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A Study on the Influence of Personality Traits on Entrepreneurial intentio
Pacific Business Review International, Vol. 9, Issue 5. pp. 12-19, ISSN No. 0974-438X -
A Study on the Influence of personality traits on entrepreneurial intention among working professionals in the Indian technical organizations
Pacific Business Review International, Vol. 9, Issue 5, pp. 12-19, ISSN 097X-438X -
A study on the influence of sustainability related factors on the online purchase decisions of customers
The e-commerce business is growing at a tremendous rate in India. More and more customers prefer to buy for the online website due to the discounts, ease of purchase and the larger variety of products available. The e-retail companies majorly use discounts as major attraction to gain more customers. In this process of trying to increase the market share the sustainability factors are not given importance in various ways. This research paper tries to find if customers give importance to sustainability related factors and if these factors can be advertised by the e-retailers. The paper tries to find if this will have an impact on the customer purchase decisions from the e retailers. Then the e-retailers will have to have a different business strategy with sustainability factors included to attract more customers. 2019 SERSC. -
A study on the relationship between internal branding and affective commitment of customer contact employees in multi-brand retail stores in Bangalore /
International Journal of Business and Administration Research, Vol.1, Issue 7, pp.189-197, ISSN No: 2348-0653. -
A Study on the Role of Tea Tourism in Assam
Tourism Development Journal Vol. 10, Issue 1, pp. 1-14, ISSN No. 0975-7376 -
A study on the self-concept of teachers working in government, aided and unaided colleges in Bangalore
The IUP Journal of Organizational Behavior, Vol-13(1), pp. 60-70. ISSN-0972-687X -
A study on the weak form efficiency of metals & mining sector in bse
This study has been conducted to observe whether the weak form of efficiency holds true when the Metals & Mining sector is examined for the same. Data is collected for a 5 year period which ranges from 1st April 2014 to 31st March 2019. A total of 1232 observations have been taken from the three selected companies of the Metals & Mining sector of the BSE, namely, Coal India Ltd, Hindustan Zinc Ltd and JSW Steel Ltd. Jarque-Bera test is used to check whether the data is normally distributed or not. Augmented Dickey Fuller test has been used to establish whether the data series possesses stationarity or not. Finally Runs test and Autocorrelation tests are used to check whether the Metals & Mining sector of BSE is weak form efficient or not through the three representative companies. Upon analyzing the data through Runs Test, except for JSW Steel Ltd, the other two companies are identified to contain randomness in their data series. Autocorrelation test also suggests that the future stock prices can be predicted only to a minimal extent for the three representative companies. Based on the derived results, it is concluded that the Metals & Mining sector of BSE does not contain any statistical dependencies between its past and future stock prices and it is found to be weak form efficient. 2021, Badebio Biotechnololgy Ltd. All rights reserved. -
A Study on Women Consumers' Preferences Towards Visual Media (With Reference to Chennai City)
Journal of Business Management & Social Sciences Research, Vol-1 (3), pp. 38-49. ISSN-2319-5614 -
A succinct analysis for deep learning in deep vision and its applications
Introduction: Deep learning methodologies can achieve forefront results on testing deep vision issues, for instance, picture portrayal, an object area, face affirmation, Natural Language Processing, Visual Data Processing and online life examination. ConvNet, Stochastic Hopfield network with hidden units, generative graphical model and sort of artificial neural network castoff to absorb competent information coding in an unproven way are deep learning plans used in deep vision issues. Objection: This paper gives a succinct survey of without a doubt the most critical Deep learning structures. Deep vision assignments, for instance, object revelation, face affirmation, Natural Language Processing, Visual Data Processing, web-based life examination and their utilization of this task are discussed with a short record of the historic structure, central focuses and impairments. Future headings in arranging Deep learning structures for Deep vision issues and the troubles included are analysed. Method: This paper consists of surveys. In Section two, Deep Learning Approaches and Changes are audited. In section three, we tend to portray the uses of Applications of deep learning in deep vision. In Section four, Deep learning challenges and directions are mentioned. At long last, Section five completes the paper with an outline of the results. Results and Conclusion: Though deep learning can recall a huge proportion of data and info, its feeble cognitive and perception of the data makes it a disclosure answer for certain applications. Deep learning despite everything encounters issues in showing various erratic facts modalities at the equal period. Multimodal profound learning is an extra notable heading in progressing deep learning research. IJCRR. -
