Browse Items (16488 total)
Sort by:
-
Enhancing Educational Engagement Among Kattunayakan Tribal Children Through Culturally Tailored Educational Activity Centres
The deep forests of the Western Ghats are home to the aboriginal tribes Kattunayakans, Categorized under the Particularly Vulnerable Tribal Groups (PVTGs) in India. Despite various government schemes to improve their economic status, the literacy rates remain very low due to geographical isolation and traditional lifestyle. The main objective is to look into designing educational activity centres that exclusively cater to the needs of the Kattunayakan children, integrating indigenous knowledge and methods with teaching-learning sessions to promote lifelong learning. Technological progress and AI can be game changers for accessing quality education, including visual learning, language translation, and personalized methods while preserving cultural heritage. The research is based on a systematic narrative review, critically examining scholarly articles, policy and government reports. The outcome shows that these programs help improve academic skills while keeping them closer to their culture and identity. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE. -
Enhancing Education Policy Estimation: A Novel Ridge Fuzzy Regression Approach for Handling Multicollinearity with Fuzzy Input Data
Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariates impact, with a specific focus on improving education policy systems. Our method utilizes the ?-level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimators performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Early Seedling Stage Salinity Tolerance in Rice Through Brassinosteroid Priming
Rice (Oryza sativa), astaple food for the major global population, faces substantial productivity challenges due to salinity stress, an increasingly prevalent issue exacerbated by climate change. Salinity impacts rice at critical growth stages especially at seedling stage, root development, shoot elongation and ultimately seedling establishment. This study evaluated the effect of brassinosteroid (BR) priming as astrategy to improve seedling stage salinity tolerance in rice seedlings across 15genotypes subjected to moderate (140?mM NaCl), and severe (200?mM NaCl) salinity stress in comparison with control. BR-primed seeds demonstrated enhanced germination rates, seedling vigor index, shoot length and root length under salinity conditions compared to non-treated seeds. BR priming led to a35% improvement in SVI under control conditions and up to 30% under severe salinity, suggesting that BRs may facilitate osmotic regulation and ion homeostasis, key for maintaining growth under stress. Furthermore, BR priming significantly increased root development, essential for water uptake and nutrient acquisition in saline environments. Our results showed the prospect of BR priming as an effective approach to enhance rice resilience to salinity stress, providing afoundation for further field-based research on BR-mediated stress tolerance mechanisms. This study underscores the relevance of BR priming in improving rice productivity in saline-prone areas, contributing to food security in the face of increasing soil salinization. The author(s), exclusively licensed to Springer-Verlag GmbH Germany, a part of Springer Nature 2025. -
Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE. -
Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and functional impairment. Early detection is crucial for effective management and intervention. This study explores the effectiveness of an integrated deep learning approach combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the early detection of Alzheimer's disease using multimodal data. A novel deep learning model was developed and validated, integrating neuroimaging data (MRI and PET scans) with clinical data using a decision-level fusion strategy. The study utilized a dataset comprising 1000 anonymized patient records from the Alzheimers Disease Neuroimaging Initiative (ADNI). Models were assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). The integrated model demonstrated superior performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 93.5%. The model's AUC was 0.97, indicating excellent diagnostic capability. The proposed deep learning approach significantly improves the early detection of Alzheimers disease by effectively analyzing complex, multimodal data. This model holds considerable potential for clinical applications, providing a robust tool for healthcare professionals to diagnose AD in its early stages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme Learning Approach
The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM. 2023 IEEE. -
Enhancing Dimensional Geometry Casting using Computer Modeling
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences -
Enhancing Digital Citizenship Through Secure Identification Technologies in the Global Unified Digital Passport
