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Classification of supply chain knowledge: A morphological approach
Purpose The purpose of the article is to create a knowledge classification model that can be used by knowledge management (KM) practitioners for establishing a knowledge management framework (KMF) in a supply chain (SC) network. Epistemological and ontological aspects of knowledge have been examined. SC networks provide a more generic setting for managing knowledge due to the additional issues concerning flow of knowledge across the boundaries of organizations. Design/methodology/approach Morphological analysis has been used to build the knowledge classification model. Morphological approach is particularly useful in exploratory research on concepts/ entities having multiple dimensions. Knowledge itself has been shown in literature to have many characteristics, and the methodology used has enabled a comprehensive classification scheme based on such characteristics. Findings A single comprehensive classification model for knowledge that exists in SC networks has been proposed. Nine characteristics, each possessing two or more value options, have been finally included in the model. Research limitations/implications Knowledge characteristics have been mostly derived from past research with the exception of three which have been introduced without empirical evidence. Although the article is primarily about SC knowledge, the results are fairly generic. Practical implications The proposed model would be of use in developing KM policies, procedures and establishing knowledge management systems in SC networks. The model will cater to both system and people aspects of a KMF. Originality/value The proposed knowledge classification model based on morphological analysis fills a gap in a vital area of research in KM as well as SC management. No similar classification model of knowledge with all its dimensions has been found in literature. Emerald Group Publishing Limited. -
Classification, source, and effect of environmental pollutants and their biodegradation
Any foreign chemical substance that is unusually present within an organism or is unexpectedly found in the environment at a higher concentration than the permissible limits can be termed a xenobiotic or a pollutant. Such substances include carcinogens, drugs, food additives, hydrocarbons, dioxins, polychlorinated biphenyls, pesticides or even some natural compounds. Pollutants are known for their higher persistence and pervasiveness, and along with their transformed products, they can remain in and interact with the environment for prolonged periods. In this article, the classification of such substances based on their nature, use, physical state, pathophysiological effects, and sources is discussed. The effects of pollutants on the environment, their biotransformation in terms of bioaccumulation, and the different types of remediation such as in situ and ex situ remediation, are also presented. 2017 Begell House, Inc. -
Classifying voice-based customer query using machine learning technique
Timely attention to issues raised by customers is critical. It is imperative that the average handling time is lesser, which in turn contributes to productivity. It was found from the data from the banking industry in the US that, on average, a customer service call last for seven minutes. The first two minutes are for the call to get redirected to the respective team. This study investigates a method using machine learning to classify and redirect the customers into the respective department directly based on their initial voice response or voice message. It will substantially reduce the service time. CRISP-DM methodology is being used to design the process of the study. The most frequently occurring issues and the department to which they are associated are created through machine learning from the dataset that contained product reviews and metadata of different issues. The programming languages that are used in this study are Python, HTML and Java. An interface is created by using HTML, which makes it quite user-friendly. The study tests the effectiveness of converting voice to text and interprets which department the call should be transferred to address the issue. A support vector machine and a logistic regression model were used for the prediction, and it was found that the models provided an accuracy of 83 and 84 percent, respectively. The study proves that using ML and voice recognition reduces the average handling time. 2021 Ecological Society of India. All rights reserved. -
Classroom mathematics learning: Association of joy of learning and school connectedness among high school students in India
Mathematics learning experiences can influence the overall academic and socio-emotional development of a child. The present study investigates the mediating effect of mathematics anxiety and emotional engagement on the relationships between teacherstudent interaction, the joy of learning, and school connectedness. Two mediation models were tested for the dependent variables: the joy of learning and school connectedness, using Hayes' process macro in SPSS on a sample of 774 eighth-standard students from Indian schools. The study's results indicate the presence of a serial mediation effect on the relationship between teacherstudent interaction and joy of learning, teacherstudent interaction, and school connectedness through mathematics anxiety and emotional engagement. The study emphasized the role of mathematics learning within the overall framework of joy of learning and school connectedness.. 2024 Wiley Periodicals LLC. -
