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A Systematic Review on e-Wastage Frameworks
The electronic devices that are targeted to the end users have become day to day essential parts. Traditional methodologies have changed drastically resulting in efficient mode of communication and fast information retrieval. As the demand and the production are exponentially growing, patterns of sales, storage and their destruction and then again, their collection have also been changed. This paper analyses many such behaviors of (electronic) waste management and recommends solutions like recycling management, different directives and policies required to be followed. Authors have emphasized on providing substantial information that can be useful to the regulating authorities responsible for waste management or the manufacturers of various electronic products and then the policy makers. With an extensive review of electronic wastages, authors have emphasized three variables (sales, stock and lifespan) for replacing/upgrading the older products with advanced versions. The root causes of electronic wastages are found in industrializing countries like India, China, Vietnam, Pakistan, the Philippines, Ghana and Nigeria whereas industrialized countries also play equally important role for its generation. This paper signifies the importance of e-waste management practice to reduce the emerging electronic waste hazards. Authors focus on todays demand of electronic devices, importance of e-waste management and management practices. The paper recommends key findings based on surveying data regarding the lack of regulation to manage the e-waste. The review concludes that the lack of regulation and improper awareness are the basic factors responsible for e-wastage and requires major focus to manage the e-waste. 2021. All Rights Reserved. -
A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence
Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society -
A Systematic Review on Prognosis of Autism Using Machine Learning Techniques
Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multisystem disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of the brain or whole nervous system which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of autism spectrum disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for the prognosis of autism. The Electrochemical Society -
A Systematic Review on the Identification and Classification of Patterns in Microservices
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. 2023 IEEE. -
A systematic review on the impact of e-tailing on Indian retail industry
The paper initially focusses on the growth and issues concerning internet retailing in India. The study also aims to explore exhaustively on the growth potential of internet retailers and their prospects in the Indian retail market. The profile of the country's customers is also discussed in detail including their expectations and the complications posed by their demography. The study also intends to analyze the challenges confronting the e-tail players, their hardships, and investments. Using a meta-analysis framework study reviewed articles from national and international journals, newspapers, and books on drivers of e-tail growth in India, E-tail players in India, and Indian E-tail customers. In the results and discussions, more was spoken about the advent and opportunity exposed by the digital wallets to promote internet retailing. In the summary and conclusions investigators talked about the digital learning mania and the dynamic online shopping behavior exercised by the subscribers of e-tailing. 2020 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
A Systematic Study on Unimodal and Multimodal Human Computer Interface for Emotion Recognition
A systematic study for human-computer interface (HCI) for emotion recognition is presented in this paper, with a focus on various methods used to identify and interpret human emotions. It delves into various methods used to identify and interpret human emotions and highlights the limitations of unimodal HCI for emotion recognition systems. The paper emphasizes the benefits of multimodal HCI and how combining different types of data can lead to more accurate results. Additionally, it highlights the importance of using multiple modalities for emotion recognition. The study has significant implications for mental health assessments and interventions as it offers insights into the latest techniques and advancements in emotion recognition. Future research can use these insights to improve the accuracy of emotion recognition systems, ultimately leading to better mental health assessments and interventions. Overall, the paper provides a valuable contribution to the field of HCI and emotion recognition, and it underscores the importance of taking a multimodal approach for this critical area of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Systematic Survey of Happiness from an Analytical Perspective
The paper is a survey paper that talks about studies around happiness. We have surveyed papers about the scales of measuring happiness, in which the scales are proposed, demonstrated and examined. Happiness is affected by various factors, which can be called indicators of happiness. Some of the papers we reviewed validate the significance of such indicators with applications. The indicators include inflation, unemployment, health, loneliness, and sports. Modern technology helps researchers estimate and forecast happiness and effectively find the relation between factors affecting happiness. Researchers use different methodologies to study happiness. The data used in the papers were retrieved from surveys and existing Happiness Report, designed surveys appropriate for the study. Models were proposed for forecasting happiness using Machine Learning and Neural Networks. From the reviews, we identify research gaps in the area for future work. This paper gives an overview of the studies around the area of happiness from an analytical approach. 2022 IEEE. -
A Systemic Review on Omicron Variant of SARS-CoV-2
