Browse Items (9795 total)
Sort by:
-
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 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 -
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 blended classroom among varied streams of undergraduate language learners An Experimental Study
Technology has become an integral part of learners today and keeping with the time, researchers have developed learning methods that address this issue. There have been several methods developed to address this issue. One of the most successful techniques that has in the recent past contributed largely to active learning is flipped classroom. Communication skills is the need of the hour, to ensure an employable community this study endeavours to develop a learning technique that will nurture active learners and improve their communication skill in a technology oriented community. Hence the paper aims at finding and deliberating the efficacy of implementing flipped classroom to improve communication skills among undergraduate learners from science and humanities majors. BEIESP. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam. -
Effectiveness of Couple Interventions in Marital Distress: A Systematic Review and Meta-Analysis
Background: Couple interventions focus on resolving relationship issues and improving partners' intimacy. Several intervention models are used on different occasions to deal with the issues. The present systematic review and meta-analysis investigated the effectiveness of such couple interventions dealing with marital distress. Methods: Using the PRISMA guidelines for reporting systematic reviews and meta-analyses, a search was carried out to identify published articles in the areas of study. The meta-analysis investigated the effectiveness of couple interventions by comparing the post-intervention assessments of the experimental and control groups. Results: The systematic review helped to identify twelve empirical studies published within the last ten years in the following databases: ScienceDirect, EBSCO, APA PsycINFO, NCBI, ProQuest, and Google Scholar. Meta-analysis showed a statistically significant overall effect size (Cohen's d = 0.85, 95% CI: 0.56-1.14). Cochran's Q showed that there was a substantial difference between the studies. There were signs of publication bias. Conclusion: The current study revealed an overall large effect size, indicating that different couple interventions had a noticeable impact on distressed couples who received interventions as opposed to couples who did not receive any interventions. 2025 Joesph et al. -
Effectiveness of dialectical behavior therapy as a transdiagnostic treatment for improving cognitive functions: a systematic review
Dialectical behavior therapy (DBT) has been found to be an efficacious treatment for disorders characterized by high levels of emotional instability. In view of the multifaceted applications of DBT and the extent to which mental disorders can incapacitate cognitive functions, the current systematic review aimed to investigate the effect of DBT in strengthening cognitive functions across various mental health conditions. Original research studies employing both experimental and quasi-experimental designs were included in the review. The literature search was done using different electronic databases, from the first available literature until June 2022, that covered an approximate period of ten years. Joanna Briggs Institute checklist was used to assess the methodological rigor of the studies. Twelve studies conducted on adolescents with emotional dysregulation, and adults with borderline personality disorder, bipolar disorder, attention deficit hyperactivity disorder, and multiple sclerosis were selected. Results indicate that DBT has the potential to improve key cognitive functions such as attention, memory, fluency, response inhibition, planning, set shifting, tolerance for delayed rewards and time perception, as assessed by neuropsychological tests, self-report of cognitive functions, and neuroimaging techniques. Considering the review's findings that showcase the effectiveness of DBT in fostering improvements in cognitive functions, DBT may possibly be chosen as a preferred treatment to ensure that patients reach optimal levels of cognitive functioning. Limitations include lack of sufficient studies encompassing all the common mental health conditions, usage of neuroimaging techniques as only an indirect measure of cognitive functioning and nuances related to the quality of individual studies. Author(s), 2023. -
Effectiveness of exponential heat source, nanoparticle shape factor and Hall current on mixed convective flow of nanoliquids subject to rotating frame