Clay-based cementitious nanofluid flow subjected to Newtonian heating
In recent years, a novel technique for producing robust cementitious materials, called nanocomposites, has emerged. These materials are comprised of clay minerals and polymers. As a result, a vertical flat plate has been used to evaluate a clay-based cementitious nanofluid in this research. The impacts of first-order chemical reactions, heat generation/heat absorption, and the Jeffrey fluid model are taken into account for the study of flow. Newtonian heating and the conditions for slippage velocity have also been considered. The mathematical problem for the flow analysis has been established in relations of partially coupled partial differential equations and the model has been generalized using constant proportional Caputo (CPC) fractional derivative. The problem is solved using the Laplace transform technique to provide precise analytical solutions. On the concentration, temperature, and velocity fields, the physics of a number of crucial flow parameters have been examined graphically. The acquired results have been condensed to a very well-known published work to verify the validity of the current work. It is important to note here that the rate of heat transfer in the fluid decreases by 10.17% by adding clay nanoparticles, while the rate of mass transfer decrease by 1.31% when the value of ? reaches 0.04. 2023 World Scientific Publishing Company. -
Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers
The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents. Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention. The current study highlights the influence of the perceived usefulness of using the Click & Collect online shopping mode on the purchase intention of customers. 2021 Transnational Press London -
Climate anxiety, wellbeing and pro-environmental action: correlates of negative emotional responses to climate change in 32 countries
This study explored the correlates of climate anxiety in a diverse range of national contexts. We analysed cross-sectional data gathered in 32 countries (N = 12,246). Our results show that climate anxiety is positively related to rate of exposure to information about climate change impacts, the amount of attention people pay to climate change information, and perceived descriptive norms about emotional responding to climate change. Climate anxiety was also positively linked to pro-environmental behaviours and negatively linked to mental wellbeing. Notably, climate anxiety had a significant inverse association with mental wellbeing in 31 out of 32 countries. In contrast, it had a significant association with pro-environmental behaviour in 24 countries, and with environmental activism in 12 countries. Our findings highlight contextual boundaries to engagement in environmental action as an antidote to climate anxiety, and the broad international significance of considering negative climate-related emotions as a plausible threat to wellbeing. 2022 The Authors -
Climate Change inflicted Environmental Degradation leading to the Crumbling of Arctic Ecosystem
The Arctic and Antarctic regions serve as the air conditioners of planet Earth. The polar regions located thousands of miles away from us determine the climatic patterns of our geographical area. They maintain our planet at bearable temperatures which are ideal for the existence of diverse flora and fauna and to support different types of ecosystems all around the world. Apart from controlling the temperatures, they also regulate ocean currents which in turn have an effect on the monsoons, winds, hurricanes etc. The poles were pristine till a few decades back. Due to mans greed, the poles started deteriorating at an alarming scale. Climate change, biodiversity changes, oil drilling, seismic testing, toxin accumulation are a few of the challenges faced by the Arctic ecosystem having serious effects on its topography, terrestrial and marine life-forms and the whole ecosystem. Due to the alarming scale of global warming, there is also the danger of permafrost meltdown which can unleash a plethora of dangerous pathogens buried underneath and also let out the huge amounts of locked down carbon. The crumbling of the polar ecosystem is leading to rampant consequences not only in the poles but also elsewhere in the world thousands of miles away. Here, we attempt to discuss the repercussions of the crumbling Arctic ecosystem due to the physical, chemical and geological changes caused by such anthropogenic activities and look at the efforts being carried out to save the Arctic ecosystem in a frantic effort to save our planet. 2024, World Researchers Associations. All rights reserved. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Climate-Smart Livelihood - A Case Study of Dodaballapura Taluk of Bangalore Rural District
