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Effective Models for Computing Optimized Storage Systems for Energy
This chapter investigates effective modeling techniques for designing optimized storage systems that minimize energy consumption. We explore various models capturing the interplay between storage performance, capacity, and energy efficiency, focusing on computational methods to enhance effectiveness. As the demand for renewable energy sources continues to increase, the need for reliable and efficient storage solutions becomes increasingly crucial. We discuss the design and implementation of optimized storage systems for energy, highlighting computational models role in improving efficiency. Starting with an overview of the energy storage system, we examine different modeling approaches such as mathematical optimization, machine learning, and simulation techniques. Each approach offers a unique approach to addressing the complexities of energy storage. Additionally, we discuss optimization models, ensuring that energy storage solutions are both technically efficient and economically viable. In summary, this section emphasizes the importance of computational modeling in developing efficient energy storage systems, which are crucial for meeting energy integration demands and ensuring stability and sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Effective ML Techniques to Predict Customer Churn
Customer churn is one of the most challenging problems that affects revenue and growth strategy of a company. According to a recent Gartner Tech Marketing survey, 91% of C-level respondents rate customer churn as one of their top concerns. However, only 43% have invested in additional resources to support customer expansion. Hence, retaining existing customers is of paramount importance to a company's growth. Many authors in the past have presented different versions of models to predict customer churn using machine learning techniques. The aim of this paper is to study some of the most important machine learning techniques used by researchers in the recent years. The paper also summarizes the prediction techniques, datasets used and performance achieved in these studies for a deeper understanding of the domain. The analysis shows that although hybrid and ensemble methods have been widely successful in improving model performance, there is a need for well-defined guidelines on appropriate model evaluation measures. While most approaches used are quantitative in nature, there is lack of research that focuses on information-rich content in customer company interaction instances, like emails, phone calls or customer support chat records. The information presented in the paper will not only help to increase awareness in industry about emerging trends in machine learning algorithms used in churn prediction, but also help new or existing researchers position their research activity appropriately. 2021 IEEE. -
Effective Methods of Waste Management Practices in Green Hotels Toward Green Brand Image: An Empirical Study
The changes in consumer tastes are a significant motivating factor for hotels to adopt environmentally friendly practices. Recently, there has been a significant focus on the perils of climate change and the significance of adopting sustainable practices. As a result, environmentalism now influences almost every consumer decision. With the increasing awareness of environmental sustainability in the hospitality industry, the options for eco-friendly hotels are expanding, providing a wider range of choices for potential customers. Thus, this study seeks to examine the efficient strategies employed by green hotels for trash management to enhance their green brand image. Customer data from hotels was gathered and examined using SPSS 25 software. The findings suggest that implementing energy efficiency measures, promoting water conservation, and adopting sustainable and environmentally conscious building practices are effective approaches to waste management that can improve a companys brand image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Effective HR practices in family business in technology disruption era
Today's world of 21st-century business is said to be a VUCA (Volatility-Uncertainty-Complexity-Ambiguity) world. VUCA describes the fast pace of change in the business environment. It has largely been led by the disruption brought about by technology-led human resources departments within organisations to revise strategy approaches and methods to face these emerging challenges. Research studies show that more than two-thirds of the companies in the world belong to family businesses. In the family business, the owners and HR should see what is going on in the business environment and update the situation. As most family businesses have family members in key positions, the tricky issues faced by family businesses are mostly about handling family and non-family members and creating effective HR policies. The COVID-19 pandemic in 2019 and 2020 has disrupted their business in unexpected ways. This chapter explores different HR practices adopted by the family business and suggests effective HR practices and procedures to meet the multiple challenges in the family business. The chapter also analyses the strategies of HR practices followed by some top family business firms worldwide. The chapter is formed as a meta-synthesis. It provides more qualitative inputs related to recent challenges and effective HR practices adopted in the technologically competitive era and during the COVID-19 pandemic period. 2022 World Scientific Publishing Co. Pte. Ltd. -
Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. -
Effective fraud detection in healthcare domain using popular classification modeling techniques
Fraud is any activity with malicious intentions resulting in personal gain. In the Present Day scenario, every sector is polluted by such fraudulent activities to fetch unauthorized benefits. In HealthCare, an increase in fraudulent insurance claims has been observed over the years which may constitute around 3-5% of the total cost. Increasing healthcare costs along with the hike in fraud cases have made it difficult for people to approach these services when required. To avoid such situations, we must understand and identify such illegal acts and prepare our systems to combat such cases. Thus, there is a need to have a powerful mechanism to detect and avoid fraudulent activities. Many Data mining approaches are applied to identify, analyze and categorized fraud claims from the genuine ones. In this paper, various frauds existing in the Health Care sector have been discussed along with analyzing the effect of frauds in the health care domain with existing data mining models. Furthermost, a comparative analysis is performed on two existing approaches to extract relevant patterns related to fraudulent claims. BEIESP. -
Effective Emoticon Based Framework for Sentimental Analysis of Web Data
The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job. 2019, Springer Nature Singapore Pte Ltd. -
Effective detection of Covid-19 using Xception net architecture: A technical investigation using X-ray images
The disastrous era of COVID-19 has altered the perspectives of nearly all nations concerning the health and education sectors. Artificial intelligence is a pressing need that needs to be implemented thoroughly in the medical and educational fields. Imperatively, the diagnosis of Covid-19 has become crucial. In this study, we have designed a classification model based on Convolutional Neural Network (CNN) and transfer learning. The COVID-19 chest X-ray images have been considered for the proposed methodology and are classified as COVID-19 positive and normal cases. The proposed shallow CNN Model achieved an accuracy of 96%, which is computationally very effective as only three Convolutional blocks are required. Then, the Xception architecture-based model is experimented with. The accuracy and loss of the proposed model have been evaluated using Adam and SGD optimizer. With the Adam Optimizer, Xception Net achieved the best classification accuracy of 99.94%. The precision, recall, and f1-score of 100% are achieved. The proposed model has outperformed the previous studies in the same domain, which highlights the models state-of-the-art performance. Our study will be helpful for decision-makers and can help further minimize mortality and morbidity by effectively diagnosing the disease. The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Effective atomic number and electron density of some biologically important lipids for electron, proton, alpha particle and photon interactions
X-ray, ?-ray and charged particle interaction parameters of biomolecules are useful in medical diagnosis and radiation therapy as exposure to radiations can cause energy of photons and charged particles to be deposited in body through various interaction processes. With this in view, the effective atomic number (Zeff) and electron density (Neff) of some biologically important lipids for X-ray, ?-ray and charged particle interactions were studied in the energy range 10 keV500 MeV using logarithmic interpolation method. A non-monotonic variation in Zeff values was observed for protons and alpha particles in low and intermediate energy regions respectively whereas a sudden increase in Zeff was observed for electron interaction in higher energy region. Zeff values were maximum in higher energy region for total electron interactions whereas maximum values of Zeff for total alpha particle interactions were at relatively lower energies. Highest Zeff values were found at lower energy region of photoelectric absorption dominance for photon interactions. Variation in Neff seems to be similar to variation in Zeff as they are inter-related. 2020 Elsevier Ltd -
Effective and Meaningful Student Engagement Through Service Learning
A paradigm shift is underway in education, challenging traditional teaching methods and calling for a more engaging and purposeful approach. It is necessary to explore how service learning empowers students to address real-world issues, fostering critical thinking, creativity, collaboration, and communication skills essential for the 21st century. Effective and Meaningful Student Engagement Through Service Learning is a comprehensive exploration of the transformative power of service learning in contemporary education. Within this text, seasoned researchers and practitioners delve into the intricacies of student engagement, emphasizing the importance of active involvement in the learning process. This book opens with a reflection on education, where traditional practices give way to innovative pedagogies. This includes a new pedagogical approach that not only imparts knowledge but also cultivates socially responsible citizens. The book provides a rich tapestry of theoretical foundations, curriculum development strategies, and innovative pedagogical approaches that move beyond passive learning. From