, F1 Score and ROC AUC were used to asses each model. We find that ensemble models perform best (ROC AUC = 0.9245) with Gradient Boosting performing best, with XGBoost and Random Forest close behind. With a competitive ROC AUC of 0.9000, neural networks showed strong potential, but fell slightly behind in recall compared with ensemble methods. Logistic Regression and Decision Tree were simpler models that did not achieve as strongly in predictive accuracy as more complex model; however they provided a baseline insight. Through this analysis, ensemble models and deep learning showed to be very efficient to predict online purchase intentions and provide actionable insights to optimize e-commerce platforms. 2025 IEEE. -
Leveraging Machine Learning: Advanced Algorithms for Soil Data Analysis and Feature Extraction in Arid and Semi-arid Regions with Expert Systems
India is culturally diverse nation at large. There are two words of symphony one is tradition and second one is inherited agriculture. India has long historical advantage having conventional agricultural practices and the scope for it to dive into day to day life as agriculturist. Happiness shrinks as people grow into modern world current trend of agriculture faces a monument challenge and needs immediate address to survive. Now withstanding with this phrase of human life on earth its necessary to give more importance to soil rather than the existence. Soil health is more paramount in this equation, as it directly influences crop growth and yield. Traditionally, analysing a few key soil properties has been the cornerstone of soil treatment practices. However, this approach often overlooks the complex interplay between various soil characteristics. To overcome the above hurdle present research incorporates the method of multivariate data analysis with selective advanced algorithms in machine learning to find suitability to predict best fit algorithm in real time data sets in arid and semi-arid zones of kolar district in Karnataka. The purpose is to draw the attention of stake holders to leveraging the new technology to deploying them into effective assessment in building expert system to incorporate in regular use on handy devices. This penetrates the results by two extremely good classifications algorithms Decision Tree and Gradient Boosting emerged as winner with 99% accuracy. In contrast, Passive Aggressive and Linear SVC produced below average of 36% accuracy. The ensemble algorithms of SMOTE on Random Forest and Stochastic Decent Gradient produced the acceptable accuracy of 83%. This input helped dynamically to build ready to use expert systems for farmers. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Leveraging ML Based Technique for Mobile Sales Forecasting
The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi. -
Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning
Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. 2023 IEEE. -
Leveraging Neural Networks for Personalized Student Engagement and Performance Prediction
Neural Networks can be used to predict students' performance and future placement opportunities. Nowadays, it is a really difficult task for students to predict their chances of getting a good campus placement, even if they have prepared well for it. There is an intense competition among peers, and many factors that influence a student's placement. To manage this data and predict their chances, they need a reliable system. In this paper, we discuss a model in which the system focuses on three main areas: predicting placement chances, analyzing skill gaps, and offering personalized recommendations for improvement. The system would predict many potential career paths by analyzing academic records, extracurricular activities, and job market trends, while also highlighting immediate opportunities and long-term growth prospects. The integration of agentic AI further enhances this system by enabling autonomous decision-making and adaptive learning. Which ensures personalized guidance for each student. By dynamically refining the predictions which are based on real-time feedback, agentic AI helps to empower students to proactively navigate their career paths with greater confidence and precision. This approach provides a genuine solution, in order to improve placement strategies, by ensuring that the students are well-equipped to meet the challenges of the modern workforce. 2025 IEEE. -
Leveraging QSPR-guided ZIF selection for MWCNTs/ZIF-8 platforms for electrochemical immunosensing of lactoferrin
This study presents a data-driven workflow integrating quantitative structureproperty relationship (QSPR) modelling with Monte Carlo (MC) adsorption simulations to guide zeolitic imidazolate framework (ZIF) selection for lactoferrin (LF) immunosensing.MC simulations calculated adsorption energies (Eads) of LF across 27 ZIFs, represented using MOFid/MOFkey-encoded SMILES notation, enabling construction of a predictive QSPR model (top-performing model: PLS, R2=0.891, Q2=0.888). The model successfully ranked ZIFs according to predicted LF affinity, with ZIF-8 emerging as the optimal candidate based on computational predictions, structural robustness, and synthetic accessibility. Following computational validation through molecular docking, ZIF-8 was integrated with multiwalled carbon nanotubes (MWCNTs) on a glassy carbon electrode (GCE), enabling noncovalent immobilization of anti-LF antibodies. Electrochemical measurements performed using square-wave voltammetry (SWV) with a ferri/ferrocyanide [Fe(CN)6] 3?/4- redox probe demonstrated a linear response over 1060ng/mL LF (R2=0.994), a limit of detection (LOD) of 4.78ng/mL, and recoveries of 95105% in spiked milk samples with reproducibility ?8% RSD (n=3). Shelf-life studies showed 74% signal retention after four weeks of storage at 4C. Electrochemical analysis revealed a synergistic enhancement of charge transfer (Rct) in the MWCNTs/ZIF-8 composite (0.572k? vs. 12.79k? for bare GCE). This work demonstrates a transferable, computationally driven framework for screening framework materials in MOF/ZIF-based biosensors, bridging predictive materials design with experimental device fabrication for broad applications in clinical diagnostics and food quality monitoring. 2026 Published by Elsevier B.V. -
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. -
Leveraging Robotic Process Automation (RPA) in Business Operations and its Future Perspective
