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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. -
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. -
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. -
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. -
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. -
Disease Identification for Tea Leaves Using Explainable Artificial Intelligence
Infection can consequently reduce both quality and yield, and causes major threats to tea production round the world. It is therefore sometime difficult to achieve fast, reliable, and precise identification of disease in tea plants and hence the need to embrace new methods of disease identification. To enable realisation of accurately understandable models for classification of the diseases in tea leaves, Explainable Artificial Intelligence (XAI) approaches are applied in this work. In order to train and test machine learning models, we collected a set of repos of high-resolution images of tea leaves affected by various diseases along with meta information. CNN models were trained with the help of our approach and adopting XAI tools as tools for explanation of predictions. From this study, the field of agricultural AI is benefitted from the illustration of how XAI might enhance disease management strategies in tea agriculture. The results demonstrate an accuracy of 87.85%, with precision, recall and F1-scores ranging between 0.78 and 0.95 across different classes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhanced Random Forest-Based Model forFlood Detection andClassification
Flooding is one of the most devastating natural disasters globally, causing extensive damage to infrastructure, the environment, and human lives. With increasing occurrences due to climate change, accurate classification and analysis of flood imagery are essential for early detection, damage assessment, and post-disaster recovery. Reliable flood classification systems are critical for early warning, resource allocation, and mitigation efforts, helping to minimize the impact on affected regions. Remote sensing and computer vision techniques, including the Bag-of-Visual-Words (BOV) model, offer powerful tools for interpreting flood images by categorizing and identifying flooded regions across vast and complex terrains. This paper presents a modification of the standard Random Forest algorithm to enhance the accuracy of image classification within a Bag-of-Visual-Words (BOV) model. The modified Random Forest achieves better adaptability and performance across flood image datasets by introducing flexibility in parameter tuning through custom hyperparameters and automatic grid search. This modification addresses challenges in balancing efficiency and accuracy for classifying high-dimensional image data sets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Smartphone Based Robust Model for Intelligent Prediction of Thyroid Disease
The thyroid is an integral organ that exerts vital functionality in regulating the bodys functionality in many different ways. Intelligent thyroid disease prediction is very crucial. Through this paper we will describe a smartphone model for predicting whether a patient has thyroid disease based on certain attributes whose values are given by the user. Here, a profound machine learning algorithm named Random Forest will help us predict thyroid disease. By exercising Random Forest within a mobile application, the users can learn about any potential thyroid disease disorder and thus seek medical help in time. This will help improve both the patients and the medical providers quality of care and the help received. With the integration of the Random Forest algorithm along with the mobile application it will prove to be a vital tool which will help everyone in the healthcare fraternity because of its availability and accessibility. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritization of Challenges in EdTech Platform to Enhance User Continuance Intention: A Multi-criteria Decision Making Approach
In the rapidly evolving digital education landscape, EdTech platforms face significant challenges that impact user continuance intention. This study employs a fuzzy logic approach within the Multi-criteria Decision Making (MCDM) framework to identify and prioritize these challenges, ensuring the long-term sustainability of EdTech solutions. Key challenges were identified through an extensive literature review and unstructured interviews with eight industry experts. The fuzzy AHP technique was used to rank these challenges, providing a structured approach for EdTech companies to enhance user continuance intention and platform effectiveness. Results reveal Personalization (32.90%) as the most critical factor, followed by Data Privacy and security (20.86%) and User Interface (12.02%). Addressing these prioritized challenges can significantly improve user engagement and contribute to the development of inclusive and accessible educational technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Personalized Medicine Recommendation Through Genomics AI
