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Next Gen Text Mining in English Literature: A Machine Learning Approach for Narrative and Stylistic Analysis
This paper presents a research work that is novel in nature, based on a new machine learning framework that focuses on a computational analysis in the field of English literature. This research uniquely aims at focusing on an intersection based on stylistic patterns and narrative structures. The newly proposed model is termed the 'Next Gen Text Mining Framework', which leverages transformer-based models (such as GPT, BERT), along with sentiment trajectory-based modeling, network analysis, and clustering algorithms for extracting stylistic features and latent semantics from the text on a large scale. A meticulously used re-processing type pipelining framework modifies and prepares the data for the model ingestion. The multi-modal approach used in this framework enables the experimental analysis of different models across various diverse corpora in the literature that demonstrate stability, superior accuracy, and robustness when compared to the traditional models, in tasks like the authorship attribution, sentimental trajectory mapping, and the theme-based classification. The proposed framework bridges the gap between distant and close reading practices, enhancing the pedagogically based engagements and translating the computational insights into interpretative forms for research and teaching. This research highlights a scalable and replicable framework model that is a transformative tool when a large-scale inquiry in literature is considered and sets a foundation towards multi-modal, future, cross-lingual, and multidisciplinary-based applications. 2025 IEEE. -
A Diabetes Detection Framework Based on Datadriven Predictive Technologies
Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE. -
Smart Irrigation System Using Soil Moisture Sensor
The impart of climatic change apparently affect accessibility of good water for agriculture irrigation system in addition to human dependency and demand on farm products for survival, increases water unavailability challenges in the farm affecting ecosystem when there is no balance between country food production capacity and population growth. Agricultural sector is challenged with unequal distribution of water in the farm plantations reducing food production strength, this is more severe in under-developing regions whereby require smart irrigation systems as promising solution to mitigate against threat of water distribution to farm products. This research work design and develop smart irrigation system with less expensive microcontroller components, implementing water distribution system using sensor captured information of soil condition, infusing precision irrigation system that calculate and supplies exact water require based on soil dryness level. The research adopts internet of things IoT technology where sensor will be used to monitor and capture soil information and control water distribution based on available soil dataset. The outcome of the research gives absolute control on over-irrigation and under-irrigation system that increases agricultural productions with advance technological means, precision irrigation system mechanisms reduces water wastage and ensure equal distributions. Multivariate soil type test will greatly enhance general acceptability of this concept in cross-functional regions. 2025 IEEE. -
An AI-Driven Framework for Computational Literary Analysis: Bridging English Literature and Technology
Artificial Intelligence is emerging to be significantly used in the field of digital humanities, yet the focus of achieving interpretability and predictive level of performance simultaneously in the field of literary analysis is challenging. This paper highlights the study of a newly proposed effective model that is the Interpretable and Pedagogical Artificial Intelligence Framework (IPAF), a novel model that is designed for the analysis of poetry, prose and drama, while suitable outputs are provided by the model that are explainable in the field of education and literature research. The traditional models, such as the Random Forest, XG Boost and the Light BGM model, were used as base learners and have been integrated into the IPAF model by using Term Frequency-Inverse Document Frequency (TF-IDF) and the BERT embedding towards feature-level representations. The evaluation of this framework is carried out by utilising standard levels of classification metrics such as precision, accuracy level, F1 score and the SHAP-based explainability, which is applied for identifying the significant features of text that are influencing predictions. The results show that the IPAF model significantly outperforms the existing baseline models, by achieving a model with high accuracy, stability and robustness, that provides actionable insights towards educators and literary researchers. The interpretability of the framework allows users towards linking the computational outputs of the model alongside stylistic and theme-based patterns, by bridging the need for qualitative analysis along critical understanding of the literature. The future scope of the work extends the IPAF model to explore multi-modal levels of literary analysis by integrating data for audio, text and image together for digital archives for the further enhancement of cross-genre interpretation and applications of pedagogy. 2025 IEEE. -
A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques
Credit card fraud has become a major concern in the FinTech industry due to the rapid growth of digital payment platforms and the increasing sophistication of fraudulent activities. Accurate and timely detection of fraud is essential to minimize financial losses and maintain trust in FinTech services. This study presents a hybrid deep learning framework for credit card fraud detection using the 2023 Credit Card Fraud Detection Dataset. The proposed approach with data preprocessing, which includes handling missing values, removing duplicate entries, and encoding categorical features to ensure clean and structured input for modeling. Normalization is applied to scale features uniformly, preventing bias from varying magnitudes and improving model convergence. Multiscale Entropic (MSE) analysis is employed for feature extraction, capturing both short- and long-term temporal patterns within transaction sequences, enhancing the representation of complex transactional behaviors. The extracted features are then processed using a Transformer-GAT classifier, which combines the attention mechanism of Transformers with Graph Attention Networks (GAT) to learn complex inter-transaction dependencies and graph-based relationships. This hybrid architecture enables the model to capture both local and global patterns, improving fraud detection performance. On the training dataset, the model achieved outstanding results with 98.65% accuracy, 98.70% precision, 98.50% recall, and an F1-score of 98.60 %, demonstrating a strong balance between correctly identifying fraudulent transactions and minimizing false alarms. The approach offers significant advantages for FinTech applications, including robust handling of imbalanced data, effective detection of subtle fraud patterns, and strong generalization to unseen transactions. 2025 IEEE. -
A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques
Pneumonia is an infection that results in inflammation of the lungs and, if not identified in time, can be life-threatening. The most frequent method of diagnosing pneumonia is chest X-rays; the pictures are scrutinized closely. Pneumonia is still a global health burden. The accurate and timely diagnosis is difficult, especially in low-resource settings. X-rays have served as a primary key for the identification of pneumonia for many years. However, with the recent advancement in artificial intelligence technologies, especially deep learning and machine learning, there's now a potential to automatically detect and classify pneumonia using chest x-ray images. This review examines the research from 2019 to 2024 to understand the current trends and future direction in various deep learning and machine learning models. These encompass the convolutional neural networks, transfer learning methods, combined network designs, explainable AI models, and the use of radiomics with conventional machine learning techniques. However, the three significant challenges remain differences in the data, an imbalance between the classes, and a limited ability to apply these methods in real clinical settings. Based on the review, this paper suggests more future research on machine learning techniques for detecting pneumonia. In this work, a new system is also introduced to improve both case identification and the clinical diagnosis process. The proposed model was evaluated using the Key Parameter Indicator (KPI) as a feature and was compared with an earlier model. Finally, recommendations are provided for future research on trustworthiness, clinical usefulness, and multi-modal AI systems. 2025 IEEE. -
Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems
The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition, sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring. 2025 IEEE. -
Machine Learning Algorithms for Optimizing Blockchain-Based Decentralized Autonomous Organizations
This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem. 2025 IEEE. -
HALC: An AI-Driven Legal Decision-Making Framework - A Qualitative NVivo Case Study on Tribal Rights
This study proposes a structured human-AI collaboration framework for legal and ethical decision-making, integrating artificial intelligence with human expertise. Unlike fully automated AI systems, it prioritizes transparency, accountability, and ethical oversight. Through expert interviews with legal professionals and AI technologists, we identified key challenges, including bias, lack of explainability, and the need for human validation. Using thematic analysis in NVivo, we developed a stepwise framework that ethically collects data, applies AI-driven analysis, ensures human oversight, and informs policy decisions. This approach enhances human judgment rather than replacing it, with potential applications in law, governance, and public policy. Future research will test and refine this framework. 2025 IEEE. -
Exploring Social Cues and Engagement in Humanoid Robots: A Robosen K1 Case Study
With the increase adoption of humanoid robots in today's world, the need to understand the ways through which these robots communicate social cues has become indispensable for effective human-robot interaction (HRI) in everyday life. The focus of this study is on the examination of the influence of nonverbal behaviour of Robosen K1 (a humanoid robots) on human perceptions and emotional responses. K1 was programmed to perform expressive full-body movements, due its lack of facial expressions, such as dancing, push-ups, and standing on its head. The research design was a mixed-method approach, which combined behavioural observations from live interactions with data from an online survey. Findings from the study revealed positive emotional reactions from participants, most of which described the robot as 'impressive', 'curious', and 'amusing'. Also, results indicated that 89.8% of participants were favourably disposed to engaging with similar robots in the future. Finally, it was found that the robot's gestures, being highly expressive, contributed to perceived personality traits such as 'playful' and 'friendly'. The study, therefore, concluded that a well-designed non-verbal cues would play critical role in enhancing emotional connection, engagement, and trust in humanoid robots, hence, their importance for successful HRI design. 2025 IEEE. -
Enhancing Healthcare Ecosystems Through the Integration of IoT for Patient-Centric Solutions
