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K-Nearest Neighbor Optimization of Silver-Graphene Fiber Optic Sensor for Lung Cancer Detection
At nearly 1.8 million deaths annually, lung cancer is among the world's top causes of mortality. Cancer is curable up to a point, after which recovery is extremely challenging. Preventing cancer requires early cancer detection, which localized surface plasmon resonance (LSPR)-based sensors high sensitivity. The phenomenon known as localized surface plasmon resonance (LSPR) occurs when nanoparticles resonate with light at certain wavelengths, leading to the development of characteristics including quick reaction times, adjustable resonance, high sensitivity, and localized light-matter interaction. Since silver-graphene has qualities that make it perfect for cancer detection, it is selected as the material composition. The silver-graphene sensor is utilized for detecting CL1-5 and A549 cell lines, for which the peak of the extinction coefficients was found to be 2.7169 and 1.8592, with a sensitivity of 107 RIU. The Silver-Graphene LSPR sensor interaction with cell lines generated a novel dataset, for which K-Nearest Neighbor Regression has been chosen due to its adaptability and robustness to outliers and has been used to improve the functionality of the sensor by optimizing sensor design, improving sensor sensitivity, and reducing experimental time. With a prediction rate of 99%, KNN and the Silver-Graphene LSPR sensor are an excellent combination for early lung cancer diagnosis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Why Some Old Songs Are Still Evergreen Even Today?
The enduring appeal of old songs across generations raises questions about the factors contributing to their timelessness. This research investigates these factors using a deep learning framework that integrates MusicBERT, a model for symbolic music understanding, with Graph Neural Networks (GNNs). By analyzing a dataset of song features, including lyrics, melody, rhythm, and emotional tone, the study identifies patterns associated with the evergreen status of songs. Key findings emphasize cultural significance, emotional resonance, and universal themes as critical contributors to their lasting popularity. The research offers novel insights into the intersection of music, culture, and technology, with implications for music production, distribution, and cultural heritage preservation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Optimizing Cybersecurity in Digital Domain Through Proactive Cyber Monitoring
In todays linked digital landscape, cybersecurity is a top priority for individuals, organizations, and governments alike. As cyber threats grow in sophistication and frequency, the necessity for proactive and comprehensive defense strategies become more pressing. This study paper goes into the topic of improving cybersecurity through proactive cyber monitoring, providing an in-depth analysis of both hacker approaches and defense strategies. The study takes a multifaceted approach, starting with a thorough examination of common hacking strategies used by cyber enemies. By deconstructing popular attack routes such as phishing, virus propagation, and social engineering, the article sheds light on the complexities of cyber threats and hostile actors strategies for exploiting system vulnerabilities. Building on this foundation, the study investigates proactive cyber monitoring as a proactive defensive measure. Organizations can improve their cybersecurity posture by using advanced monitoring technologies, anomaly detection algorithms, and threat intelligence feeds to identify and mitigate possible threats before they become full-scale attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comparative Analysis of Predictive Models to Detect Alzheimers Disease
Alzheimers disease is the most common type of dementia, often affecting people above the age of 60, as all the brain connections and cells themselves start to die, affecting motor, speech and memory, slowing eating away a person once it sets out as it is a non-curable disease as of now. But an early and easy diagnosis may help slow down the process and start treatment, so it is essential to diagnose it quickly. But this disease needs a number of tests and time to determine the diagnosis, and time is of the essence. Various Machine Learning (ML) algorithms are being applied nowadays, with newer methods being trialed every day for the detection of Alzheimers more consistently and easily, but it is essential to apply the most accurate of models and require only the optimum number, and cost efficient tests for reliable diagnosis so this horrid disease could be started the treatment for as soon as possible. This paper is presenting its arguments for various methods of prediction of Alzheimers to improve efficiency of detection, a comparison of models taking into consideration the costs, the accuracy and the true benefit of the test for early tackling of this illness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Algorithmic Justice: Navigating the Ethical and Legal Landscape of AI-Powered Predictive Policing in Twenty-First Century
