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Cobb douglas production function analysis of total factor productivity in Indian textile industry in the post multi-fiber agreement (MFA) period
Internnational tradee in textiles aand clothing wwas governedd by the systemm of bilateral quotas undeer the MMulti Fibre AgreementA (MMFA) since 11973 and thiss agreement has been repeplaced by thhe Agreeement on Texxtiles and Cllothing (ATC)C) from Januaary 1, 2005. The dismanntling of quotta restricctions had brrought about a significant change in thee structure off worldwide trrade in textilees as theere are no quuota barriers for the exporrt of textiles aand apparelss and the hithherto protecteed textilee industry wouldw be exposed to interrnational commpetition. It was widely believed annd highliighted in mosst of the studies that develloping economies, especiaally India andd China woulld be thee major gaineers in the neww policy regiime. In view of the changged trading ennvironment foor textilee trade, it wass considered important to analyze the immpact of quotta removal onn Indian textille industry, especiallyy with respecct to the prodductivity on thhis sector. Thee study has coonsidered fouur sub seectors of Indiian textile inddustry as givven in the Annnual Survey oof Industries, viz, Spinningg, Weavving and Finiishing of Texxtiles, Manuffacture of Other Textiles,, Manufacturre of Wearinng Apparrels and Manufacture off Crocheted aand Knitted Fabrics. Esstimates of CCobb Douglaas produuction function shows that the removal of textile quoota had impaccted all the foour sub sectorrs considdered in the study.s The imppact of quota removal on pproductivity iis positive andd significant iin the caase of spinninng, weaving aand finishingg of textiles ass well as in tthe manufactuure of wearinng apparrels. In the casec of mannufacture of other textiless and the mmanufacture oof knitted annd crochheted fabrics, the quota duummy coefficient is negatiive and signifificant at onee percent leveel. This sshows that thhe removal ofof quota restrrictions have adversely immpacted the ooutput of thesse sectorrs. 2020, North Carolina State University. All rights reserved. -
Smart songs selection in playlists using parallel k-means clustering
Most songs today are of different tempo, pitch and time signature. In a music player application, the typical shuffle picks the succeeding song or preceding song at random with no parameters to choose the songs. Different songs from different genres can have a tempo range anywhere between forty beats per minute and three hundred beats per minute. In this paper, the quick and efficient parallel k means clustering algorithm is implemented in Hadoop on the million-song dataset subset to form clusters for the songs based on tempo and pitch. The aim of this paper is to reduce the variation that occurs when a typical shuffle picks the succeeding song at random. This variation can be in the form of tempo or other parameters. The formation of clusters and intern the reduction in the variation of tempo can be used in a new 'smart shuffle'. After the clusters have been formed, the smart shuffle picks the songs within that specific cluster. This paper aims at reducing the variation by 50%. This would have many musical benefits and would also be more pleasing to the listener. 2018 IAEME Publication. -
Synthesis and characterization of porous, mixed phase, wrinkled, few layer graphene like nanocarbon from charcoal /
Russian Journal Of Physical Chemistry A, Vol.89, Issue 13, pp.2438-2442, ISSN No: 0036-0244. -
Export performance of Indian textile industry in the post multi fibre agreement regime /
Artha Journal Of Social Science, Vol.13, Issue 4, pp.63-86, ISSN No: 0975-329X. -
Cyber-Secure Framework for the Insecure Designs in Healthcare Industry
Sensitive data protection has been a top priority in the healthcare industry. This has led to the investigation of safe data storage and transaction. Despite various attempts to address this issue, data breaches continue to plague the healthcare industry. This study aims to investigate prevalent storage practices and security methodologies in the healthcare, recognizing the need for a robust framework. The work further extends with design of new security framework for healthcare industry. This framework identifies critical data and implement measures to prevent unauthorized access and data tempering. The industrial hype towards the implementation of adaptive machine learning craves the need for hybrid machine learning approaches to be adapted in the cyber secure framework. In order to improve security and confidentiality in the healthcare sector. Blockchain is used in the proposed cyber secure framework promising integrity of data with the features of immutability. This proposal aims to provide a comprehensive solution to the ongoing problem of protecting medical data. Grenze Scientific Society, 2024. -
Game Rules Prediction Winning Strategies Using Decision Tree Algorithms