Passports play a vital role in enabling international movement and security, as well as confirming one's identity. However, the existing passport system has many problems and limitations, such as identity fraud, passport falsification, human smuggling, terrorism, and border control. Despite the fast growth and adoption of digital technologies in various fields, the passport system has not been able to adapt to the changing demands and expectations of the global community. Therefore, there is a pressing need to investigate and develop a digital passport and verification system that can address the shortcomings of the conventional passport system and provide a more safe, convenient, and effective way of managing and verifying the identity and travel history of individuals across the world. This paper presents the solution and requirement for the development of a digital passport system that can be applied globally and universally. The paper proposes a conceptual framework and a technical architecture for the digital passport system, based on the principles of blockchain, biometrics, and cryptography. The paper also discusses the possible benefits, challenges, and implications of the digital passport system for various stakeholders, such as travelers, governments, airlines, and immigration authorities. The paper aims to contribute to the research and innovation of digital identity and citizenship, as well as to the progress of the sustainable development goals (SDGs) related to peace, justice, and strong institutions. 2024 IEEE. -
Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques
Worldwide, Lung cancer is the primary cause of death from cancer, and chances of surviving are considerably raised by early detection. While traditional diagnostic approaches heavily rely on imaging and specialized infrastructure, they often fail to serve low-resource or early screening environments. In this work, based on deep learning, lightweight framework for detecting lung cancer from structured survey data is presented. The research tackles the prevalent the problem of class disparity using the Synthetic Minority Over-sampling Technique (SMOTE), enhancing the sensitivity of predictive models. A comparative evaluation was conducted across six models Logistic Regression, SVM, KNN, Naive Bayes, Random Forest, and XGBoost. Among these, Random Forest and XGBoost achieved 95% accuracy, 0.98 recall, and ROC-AUC scores of 0.9943 and 0.9835 respectively. The proposed hybrid ensemble model (Random Forest + XGBoost) outperformed all with 96% accuracy, 0.95 precision, 0.98 recall, and a ROC-AUC score of 0.9961. These findings demonstrate that the hybrid strategy is effective in providing high diagnostic precision using clinical survey data that is not imaging. 2025 IEEE. -
Enhancing Diagnostic Accuracy in Familial Alzheimers Disease Through Gene Expression Profiling and Optimized Machine Learning Algorithms
The abstract should summarize the contents of the paper in short terms, i.e. 150250 words Early and accurate diagnosis of the Familial Alzheimers Disease (FAD) is critical for effective treatment of this genetically inherited form of Alzheimers disease. A prediction of FAD from gene expression data is investigated and the performance of various machine learning models on the discovered patterns is evaluated. We compare the output of Linear, Ridge Regression and a LightGBM model with hyper-tuned parameters on data from the Gene Expression Omnibus. The LightGBM model is then hyperparameter tuned to better capture the non-linear complexity of the data. To find the predictive performance, a model is evaluated using MSE, R squared and accuracy. The results show that both the LightGBM model and the traditional models have lower MSE, higher R squared and better accuracy. By examining FAD data on high-dimensional gene expression data these results show that when dealing with high-dimensional gene expression data, sophisticated machine learning models perform better than other approaches, such as LightGBM show higher diagnostic accuracy in FAD. It is shown in this research the power of machine learning is immense and is a powerful tool for the predictive modeling of Alzheimers Disease, as well as possible early detection and personalized treatment. Future work might also aim to further improve model performance with other more complex genetic datasets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning
In the quest to enhance the diagnostic accuracy of neural disorders, particularly autism, this paper presents a novel approach for brain region classification using advanced machine learning techniques. The study utilizes the ABIDE and AAL116 atlas datasets, focusing on segmenting and classifying brain regions from resting-state functional MRI (rs-fMRI) images. We propose a three-stage process. In the first stage, data collection and preprocessing are conducted, where rs-fMRI images are preprocessed into SPM12-NIfTI format. The second stage involves the segmentation of brain regions using a Regularized VNet, resulting in the extraction of AAL116 brain region images, which are then split into training, testing, and validation sets. In the third stage, we introduce a custom-designed BRCNet (Brain Region Classification Network), which discriminates between Autism and Normal classes. Our segmentation methods are rigorously evaluated using metrics such as Dice Score, Recall, and Precision, with the proposed method achieving a Dice Score of 0.985, Recall of 0.962, and Precision of 0.991, surpassing other tested methods like UNet, Active Contour, and Binary Unit. For classification, various methods, including Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks like ResNet, are compared. Our findings demonstrate that ResNet achieves an exemplary performance with an Accuracy of 97.5%, Sensitivity of 96.2%, Specificity of 97.1%, Precision of 97%, and an F-Measure of 97.93%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Data Security Through Semi-parametric Shrinkage Estimation of Shannon and Past Entropy in Geometric Distributions