As the new strains spread around the world, scientists have been trying to learn more about the different strains, especially Omicron, and how SARS-CoV2 acts in general. Studying historical trends of virus spread and the structure of the virus and its strains, as well as all the mechanisms it needs to survive, can help identify the symptoms and diagnose and treat the disease. The research has shown that the new strains, including Omicron, have a higher rate of mutation and transmissibility. Additionally, due to the rapid spread of the virus, there has not been a significant amount of time to understand the severity of the infection. To better understand the novel variants, a detailed analysis of the basic pathophysiology of the virus is needed. This includes transcriptome analysis for the recombination index to identify variation in the strand. This aided in the diagnostic process, and therapeutics for mutants of the virus could be treated. The Omicron strain is particularly threatening due to its rapid transmission rate and its property of immune evasion, which can make it less vulnerable to vaccination. 2023 Biomedical & Pharmacology Journal. -
A taxation perspective on how domestic double taxation on corporate taxes affects Indian commerce
This paper examines the impact of domestic double taxation on corporate taxation in India after the abolition of the dividend distribution tax (DDT) and the introduction of the new tax rates and rules in 2020. Domestic double taxation occurs when the same income is taxed twice by the same jurisdiction, such as at the corporate and shareholder level. Using data from the Income Tax Department and the Reserve Bank of India, this paper estimates the effective tax rate on corporate income and dividends in India under the current tax system. It compares it with the previous tax system and the international standards. It also analyses the effect of domestic double taxation on corporate financial decisions, such as the dividend payout ratio, the retained earnings, the debt-equity ratio, and the investment rate. It evaluates the effect of domestic double taxation on corporate tax revenue, tax incidence, and tax efficiency. The authors identify that, between 2019 and 2023, corporate income tax revenue in India increased alongside nominal GDP growth, with a notable positive correlation coefficient between the two variables. The empirical analysis technique involves gathering and analyzing quantitative data to assess the real impact of new tax reforms and reduced corporate tax rates. Finally, this study proposes policy recommendations to mitigate the adverse effects of domestic double taxation and improve India's corporate taxation system and GDP. This paper contributes to the literature by providing updated and comprehensive empirical evidence on domestic double taxation and corporate taxation in India and by offering some insights and suggestions for the policymakers, the tax authorities, the corporate sector, and the academic community. 2025 Malque Publishing. All rights reserved. -
A technical physical assessment of the structural steel manufacture for improving the performance /
Patent Number: 202241004392, Applicant: Dr.Gopala Rao Thellaputta.
Off-site development seeks to move building activity to a production setting, allowing for autonomous modularity paneling. While this strategy has proved to be beneficial to the Indian development sector, developments in panelized wall production techniques provide new problems and possibilities for the architecture industry. Assessment for protection and labeled compounds manufacturing quality may be automated in such a supervised environment. Regarding framework assemblies, optical sensing may fulfill several functions. -
A Technological Framework for ICT Implementation in Teaching-Learning Process
Information and Communication Technology (ICT) has penetrated into all walks of life and education sector is no exception. Many studies reveal that ICT is an extremely powerful tool that could bring about tremendous changes in the education process. Innovation has become mandatory to sustain in the global competitive environment. ICT plays an important role in reaching various goals. Its ability to transcend time and space allows learning to take place 24 hours a day, 7 days a week eliminating geographical barriers. Information and Communication Technology (ICT) is foreseen as a tool to overcome various challenges in education. Many education institutions have taken a keen initiative towards the usage of ICT based teaching-learning systems so as to enhance efficiency. In this context, LMS has been identified as the most accepted ICT-based tool in the present education system. Through review of literature, it is evident that LMS is widely implemented in education sector. However, it is identified that various essential parameters that could enhance the performance are not adequately addressed in the present ICT-based LMS systems. The primitive and essential features of ICT- based LMS were identified. A detailed analysis on applicability of the identified features was done from the perspective of three major stakeholders namely students, teachers and technical experts. The inference of this analysis revealed that current LMS systems address only 25% of interoperability issues, followed by 21% accessibility and 19% of adaptability. This analysis further revealed that reusability accounts for 11% and affordability for 8% only. Hence, it is evident that the essential parameters are not completely addressed in the present LMS system. A comparative study of various existing LMS systems pertaining to various parameters identified was performed. A technological hybrid LMS framework was developed at macro and micro level to address the various LMS challenges identified like interoperability, accessibility, durability, adaptability and affordability so as to improve the system. Finally, a SCORM (Sharable Content Object Reference Model) enabled hybrid LMS framework was developed as the outcome of the research. This framework would enhance the overall performance and provide complete ICT based solutions for educational institutions. -
A Textual Analysis of Panchatantra, Enhanced by Technology from the Psychological Perspective
This research paper offers a textual analysis of the portrayal of animals in the Panchatantra tales, leveraging technology, Natural Language Processing (NLP) for enhanced insights. The study focuses on the interplay of anthropomorphism and stereotypes within these narratives, delving into the diverse stereotypes associated with specific animals in the stories. This analysis enhances our understanding of animal portrayal in children's literature. Natural Language Processing (NLP) techniques like textual classification and thematic analysis have been employed to examine the underlying archetypes embedded within the tales to comprehend stereotypes. Through a close examination of literary examples employing AntConc, a corpus analysis software, this paper provides readers with a nuanced understanding of how anthropomorphism and stereotypes influence human perceptions of animals and contribute to our understanding of the natural world. 2024 IEEE. -