The Internet of Things (IoT) is a newly implemented technology in the field of healthcare and can enhance patient-centrical care and efficiency in the healthcare field. IoT may be used to assist in delivering real-time health data and predictive diagnostics and custom care plans by interconnecting medical equipment, sensors, and information systems. This paper will discuss how the IoT technologies, particularly wearable sensors, cloud-based analytics and smart health architectures are changing the way healthcare is delivered. The article highlights how the data merge on the utilization of clinical and non-clinical sources to aid in remote patient monitoring, resources use optimization, and positive patient outcomes. It also identifies the concerns of the implementation of IoT such as the security risks, data privacy and the failure to connect the devices and systems. To address them, the paper discusses the new structures that integrate blockchain and artificial intelligence and ensure safe implementation of data management and heightened clinical decision-making. The results of various works of 2015-2020 have revealed that IoT applications and patent health care-related tendencies are growing, which implies that the shift towards interconnected and intelligent ecosystems is rapid. The consequences of this digital transformation are not confined to the hospital sphere only, as it is extended to homecare, telemedicine, and state population management. Lastly, IoT will allow healthcare stakeholders to shift their healthcare systems to patient-centered, rather than hospital-centered systems, in which a focus on accessibility, efficiency, and personalization would be placed on healthcare provision. 2025 IEEE. -
Artificial Intelligence Personalization: Opportunities, Risks, and the Need for Ethical Data Practices
The benefits of AI personalization are numerous; however, the question still remains, what are the side effects of this feature? Will it help make shopping and enjoying content more enjoyable and efficient or will it destroy the trust of the users by creating privacy concerns. These are the issues this article has attempted to discuss and investigate. Artificial Intelligence (AI) personalization is a very helpful and useful feature, but there must remain a proper balance between personalization and data collection in order to ensure client comfort. A transparent and honest collection of data is to be expected for almost all the companies and this data is to be used responsibly. Activities like profiling must be regulated and controlled and should not be left unregulated. Despite laws being updated to be more considerate towards the privacy of users, the development of better enforcement is imperative. Regardless of the presence of appropriate laws, it is important that each individual practice the respective ethics while present in the digital universe. This article is discussed about AI personalization issues and its research challenges. 2025 IEEE. -
Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models
This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market. 2025 IEEE. -
Improving Voltage Regulation in High-Power Solar Applications
This paper presents an advanced solar-powered isolated DC-DC converter optimized for high-power applications, with a focus on precise voltage regulation at the output stage. To mitigate high-voltage stresses typically encountered in single-stage DC-DC converters, a Lossless Active Clamp Flyback circuit is integrated, offering soft switching capabilities and regenerative energy features. The proposed topology is designed using low-voltage devices, enhancing overall system efficiency. A hardware prototype rated at 2 kW has been developed to empirically validate the circuit's performance. Additionally, a novel control algorithm is introduced to further optimize the converter's operational characteristics. The proposed converter is benchmarked against existing solutions, highlighting significant improvements in terms of component count, voltage handling, and energy regeneration. The results demonstrate superior efficiency and robustness, making the system highly suitable for high-power renewable energy applications. Through this innovative approach, the converter offers substantial gains in performance and operational feasibility, especially in scenarios demanding high power density and stringent efficiency standards. 2025 IEEE. -
Innovative Power Conversion Solutions for Renewable Energy and Electric Mobility
The global transition to renewable energy sources and electrification demands efficient power conversion systems for applications like hybrid electric vehicles (HEVs) and energy storage systems. This paper introduces a novel Multi-Port Bidirectional DC-DC/DC-AC Converter (MBPC) with high efficiency, compact design, and versatile functionality. The MBPC supports two input and two output ports, enabling energy flow between renewable energy sources, storage systems, and loads. Its efficiency exceeds 95%, with a power density of over 10W/cm2. The innovative design minimizes component count, reducing manufacturing costs by 30% compared to conventional converters. Extensive experimentation validates its ability to handle varying current-voltage profiles in multiple operational modes, including DC-DC and DC-AC conversions. With applications in grid-tied systems and electric vehicles, the MBPC addresses efficiency, cost, and flexibility challenges in modern power systems. This work contributes to advancing renewable energy integration and efficient electrification solutions. 2025 IEEE. -
AI-Powered Solutions for Legal Compliance in Industrial Workspaces a Psychological and Labour Law Perspective