Purpose: The study of novel exponential heat source (EHS) phenomena across a flowing fluid with the suspension of nanoparticles over a rotating plate in the presence of Hall current and chemical reaction has been an open question. Therefore, the purpose of this paper is to investigate the impact of EHS in the transport of nanofluid under the influence of strong magnetic dipole (Hall effect), chemical reaction and temperature-dependent heat source (THS) effects. The Khanafer-Vafai-Lightstone model is used for nanofluid and the thermophysical properties of nanofluid are calculated from mixture theory and phenomenological laws. The simulation of the flow is also carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e. sphere, hexahedron, tetrahedron, column and lamina). Design/methodology/approach: Using Laplace transform technique, exact solutions are presented for the governing nonlinear equations. Graphical illustrations are pointed out to represent the impact of involved parameters in a comprehensive way. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for 20 different nanofluids are presented. Findings: It is established that the nanofluid enhances the heat transfer rate of the working fluids; the nanoparticles also cause an increase of viscous. The impact of EHS advances the heat transfer characteristics significantly than usual thermal-based heat source (THS). Originality/value: The effectiveness of EHS phenomena in the dynamics of nanofluid over a rotating plate with Hall current, chemical reaction and THS effects is first time investigated. 2019, Emerald Publishing Limited. -
Effectiveness of Farmers Risk Management Strategies in Smallholder Agriculture: Evidence from India
Smallholder farmers in developing countries are more vulnerable to climate risks, and most of them, because of a lack of access to institutional risk management measures such as crop insurance, rely on traditional measures to offset the adverse effects of such risks on agricultural production. Employing a multinomial endogenous switching regression technique to the farm-level data, this study first identifies the determinants of farmers own risk management measures and then evaluates their impacts on farm income and downside risk exposure. There are three key highlights of this analysis. One, farmers, based on their past exposures to climate risks, endowments of resources, and access to credit and information, often use more than one measure or strategy to mitigate, transfer, and cope with the climate risks. Two, all the risk management strategies are found to be effective in improving farm income and reducing risk exposure, but it is their joint implementation that yields larger payoffs. Three, the joint adoption of different adaptation strategies is positively associated with farm size, but with liquidity and information constraints relaxed, the probability of their joint adoption is expected to increase further. These findings impinge on the concept of climate-smart agriculture and suggest the need to identify and integrate traditional farm management practices with science-based innovations to provide an effective solution to climate risks. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Effectiveness of Financial Inclusion through PMJDY Scheme: A Study of the PMJDY Beneficiaries in Tamil Nadu
The study explored whether various banking dimensions, viz. savings and borrowings, literacy and promotions, bank facilities and other bank services, contributed to the PMJDY beneficiaries' satisfaction in the Coimbatore region. Moreover, the study examined whether the satisfaction of the beneficiaries obtained through banking dimensions led to the frequent usage of bank accounts under the PMJDY. The data were collected from 380 beneficiaries of PMJDY from 12 administrative blocks in the Coimbatore district of Tamil Nadu, the Southern part of India. Factor analysis and Structural Equation Modeling (SEM) were used for the analysis. The results showed that the banking dimensions, viz. savings and borrowings and literacy and promotions, had positively influenced the beneficiaries' satisfaction. There was a linkage between the beneficiaries' satisfaction with frequent bank accounts under the PMJDY in rural areas of the Coimbatore region. It was found that an enriched banking service through politeness and benevolence of bank employees would enhance satisfaction, which helped the bank to acquire and retain existing beneficiaries for a thriving business environment. 2024 The Society of Economics and Development, except certain content provided by third parties. -
Effectiveness of gamification in facilitating microlearning for gen Z
This chapter offers a thorough examination of the uses, advantages, and difficulties of gamification in higher education. In contrast to game-based learning, gamification uses specific game features to improve the learning experience. This chapter investigates the use of gamification to engage and inspire Generation Z (Gen Z) pupils with the goal of enhancing their academic performance. It underlines the necessity for game development that increases motivation and engagement in educational settings and highlights the measurement of student progress based on completed activities. Effective instructional approaches are crucial in a time where there is a constant stream of information and people have short attention spans. A promising approach to overcoming these difficulties in both online and offline education utilizing ICT technologies is offered as gamified microlearning, which combines microlearning and games. 2024, IGI Global.