A Survey Instrument for Ranking of the Critical Success Factors for the Successful ERP Implementation at Indian SMEs
Bioinfo Business Economics, Vol-1 (1), pp. 06-12. ISSN-2249-1775 -
A survey of blockchain: concepts, applications and challenges
With the development of Bitcoin, organisations, be it businesses or institutions, are centring on leveraging Bitcoins blockchain technology to non-monetary based applications to improve efficiency of the activities. Having various benefits like anonymity, decentralised, audibility etc. Blockchain technology can be vastly implemented in various sectors other than financial too. This paper gives an overview the blockchain technology. It briefs about various technical concepts used in the blockchain, its types and where it can be used. It also discusses some proposed applications of the technology and tools or frameworks that can be used to develop such. It also presents the limitations of the technology. Copyright 2023 Inderscience Enterprises Ltd. -
A Survey of Sentiment Analysis from Social Media Data
In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future. 2014 IEEE. -
A survey of the studies on Gallai and anti-Gallai graphs
The Gallai graph and the anti-Gallai graph of a graph G are edge disjoint spanning subgraphs of the line graph L(G). The vertices in the Gallai graph are adjacent if two of the end vertices of the corresponding edges in G coincide and the other two end vertices are nonadjacent in G. The anti-Gallai graph of G is the complement of its Gallai graph in L(G). Attributed to Gallai (1967), the study of these graphs got prominence with the work of Sun (1991) and Le (1996). This is a survey of the studies conducted so far on Gallai and anti-Gallai of graphs and their associated properties. 2021 Azarbaijan Shahid Madani University. -
A Survey of Traditional and Cloud Specific Security Issues
The emerging technology popularly referred to as Cloud computing offers dynamically scalable computing resources on a pay per use basis over the Internet. Companies avail hardware and software resources as service from the cloud service provider as opposed to obtaining physical assets. Cloud computing has the potential for significant cost reduction and increased operating efficiency in computing. To achieve these benefits, however, there are still some challenges to be solved. Security is one of the prime concerns in adopting Cloud computing, since the user's data has to be released from the protection sphere of the data owner to the premises of cloud service provider. As more Cloud based applications keep evolving, the associated security threats are also growing. In this paper an attempt has been made to identify and categorize the security threats applicable to Cloud environment. Threats are classified into Cloud specific security issues and traditional security attacks on various service delivery models of Cloud. The work also briefly discusses the virtualization and authentication related issues in Cloud and tries to consolidate the various security threats in a classified manner. Springer-Verlag Berlin Heidelberg 2013. -
A Survey on 5G Standards, Specifications and Massive MIMO Testbed Including Transceiver Design Models Using QAM Modulation Schemes
Massive MIMO (Multiple Input Multiple Output)is the advanced technology in 5G architecture which improves mobile and data wireless system parameters in multiple folds. The basic idea of this technology is to include huge number of antennas in the base stations serving limited user equipment. This will enhance the parameters like spectral efficiency, data rate, wireless devices connectivity, energy or power efficiency and also, significant reduction in interference and error rates. The Third Generation Partnership Project (3GPP)consortium, International Mobile Telecommunication (IMT)and various partner telecom companies are on the way to develop unified architecture to meet the proposed 5G standards by the year 2020. Initial test beds and field-trials are already in process at various universities and telecom companies considering Long Term Evolution (LTE)releases features in the 5G architecture framework. However, the research is still an open issue on improving the parameters. This research paper provides a detailed overview on 5G standards, specifications and Field trials and test beds implemented by various universities and telecom industry utilizing Massive MIMO technology. This literature survey paper aims to enlighten the researchers working in the area of Massive MIMO to understand the test bed and field trials designs existing till date. This paper also motivates to complete experiments on Bit error rate (BER)estimation in various modulation schemes for single transmitter-receiver as well as in MIMO configuration. The reduction in BER is observed when MIMO models are used for transceiver design. The hardware utilization and simulation work of the field trials and testbed provide different existing techniques to develop a transceiver system which meets 5G standard. 2019 IEEE. -
A Survey on Adaptive Authentication Using Machine Learning Techniques