More than a billion farmers around the world are on the frontier of climate change. These farmers' livelihoods are directly and indirectly affected by the impact of climate change. Climate smart livelihood explains the practices in agriculture sector which sustainably contributes to productivity and income. This study tries to explore the adaptation of climate smart livelihood techniques by the farmers in the Doddaballapur taluk of Bangalore rural district. The data was collected primarily from the five villages and 50 households of Doddaballapur taluk. The survey revealed that 81.67% of the respondents faced problems during adaptation of climate smart agriculture was due to poor support of local and national authorities with climate related issues and ranked it one of the major constraints. This was followed by lack of financial constraints, lack of knowledge about adaptive practices (78.50%), non-availability of agriculture inputs in time (76.17%), lack of education about the adaptation strategies (75.33%), unavailability of new technologies (78.83%), higher cost of the agricultural inputs used for the practices (71.17%), lack of improved communication facility about the climate change (71 %), migration of youth due to urbanization and better employment (70.83%), lack of knowledge about post-harvest technology (68.83%), lack of awareness about climate change issues (59.83 %). The study reveals that as most farmers believe they have low capacity to adapt to climate-smart agriculture due to lack of availability of resources. Government can help farmers through National Agricultural Extension Project (NAEP), Krishi Prashasthi, etc. 2022 - Kalpana Corporation. -
Clinical Study Macular Oedema
Prior to the development of the ophthalmoscope, macular oedema remained mostly unknown. Macular oedema is caused by fluid buildup in the retinal layers around the fovea. It causes vision loss by changing the functional cell connection in the retina and stimulating an inflammatory reparative response. The clinical profile, aetiology, and varied types of Macular Oedema are hence the focus of research, and also to investigate the aetiology of macular oedema as well as the various forms of macular oedema in patients attending Krishna Hospital in Karad. The male to female ratio among the 60 participants was 2.53:1. Macular oedema is the major cause for loss in vision which is common vitreo retinal diseases, with diabetes being the most prevalent cause (35% of cases) in our study. Its early detection and treatment are critical for preventing blindness. It is consequently critical to understand the aetiology, pattern, and chronicity of macular oedema in order to customize treatment and monitor response to it. RJPT All right reserved. -
Cloud computing security for public cloud using ciphers and queueing petri nets
Cloud computing is the most used word in the domain of Information Technology, which is making colossal differentiations in the IT business. Nowadays, a massive proportion of data is being made, and the masters are discovering better approaches for managing this data. In a general sense, the word cloud implies a virtual database that stores immense data from various clients. There are three sorts of cloud public, private and hybrid. A public cloud is fundamental for general customers where customers can use cloud benefits free or by paying. Private cloud is for explicit associations, and hybrid one is in a broad sense a mix of both. Cloud offers diverse kind of administrations, for instance, IAAS, PAAS, SAAS where administrations like a stage for running any application, getting to the enormous information extra room, can use any application running under the cloud are given. The cloud similarly has a shortcoming concerning the security for the data warehouse. In a general sense, public cloud is inclined to data modification, data hacking and therefore, the integrity and privacy of the data are being undermined. Here in our work our motive is to verify the information that will be taken care of in the public cloud by using the multi-stage encryption. The estimation that we have proposed is a mix of Rail Fence cipher and Play Fair cipher. 2020, IJSTR. -
Cloud databases: A resilient and robust framework to dissolve vendor lock-in
Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper. 2024 The Author(s) -
Cloud Dynamic Scheduling for Multimedia Data Encryption Using Tabu Search Algorithm
The cloud computing is interlinked with recent and out-dated technology. The cloud data storage industry is earning billion and millions of money through this technology. The cloud remote server storage is on-demand technology. The cloud users are expecting higher quality in minimal cost. The quality of service is playing a vital role in any latest technology. The cloud user always depends on thirty party service providers. This service provider is facing higher competition. The customer is choosing a service based on two parameters one is security and another one is cost. The reason behind this is all our personal data is stored on some third party server. The customer is expecting higher security level. The service provider is choosing many techniques for data security, best one is encryption mechanism. This encryption method is having many algorithms. Then again one problem is raised, that is which algorithm is best for encryption. The prediction of algorithm is one of major task. Each and every algorithm is having unique advantage. The algorithm performance is varying depends on file type. The proposed method of this article is to solve this encryption algorithm selection problem by using tabu search concept. The proposed method is to ensure best encryption method to reducing the average encode and decode time in multimedia data. The local search scheduling concept is to schedule the encryption algorithm and store that data in local memory table. The quality of service is improved by using proposed scheduling technique. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Cloud Intrusion Detection Using Hybrid Convolutional Neural Networks