Every individual is genetically different. This highlights the importance of exploring the possibility of personalized medicine tailored for each persons unique genetic profile. Due to this genetic variability, people have varied responses to the same medication. Thus, it is all the more necessary to withdraw from traditional medication and adopt personalized medicine. Using genomic data analysis techniques, healthcare specialists will be able to distinguish the minute genetic differences to produce unique therapies for each patient. This paper traverses the development of an algorithm to integrate the patients genomic data with his medical history. Personalized treatment can be recommended based on the inferences using genomic AI. These insights derived from the algorithm can be scrutinized by decision support systems. This step ensures the reliability of the prescribed personalized treatment and confidence of patients on the medication. Each time the model predicts medication compositions and dosages using genomic data, it becomes more accurate improving therapeutic results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Multi-criteria Decision-Making Approach for Prioritising Customer Churn Factors in OTT Video Platforms
Over-the-top (OTT) platforms have revolutionised media consumption by providing on-demand streaming services. Despite their growing popularity, customer churn remains a significant challenge for the platform. This research paper analyses the factors affecting customer churn in OTT video platforms. The factors are identified through unstructured interviews with industry experts and an extensive literature review. This research paper employs a novel approach to prioritising customer churn factors by incorporating multi-criteria decision-making (MCDM) techniques like AHP and fuzzy AHP. The importance of each customer churn factor is measured based on the analytic hierarchy process (AHP) and fuzzy AHP to develop a hierarchy of churn factors. The MCDM analysis results indicated that the content variety and recommendation system, video streaming issues, and high subscription prices are the most significant factors that cause customer churn. Through comprehensive analysis, the study aims to provide insights for OTT service providers to enhance customer retention and mitigate churn rates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Human Activity Analysis Based on Smartphones and Smart Glasses
The study explores the application of smart glasses and smartphones to study human behavior. Through ensemble and deep learning methodologies, the study seeks to autonomously scrutinize data from each device to improve accuracy and resilience in activity identification. The methodology adopted entails the utilization of distinct models for data derived from smartphones and smart glasses, as opposed to amalgamating attributes, to acquire distinctive insights into user activities. The study outcomes demonstrated promising results, showcasing elevated precision in activity recognition across various machine learning models. Comparative analyses with prior research work reveal enhancements in algorithmic efficacy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Proposing an AI-Enabled Waste Segregation System for Domestic Settings
This paper proposes an innovative AI-based system for automated domestic waste segregation. Utilizing Teachable Machine and MobileNet, the system accurately categorizes waste into dry and wet components, laying the foundation for sustainable waste management practices. Embedded in a Raspberry Pi 4, the system integrates real-time image processing with various sensors to streamline the sorting process. While the model has been simulated due to budgetary constraints, future implementation envisions real-world application. Potential advancements include expanding the dataset, enabling multi-category waste classification, and exploring low-power alternatives. This research contributes to the evolving landscape of smart waste management, addressing environmental sustainability and the pressing need for automated, efficient waste segregation at the domestic level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI-Powered Wheels: Machine Learning Approaches for Predicting Used Car Prices
Predictive analytics is now an essential tool for dealers, buyers, and sellers due to the used car markets increasing need for precise pricing models. This study compares the capability of Logistic Regression, Random Forest, Linear Regression, Support Vector Machine (SVM), and Gradient Boosting Machines (GBM) for predicting used car pricing. The results demonstrate that Random Forest and Gradient Boosting scored the best accuracy (87%), with Random Forest also demonstrating better precision (90%). Logistic and Linear Regression both achieved comparable accuracy of 85%, with precision scores of 88% and 89%, respectively. SVM, while significantly less accurate (83%) and precise (86%), produced comparable results for high-dimensional data. In terms of training time, Linear Regression (0.0089 seconds) and Logistic Regression (0.0094 seconds) were the fastest, whereas Gradient Boosting (0.8312 seconds) and Random Forest (0.4766 seconds) took much longer. These results demonstrate a trade-off between model complexity, accuracy, and computing efficiency, with simpler models performing better in terms of speed and ensemble models doing better in terms of prediction accuracy. This study presents practical insights to help stakeholders choose machine learning models for predicting used car prices depending on their specific requirements. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Predicting Football Players Market Value via Machine Learning