The complicated issue of the ways to ensure the workplaces in the industries meet the legal standards brings together the psychology of the workplaces, the labour law, and the convolution of the AI design. In this dissertation, the author investigates the use of AI-mediated options to deal with regulatory compliance and psychological and legal issues. The risk associated with the fair labour, discrimination, and safety issues can be addressed with the help of predictive analytics, automated compliance, and AI-compliance monitoring. The use of AI can also be expanded to support worker well-being and mental health through the identification of work stressors, burnout prevention, and creation of a physiologically safe workplace. But in the case of AI, there are ethical or legal considerations around the agency of workers, bias in algorithms, as well as privacy or confidentiality of data. Due to these reasons, it is necessary to adopt the strategy approach, where AI and human observation are used to determine the work decisions trade-offs in an observable and just manner to employers and employees alike. This dissertation also added to the contribution of how AI can benefit the responsible design of industrial workplaces that do not fail to achieve ethical standards, hold to psychological sustainability, and adhere to labour laws, evaluating the psychological effects of labour law actors as well as effects on the workplace. 2025 IEEE. -
Explainable Intrusion Detection System for Internet of Things-explainability with reliability
Explainable Artificial Intelligence (XAI) based Intrusion Detection System (IDS) (X-IDS) has transformed the traditional IDS into interpretable and transparent system with the goal of providing interpretable justification for IDS models. XAI is now being used to extract more appropriate features for specific cyber-attacks. The black-box model of ML based IDS is not capable of giving reason for false positive to the cyber defense personnel. XAI tools reduces this abstraction by locally interpreting the model's behaviour at some datapoints along with global interpretability. This article proposes an explainable IDS by using XAI tools. We used SHAP (SHapley Additive exPlanations) to identify the variations in feature importance of selected ML based IDSs and explain the variations of their detection accuracies. Also, we have shown that with same dataset, feature importance varies differently with different ML models. This leads us to the conclusion that specific set of features are required for specific ML models while other can be discarded. The explainability proposed in this study also help to select less set of features to overcome time of execution and cost. 2025 IEEE. -
Innovative Hybrid Models for Predicting Diabetes: CNN-LSTM Hybrid and Calibrated Soft Voting Model
This study assesses four ensemble techniques - stacking, soft voting, hard voting, and calibrated soft voting - for predicting diabetes onset using the Pima Indians Diabetes dataset. Traditional single-model methods are contrasted with these advanced ensemble approaches, which integrate multiple models to enhance predictive accuracy. The evaluation included metrics such as accuracy, precision, recall, F1 score, and AUC. The CNN-LSTM model was also examined, achieving an accuracy of 75%, precision of 70%, recall of 69%, and an F1 score of 72%. Among the suggested methods, the calibrated soft vote model was the most effective, with improved performance compared to the rest of the techniques. Upcoming studies will address the combination of these models with real-time monitoring systems and deploying their use across a broad range of datasets and medical conditions. 2025 IEEE. -
Indoor Localization and Tracking with IoT: A Critical Survey of Technologies, Challenges, and Future Trends
Indoor localization and tracking have been important areas of research throughout the past 10 years, driven by the expanding Internet of Things (IoT) technologies. The shortcomings of conventional GPS in indoor environments have called for the development of replacement localization methods. This paper presents a methodical review of IoT-enabled indoor localization techniques covering both well-known technologies such as Bluetooth Low Energy (BLE), Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), and Wi-Fi fingerprinting, as well as newer approaches such as Visible Light Communication (VLC). We critically evaluate these technologies by way of a comprehensive analysis of modern research and case studies, emphasizing significant performance criteria such as accuracy, scalability, and energy efficiency as well as pragmatic concerns such as cost and security. Our work looks at field trends still in development, highlights significant gaps and problems, and integrates the current state of the art. We also stress potential application fields - such as smart homes, healthcare, and industrial automation - that stand to benefit significantly from advances in indoor localization. Finally, we outline future research intended to address current limitations, including the need of higher accuracy in complex environments and more robust security measures. 2025 IEEE. -
DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection
The significance of early skin cancer detection for effective prevention and treatment is underscored by the limitations of traditional manual diagnostic methods used by dermatologists. Leveraging Convolutional Neural Networks (CNNs) and the HAM10000 dataset, this research aims to automate skin cancer classification through dermatoscopic image analysis. The primary objective of the research is an accurate classification system identifying seven specific skin cancer types. The novelty is the deployment of the classification system using a Mobile Application - DermAI. The trained CNN model, spanning 10 epochs, achieved remarkable precision, peaking at a 97.90 percentage test accuracy during the 7th epoch. Evaluation metrics like the confusion matrix confirm its reliability in categorizing lesions, minimizing misclassifications, and validating its efficiency as a diagnostic tool. Transforming the model into TensorFlow Lite format enables seamless integration into mobile platforms, optimizing computational resources. This allows users to access prompt skin cancer classification via an Android application, fostering accessibility to preliminary assessments. Early identification facilitates timely medical intervention, a crucial factor in enhancing prognosis. Through CNNs, TensorFlow Lite, and mobile deployment, this research strives to bridge technology and healthcare accessibility, empowering individuals to proactively manage their skin health based on classification results and initiate timely discussions with healthcare professionals. 2025 IEEE.