The concept of entropy has been introduced in statistical methods to measure the amount of information contained in a random observation, and it plays a crucial role in various fields, especially in data security. This paper focuses on the semi-parametric shrinkage estimation of Shannon entropy and past entropy measures of the geometric distribution under complete, right, and time-censored sampling procedures. Shannon entropy, a key measure of uncertainty, along with past entropy (or min-entropy), which assesses the least predictable outcomes, plays a crucial role in ensuring strong data security, particularly in cryptographic systems and secure communications. While most existing literature addresses estimating these entropy measures for continuous distributions, this paper evaluates shrinkage estimators to enhance the efficiency of the ordinary semi-parametric least squares estimator for geometric distributions. This study explores the constant shrinkage factor and modified Thomson-type estimators, evaluating their effectiveness against traditional methods such as maximum likelihood estimators. Empirical investigations conducted with simulated samples indicate that shrinkage estimators consistently outperform maximum likelihood estimators, showcasing better relative efficiency. These results emphasize the potential of shrinkage estimators to enhance entropy-based measures in data security applications, which can lead to more robust cryptographic key generation, password strength analysis, and intrusion detection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks
Cyberthreat Intelligence (CTI) feeds serve as crucial resources for organizations seeking to fortify their defenses against emerging cyberthreats. However, these feeds often suffer from deficiencies such as incomplete data, false positives, and a lack of contextual information. This chapter proposes an innovative approach to address these challenges by leveraging Generative Adversarial Networks (GANs) to enhance CTI feeds. We introduce ThreatGAN, a novel GAN architecture specifically designed for cyberthreat modeling. Trained on accurate CTI data, ThreatGAN learns to generate synthetic yet realistic threat indicators, including malicious uniform resource locators (URLs), Internet Protocol (IP) addresses, and attack patterns. We demonstrate the efficacy of ThreatGAN in filling gaps in existing feeds, reducing false positives, and providing essential contextual information. The quantitative and qualitative evaluation shows that ThreatGAN significantly improves CTI quality. This technique can strengthen organizations cyber defenses by enabling them to work with higher quality, more complete Threat Intelligence. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
Enhancing Cybersecurity: Machine Learning Techniques for Phishing URL Detection
Phishing attacks exploit user vulnerabilities in cybersecurity awareness by tricking them to fake websites designed to steal confidential data. This study proposes a method for detecting phishing URLs using machine learning. The proposed method analyzes various URL characteristics, such as length, subdomain levels, and the presence of suspicious patterns, which are key indicators of phishing attempts. Gradient Boosting was selected due to its robustness in handling complex, non-linear relationships between features, making it particularly effective in distinguishing between legitimate and phishing URLs, by evaluating the Gradient Boosting classifier on a dataset with 10,000 entries and 50 features, the method achieves an accuracy of 99%.This approach has the potential to enhance web browsers with add-ons or middleware that alert users from potential phishing sites which will be based solely on URL. 2024 IEEE. -
Enhancing cybersecurity with distributed models and sparse mixture of experts
[No abstract available] -
Enhancing customer satisfaction through artificial neural networks (ANNS): Principles, architectures, and practical applications
Artificial neural networks (ANNs) are a powerful paradigm in AI, inspired by the complex structure of the human brain. Their design mirrors biological neural networks that govern the human nervous system, allowing ANNs to excel in tasks from pattern recognition to decision-making. This chapter explores the foundational principles of ANNs, highlighting the interplay between neuroscience and computer science, where digital systems replicate or sometimes surpass human cognitive abilities. The study examines ANNs in various customer-focussed applications, where businesses can leverage ANNs to increase customer satisfaction by predicting and influencing consumer behaviour. This chapter provides an overview of the core principles, architectures, and broad applications of ANNs. It seeks to offer historical insights, understanding the evolution of ANNs and their mathematical foundations. The research explores the building blocks of neural networks, including neurons, layers, and activation functions, and their importance in pattern recognition and information processing. Special emphasis is given to popular architectures like feedforward, recurrent, and convolutional neural networks. ANNs' versatility is demonstrated through surveys of their applications in finance, robotics, and healthcare. This chapter addresses real-world challenges and potential solutions in these domains. This chapter serves as a valuable resource for researchers and practitioners interested in understanding ANNs, how they work, their development, and future advancements in the field. It discusses ANNs' impacts in finance, healthcare, and robotics, alongside ethical considerations. This original contribution provides fresh insights into ANNs, making it valuable for those exploring this emerging topic. 2025 Diva Kaur Grewal, Vanishree Senthilkumaran and Hridhya P. K.. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Enhancing Customer Satisfaction and Sales in Retail Environments: A Personalized Augmented Reality Approach for Dynamic Product Recommendations