evaluating the impact of service learning to incorporating technology and measuring learning outcomes, each chapter offers theoretical frameworks, practical experiments, and real-life examples for educators, administrators, and policymakers. The book addresses the challenges and barriers to achieving meaningful student engagement, proposing practical solutions and recommendations. It emphasizes the role of service learning in building reciprocal relationships with communities and fostering inclusivity. Case studies and best practices from diverse educational settings showcase the effectiveness of different approaches to student engagement. The diverse audience within and beyond the education sector, including students, faculty members, parents, policymakers, NGOs, and community organizations, will find within the pages of this book valuable insights and tools to create more effective and meaningful learning experiences. The book covers a broad spectrum of topics, from the institutionalization of service learning to motivations for sustainable engagement, making it an indispensable resource for anyone passionate about shaping the future of education. 2024 by IGI Global. All rights reserved. -
Effective and Efficient Video Compression by the Deep Learning Techniques
Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing highquality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video. 2023 CRL Publishing. All rights reserved. -
Effect of Work Experience on Psychological Capital and Job Satisfaction among Employees
In todays fast-paced workplaces, where technology is evolving at a dizzying rate, professionals face a myriad of problems. Their inability to strike a healthy work-life balance may lead to feelings of dissatisfaction with their job. Consequently, in order to achieve flexible, long-term growth and job happiness, businesses should support their employees good psychological development. Primary data was acquired from employees in the automotive manufacturing company, totalling 95 individuals, using standardized questionnaires that had a good level of reliability and validity. The results indicated that there is no significant effect of work experience on the psychological capital of employees (F = 1.21; p < 0.30) and their job satisfaction (F = 0.35; p < 0.70). The major findings indicate that regardless of an employees level of experience, there is no substantial variation in the psychological capital and job satisfaction of the employees. This variation may also arise because of other specific factors. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Effect of Work Experience on Psychological Capital and Job Satisfaction among Employees
In todays fast-paced workplaces, where technology is evolving at a dizzying rate, professionals face a myriad of problems. Their inability to strike a healthy work-life balance may lead to feelings of dissatisfaction with their job. Consequently, in order to achieve flexible, long-term growth and job happiness, businesses should support their employees good psychological development. Primary data was acquired from employees in the automotive manufacturing company, totalling 95 individuals, using standardized questionnaires that had a good level of reliability and validity. The results indicated that there is no significant effect of work experience on the psychological capital of employees (F = 1.21; p < 0.30) and their job satisfaction (F = 0.35; p < 0.70). The major findings indicate that regardless of an employees level of experience, there is no substantial variation in the psychological capital and job satisfaction of the employees. This variation may also arise because of other specific factors. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Effect of Waste Materials in Partial Replacement of Cement Fine Aggregate and Course Aggregate in Concrete
International Journal of Inventive Engineering and Sciences (IJIES), Vol.2, Issue 4, ISSN: 2319-9598 -
Effect of VR Technological Development in the Age of AI on Business Human Resource Management
Human resource management (HRM) strategies are increasingly using AI and other AI-based technologies for managing employees in both local and foreign enterprises. An exciting new field of study has emerged in the last decade on topics like the media interaction of AI and robotics, the possessions of AI acceptance on independence and consequences, and the evaluation of AI-enabled HRM practices due to the proliferation of AI-based implementations in the HRM function. The use of these technologies has influenced the way work is organized in both domestic and global corporations, presenting new possibilities for better resource management, faster decision-making, and more creative issue resolution. Research on AI-based solutions for HRM is scarce and dispersed, despite a growing interest in academia. Human resource management (HRM) roles and human-AI interactions in major multinational corporations disseminating such advances need more study. As computing and networking infrastructure has advanced rapidly, so has the era of artificial intelligence. Now that in the age of AI, virtual reality technology has found many applications beyond gaming. Human resource management has emerged as a hot topic, with interest coming from both large businesses and government agencies. Many studies have been conducted on HRM in the business world, but in order to stay up with the trends, HRM must be constantly updated. This article does a demand analysis, and sets up and tests a fully-featured VR business human resource management system, all against the backdrop of the age of artificial intelligence and the present popularity of VR technology. 2023 IEEE. -