With the Fourth Industrial Revolution gaining traction, cyber-physical cognitive systems have thrived, fundamentally reshaping the fabric of the society we inhabit. Artificial intelligence (AI) is one such pioneering system, which thrived as a result of the intricate interplay between machine learning algorithms, deep learning (employs neural networks to facilitate automated learning) and vast repositories of structured, semi-structured or unstructured data called big data. AI was further revolutionized by development of techniques such as generative adversarial networks (GANs), transformers and large language models (LLMs) which initiated a new era of predictive analytics. One such prominent deployment of predictive data analytics has been witnessed in the sphere of law enforcement and crime controlling popularly termed as predictive policing (PP). PP is a convulsion of smart society and smart policing that uses data, algorithms and statistical modelling to foster safe, sustainable and better quality of life by optimizing resources and actions of law enforcement agencies (LEAs) to change the landscape of crime management in the society. AI-powered PP is one such breakthrough in advancement of this goal, but there are certain ethical and legal conundrum such as AI ethics, privacy, algorithmic bias and legal uncertainty that are barrier to the adoption of AI for PP. This research paper discusses the ethical and legal quandaries of AI-powered PP with further comparative analysis of key issues in two prominent democraciesUSA and India. Additionally, the paper also puts forth a way forward to achieve sustainable and harmonious use of AI for PP. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Chronic Disease Diagnosis Using Multi-modal Transfer Learning Model
Chronic diseases are on rise in recent times and symptoms of these disorders need to be detected at an early stage. Traditional methods in handling these risks are quite resource intensive and costly with less accuracy. This paper explores transfer learning to address the challenge of limited data in diagnosing chronic diseases like chronic kidney disease, breast cancer, hepatitis, and Alzheimers. We propose a multi-modal framework utilizing pre-trained models: image recognition for medical images and natural language processing for textual data. Transfer learning aims to improve diagnostic accuracy and reduce training time, enabling development of adaptable tools for various chronic diseases. The implementation results show the performance of the model is promising generating an accuracy rate of 94%. Also the model gave a mean accuracy of 93.3% when tested with different chronic disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine-Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm
This article is investigating the connection between sleep patterns and concentration spans among university students while exploring the potential influence of MyersBriggs Type Indicator (MBTI) personality types on these aspects. The primary objective is to understand how sleep duration affects students ability to maintain focus and how their personality traits might interact with this relationship. Data was collected from university students aged 1619 using a multiple-choice form. The key variables analyzed were age, MBTI personality types, sleep duration, concentration span, and effective study ranking. Pearson's correlation was employed to examine these relationships. Additionally, a linear regression model was developed to predict concentration span based on sleep hours. The findings revealed a strong positive correlation 0.758 between sleep duration and concentration span, suggesting that increased sleep is associated with longer concentration spans. A moderate positive relationship 0.249 was also observed between concentration span and effective study ranking. However, the analysis showed a negligible relationship ? 0.008 between MBTI personality types and concentration span, indicating that within the context of this study, personality type does not significantly influence concentration span. This research emphasizes the critical role of sleep in academic settings and challenges the assumption that personality types significantly impact concentration span and sleep patterns. The linear regression model developed provides a predictive tool for understanding the impact of sleep on concentration, underscoring the importance of adequate sleep for academic success. This research is contributing to the broader understanding of factors influencing student performance and offers practical insights for optimizing study habits and educational strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Performance Evaluation of CPU and GPU Processors Using Advanced Data Analysis Techniques