Instead of storing data on a hard drive, cloud computing is seen as the best option. The Internet is used to deliver three different kinds of computing services to users all over the world. One advantage that cloud computing provides to its customers is greater access to resources and higher performance while at the same time increasing the risk of an attack. Intrusion detection systems that can handle a large volume of data packets, analyse them, and generate reports based on knowledge and behaviour analysis were developed as part of this research. As an added layer of protection, the Convolution Neural Network Algorithm is used to encrypt data during end-to-end transmission and to store it in the cloud. Intrusion detection increases the safety of data in the cloud. In this paper demonstrates the data is encrypted and decrypted using a model of an algorithm and explains how it is protected from attackers. It's important to take into account the amount of time and memory required to encrypt and decrypt large text files when evaluating the proposed system's performance. The security of the cloud has also been examined and compared to other existing encoding methods. 2024, Iquz Galaxy Publisher. All rights reserved. -
Cloud security based attack detection using transductive learning integrated with Hidden Markov Model
In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. 2022 -
Cloud service negotiation framework for real-time E-commerce application using game theory decision system
A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. 2021 - IOS Press. All rights reserved. -
Cloud-enabled Diabetic Retinopathy Prediction System using optimized deep Belief Network Classifier
Diabetic retinopathy disease is one of the notorious metabolic disorders happens due to increase of blood sugar level in human body. In computer vision, images are recognized as the indispensable tool for precise prediction and diagnosis of diabetic retinopathy. Therefore, the proposed research study considers the fundus images of various patients containing the diabetic disease. Basic idea behind this research is to introduce a stochastic neighbor embedding (SNE) feature extraction approach for the sake of dimensional reduction and unnecessary noise removal from the fundus images. After feature extraction, the proposed optimized deep belief network (O-DBN) classifier model is capable of measuring the image features into various classes that gives the severity levels of diabetic retinopathy disease. Moreover, the proposed cloud-enabled diabetic retinopathy prediction system using the SNE feature extraction and O-DBN classification model could outperform the existing online prediction systems in terms of sensitivity, specificity, F1-score, prediction time and accuracy. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Cluster institutional isomorphic pressures: A case of Tirupur knitwear cluster /
Journal Research Journal of Social Science & Management (RJSSM), Vol.2 Issue 4, pp.95-102, ISSN No. 2251-1571. -
Clustering of low-mass stars around Herbig Be star IL Cep - Evidence of 'Rocket Effect' using Gaia EDR3 ?
We study the formation and the kinematic evolution of the early-type Herbig Be star IL Cep and its environment. The young star is a member of the Cep OB3 association, at a distance of 798 9 pc, and has a 'cavity' associated with it. We found that the B0V star HD 216658, which is astrometrically associated with IL Cep, is at the centre of the cavity. From the evaluation of various pressure components created by HD 216658, it is established that the star is capable of creating the cavity. We identified 79 co-moving stars of IL Cep at 2-pc radius from the analysis of Gaia EDR3 astrometry. The transverse velocity analysis of the co-moving stars shows that they belong to two different populations associated with IL Cep and HD 216658, respectively. Further analysis confirms that all the stars in the IL Cep population are mostly coeval (?0.1 Myr). Infrared photometry revealed that there are 26 Class II objects among the co-moving stars. The stars without circumstellar disc (Class III) are 65 per cent of all the co-moving stars. There are nine intense H ? emission candidates identified among the co-moving stars using IPHAS H ? narrow-band photometry. The dendrogram analysis on the Hydrogen column density map identified 11 molecular clump structures on the expanding cavity around IL Cep, making it an active star-forming region. The formation of the IL Cep stellar group due to the 'rocket effect' by HD 216658 is discussed. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.