With the availability of extensive data spanning over the years, sports have become an emerging field of research. The application of analytics in cricket has become prominent over the years. Cricket, the most loved sport in India, draws the attention of fans worldwide. The Indian Premier League is no exception. Created in 2008, this franchise-based T20 format of cricket has gripped the attention of cricket enthusiasts. With ardent fans cheering for their favorite teams, teams have mounting pressure to maintain their winning streak. One such team is the beloved Chennai Super Kings. Statistical techniques for winner prediction have become popular over the last decade. In this study, we try to frame decision rules for IPL teams to win a series using the CART algorithm. By considering Chennai Super Kings, this study aims to understand the criteria for winning and identify potential weaknesses, allowing the team to predict the likelihood of winning the IPL series. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Industry 5.0 - The co-creator in marketing
The unavoidable connection between automation and digitalization is already in the business horizon in the name of Industry 5.0. Industry 4.0, the robotic and technological revolution were largely hit among the manufacturing industries, but Industry 5.0 is meant for all sectors across ranges from manufacturing to services. Evolution from the days of mechanization (Industry 1.0) to smart factory (Industry 4.0) witnessed the improvisation of metrics related to efficiency and optimization. And now its turn for the balance between optimization and efficiency with the support from robots in assisting the smarter generation's technologies and machineries and tools through Industry 5.0 in the domain of marketing too where the change is constant and dynamic would be more accommodative to opportunities and challenges through the next wave of 5.0. The disruption by Industry 5.0 will change existing nature of marketing in terms of customer experience, supply chain, procurement, product development, retail operations, etc. The market which predominantly flourishes with the help of customers in co-creation is going to have robot as bystander with the intervention of this Revolution 5.0 which will level up the existing customer experience. Marketing by its nature demands the cooperation at multiple levels and is becoming easier prey for the Industry 5.0 revolution as it's expected to create the cooperation between the humans and machines. Product development, customer engagement and customer experience will undergo the transformation due to this industry revolution and also there are other areas in the marketing domain to go through the impact are addressed in this chapter. 2023 by A. Mansurali, V. Harish and Swamynathan Ramakrishnan. All rights reserved. -
Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry
HR analytics has been envisaged as recent research trend for providing a comprehensive decision support system to the top level management in terms of employee's performance, recruitment and behaviour analysis. Globally, organizations are using technology to support and ease HR processes. Every organization should give maximum value to every available human resource, and they should minimize the attrition and absenteeism rate and ensure what are the factors that contribute towards employee attrition as well as the causes for workmen absenteeism. The ultimate objective is to correctly identify attrition and absenteeism in order to assist the company to improve retention tactics for key personnel and increase employee satisfaction. Through this chapter, a machine learning-based model is proposed to get quick results for such employee attrition and workmen absenteeism. The model is trained and tested for its accuracy. The result shows that the proposed model has high sensitivity. The managerial implications are also discussed for taking informed decisions. 2023, IGI Global. All rights reserved. -
Through the Lens of Recession 2.0: Diversification Dynamics Between the Leading Asian Stock Markets
The focus of this article is to analyse the inter-linkages between eight leading stock markets in Asian continent from the period of July 2011 to February 2018. This period holds relevance as this was the time when Recession 2.0 set in, which adversely affected the developed economies; however, the developing economies withstood the crisis without much of an impact. Co-integration and Granger causality tests were conducted to probe the inter-linkages. Study revealed a positive impact on Asian stock market indices collectively on each of the indexes. The highest number of unidirectional causalities was to KOPSI and NIFTY from rest of the stock indices. Results confirmed that no co-integration relationship existed among the selected indices indicating favourable diversification opportunities. Thus, the study fosters global market participants and policymakers to consider the nitty-gritties of stock market integration so as to benefit from international stock market diversification in the Asian region. 2022 Management Development Institute. -
Blockchain and the Evolving Internal Audit Function