The modern industry is developing advanced CPU and GPU processors. The standard efficiency difference between the Intel Core i5 and 11th Gen with Nvidia GTX3050 processors is being discussed in this article. However, the reduction factor between these processors is determined to be as 2.5. Various data visualization techniques were applied to give a comprehensive analysis of the performance of CPU and GPU-based processors for execution of intensive tasks. The results were analyzed to understand the various performance parameters related to their functioning and efficiency. A model was proposed for enhancing the performance and throughput of the processor by easing the internal communication process between the CPU and the GPU by converting from electrical signals to light signals, though being faced with many challenges in the current time, holds a large scope of further research in the pursuit of higher computational efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial Intelligence: A Catalyst for Change in the Indian Automobile Industry
AI is becoming a major game-changer economically and technologically across various sectors in the world. The Indian automotive industry is one such area of development. This paper discusses AIs impact on Indian automotive sector right from supply chain management, boost in production, smart AI systems through predictive maintenance, customization capabilities and development of autonomous vehicles. The Indian automotive industry is one such industry that greatly adds to the countrys GDP and employment, but at the same time presents challenges in terms of infrastructure, logistics and changing consumer needs that AI can address. With the advent of campaigns like Make in India and Digital India, India seeks to position itself as one of the leading figures in international production, and for this, the adoption of AI measures seems of strategic importance as this will facilitate productivity growth, competitiveness and meeting the aims of sustainable development (Aggarwal et al. in J Technol Forecast Soc Change 170, 2021 [1]; Chui et al. in AI adoption and economic growth: The case of India. McKinsey Global Institute, 2022 [6]). Through case studies of Indian companies and new startups using AI technologies, this research focuses on how AI can tackle complex supply chains, cut production costs and satisfy consumer expectations for going green, safety and personalization. At the same time, AI usage in India has its own challenges such as expensive introduction, lack of skilled labour, protection of personal data and strict rules. This paper posits that given the necessary assistance from the state, together with the cooperation of the industry and investment in AI specialists, the Indian auto industry is able to use AI for scaling in a competitive environment and to become part of Indias economy in a larger context. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Study on Impact of Dopants on Piezoelectric Parameters of Aluminium Nitride
Doping is a critical in piezoelectric base materials used as sensors or energy harvesters. It has a significant effect in determining the output parameters of an energy harvester. This paper studies the effect and impact on piezoelectric properties when materials are co doped and singly doped by considering some common dopants found in the literature. From the study, the elastic constant and c/a ratio are found to be decreasing and all other lattice parameters such as piezoelectric stress coefficient, electromechanical coupling constant, Born effective charge etc. are increasing, although some discrepancies are there. Scandium, magnesium, Lithium and its co-dopants are mainly considered for the study. Another factor that should be considered is doping concentration. Excessive doping with scandium (Sc) has been demonstrated to reduce the coercive field (Ec), enabling ferroelectric switching in AlScN thin films. The critical factor for achieving ferroelectric switching lies in sufficiently lowering the energy barrier between the two polarization states made of the wurtzite structure. This can be accomplished either by increasing the proportion of the non-Group-III metal or through strain engineering. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritizing Risks in IoT-Enabled EdTech Platforms: A Fuzzy AHP Approach to Maximize User Satisfaction
The integration of the Internet of Things (IoT) in educational technology (EdTech) platforms offers personalized, adaptive learning but introduces significant risks. This study identifies and prioritizes these risks using the Analytic Hierarchy Process (AHP) and fuzzy AHP techniques. Seven industry experts provided input, complemented by a comprehensive literature review. The analysis reveals ethical considerations (30.17%) and data privacy/security (29.22%) as top concerns, followed by regulatory compliance (12.91%), high implementation costs (10.99%), and technical expertise requirements (8.37%). Surprisingly, scalability concerns (1.70%) and data accuracy/reliability (2.63%) rank lower. These findings emphasize the need for a human-centric approach in IoT-enhanced EdTech deployment, focusing on responsible implementation and regulatory adherence. The study provides valuable insights for EdTech companies and educational institutions, guiding strategic decision-making to enhance user satisfaction and ensure sustainable development of IoT-enhanced educational platforms. Future research could explore more advanced mathematical models and context-specific challenges to refine risk prioritization strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Fuzzy Logic Approach for Prioritizing Customer Retention Strategies in OTT Video Platforms
