Browse Items (11808 total)
Sort by:
-
An exploration of the impact of Feature quality versus Feature quantity on the performance of a machine learning model
About 0.62 trillion bytes of data are generated every hour globally. These figures have been increasing as a result of digitalization and social networks. Some data ecosystems capture, store, and manage this big DATA. The basis is to be able to analyze their information and extract their value. This fact is a gold mine for companies researching and using this data. This leads us to follow how essential and valuable data is in this growing age. For any machine learning model, the selection of data is necessary. In this paper, several experiments have been performed to check the importance of data quality vs. data quantity on model performance. This clearly indicates comparing the data's richness regarding feature quality (e.g., features in images) and the amount of data for any machine learning model. Images are classified into two sets based on features, then removing redundant features from them, then training a machine learning model. Model getting trained with non-redundant data gives highest accuracy (>80%) in all cases versus the one with all features, proving the importance of feature variability and not just the feature count. 2023 IEEE. -
Unveiling the synergistic effect of amorphous CoW-phospho-borides for overall alkaline water electrolysis
Amorphous transition-metal-phospho-borides (TMPBs) are emerging as a new class of hybrid bifunctional catalysts for water-splitting. The present work reports the discovery of CoWPB as a new promising material that adds to the smaller family of TMPBs. The optimized compositions, namely Co4WPB5 and Co2WPB1 could achieve 10 mA/cm2 at just 72 mV and 262 mV of overpotentials for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively, in 1 M KOH. Furthermore, the catalyst showed good performance in a 2-electrode assembly (1.59 V for 10 mA/cm2) with considerable stability (70 h stability, 10,000 operating cycles). Detailed morphological and electrochemical characterizations unveiled insights into the role of all elements in catalyst's improved performance. The presence of W was found to be crucial in improving the electronic conductivity and charge redistribution, making CoWPB suitable for both HER and OER. In computational simulation analysis, two configurations with different atomic environments, namely, CoWPBH and CoWPBO were found to have the lowest calculated overpotentials for HER and OER, respectively. It was found that the surface P-sites in CoWPBH were HER-active while the Co-sites in CoWPBO were OER-active sites. The study presents new knowledge about active sites in such multi-component catalysts that will foster more advancement in the area of water electrolysis. 2024 Hydrogen Energy Publications LLC -
Unveiling the kinetics of oxygen evolution reaction in defect-engineered B/P-incorporated cobalt-oxide electrocatalysts
Defect-rich transition-metal oxide electrocatalysts hold great promise for alkaline water electrolysis due to their enhanced activity and stability. This study presents a new strategy that significantly improve the OER activity of Co-oxide nanosheets through incorporation of B and P (B/P-CoOx NS), eventually leading to abundant surface defects and oxygen vacancies. The B/P-CoOx NS demonstrates low overpotential of 220 mV to achieve 10 mA/cm2. The electrochemical and kinetic studies coupled with conventional morphological and structural characterizations, reveal that various crystalline defects like vacancies, dislocations, twin planes, and grain boundaries play crucial roles in promoting the OH? ion adsorption, the formation of intermediates, and the desorption of oxygen molecules. The industrial viability of the developed electrocatalyst is substantiated through assessments under harsh industrial conditions of 6 M KOH at 60 C in a zero-gap single-cell alkaline electrolyzer which achieves 1 A/cm2 at 1.95 V. Chronoamperometry tests (100 h) highlight remarkable robustness of the electrocatalyst. This work establishes a new strategy to fabricate defect-rich OER electrocatalysts, setting a precedent to achieve better OER rates with non-noble materials. 2024 -
Exploring In Situ Kinetics of Oxygen Vacancy-Rich B/P-Incorporated Cobalt Oxide Nanowires for the Oxygen Evolution Reaction
Defect-engineering of transition-metal oxide-based nanocatalysts is an innovative approach for improving the oxygen evolution reaction (OER) owing to their enhanced activity and stability. The present study introduces a facile approach aimed at enhancing OER activity by incorporating boron and phosphorus into cobalt oxide nanowires (B/P-CoOx NWs). The resulting material, enriched with oxygen vacancies (Ov), as confirmed by X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR), induced a complete structural transformation from Co3O4 to a CoO phase. The B/P-CoOx NWs exhibited an impressive overpotential of only 230 mV to achieve a current density of 10 mA cm-2 in 1 M KOH. The presence of Ov was proved to be responsible for the improvement in conductivity along with the quantity and quality of active sites. Electrochemical kinetic analysis was performed to reveal the crucial role of Ov in facilitating the OER mechanism by enhancing the adsorption and desorption of OH- ions and O2 molecules from the surface. The robustness of the developed electrocatalyst was demonstrated through a chronoamperometric test conducted over 80 h and a recyclability test spanning 10 000 cycles. This study focuses on the fabrication and dynamic investigation of the electrocatalyst, laying the groundwork for further advancements in non-noble material-based electrocatalysts. 2024 American Chemical Society. -
