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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 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 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 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 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 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 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 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 Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
Detection and effective remedies play a crucial role in revolutionizing banana crop health. The banana industry faces numerous challenges, including the prevalence of diseases and pests that can lead to significant yield losses. This paper explores the potential impact of detection techniques and remedies on improving banana crop management. Disease detection models based on machine learning, image processing and deep learning offer high accuracy in identifying diseases like Fusarium Wilt, Yellow Sigatoka, and Black Sigatoka. Implementing detection and targeted treatments can enhance crop productivity, reduce pesticide usage, and ensure sustainable banana production. 2024 IEEE. -
Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles
Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing authentication in blockchain bridges: A smart contract-based approach leveraging polynomial interpolation
This work focuses on the integration of blockchain for enhancing the security, privacy, and trust management within Vehicle Ad Hoc Networks (VANETs). In the context of smart transportation, VANETs offer essential safety but the open and dynamic nature of these networks makes secure, anonymous authentication a major challenge. Blockchain's decentralized nature can provide a secure, tamper- resistant ledger for managing data across the network nodes, helping address these security concerns. Cross- chain bridges enable the transfer of data, money and assets across blockchains. It has thus become important to enhance existing authentication mechanisms in blockchain bridges. In this research, we analyze existing authentication approaches, highlighting their limitations, such as reliance on centralized entities, private key leaks and weakness in smart contract functions. We then propose a novel approach to strengthen existing authentication mechanisms with the combined capabilities of Smart Contracts and Polynomial Interpolation, to establish a secure authentication layer. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhancing Angle Modulation Using Fractional Calculus: Theory and Performance Analysis
Angle modulation originally forms part of the backbone of the telecommunication and signal processing, where current studies are being carried out to improve its susceptibility to noise interference. This paper aims to analyze the possibility of the application of fractional calculus for optimization of angle modulation, as a requirement for development of enhanced and flexible communication networks. The main purpose of this study is to design and model new angle modulation technique which are Fractional Phase Modulation (FPM) and Fractional Frequency Modulation (FFM) by using fractional calculus. A generalized form of angle modulation and an introduction to the use of fractional calculus was proposed and a mathematical analysis of FM and FFM detectors was done. In evaluating the findings, the interaction between the fractional order of ? and some performance parameters like Signal-to-Noise Ratio (SNR) and Figure of Merit (FoM) was also considered. It has been shown that FPM and FFM detectors also show high SNR and FOM performance, and when ? is replaced. The FPM detector demonstrated a steady trend and increased from SNR 0 to 1 when the ? was diverse, while the FFM detector had a huge increase in SNR from ?=-0.9 to 0. These results indicate that the angle provides additional benefits in partial stones, signaling purity and system flexibility for the modulation technique. Thus, the ability to achieve better stability in communication for modulation techniques indicates the ability to achieve better stone purity and system flexibility for modulation techniques. Since partial order ? can be adjusted to fit the application, the proposed method shows interesting applications in many communication settings, especially when the signal is noisy or dynamic. 2025, School of Electrical Engineering and Informatics. All rights reserved. -
Enhancing academic credential verification through blockchain technology adoption in university academic management systems
Blockchain technology has emerged as promising solution in various sectors, including higher education. This research investigates the impact of usage of blockchain technology in student credential verification within university academic management system. This study employs a descriptive research through quantitative analysis of data collected from universities that have integrated or planning to integrate blockchain technology into their academic management systems. Key parameters examined include awareness and familiarity with blockchain, extent of blockchain usage, user experience and satisfaction, the perceived impact and benefits. The findings suggest that blockchain technology positively influences academic credential verification process, streamlining data sharing and reducing administrative burdens. As blockchain continues to transform the academic management landscape, this study offers timely guidance for stakeholders navigating the intersection of technology and education. 2024, IGI Global. All rights reserved. -