The emergence of over-the-top (OTT) video platforms has significantly transformed the way people consume content and the entire media landscape. The significant growth of OTT video platforms in recent years, amidst fierce competition and changing consumer preferences, has posed a challenge for the platforms in retaining customers. Elevated churn rates in the OTT video platforms have prompted them to focus on customer retention. OTT platforms implemented different strategies to retain customers. The customer retention strategies are identified through a literature review and unstructured interviews with industry experts. This paper presents a novel approach to prioritize customer retention strategies using a fuzzy analytic hierarchy process (fuzzy AHP). The fuzzy AHP analysis results show that content strategy is the most significant, followed by pricing, customer experience, and platform extension. This paper provides actionable insights for OTT platform managers, helping them enhance user satisfaction and retain customers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Integrating SMOTE and Heterogeneous Ensemble Methods for Online FraudDetection
In the continuous evolving digital era, the escalation of online fraud demands a robust and efficient mechanism for its detection and prevention. In the recent years there has been a significant increase in the online bank transactions. The research delves into the integration of different machine learning algorithms and to enhance the models adaptability, Synthetic Minority Oversampling Technique (SMOTE) has been utilized. The approach addresses the challenges of data imbalance and also strengthens the overall detection performance. Through an extensive literature review the study highlights the limitations in the existing issues in online financial fraud. The proposed model employs a heterogeneous ensemble model consisting of K-Nearest Neighbors (KNN), Random Forest, and XGBoost. KNN functions as an anomaly detector, identifying irregularities in transactional data. Simultaneously, Random Forest assesses feature significance and detects intricate patterns, contributing to a comprehensive understanding of fraudulent activity. XGBoost, known for its computational efficiency, ensures real-time responsiveness by adapting to emerging fraud tactics. The system also introduces a soft voting mechanism that seamlessly integrates individual algorithm predictions, resulting in a robust and highly accurate ensemble fraud detection system. Validation on an authentic bank fraud dataset underscores the framework's prowess, showcasing superior fraud detection capabilities and a significant reduction in false positives. The purpose of adopting this approach is to enhance the financial security and safeguard the consumers assets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Designing of a Human-Centric Autonomous Fish-Feeding Robot: Advancing Aquaculture Sustainability Through Enhanced HumanRobot Interaction
The increasing need for sustainable and efficient aquaculture practices due to rising global seafood consumption and challenges such as labor shortages, environmental degradation, and inefficiencies in manual feeding methods highlights the crucial requirement for an autonomous fish-feeding robot. Service robots are usually designed as a mechatronic design issue focused on implementing essential technological functions, frequently neglecting the significance of intuitive operation and usability. Using the double diamond design framework, this study details the development of a human-centered autonomous fish-feeding robot for fish farming. The goal was to make it more user-friendly and sustainable. The project began the discovery phase to gather insights into aquaculture difficulties and find potential for innovation in fish-feeding procedures. Around 38 small- and medium-sized fish farmers from Andhra Pradesh, Tamil Nadu, and Odisha were interviewed to gather their needs for fish farm feeding systems. In the Define phase, the focus shifted to developing a system that optimizes feeding efficiency while considering environmental factors and user requirements. During the development process, the team utilized iterative design and prototyping, incorporating modern technology such as AI for accurate feed distribution and sensors for evaluating water quality and fish health. User comments played a vital role in enhancing the robots usability and functionality. The Deliver phase concentrated on deploying the robot in simulation environments using ROS AND GAZEBO for the technical feasibility test, assessing its influence on operational efficiency and waste reduction, and advocating for sustainable aquaculture methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Advancing Brain Tumor Recurrence Prediction: Integrating AI andAdvanced Imaging Technologies forEnhanced Prognosis