Adaptive authentication is a reliable technique to dynamically select the best mechanisms among multiple modalities to authenticate a user based on the users risk profile generated using behavior and context-based information. Websites or enterprise applications enabled with adaptive authentication will have a more robust security system as analyzing the large volume of the user, device, and browser data in real time generates a risk score that decides the appropriate level of security. Though a significant amount of research is being carried out on adaptive authentication, no single model is suitable for a global attack. This paper provides a structured (extensive) survey of current adaptive authentication techniques available in the literature to identify the challenges which demand future research. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Survey on Arrhythmia Disease Detection Using Deep Learning Methods
The Cardiovascular conditions are now one of the foremost common impacts on human health. Report from WHO, says that in India 45% of deaths are caused due to heart diseases. So, heart disease detection has more importance. Manual auscultation was used to diagnose cardiovascular problems just a few years ago. Nowadays computer-assisted technologies are used to identify diseases. Accurate detection of the disease can make recovery simpler, more effective, and less expensive. In this proposed work, 11years of research works on arrhythmia detection using deep learning are integrated. Moreover, here presents a comprehensive evaluation of recent deep learning-based approaches for detecting heart disease. There are a number of review papers accessible that focus on traditional methods for detecting cardiac disease. This article addresses some essential approaches for categorizing ECG signal images into desired classes, such as pre-processing, feature extraction, feature selection, and classification. However, the reviewed literatures consolidated details have been summarized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A survey on artificial intelligence for reducing the climate footprint in healthcare
The primary mission of the healthcare sector is to protect from various ailments with improved healthcare services and to use advanced diagnostic solutions to promote reliable treatments for complex diseases. However, healthcare is among the significant contributors to the current climate crisis. Therefore, research is underway to identify various measures to reduce the emissions from advanced healthcare systems. Modern healthcare facilities invest significantly in renewable energy, efficient energy solutions, and intelligent climate cooling and control technologies. Furthermore, innovative technologies like artificial intelligence (AI) are proposed to enable automation for patient health monitoring. With the advances in AI, there are green AI goals for potentially reducing emissions through data-driven and well-optimized models for healthcare. Furthermore, novel machine learning and deep learning techniques are continually proposed for improved efficiency to reduce emissions. Therefore, the scope of the research is to review the potential of AI in healthcare for lowering emission rates and its methodologies, current approaches, metrics, challenges, and future trends to attain a straightforward pathway. 2022 -
A Survey on Domain-Specific Summarization Techniques
Automatic text summarization using different natural language processing techniques (NLP) has gained much momentum in recent years. Text summarization is an intensive process of extracting representative gist of the contents present in a document. Manual summarization of structured and unstructured text is a tedious task that involves immense human effort and time. There are quite a number of successful text summarization algorithms for generic documents. But when it comes specialized for a particular domain, the generic training of algorithms does not suffice the purpose. Hence, context-aware summarization of unstructured and structured text using various algorithms needs specific scoring techniques to supplement the base algorithms. This paper is an attempt to give an overview of methods and algorithms that are used for context-aware summarization of generic texts. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Survey on Enhancing System Performance of Wireless Sensor Network by Secure Assemblage Based Data Delivery
To provide secure data transmission in Cluster Wireless Sensor Networks (CWSNs), the challenging task is to provide an efficient key management technique. To enhance the performance of sensor networks, clustering approach is used. Wireless Sensor Network (WSN) comprises of large collection of sensors having different hardware configurations and functionalities. Due to limited storage space and battery life, complex security algorithms cannot be used in sensor networks. To solve the orphan node problem and to enhance the performance of the WSN, authors introduced many secure protocols such as LEACH, Sec-LEACH, GS-LEACH and R-LEACH, which were not secure for data transmission. The energy consumption in existing approach is more due to overhead incurred in computation and communication in order to achieve security. This paper studies about different schemes used for secure data transmission. We are proposing new methodology called IBDS and EIBDS that will increase the performance of WSN by reducing computational overhead and also increases resilience against the adversaries. 2017 IEEE.