Enhanching the Performance Metrics of Overlay Network for QoS in Media Transfer Using Genetic Algorithm
Quality of Service (QoS) of real time video applications is difficult to realize in wireless mobile networks because of the limited resource availability. Software-Defined Networking (SDN) Overlay networks are becoming popular to solve routing, traffic engineering and QoS due to the rapid increase in the adoption and investment in SDN. The SDN market size is projected to grow by a double-digit CAGR within the next decade and reached the low tens of billions USD in 2023, which shows a positive adoption of the industry. Real-time streaming and live content demand have also risen to an all-time high - the live-streaming market is growing at an average rate of about -20-23% CAGR through 2030, and the role of QoS in high-volume media is becoming more and more relevant. 2025 IEEE. -
Enhancements to randomized web proxy caching algorithms using data mining classifier model
Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective web pages, behaves as the proxy for the server, and services the requests that are made to the servers by the users. In this paper, the performance of a proxy system is measured by the number of hits at the proxy. The higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this work, the performance of the randomized replacement policies such as LRU-C, LRU-S, HARM, and RRGVF are adapted by the data mining classifier based on the weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. Springer Nature Singapore Pte Ltd. 2019. -
Enhancements to greedy web proxy caching algorithms using data mining method and weight assignment policy /
International Journal of Innovative Computing, Information And Control, Vol.14, Issue 4, pp.1311-1326, ISSN No. 1349-4198. -
Enhancements to greedy web proxy caching algorithms using data mining method and weight assignment policy
A Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective Web objects and behaves as the proxy for the server and service the requests that are made to the servers by the users. In this paper the performance of a proxy system is measured by the number of hits at the proxy. A higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this paper, the performances of the greedy replacement policies such as GDS, GDSF and GD* are adapted by the data mining method and weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. 2018 ISSN. -
Enhancements to Content Caching Using Weighted Greedy Caching Algorithm in Information Centric Networking
Information-Centric Networks (ICN) or Future Internet is the revolutionary concept for the existing infrastructure of the internet that changes the paradigm from host-centric networks to data-centric networks. Caching in Information-Centric Networks (ICN) has become one of the most critical research areas in today's world, especially for the leading in content delivery over Internet companies like Netflix, Facebook, Google, etc. This paper is intended to propose a novel Caching strategy called Weighted Greedy Dual Size Frequency for caching in Information-Centric networks. In this paper, the WGDSF considers multiple critical factors for maintaining the Web Content efficiently in ICN Caching Router. Simulation is done for the various performance metrics like Cache Hit ratio, Link load, Path Stretch, and Latency for WGDSF cache replacement algorithm, and results shown that WGDSF outperforms well compared with LRU, LFU, and RAND Caching Strategies. 2020 The Authors. Published by Elsevier B.V. -
Enhancements of women's entrepreneurship: A theme-based study
Woman entrepreneurs are defined as a group of women who initiate, organize, and run a business concern, from a situation where a woman was not even allowed to get out of their home, to today, running most of the successful brands of the world, contributing a major part to the economic growth, and breaking the stereotypes by providing a reality check to the male dominance. There has been a wide range of public policies enrolled out to facilitate and encourage the growth of women's entrepreneurship. A few such policies from India have proved to be successful, which will be outlined in this book chapter. From the past times of not gaining adequate recognition for their support, women have emerged successful in overcoming hardships such as lack of visibility, lack of training and educative support about public policies provided by governments to women entrepreneurs, fewer opportunities, and walking out of the social stigma. 2023, IGI Global. All rights reserved. -
Enhancements in anomaly detection in body sensor networks
Anomaly detection in Body Sensor Networks (BSNs), have recently received much attention from the healthcare community. This is partly due to the development of sensor based real-time tracking and monitoring networks. These networks have been responsible not only for ensuring critical medical treatment at times of emergency, but have also made it easier for health-care personnel to administer critical treatment. In this paper we consider improvements to existing machine learning methods that detect anomalous sensor measurements. The improved methods are a step in the right direction in ensuring unduly overheads due to faulty sensors don't interfere while administering life-critical treatment in a limited resources scenario. 2019 IEEE.