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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. -
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 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 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 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 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. -
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. -
Enhancement of thermoelectric efficiency in vapor deposited Sb2Te3 and Sb1.8In0.2Te3 crystals
Crystal Research & Technology, Vol-49 (4), pp. 212-219. ISSN-0232-1300 -
Enhancement of thermoelectric efficiency in vapor deposited Sb 2Te3 and Sb1.8In0.2Te3 crystals
Pure and indium doped antimony telluride (Sb2Te3) crystals find applications in high performance room temperature thermoelectric devices. Owing to the meagre physical properties exhibited on the cleavage faces of melt grown samples, an attempt was made to explore the thermoelectric parameters of p-type crystals grown by the physical vapor deposition (PVD) method. The crystal structure of the grown platelets (9 mm8 mm2 mm) was identified as rhombohedral by x-ray powder diffraction method. The energy dispersive analysis confirmed the elemental composition of the crystals. The electron microscopic and scanning probe image studies revealed that the crystals were grown by layer growth mechanism with low surface roughness. At room temperature (300 K), the values of Seebeck coefficient S (c) and power factor were observed to be higher for Sb1.8In0.2Te 3 crystals (155 ?VK-1, 2.669 10-3 W/mK2) than those of pure ones. Upon doping, the thermal conductivity ? (c) was decreased by 37.14% and thus thermoelectric efficiency was improved. The increased figure of merit, Z = 1.23 10-3 K -1 for vapour grown Sb1.8In0.2Te3 platelets indicates that it could be used as a potential thermoelectric candidate. Pure and indium doped antimony telluride (Sb2Te 3) crystals were grown by the physical vapor deposition (PVD) method. Incorporation of indium atoms into the antimony sub lattice improved Seebeck coefficient and reduced thermal conductivity. The increased figure of merit, Z = 1.23 10-3K-1 for vapor grown Sb 1.8In0.2Te3 platelets indicates that it could be used as a potential thermoelectric candidate. 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. -
Enhancement of the thermal conductivity of a near room temperature magnetocaloric composite using graphene-like hybrid nanosheets derived from organic waste
Polymer matrix composites, fabricated to counter the inherent brittleness of magnetocaloric Heusler alloys, suffer from low thermal conductivity. Here, we demonstrate a low-cost, scalable route towards developing thermally conductive, mechanically robust near-room-temperature magnetocaloric composites by incorporating graphene-like hybrid nanostructures chemically synthesized from discarded sugarcane. Micron-sized particles obtained by manually grinding Ni50.2Mn36.7Sn13 ribbons possessing a strong magnetostructural transformation near room-temperature were chosen as the active magnetocaloric fillers. Both the functional fillers were incorporated into a polysulfone matrix by solution casting. Large values of isothermal entropy change ? 0.43 and -0.46 J/kg.K were observed for a ?H = 2T, driven by two successive first and second-order transformations within the alloy fillers. Additionally, an enhanced value of the in-plane thermal conductivity ? 3.06 0.4 W/m.K was observed in the composites owing to the formation of efficient thermal bridges/pathways by the graphene-like hybrid nanostructures, rendering them promising candidates for magnetic refrigeration applications. 2023 Acta Materialia Inc. -
Enhancement of the Electrochemical behaviour of Carbon Black via a defect induced approach
In order to address the rising global concern of energy storage, carbon-based materials have established themselves due to their distinct features. Despite the demand for the fabrication of supercapacitors from natural, inexpensive carbonaceous materials is on the rise, the intrinsic disorders present in such materials hinder their performance, and hence, tuning these defects can aid in the improvement of their electrochemical performance. In this study, carbon black is introduced with defects in the form of oxygen functional groups via oxidation and thermal exfoliation and the impact on its electrochemical performance is studied. Careful tuning of the type of oxygen functional moieties at the basal plane of the carbon lattice is observed to be the contributing factor for the electrochemical behaviour. The distortion in the graphitic lattice caused by the epoxy and hydroxyl groups alters the specific surface area, porosity, and thermal stability, facilitating easier ion diffusion rates and enhanced faradaic reactions. The obtained specific capacitance of the thermally exfoliated carbon black is as high as 246.49 Fg?1 in a three-electrode system and 82.85 F/g in a two-electrode setup, owing to an energy density of 5.63 Whkg?1 and a power density of 189.75 Wkg?1. It has also exhibited excellent cyclic stability and capacitance retention up to 4000 cycles. The equivalent series resistance is found to decrease from 5.67 to 4.96 ? making the material conductive. As a result, the electrochemical properties of carbon black can be enhanced by tuning the oxygen functional groups, making it a promising supercapacitive material. Graphical Abstract: (Figure presented.). Qatar University and Springer Nature Switzerland AG 2024. -
Enhancement of tensile strength and elastic modulus using bio-waste based carbon nanospheres doped polymer nanocomposites
The Carbon Nano Spheres (CNS) derived from areca nuts were synthesized from pyrolysis process and were used as fillers for fabrication of polymer nano composite materials. The filler materials are loaded in 0.05%, 0.1% and 0.5% loading percentages. The optimum sample was subjected to heat treatment. The tensile strength, elastic modulus and % of elongation were investigated for all samples. The Scanning Electron Microscope (SEM) images revealed the morphological features of optimum samples and hence the uniform dispersion of CNS in polymer matrix. The 0.1% samples showed 10% improvement in Ultimate Tensile Strength (UTS) and 24% improvement in Elastic modulus compared to bare epoxy material. When 0.1% samplewas subjected to heat treatment under 200C the UTS improved by 23%. Hence, CNS reinforced composite materials exhibited unique properties like high strength, less weight and low cost making them suitable for various structural applications such as aerospace, automotive, construction, and electronics industries. The Polymer Society, Taipei 2024.