Effect of viscous dissipation on three dimensional flow of a nanofluid by considering a gyrotactic microorganism in the presence of convective condition
This article deals with the combined effects of viscous dissipation and convective condition on 3D flow, heat and mass transfer of a nanofluid over a stretching sheet by considering gyrotactic microorganism. Appropriate transformations yield the nonlinear ordinary differential systems. The resulting nonlinear system has been solved. Role of substantial parameters on flow fields as well as on heat, mass and microorganism transportation rates are determined and conferred in depth through graphs. It is found that, the larger values of bio-convection Schmidt number decreases the microorganisms profile. 2018 Trans Tech Publications, Switzerland. -
Effect of viscous dissipation and joule heating on three-dimensional mixed convection flow of nano fluid over a non-linear stretching sheet in presence of solar radiation
The present exploration deals the study of viscous dissipation and Joule heating effects on three-dimensional flow and heat transfer of nanofluid over a nonlinear stretching sheet. The fluid is assumed to be electrically conducting and the flow is persuaded by a stretching of an elastic sheet in two lateral directions. The governing partial differential equations are reduced to a set of nonlinear ordinary differential equations by applying the suitable similarity transformations. The so obtained similarity equations are solved by employing the fourth-fifth order Runge-Kutta-Fehlberg method. The impact of various pertinent parameters on the velocities, temperature, skin friction coefficients and Nusselt number are computed and illustrations are provided by the inclusion of figures and tables. The present results have an excellent agreement with previously published results in a limiting sense. It is found that the heat transfer rate increases when radiation parameter is increased and the effect of nanoparticle volume fraction and thermal radiation stabilizes the thermal boundary layer growth. 2017 by American Scientific Publishers All rights reserved. -
Effect of Various Double-Frequency Modulations on Rayleigh-Benard Convection
Rayleigh-Bard convection in Newtonian fluid under different types of modulations are studied in this thesis by replacing the single frequency modulations with two frequency modulations with different amplitude and frequency. Linear and non-linear analysis of Rayleigh-Benard convection is considered under two-frequency gravity, rotation, temperature, magnetic field and internal heat generation modulation. The sixteen combinations of sinusoidal (trigonometric sine) and non-sinusoidal (square, triangular, sawtooth) wave forms of different modulations are considered to study the impact of modulations on the onset of convection and heat transport. The expressions for unmodulated Rayleigh number and correction Rayleigh number in the linear case are obtained from linearized Lorentz model using Venezian approach. To study the impact of different types of modulations and wave forms on the heat transport, the expression for the Nusselt number is obtained by solving the non-linear Lorentz model numerically. From the study it is found that the two-frequency modulations make the system more stable compare to no-modulation and single-frequency modulations. The mixing angle of the two frequency plays major role in deciding the stability of the system. The results pertaining to no-modulation and single frequency are obtained as the limiting cases. Onset of Rayleigh-Bard Convection and Heat Transfer under Two-frequency newlineRotation Modulation The study investigates the effect of sixteen sinusoidal (sine) and non-sinusoidal combinations (square, triangular, sawtooth) of time-periodic Coriolis force (rotation modulation) on Rayleigh-Bard convection (RBC) in a Newtonian liquid. The consideration captures the potential effects of two-frequency rotation modulation on stability, newlinespecifically the onset of convection and the amount of heat transfer in the system simultaneously. -
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Effect of variable viscosity on marangoni convective boundary layer flow of nanofluid in the presence of mixed convection
The effect of variable viscosity on Marangoni convection in immediate vicinity of the plate is discussed. The mathematical model of the problem is highly nonlinear partial differential equations transforms into two nonlinear ordinary differential equations by applying suitable similarity transformations. The reduced similarity equivalences are then solved numerically by RungeKutta Fehlberg-45 order method. The consequences of pertinent parameters like variable viscosity parameter, convection parameter and volume fraction are analyzed on various flow fields. The results acquired are on par with erstwhile published results. The results of the present study shows that for greater values of angular momentum the buoyancy effects dominate, augmentation in mixed convection carries away the free convection currents from the plate, increase in volume fraction of solid enhances the thermal conductivity of the fluid and it is important to note that Marangoni effect is constructive for cooling processes. 2019 by American Scientific Publishers All rights reserved.