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A Theoretical Article: Exploring the Evolutionary Dynamics of Couples and Family Therapy
This article provides a comprehensive review of how the field of family therapy has evolved, tracing its roots from early practices influenced by eugenics to its current diverse theoretical frameworks, which are ever-expanding. In the mid-20th century, family therapy expanded beyond its eugenic roots, embracing diverse theoretical frameworks and giving rise to various therapeutic modalities like behavioral and emotionally focused family and couples therapy. However, due to cultural disparities, these concepts and models cannot holistically capture the essence of family therapy in India. They do not compute the central role of the intergenerational subsystem or understand hierarchical dynamics. What is deemed okay in the Western context does not hold true in the Indian context. Postmodern approaches show a marked improvement in dealing with these problems in cultural adaptations of family and couples therapy by integrating diverse therapeutic practices, technological advances, and cultural and diversity-sensitive practices. However, despite these advancements, the adapted modalities have a scope for improvement, posing a pressing need for research that bridges this gap. Moving forward research should focus on family change mechanisms, symptom improvement factors, and prioritizing culturally sensitive approaches to meet the unique needs of Indian families. The Author(s) 2024. -
A theoretical framework for gamified learning
The term gamified has been applied to a large number of processes in the organization. Marketing professionals have attempted to gamify customer experiences, while human resource managers have attempted to gamify employee processes like recruitment and onboarding. Being a powerful driver for goal-oriented behavioural change, gamification has the potential to revolutionise the way people work, collaborate, and develop. However, the application of gamification has met with limited success in the organization. Researchers have attributed this lack of success to incomplete understanding of the concept. The current study reviews literature in the area of Gamification in an attempt to arrive at a conceptual model explaining how gamification drives learning. The model proposed in this study is simple and draws from key theories related to Learning and use of technology. The purpose of the review is to provide a base for future researchers and a basic understanding for practitioners attempting to introduce gamified learning. BEIESP. -
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A Theoretical Study of Rayleigh-Benard Convection Problem with Realistic and Artificial Boundary Conditions
In this thesis we present linear and weakly non-linear study of Rayleigh Bard newlineconvection subject to general boundary condition, which includes both physically newlinerealistic and artifcial boundaries. A horizontal confguration is adopted, wherein newlinethe horizontal surfaces are attached to porous blocks, which allows for the inclusion newlineof rough boundaries modelled by the Robin boundary condition on velocity. The Robin boundary condition is utilised to model boundary condition on temperature as well. Adding nanoparticles to a base and#64258;uid results in an increased thermal conductivity of the base and#64258;uid. The objective of this research is to present a conducive understanding of the eand#64256;ect of nanoparticles and its enhanced thermophysical properties eand#64256;ects on the onset of convection. Eand#64256;ects of Rough Boundaries on Rayleigh-Bard Convection in Nanoand#64258;uids A linear and weakly non-linear stability analysis of Rayleigh-Bard convection in a Newtonian nanoand#64258;uid between two rough boundaries is carried out. A newlinesingle-phase description of nanoand#64258;uids is adopted in the study. Water-alumina and newlinewater-copper are nanoand#64258;uids in consideration for the study. The values of thermophysical quantities of nanoand#64258;uids are obtained using either the mixture theory or phenomenological laws. The boundary eigenvalue problem arising in the study is solved using the Maclaurin series. Also, a single-term Galerkin technique is adopted to obtain the guess value of the Rayleigh number and the wave number. Further, improved values of the Rayleigh number and the wave number are obtained using the Newton-Raphson method. The minimal Fourier series representation is used to arrive at the generalised Lorenz model. A detailed discussion is made on the eand#64256;ect newlineof rough boundaries on the onset of convection in nanoand#64258;uids. The study aims to newlinepresent a theoretical comparison between the results of the two nanoand#64258;uids considered and the destabilizing eand#64256;ect showcased by each of the nanoparticles on the onset of convection. -
A thorough investigation of various goals and responses for mobile software-defined networks
Cloud computing has caused some companies to modify their IT infrastructure and maintenance procedures and may eliminate their current hardware altogether. Conventional methods of setting up a switch or router may be error-prone and unable to make full use of the capabilities of current network architectures. As many intelligent networking designs as possible must be developed for intellectualization, activation, and customization in future networks. Due to software-defined networking (SDN) technology, it's possible to control, secure, and optimize network resources, eliminating the rigid coupling between the control plane and the data plane in traditional network architectures. Here, the chapter explores the problems, difficulties, and potential solutions associated with software-defined networks (SDN), a novel concept in computer networking. Through SDN, the network gains the ability to be programmable, quick, and adaptable thanks to its separation of data and its ability to control traffic. 2023, IGI Global. All rights reserved. -
A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis
Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. -
A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein
The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and ?8.66 kcal mol?1, respectively), when compared to the control drug, Ribavirin (-7.01 and ?6.52 kcal mol?1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol?1) and NiV M (-40.26 kJ mol?1) proteins in comparison with the control drug Ribavirin (-13.12 and ?13.94 kJ mol?1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection. 2024 Elsevier Ltd