Football, arguably the most popular sport in the world, has become much more than just a sport, it is a multibillion-dollar industry with its center in Europe. Every year millions of euros are spent in transfer window to buy and sell players and a common theme that has been seen is players not living up to the price the clubs paid for them. This research aims to predict football players market values using machine learning techniques. Departing from traditional methods that broadly categorize players into positions like Goalkeeper, Defender, Midfielder, and Forward, this study provides a more nuanced approach by classifying players into specific roles such as Center-back, Full-back, Defensive Midfielder, Attacking Midfielder, and Winger. By incorporating performance metrics tailored to each position and weighing the performance indicators based on the relevance to that specific position, the research aims to provide a robust method to predict players market value within a negotiation tolerance range. Using data from the past three seasons, including detailed player performance statistics and contractual details, models were developed to assist clubs in making data-driven transfer decisions. Machine learning algorithms, including Random Forest Regressor and Light GBM, were utilized, with RMSE and R2 Score as evaluation metrics. Both algorithms demonstrated robust performance, with some positional models predicting market values within an acceptable error range of 312million, enabling clubs to negotiate transfer fees with greater precision based on empirical evidence of player performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluating Social Priorities in Environmental Social Governance for the BFSI Sector: A Fuzzy Analytic Hierarchy Process Perspective
As global financial systems evolve, the Banking, Financial Services, and Insurance (BFSI) sector faces increasing pressure to balance financial performance with Environmental, Social, and Governance (ESG) obligations. However, integrating social factors such as employee welfare, community engagement, customer satisfaction, and diversity and inclusion remains challenging due to their subjective and often intangible nature. This study addresses this issue by applying the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to evaluate and prioritize social factors within the ESG framework. The Fuzzy AHP method, which combines traditional AHP with fuzzy logic to manage uncertainty in expert judgments, was used to gather and analyze input from BFSI sector experts. The study assessed the relative importance of social factors through structured pairwise comparisons, providing a clear hierarchy of priorities for BFSI institutions. The results reveal that employee welfare and customer satisfaction emerged as the most critical social aspects, reflecting stakeholder expectations and regulatory pressures. By focusing on these key areas, BFSI institutions can enhance their ESG performance and meet sustainability goals. These findings offer actionable insights for decision-makers in the BFSI sector, allowing them to better allocate resources to social initiatives that not only satisfy regulatory requirements but also contribute to long-term business value and societal impact. This study underscores the importance of prioritizing social factors in sustainable strategies and provides a robust framework for navigating the complexities of ESG integration. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Deep Reinforcement Learning with Meta-Learning and Signal Bands for Indian Equity Portfolio Management
The portfolio is a collection of assets belonging to an investor. Managing the portfolio depends on the goal of the portfolio management. This paper proposed a new portfolio managing technique using a deep reinforcement learning framework combined with meta-learning and signal bands to optimize the returns and risk of the Nifty 50 index. The objective is to maximize portfolio returns by minimizing the risk, portfolio volatility, and drawdowns with constraints of transaction cost, maximum and minimum allocation, and availability of cash and holdings. The model executes the actions of buy, sell, and hold with the constraints, and the model executes any of those actions depending on the situation and model training. Proposed model recorded a 4.68 Sharpe ratio and 7.53 Sortino ratio while training the model. While testing the model, it recorded a 4.5 Sharpe ratio and 7.64 Sortino ratio, which aligns with the aim to achieve a higher Sortino and Sharpe ratio to build a robust model for risk-adjusted returns. Proposed approach aims to create a strong model for a portfolio management system that adapts to dynamic market conditions and optimizes investment strategies by integrating these techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning
Breast cancer remains a major global health challenge, with the complexity of managing diverse diagnostic tests often hindering timely and accurate detection. This system proposes a solution by unifying various test results, such as imaging, biopsy and genetic data, into a single platform that leverages machine learning (ML) to predict the likelihood of breast cancer. The platform features an intuitive dashboard that visually represents deviations from normal values, enabling healthcare providers to make informed decisions for early detection and treatment planning. In addition, the system includes an interactive chatbot powered by natural language processing, which assists both doctors and patients by interpreting test results, explaining predictions and offering real-time suggestions for treatment options. This comprehensive approach not only integrates ML models to enhance diagnostic accuracy but also provides real-time updates and alerts for critical changes in patient data. By consolidating fragmented information and incorporating predictive analytics, the system aims to improve the precision of cancer monitoring and offer personalized treatment guidance. About 98% early detection accuracy is achieved to do decision-making processes better which leads to efficient treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.