Integrating artificial intelligence (AI) and advanced imaging technologies in medical diagnostics is revolutionizing brain tumor recurrence prediction. This study aims to develop a precise prognosis model following Gamma Knife radiation therapy by utilizing state-of-the-art architectures such as EfficientNetV2 and Vision Transformers (ViTs), alongside transfer learning. The research identifies complex patterns and features in brain tumor images by leveraging pre-trained models on large-scale image datasets, enabling more accurate and reliable recurrence predictions. EfficientNetV2 and Vision Transformers (ViTs) produced prediction accuracy of 98.1% and 94.85%, respectively. The studys comprehensive development lifecycle includes dataset collection, preparation, model training, and evaluation, with rigorous testing to ensure performance and clinical relevance. Successful implementation of the proposed model will significantly enhance clinical decision-making, providing critical insights into patient prognosis and treatment strategies. By improving the prediction of tumor recurrence, this research advances neuro-oncology, enhances patient outcomes, and personalizes treatment plans. This approach enhances training efficiency and generalization to unseen data, ultimately increasing the clinical utility of the predictive model in real-world healthcare settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Smart Healthcare Systems and Artificial Intelligence: Leveraging Data-Driven Solutions in Healthcare
Smart healthcare systems, powered by artificial intelligence (AI), enables drastic shift in healthcare sector. The benefits of data-driven solutions anchored in advanced disease diagnosis, efficient administrative records management, and operational efficiency. AI algorithms, including machine learning and deep learning, enable real-time data analysis from diverse sources such as Internet of medical Things (IoMT), wearable devices, and medical imaging. This facilitates early disease detection, personalized treatment plans, and predictive analytics, leading to improved health outcomes. Additionally, AI-powered systems optimize resource management, reduce human error, and streamline administrative tasks. While challenges such as data privacy, integration complexities, and ethical concerns exist, the potential of smart healthcare systems to revolutionize healthcare delivery is undeniable. This study explores the role of AI in advancing smart healthcare, focusing on the integration of data-driven technologies to create a more efficient, accessible, and patient-centered healthcare ecosystem. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection
Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, nonlinear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing
This research elucidates the transformative potential of big data analytics and artificial intelligence in optimizing health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Dreamscapes and Virtual Realms: Exploring VRs Impact on Dream Patterns
In recent years, psychologists have been exploring the impacts of virtual reality on mental health, discovering its potential toward curing diseases and therapeutic tools for many such mental conditions. In conventional exposure therapy, the ability of patients to successfully visualize particular feared stimuli is a prerequisite for imaginal exposures. On the flip side, various VR-related tasks have been conducted to test the impact on dreams. This study examines the impact of VR on dreams and analyze the changes in its patterns. By reviewing the recent papers, we concluded that the potential harm caused by VR is well established, with negative side effects reported since the early 1990s. Seven of these twenty-three studies either did not report global incorporation rates or failed to provide sufficient data to determine them. The side effect profile associated with the clinical use of VR and AR remains largely unknown; therefore, we systematically reviewed available evidence of their adverse effects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Path Planning of KR6 R900 Vision Sensor Assisted KUKA Industrial Robot for Pick and Place Application
In this paper, a new multi-objective functions comprising of squared values of joint jerk, acceleration, torque rate and total travel time subjected to kinematic and dynamic constraints have been formulated for achieving optimal trajectory for industrial applications. Then four different multi-objective optimization algorithmsthe multi-objective particle swarm optimization technique (MOPSO), the multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm-II (NSGA-II) and the proposed multi-objective enhanced teaching learning-based optimization (MOETLBO)have been utilized to obtain the optimal solution for trajectory planning. Finally, the experimental validation of the proposed technique and the summarization of simulation results have been done as a comparative study of the four different metaheuristic techniques for pick and place application of KR6 R900 KUKA industrial manipulator. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.