IMPACT OF GOVERNMENT ADVERTISEMENTS ABOUT AGRICULTURAL INFORMATION IN SENSITISING RURAL FARMERS: A STUDY OF MARATHWADA REGION IN MAHARASHTRA
To stimulate the agricultural sector, the Ministry of Agriculture, Government of India, has initiated a myriad of welfare initiatives aimed at farmers, entailing a substantial financial commitment. A portion of this financial outlay is also allocated to the dissemination of pertinent information among the farming community. However, a pertinent question arises: does this information reach the rural farmers and do they avail the benefit? The present study attempts to address this issue. Based on a tested questionnaire, a primary survey was conducted in Marathwada region of Maharashtra where the incidence of agrarian crisis is insurmountable. The study found a limited impact of government advertisements regarding agricultural information on farmers in terms of increasing their awareness level. The primary conduit for agricultural information, as ascertained by the study, predominantly stems from informal sources. Among the socio-economic characteristics, only education and land ownership are found to have an impact on the level of awareness and their willingness to acquire information on agriculture. The percentages of farmers, who are aware of the scheme and get its benefits, do not even exceed 30 per cent, irrespective of the scheme under consideration. It was found that only limited farmers are taking benefit of the scheme even after receiving information. A general disinterest was observed among the farmers because of the heavy paperwork of availing those benefits. To engender a constructive transformation in farmers awareness levels regarding agricultural schemes and programmes, a well-thought-out and strategic endeavour becomes indispensable. 2022 National Institute of Rural Development. All rights reserved. -
Big Data De-duplication using modified SHA algorithm in cloud servers for optimal capacity utilization and reduced transmission bandwidth; [Big Data Deduplicaci utilizando algoritmo SHA modificado en servidores en la nube para una utilizaci tima de la capacidad y un ancho de banda de transmisi reducido]
Data de-duplication in cloud storage is crucial for optimizing resource utilization and reducing transmission overhead. By eliminating redundant copies of data, it enhances storage efficiency, lowers costs, and minimizes network bandwidth requirements, thereby improving overall performance and scalability of cloud-based systems. The research investigates the critical intersection of data de-duplication (DD) and privacy concerns within cloud storage services. Distributed Data (DD), a widely employed technique in these services and aims to enhance capacity utilization and reduce transmission bandwidth. However, it poses challenges to information privacy, typically addressed through encoding mechanisms. One significant approach to mitigating this conflict is hierarchical approved de-duplication, which empowers cloud users to conduct privilegebased duplicate checks before data upload. This hierarchical structure allows cloud servers to profile users based on their privileges, enabling more nuanced control over data management. In this research, we introduce the SHA method for de-duplication within cloud servers, supplemented by a secure pre-processing assessment. The proposed method accommodates dynamic privilege modifications, providing flexibility and adaptability to evolving user needs and access levels. Extensive theoretical analysis and simulated investigations validate the efficacy and security of the proposed system. By leveraging the SHA algorithm and incorporating robust pre-processing techniques, our approach not only enhances efficiency in data deduplication but also addresses crucial privacy concerns inherent in cloud storage environments. This research contributes to advancing the understanding and implementation of efficient and secure data management practices within cloud infrastructures, with implications for a wide range of applications and industries. 2024; Los autores. -
Regression testing on services in mobile applications
The power of mobile devices has increased dramatically in the last few years. The Mobile apps market increases every day and Mobile device has become one of the most important equipment in peoples daily life, which brings us not only convenience of communication, but more and more work and entertainment applications. Mobile testing becomes very crucial as the mobile applications and mobile users are growing rapidly. Test consultants, test specialist, test managers and software engineering researchers are finding ways to do effective verification methods and to ensure reliability of mobile applications. In this paper, we propose regression testing framework on services in Remote Link Lite (RLL) mobile application. In order to perform regression test on mobile application, we have considered RLL application for POC purpose. Research India Publications. -
Examining the Impacts and Obstacles of AI-Driven Management in Present-Day Business Contexts
This paper explores the growing role of Artificial Intelligence (AI) in the management structures of modern business organizations. In order to improve operational effectiveness and overall success, it focuses on the integration and effects of AI within Management Information Systems (MIS). The study finds the many advantages artificial intelligence (AI) offers to knowledge management, resource management systems, and organizational effectiveness through a thorough analysis. The paper uses a wide range of scholarly references to explain different aspects of AI-powered management, such as strategic planning, decision-making, and sustainable marketing tactics. The study highlights a notable void in the all-encompassing comprehension of artificial intelligence's concrete contribution to business improvement, thereby promoting a deeper and more empirical investigation of AI's incorporation into business operations. 2024 IEEE. -