This article explores the potential transformative impact of integrating augmented reality (AR) technology with personalized product recommendations in the retail industry. By leveraging ARs ability to overlay digital information onto the physical world, retailers can offer tailored suggestions based on individual preferences, past purchases, and real-time contextual cues, thereby enhancing customer satisfaction and driving sales. Through a comprehensive literature review and empirical analysis, the study investigates user experience, adoption factors, and the long-term effectiveness of AR-deep learning integration in retail settings. Findings reveal significant improvements in customer satisfaction, sales performance, inventory management, and employee productivity with the implementation of AR-Deep Learning technology. Additionally, the article presents an innovative framework that seamlessly integrates AR and deep learning models, demonstrating high accuracy in object recognition, real-time interaction, and enhanced user experience across various industries. While highlighting the studys limitations and areas for further research, this article underscores the importance of customer-centric strategies and technological innovation in optimizing the retail experience and driving business growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Customer Satisfaction and Sales in Retail Environments: A Personalized Augmented Reality Approach for Dynamic Product Recommendations
This article explores the potential transformative impact of integrating augmented reality (AR) technology with personalized product recommendations in the retail industry. By leveraging ARs ability to overlay digital information onto the physical world, retailers can offer tailored suggestions based on individual preferences, past purchases, and real-time contextual cues, thereby enhancing customer satisfaction and driving sales. Through a comprehensive literature review and empirical analysis, the study investigates user experience, adoption factors, and the long-term effectiveness of AR-deep learning integration in retail settings. Findings reveal significant improvements in customer satisfaction, sales performance, inventory management, and employee productivity with the implementation of AR-Deep Learning technology. Additionally, the article presents an innovative framework that seamlessly integrates AR and deep learning models, demonstrating high accuracy in object recognition, real-time interaction, and enhanced user experience across various industries. While highlighting the studys limitations and areas for further research, this article underscores the importance of customer-centric strategies and technological innovation in optimizing the retail experience and driving business growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Customer Experience and Sales Performance in a Retail Store Using Association Rule Mining and Market Basket Analysis
The retail business grows steadily year after year andemploys an abounding amounts of people globally, especially with the soaring popularity of online shopping. The competitive character of this fast-paced sector has been increasingly evident in recent years. Customers desire to blend the advantages of old purchasing habits with the ease of use of new technology. Retailers must thus guarantee that product quality is maintained when it comes to satisfying customer demands and requirements. This research paper demonstrates the potential value of advanced data analytics techniques in improving customer experience and sales performance in a retail store. Apriori, FP-Growth, and Eclat algorithms are applied in the real time transactional data to discover sociations and patterns in transactional data. Support, confidence and lift ratio parameters are used and apriori algorithm puts out several candidate item sets of increasing lengths and prunes those that fail to offer the assistance that is required threshold. We identified lift values are more when considering frozen meat, milk, and yogurt. if the customer decides to buy any of these items together, there is a chance that the customer will buy 3rd item from that group. Research arrived High confidence score is for Items like Semi Finished Bread and Milk so these products should be sold together, Followed by Packaged food and rolls. As retailers continue to face increasing competition and pressure to improve their operations, The aforementioned techniques may provide you a useful tool to comprehend consumer buying habits and tastes and for utilising that knowledge to come up with data-driven decisions that optimise product placement, enhance customer satisfaction, and attract sales. 2023 IEEE.