Robotic Process Automation (RPA) is used to automate the business process operations including its capabilities to mimic the routine tasks, which requires less human intervention. RPA has seen crucial take-up practically throughout the last few years because of its capacity to reduce expenses and quickly associate heritage applications. Fundamentally RPA would perform automated tasks much like as an individual to accomplish objectives productively and adequately. This article analyses the features in current business conditions to comprehend the movement of RPA and automated interaction has carried to substitute the businesses with automated tasks. RPA is an innovative technology which utilizes software programming to execute enormous capacity assignments that are routine and time-consuming in the business cycle. RPA streamlines by playing out those undertakings proficiently as it reduces cost and saves assets of an association as programming works till the finishing of the assignment. This study aligns with the descriptive approach and leveraging Robotic Process Automation into business operations. This article also addresses the different players in the RPA Technological segment. This study also discussed and suggested selecting RPA Vendors in a future perspective. 2023 American Institute of Physics Inc.. All rights reserved. -
Leveraging social media and natural language processing for early detection of depressive disorders
Depression is a prevalent mental health disorder impacting over 280 million people worldwide, according to recent World Health Organization (WHO) estimates. It poses a substantial burden on individuals and societies, emphasizing the need for early detection and timely intervention. Despite the availability of treatment options, many affected individuals do not seek professional help due to barriers such as stigma, lack of awareness, and insufficient access to mental health services. With the widespread adoption of social media, people increasingly share their thoughts, feelings, and daily experiences online, providing an abundant source of user-generated content. This information can be harnessed to detect early signs of depression. In recent years, advancements in Natural Language Processing (NLP) and Machine Learning (ML) have paved the way for innovative approaches to analyzing social media data for mental health insights. By processing text-based content from platforms such as Twitter, Facebook, and Reddit, NLP techniques can identify linguistic patterns. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging sustainable finance to attain United Nations Sustainable Development Goals (SDGs)
This chapter is a deep exploration of the role that sustainable finance can take on as a crucial factor to attain the United Nations SDGs by pointing out how innovation financial instruments and regulatory frameworks play in aligning the goal. This chapter simplifies the explanation on the holistic discussion of how financial strategies would not only ensure long-term growth in the economy but at the same time not have an adverse impact on society and nature. This is further supported by greater interest from investors, businesses, & regulators. It keeps gaining in value as investors seek opportunities not just with financial returns but also with values they hold and contribute to sustainable development. Businesses find out that ESG factors included in their strategies could make them more resilient, better in reputation, and have longer-term profits. It triggers regulatory bodies to react and develop frameworks for transparency, accountability, and consistency with practices in sustainable finance. It is important to know that sustainable finance is more than an ethical choice. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging technology for climate resilience in urban areas
Urban areas, densely populated and often located in vulnerable coastal regions, are particularly susceptible to the impacts of climate change. This chapter explores the diverse ways technology can be leveraged to enhance climate resilience in these urban environments. Focusing on mitigation and adaptation strategies, the study examines the potential of smart city technologies, including sensor networks, data analytics, and AI, to improve infrastructure management and disaster preparedness. Specific examples include real- time flood monitoring systems, predictive modelling for extreme weather events, and optimized energy grids for reduced carbon emissions. Furthermore, the chapter investigates the role of digital platforms in facilitating community engagement and fostering collaborative responses to climate- related challenges. Furthermore, the importance of equitable access to technology and the need for robust data governance frameworks to ensure that technological interventions effectively contribute to building climate- resilient and sustainable urban futures is explored. 2025, IGI Global Scientific Publishing. All rights reserved. -
Leveraging technology for sustainable economic growth: Advancing the SDGs through innovation
Raising long-term competitiveness of national economies is an important requirement with the broad use of digital technologies. In addition to offering the possibility of economic restructuring, information and communication technologies open up new avenues for all citizens to access a range of services, such as first-rate healthcare and education. As a result, these developments promote inclusive growth and significantly aid in the achievement of the UN Sustainable Development Goals (SDGs). Significant economic changes that raise living standards and boost global competitiveness can be sparked by the promise of digital transformation within a framework of sustainable development. Keeping in view the above, the chapter thoroughly examines practical and theoretical frameworks pertaining to the application of sustainable development, as well as an assessment of the possibilities for using digital technologies to promotesustainable competitiveness. The relationship between digital inclusion and its longterm effects on global economic development is also analyzed. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging the Synergy of Edge Computing and IoT in Supply Chain Management
This article investigates the possibilities of integrating edge computing and IoT in supply chain management, as well as the adoption of disruptive technologies such as blockchain integration, digital twins, robotics, and autonomous systems. Operational efficiency can be considerably enhanced by establishing a linked and intelligent supply chain ecosystem. The benefits of this technology include increased openness, efficiency, and resilience in supply chain processes. Among the benefits include real-time product tracking, environmental sustainability, enhanced production, and cost savings. The use of blockchain technology in a three-tiered Supply Chain Network (SCN) shows promise in terms of boosting supply chain transparency and security. The SCOR model is also discussed as a comprehensive framework for optimising supply chain processes. However, concerns such as data privacy, security, and employment displacement must be solved before firms can fully reap the benefits of new technologies. Overall, embracing these innovations has the potential to revolutionise supply chain management and create trust among stakeholders. 2023 IEEE.