Security and Privacy in Internet of Things (IoT) Environments
Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE. -
Rejuvenating human resource accounting research: a review using bibliometric analysis
The current study attempts to map the intellectual structure of Human Resource Accounting to understand the research gaps and future trajectories. The study employs systematic literature review technique to extract relevant literature, bibliometric analysis to map the intellectual structure of research in human resource accounting, to identify underlying research themes and content analysis to identify avenues for future research. Based on 2438publications, author keyword co-occurrences extracted four themes namely, Human Resource Management, Intellectual Capital, Human Capital, and Voluntary Disclosure. The study also summarizes significant findings of papers under each cluster through content analysis identifying areas for future research. The study provides a birds eye view of the intellectual structure of academic research efforts in the field of human resource accounting. The study is one of the first attempt to comprehensively review the academic literature from Scopus database employing systematic literature review, bibliometric methods, and content analysis in the field of human resource accounting. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Covid 19 impact assessment index for manufacturing MSMES /
Patent Number: 202141035478, Applicant: Theresa Nithila Vincent.
The COV1D 19 pandemic disrupted the functioning of enterprises and posed a significant effect on the Micro, Small and Medium Enterprises (MSMEs) sector. The level of impact was varying depending on the type of business. This study aims to develop an index to assess the impact of COVTD 19 on the manufacturing MSMEs by studying the impact on the Key Performance Indicators, namely; Labour, Supply Chain, Production and Revenue. -
Insinuating cocktailed components in biocompatible-nanoparticles could act as an impressive neo-adjuvant strategy to combat COVID-19
The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread, freezes all sectors, and was declared as a life-threatening disease by the World Health Organization on Jan 30, 2020. So far, no specific drugs are identified or approved for treating SARS-CoV-2. In the past few years, nanomaterials are in the limelight for their ability to deliver the drugs effectively and selectively like siRNA to target/prime infection sites, and benefits us to visualize the particular regions, treatment reactions via non-intruding imaging techniques. As intranasal delivery interacts directly to the infection site with minimal side effects on the healthy cell, we postulate to administer a mixture of few polyherbal formulations like the golden spice curcumin, sitopaladi churna (SPC), and kaempferol in zein-chitosan nanoparticles as a life-saving measure for treating Coronavirus disease (COVID-19) cases. This viewpoint will shed light on the antiviral role of curcumin, SPC, and kaempferol zein-chitosan nanoparticle to modulate immune responses and observe its curative approach to the current pandemic COVID. 2021, Visagaa Publishing House. All rights reserved. -
Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
Monetary fraud, which is a deceptive method for getting cash, has turned into a typical issue in organizations and associations as of late. Customary techniques like manual checks and reviews aren't extremely precise, are costly, and consume most of the day. Attempting to get cash by lying. With the ascent of simulated intelligence, approaches based on machine learning have become more well known. can be utilized shrewdly to track down fraud by dissecting an enormous number of monetary exercises information. Thus, this work attempts to give a systematic literature review (SLR) that ganders at the literature in a systematic manner. reviews and sums up the exploration on machine learning (ML)-based fraud recognizing that has proactively been finished. In particular, the review utilized the Kitchenham strategy, which depends on clear systems. It will then, at that point, concentrate and rundowns the significant pieces of the articles and give the outcomes. Considering the Few investigations have been finished to accumulate search systems from well-known electronic information base libraries. 93 pieces were picked, examined, and integrated in light of measures for what to incorporate and what to forget about. As the monetary world gets more confounded, robbery is turning into a more serious issue in the accounting and finance industry. Fraudulent activities cost cash, yet they likewise make it harder for individuals to trust monetary frameworks. To stop this danger, we want further developed ways of tracking down fraud straightaway. This theoretical gives an outline of how machine learning strategies are utilized to further develop fraud detection in accounting and finance. 2024 IEEE. -
Adopted online marketing strategies for newly opened cafes in Delhi and Bengaluru /
Cafes in India have become a major business activity. It’s demand has grown so much so that people no longer want to go for fine dining and eat their food in an enclosed space with sophisticated colored walls. Though cafes are a borrowed concept from West but in the last seven to eight years, cafes have become a booming sector in the gastronomic industry. Particularly, if we look at the metropolitan cities of India, they all are bustling with cafes and pulling crowd like never before. -