Blockchain Technology indicates a transformative era for internal audit practices in the evolving digital finance and operations landscape. This research explores the internal audit function in a Blockchain-driven world, emphasizing the changing perspectives and methodologies necessitated by this disruptive technology. With its foundational principles of transparency, immutability, and decentralization, Blockchain presents challenges and opportunities for internal auditors. The paper delves into how Blockchain is poised to redefine traditional audit practices, moving towards more real-time and continuous auditing techniques. It examines the implications of Blockchain for risk assessment, fraud detection, and compliance, highlighting the shift towards proactive rather than reactive audit strategies. Furthermore, the research examines Blockchains opportunities and challenges to the internal audit function. This study provides insights into integrating Blockchain Technology in internal auditing through a comprehensive secondary data analysis. It proposes a roadmap for auditors to adapt and thrive in this new era. The findings underscore the importance of embracing technological advancements, advocating for a dynamic approach to audit practices that aligns with the complexities of a blockchain-driven world. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
AI in IA: Impact of Artificial Intelligence in Internal Audit: A Qualitative Study
Internal auditing is becoming more crucial as businesses become more complex and extensive. Artificial intelligence (AI) in internal auditing is a trend change that promises to revolutionize how internal auditing functions are performed and delivered through significant improvements in audit quality and operational discipline. This paper reflects on many of the multifaceted impacts of AI on internal auditing functions. This paper intends to investigate how this AI will impact the audit profession. By interviewing ten individual internal audit experts qualitatively, the study shows that AIs implementation will impact the following six critical levels. AI makes it possible for an auditor to (1) spend less time and make the audit more productive, (2) increase coverage, (3) real-time auditing, (4) enhance decision-making, (5) risk assessment and management, and (6) create new advisory services. The findings thus imply a need for a well-defined and consistent audit structure that is flexible enough for auditors to improve their audits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Phytogenic synthesis and antimicrobial activity of ZnO nano bow ties (ZnO NBTs): An experimental and computational study
Phytogenic synthesis is a sustainable and eco-friendly approach for producing nanoscale particles, using biological entities such as plants and their byproducts. In this study, Allium sativum extract was selected as a capping and reducing agent due to the presence of phytochemicals such as allicin, diallyl disulfide (DADS), vinyl dithiins, ajoene (E- and Z-ajoene), diallyl trisulfide (DATS), and thiol (sulfhydryl) groups. The resulting ZnO Nano Bow Ties (ZnO NBTs) were characterized using FE-SEM, XRD, EDX, DLS, zeta potential, FTIR, and UV-Vis spectroscopy to evaluate the size, morphology, and crystallinity. The obtained XRD, SEM, and DLS results suggested an average longitudinal length of ?372 nm with a maximum lateral width of ?64 nm and a Bow Tie shape. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was employed to elucidate the prominent phytochemical constituents of the Allium sativum extract. Preliminary antibacterial assays reveal significant inhibition zones and growth inhibition effects against gram-negative bacteria of both Klebsiella pneumoniae and Escherichia coli, suggesting the promising antimicrobial potential of these ZnO NBTs. Monte Carlo simulations revealed that the cone-shaped ZnO NBTs bind strongly to the active sites of the target proteins with binding affinities of ?36.20 and ?32.14 kcal/mol for Klebsiella pneumoniae and Escherichia coli respectively, which correlates with their activities. The ZnO NBTs complexes formed stronger hydrophobic interactions and hydrogen bonds with amino acid residues of Escherichia coli than with Klebsiella pneumoniae. This integrated experimental and computational study underscores the potential of the use of ZnO NBTs as a sustainable and effective strategy to combat bacterial pathogens. The findings of this study indicate that efficient morphology (shape) is a major contributor to the protein binding affinities of ZnO NBTs, with promising implications for the design of antibacterial drugs in nanomedicine. 2024 The Authors -
Review Article: A Review on Starch and CelluloseEnhanced Superabsorbent Hydrogel
