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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 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 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 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 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 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 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 proactive routing protocol using smart nodes system
Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires. 2022 The Authors -
Effective storage of goods in a warehouse using farm optimisation algorithm
Effective organisation of a warehouse's incoming goods section is important for its productivity as ensuring efficient shelving systems. When the incoming goods section is not properly configured, this almost automatically causes major interruptions throughout the subsequent storage phase. For effective storage of goods in warehouse farm optimisation algorithm (FOA) is proposed. The efficacy of the proposed approach was demonstrated using BR datasets and it is compared with different optimisation algorithms. From this experiment, it is noted that the suggested FOA fulfils the objective of efficient arrangement of goods in the warehouse. The order in which the goods are placed into the warehouse is also noted to be ideal than other competitive optimisation algorithms. 2020 Inderscience Enterprises Ltd.. All rights reserved. -
Effective Techniques Non-linear Dynamic Model Calibration using CNN
This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability 2024 IEEE. -
Effective Temperature Prediction for An Enhanced Climate Forecast System
Ever since the first industrial revolution, there has been a subtle temperature change. The transition to new manufacturing processes in conjunction with the surge in population has a negative consequence on the earths atmosphere. Climate change has been identified as the most crucial environmental issue of this century, and it has sparked heated discussions [1]. Temperature is the most common metric to evaluate the change in climate/global warming. It is anticipated that climate change will result in an adverse and enduring impact on the ecosystem. Weather forecasting today extensively depends on conventional methodologies and requires complex and complicated infrastructure [2]. Prime problems concern quality of acquired data, timeliness, availability, reliability, and usability constraints on forecast preparation. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. -
Effective Testing: A case study approach for improving test efficiency
The study presented in this thesis investigates the methods for improving the software test efficiency. Test efficiency measures the cost-effectiveness of a test organisation and it is measured by dividing the number of defects found in a test by the effort needed to perform the test. A review of the literature suggests that software test efficiency improvement depends on direct and indirect success factors like test process, test management, test tools, test object delimitation, test case determination, test infrastructure, configuration management, release management etc. This thesis was a case study approach for improving the test efficiency of an existing test setup in a database environment. Most of the thesis work followed an action based research approach by giving importance to the test setup. Work started with an analysis of the initial test environment, identified the issues and improvement areas in existing test setup and given an implementation proposal for the identified problems. Based on the proposal, team implemented the solutions, which lead to a test environment containing number of actions like automation using standard framework, risk based testing, parallel execution, modularization, avoiding code redundancy and proper test management. The results of the case study suggest that the software products that has multiple releases should seriously consider the test improvement factors like regression environment, risk based testing, light weight test automation etc., in the initial stages of the testing. This will lead to cost savings, quality, flexibility and higher productivity. The investigation further identifies the issues in test management and introduced new method called test point method for proper test execution tracking. Based on the implementation results and their discussions, this study presents a new approach and practical guidelines for improving test efficiency of a software test project. IBM has recognised this case study by giving eminence and excellence award for saving one person year of testing effort in their indexing tool test environment. -
Effective time context based collaborative filtering recommender system inspired by Gowers coefficient
The fast growth of Internet technology in recent times has led to a surge in the number of users and amount of information generated. This substantially contributes to the popularity of recommendation systems (RS), which provides personalized recommendations to users based on their interests. A RS assists the user in the decision-making process by suggesting a suitable product from various alternatives. The collaborative filtering (CF) technique of RS is the most prevalent because of its high accuracy in predicting users' interests. The efficacy of this technique mainly depends on the similarity calculation, determined by a similarity measure. However, the traditional and previously developed similarity measures in CF techniques are not able to adequately reveal the change in users' interests; therefore, an efficient measure considering time into context is proposed in this paper. The proposed method and the existing approaches are compared on the MovieLens-100k dataset, showing that the proposed method is more efficient than the comparable methods. Besides this, most of the CF approaches only focus on the historical preference of the users, but in real life, the people's preferences also change over time. Therefore, a time-based recommendation system using the proposed method is also developed in this paper. We implemented various time decay functions, i.e., exponential, convex, linear, power, etc., at various levels of the recommendation process, i.e., similarity computation, rating matrix, and prediction level. Experimental results over three real datasets (MovieLens-100k, Epinions, and Amazon Magazine Subscription) suggest that the power decay function outperforms other existing techniques when applied at the rating matrix level. 2022, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering
As well as setting up the sources, working with editable poly, information in the interior of the swimming pool, using turbo-smooth and symmetry modifier, this procedure of making a 3D swimming pool model is clarified. The lighting the scene and setting up the rendering, the method in which substances are added to the replica is defined. The methods and techniques of rendering are defined, too. The final rendering is the result of multiple images being drawn. The aim of our research work is to create a swimming pool design with enhancing models with materials affect. The shapes used for that are cylinder, sphere, box, plane and splines. The modifiers are editable poly, editable spline and UVW map. Finally, we used a material editor and target lights for enhancing the model. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effectiveness and Perception of 4P's on Green Products in FMCG
International Journal of Multidisciplinary Research and Development, Vol. 3, Issue 11, pp. 311-355, ISSN No. 2349-4182 -
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Effectiveness of activity based program in enhancing fine motor skills of children with dyspraxia /
Scholedge International Journal Of Multidisciplinary & Allied Studies, Vol.2, Issue 5, pp.502-510, ISSN No: 2394-336X. -
Effectiveness of adjuvant psychological therapy on alexithymia fatigue and affective dimensions among women with breast cancer
Psychological aspects in women with breast cancer are many. Among them are those related to what patients go through at different phases of treatment such as newlinediagnosis, pre and post-surgery, chemotherapy and radiotherapy. Women with breast cancer experience psychological repercussions which are specific to them. Some of them are poor body image, self-depreciation, weight changes and hair loss can be distressing to women with breast cancer. newlineThe underlying cause could be a deficit in emotional processing and affect regulation. This could lead to an inability in verbalising and identifying feelings newlinewhich is known as alexithymia. Closely related is the concept of fatigue which is newlinesubjective and tiredness which could last beyond treatments related to cancer. An newlineoverriding concept which could explain and understand these concepts is affect and newlinemood. Towards this end the objective of the study was to examine the efficacy of adjuvant psychological therapy in breast cancer in terms of alexithymia, fatigue, newlinedepression, anxiety, stress and positive and negative affect. newlineThe study also explored if there was an association among alexithymia, fatigue, depression, anxiety, stress and negative affect. The study consisted of 20 patients in the intervention and control groups each. newlineThey were administered the following scales namely, Toronto Alexithymia Scale (TAS-20), Checklist of Individual Strength, Positive and Negative Affect Scale and Depression, Anxiety and Stress Scale. Towards the end of the sessions, they were administered Revised Sessions Reactions Scale. Adjuvant Psychological Therapy is a therapy tailor made for those with cancer which includes both cognitive and behavioral techniques. The results indicated that among the subscales and total alexithymia scores, newlinethere were statistically significant differences across three time-frames in the newlineintervention group. -
Effectiveness of anti-smoking PSAa: A comparative study /
The purpose of the study is to find out whether anti-smoking Public Service Advertisements are well strategized attempts to create and spread awareness about a public issue that could affect deeply seated public attitudes and behaviour. The study also highlights the ways in which anti-smoking PSAs are produced in different parts of the world and how it has brought changes in the public behaviour.