Relationship Between Industry-Associated Value Premium and Firm Risk Charaterstics on Stock Returns : Evidence From Indian Stock Market
The body of academic literature consistently debates that firms with low PB (price to book) outperform firms with high PB characteristics. This study examines whether the academic literature-promised value premium has any industry association in the Indian equity market and tests the existence of other anomalies: size, investment, profitability, and R&D, in explaining the cross-sectional variability of stock returns. The study considers all BSE-listed firms actively trading between 1999-2021, using time-series, multivariate, and cross-sectional models on each industry-level portfolio. Results indicated that a significant value premium exists in 18 out of 21 industry groups. Both industry and firm-level value premiums are identified; however, the firm-level premium seems more prominent. The value premium is most substantial in small-cap value stocks of value-and-growth-oriented industries, large-cap value stocks of value-oriented industry groups, then small-cap growth stocks of value-and growth-oriented industries and large-cap growth stocks of value- and growth-oriented industries. Interestingly, the sub-period analysis revealed variation in the value premium, indicating that the industry-associated value premium has been relatively low in the current decade. It is due to decreasing tendencies in industry returns and increasing PB in industries. The study explores R&D premium and compares existing factor premiums. Results showed that India's annualized average R&D premium is significantly higher than the current value, profitability, size, and investment premiums, particularly for highly R&D intensive firms. To check the robustness of the findings, the study used the multivariate GRS (Gibbons Ross Shanken) test and the regression models. It confirmed that size and value premiums are the most prominent determinants of industry-level equity returns. The profitability and investment premiums also influence industries' returns. Investors who seek to allocate assets within and across industries are likely to have predictable and stable returns. -
Compression Based Modeling for Classification of Text Documents
Classification of text data one of the well known, interesting research topic in computer science and knowledge engineering. This research article, address the classification of text files issue using lzw text compression algorithms. LZW is a lossless compression technique which requires two pass on the input data. These two passes are treated separately as training stage and text stage for classification of text data. The proposed compression based classification technique is tested on publically available datasets. Results of the experiments shows the effectiveness of the proposed algorithm. 2019, Springer Nature Singapore Pte Ltd. -
The LimbuTamang Communities of Sikkim History and Future of Their Demand for Reservation
Since its merger in 1975 with the Indian union, one of the major sociopolitical issues in Sikkim has been the demand for reservation in the state legislative assembly for two communitiesLimbu and Tamang. The demand of reservation for the Limbus and Tamangs crystallised in Sikkim when these communities were notified as Scheduled Tribes under the Scheduled Castes and Scheduled Tribes Orders (Amendment) Act, 2002. The history and future of this political demand has been analysed. 2023 Economic and Political Weekly. All rights reserved. -
Energy Management System for EV Charging Infrastructure
The increasing adoption of electric vehicles (EVs) has led to a significant rise in the demand for efficient and sustainable charging infrastructure. Managing the energy supply to meet this growing demand while ensuring grid stability presents a critical challenge. This paper presents an energy management system designed for electric vehicle charging infrastructure that balances demand and supply in real time. The proposed system dynamically allocates available power to connected EVs based on their charging demands and the total power available, ensuring optimal utilization of energy resources. By simulating various scenarios, the system demonstrates its capability to prevent overloading, efficiently distribute power, and prioritize critical energy needs. The results of the simulation show that the system can effectively manage power distribution, reduce peak load impact, and enhance the reliability of EV charging networks. This approach offers a scalable and adaptable solution for integrating EVs into the existing power grid, contributing to the development of smart and sustainable transportation systems. The Authors, published by EDP Sciences. -
AI and Real-Time Business Intelligence
Timely and accurate knowledge that can be provided to different stakeholders in an enterprise improves the performance and decision-making capabilities with better insight. The information, be it qualitative or quantitative, when made available to decision makers becomes the basis of the business intelligence (BI) that improves functionality, scalability and reliability. The knowledge is managed by application of various data warehousing techniques, and artificial intelligence comes into play by providing an ample number of data mining and machine learning techniques. The chapter aims at analyzing the origin, evolution and development of BI systems and their relationship with artificial intelligence (AI). The chapter also aims to provide new research horizons in the scientific activities and advancements in BI, knowledge management and analysis. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors. -