Superabsorbent hydrogels are hydrophilic polymer units that can absorb water and organic fluids into the three-dimensional network and mimic biological cells when swollen. Hydrogels are categorized as natural, synthetic, and hybrid, depending on their constituent polymer. The novel green synthesis includes the combination of natural polymers with synthetic ones to produce eco-friendly Hydrogels. The networks are established using crosslinkers formed chemically as covalent bonds or ionic bonds and physically if intermolecular forces are involved. Starch and cellulose are naturally occurring biopolymers that make significant applications for hydrogel production. This article reviews hydrogel, its properties, classification, synthesis mechanism, and application in various sectors using starch and cellulose as copolymers. Due to the high range of availability, nontoxic nature, and biodegradability, starch and cellulose-based hydrogels find high regard in the present research era. The biopolymers beneficiation can result in the evolution of economic and sustainable methods for transforming this natural biopolymer into utilitarian organic products. 2023, Sami Publishing Company. All rights reserved. -
Construction of a waste-derived graphite electrode integrated IL/Ni-MOF flowers/Co3O4 NDs for specific enrichment and signal amplification to detect aspartame
A novel and cost-efficient electrochemical sensor was designed by immobilizing IL/Ni-MOF/Co3O4 nanodiamonds on the graphite (GE) electrode, marking the first application for the detection of aspartame. The graphite electrode was extracted and recycled from discharged batteries to serve as a working electrode. The nanocomposite features unique Co3O4 nanodiamonds, generated using Coriandrum sativum seed extract, alongside Ni-metal organic framework (MOF), which were synthesized through a solvothermal method. The conductivity and stability of the electrochemical sensor were enhanced through the incorporation of the ionic liquid (IL) ([BMIM][MeSO4]. The phytochemical profile of Coriandrum sativum seed extract, analyzed by GC-MS, identified key compounds involved in the synthesis of Co3O4 nanodiamonds. A comprehensive characterization of the nanocomposite was performed using UV-Vis, FTIR, DLS, Zeta potential, XRD, XPS, FE-SEM, TEM, optical profilometry, and AFM to confirm the structural and elemental modifications. Electrochemical characterization of the bare and modified electrodes was conducted through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The GE/IL/Ni-MOF/Co3O4 nanodiamonds modified electrode displayed enhanced electroanalytical performance for aspartame detection, characterized by signal amplification at +7.0 V. Quantitative analysis by Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) revealed a linear detection range of 315 M for aspartame. A comparison of SWV and DPV revealed superior analytical performance for SWV, with limit of detection (LOD) and limit of quantification (LOQ) values of 1.02 M and 3.1 M (R2 = 0.993) compared to 1.81 M and 5.5 M (R2 = 0.986) for DPV. This study reveals the excellent adsorption capabilities of Ni-MOF and Co3O4 nanodiamonds (Co3O4 NDs), attributed to their high porosity and large surface area, paving the way for the development of affordable sensing devices for artificial sweeteners. 2024 Elsevier B.V. -
Ni-Co MOF Flowers/ZnO NRs Mediated Electrochemical Sensor for Rapid and Ultrasensitive Detection of Neotame in Food Samples
This study focuses on the bioreduction of waste-derived graphite rods into reduced graphene oxide(rGO), followed by the fabrication with Ni-Co metal-organic flowers and Zinc oxide nanorods(ZnO NRs) using Nafion, for sensitive detection of neotame. The Ni-Co metal-organic flowers and ZnO NRs were synthesized using solvothermal synthesis and Azadirachta indica leaf extract, respectively. Additionally, Nafion polymer enhances the stability and conductivity of the nanocomposite. The nanocomposite was characterized using UVvis, Fourier transform infrared spectorscopy, X-ray diffraction, Raman spectroscopy, Dynamic light scattering, X-ray photoelectron spectroscopy, Field-emission scanning electron microscopy, Energy-dispersive X-ray analsysis, Transmission electron microscopy, and Atomic force microscopy. The electrochemical studies were carried out using Electrochemical impedance spectroscopy and Cyclic voltammetry. The modified electrode (rGO/Nafion/Ni-Co MOF/ZnO NRs) demonstrated improved electrochemical activity (34.01 ?A) for neotame with an enhanced peak current at +0.73 V. The LOD and LOQ values were calculated and found to be 0.32 and 0.99 ?M with a recovery (%) ranging from 94.50 to 101.34%. The outcome of this study identifies the morphological and electrochemical factors as major contributors to the adsorption affinities and catalytical activities, with promising possibilities for the design of electrochemical sensing of artificial sweeteners. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Experimenting with scalability of Beacon controller in software defined network
In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance. BEIESP. -
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification
Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms. 2021,International Journal of Intelligent Engineering and Systems. All Rights Reserved. -
A prediction technique for heart disease based on long short term memory recurrent neural network
In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naive Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods. 2020 by the authors. -
Machine Learning based Candidate Recommendation System using Bayesian Model
Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter. 2023 IEEE.