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Enhancing Biodegradability and Ecological Impact: Treatment of Low-Density Polyethylene for Sustainable Plastic Management with Eudrilus eugeniae Earthworms
Low-density Polyethylene (LDPE) is widely used in food packaging and agricultural mulching, but its disposal creates harmful macro-, meso- and microplastics. To address this, LDPE has been treated to become biodegradable. The treatment involved dissolving LDPE in trichloroethylene and treating it with starch, hydrogen peroxide, nitric acid and acetic acid, reducing its crystallinity from 48.48% to 32.98% through Single (T), double (TT) and triple (TTT) treatments. This 15.5% decrease in crystallinity enhanced polymer degradation. When LDPE microplastics with 40.02% crystallinity (TT) were tested on Eudrilus eugeniae earthworms, they showed a lower mortality rate compared to other treated and untreated LDPE. The 40.02% crystallinity LDPE exhibited hydroxyl and carboxylic functional groups. Treated LDPE (TT) introduced to earthworm casts showed microbiota, including Mycobacterium and Rozellomycota, known for Polyethylene degradation. Additionally, microbial examination of treated LDPE revealed Aeromonas and Pyrenochaetopsis leptospora in the earthworm gut, potential LDPE degraders. X-ray differaction (XRD) analysis and Fourier transform infrared (FTIR) spectra indicated distinct degradation patterns. After 21 days with Eudrilus eugeniae, treated LDPE's crystallinity decreased from 40.02% to 22.84%. This study highlights the significance of oxidized treated LDPE for microbial colonization and degradation, supporting Eudrilus eugeniae survival and improving soil biota health. 2025 - Kalpana Corporation. -
Enhancing business capabilities through digital transformation, upscaling, and upskilling in the era of Industry 5.0: A literature review
This literature review aims to understand the recent developments in the field of upscaling and upskilling in the digital transformation of business, from an Industry 5.0 prospective. It used a comprehensive search of relevant peer-reviewed journal articles, industry reports, and online sources to gather the relevant data. The findings indicate that upscaling is essential for industry 5.0, and that businesses should invest in upskilling and upscaling programs to meet the changing demands of the digital economy. This literature review provides a comprehensive analysis of the current state of upscaling and upskilling in the digital transformation of business and provides insights into the future direction of this field. It also highlights the importance of collaboration between businesses, governments, and educational institutions to ensure that the workforce is prepared for the future of work. 2024, IGI Global. All rights reserved. -
Enhancing Cloud Security and Privacy With Blockchain Technology
This chapter explores blockchain's potential to address cloud computing security challenges. Despite cloud computing's scalability and cost efficiency, it faces risks like data breaches and regulatory non-compliance, as seen in the 2019 Capital One AWS breach. Blockchain's decentralized ledger, cryptographic hashing, smart contracts, and consensus mechanisms (e.g., PoW, PoS) enhance security through decentralized access control, secure storage, and intrusion detection. Privacy techniques like homomorphic encryption and zero-knowledge proofs protect data. Case studies, including IBM Food Trust and MedRec, show practical applications. However, scalability, interoperability, regulatory conflicts (e.g., GDPR), and high costs pose barriers. Solutions like sharding and layer-2 protocols aim to overcome these. Future research focuses on scalability, privacy, hybrid cloud integration, and AI-driven security. Blockchain strengthens cloud security but requires innovation to achieve widespread adoption. 2026, IGI Global Scientific Publishing. All rights reserved. -
Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm
UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This studys principal objective is to further develop UAV network routing decisions by using CNNs ability for design recognition and the Modified SBOs optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAVs, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. 2024 River Publishers. -
Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
This research introduces a hybrid model for copper price prediction, employs advanced machine learning models (linear regression, random forest, SVM, Adaboost, ARIMA), and utilizes the SHAP method for model interpretability. The study focuses on transportation-related variables over a 10-year period from Bloomberg Terminal, employing STL decomposition for time series forecasting. Key features impacting copper prices are identified, emphasizing the significance of demand, transportation, and supply. The Random Forest model highlights the critical role of demand. Addressing transportation supply constraints is crucial for enhancing model output in the dynamic copper market. 2024 IEEE. -
Enhancing Crude Oil Price Prediction with Neural Network Models
A nations economic, social, and national security are all severely affected by variations in crude oil prices, which is a basic energy source. Research on accurately forecasting price changes for crude oil is always progressing. This research presents a forecasting strategy for crude oil pricing using artificial neural networks. The presented model uses standardization techniques to prepare the historical data for the subsequent processes. It is possible to predict future prices by using a Feed Forward Neural Network (FFNN) with four layers. West Texas Intermediate (WTI) and Brent crude oil prices are utilized on a daily, weekly, and monthly basis to demonstration and confirmation. Directional statistic, accuracy of prediction, the model is evaluated using root mean square error and mean absolute error expressed as percentages. Empirical findings confirm that the suggested approach performs better than any of the previous approaches. Additionally, it is noted that the presented method achieved higher prediction in contrast to other methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing Cultural Learning Through Spatial Audio in a Virtual Reality-Based Chettinad Experience
This chapter examines how immersive cultural learning using spatial audio and Virtual Reality (VR) can be used for enhanced learning. The study has taken a heritage building in Chettinad, Tamil Nadu, known for its palatial mansions with unique architectural properties and sustainable measures for preservation of various resources. On the basis of an experiment to understand user engagement, the chapter focuses on proposing an Immersive Cultural Learning Framework (ICLF), which can be implemented for immersive cultural learning. The study uses a customdesigned VR- based immersive learning environment, to demonstrates how the ICLF can be utilized to deepen cultural understanding and enhance learner engagement. This framework is suitable for representing intangible heritage, particularly through audio cues. The authors hope that this multi- use case study is relevant to various stakeholders, such as educators, cultural practitioners, and immersive media designers, by providing a structured model for facilitating meaningful and inclusive cultural learning in the digital age. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing curricula with service learning models
In today's digital age, technological advancements permeate every sector, especially higher education. However, higher education must go beyond merely integrating AI into the curriculum. Additionally, it needs to prioritize educating students about societal issues. Integrating service learning into higher education curriculums, however, is a significant challenge facing schools today. There is a need for comprehensive research on its effectiveness and guidance on institutionalizing it effectively. This hampers its potential to foster civic engagement and social responsibility among students. With clear strategies and best practices, institutions can implement service learning programs that benefit all stakeholders. Enhancing Curricula with Service Learning Models provides a comprehensive blend of theoretical frameworks, practical experimentation, and real-world examples to guide educators, administrators, and policymakers in fostering profound student engagement. It emphasizes the role of emerging educational paradigms, like service-learning, in instilling a sense of civic duty and purpose in students. By enriching the educational dialogue with an emphasis on the pivotal role of student engagement in creating transformative and purposeful learning experiences, this book empowers educators and institutions to create impactful and sustainable programs. To ensure that educators and stakeholders are equipped with the knowledge and tools necessary to cultivate environments that encourage active student participation, Enhancing Curricula with Service Learning Models provides practical guidance on building effective tri-party relationships between community partners, academia, and students. By offering a meta-analysis of service learning practices, this book is a valuable resource for institutions looking to enhance their academic quality and community engagement. 2024 by IGI Global. All rights reserved. -
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. -
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 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 cybersecurity with distributed models and sparse mixture of experts
[No abstract available] -
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 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 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 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 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 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 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.