Volume 11 | Issue 8 |

 Abstract

Purpose - The objective of the study is to describe the potential risks and benefits of cryptocurrency adoption on the Indian economy and to analyze the factors for the growth of cryptocurrency in India. Design/methodology/approach - Descriptive research is undertaken to describe the impact of cryptocurrency on the Indian economy. The primary data is collected from a sample of 700 consumers residing in India covering their attitudes, perceptions, and usage of cryptocurrency. The survey is conducted online through a web-based platform, and is distributed using social media and email channels. Findings - On the positive side, cryptocurrencies can provide financial inclusion to the unbanked population, provide a new avenue for investment, and reduce transaction costs for cross-border transactions. It can also attract foreign investment in the Indian economy and boost the country's technological development. However, there are also concerns regarding the use of cryptocurrencies in unlawful activities such as money laundering and financing terrorism. There is also the risk of the Indian economy losing control of its monetary policy and the potential for instability in the financial system. Originality/value - The impact of cryptocurrencies on the Indian economy remains uncertain. While it presents both opportunities and challenges, a cautious and balanced approach is necessary to harness the potential benefits while mitigating the risks. However, in the future, the rigorous empirical studies are to be undertaken to know the real benefits are being enjoyed the people who invested money in cryptocurrency. Paper Type - Research paper

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Cryptocurrency, digital currency, Indian economy, demographical factors, adoption of cryptocurrency, etc.

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The present study aimed to know the suicidal tendency among boys and girls. It also aimed to check suicidal tendency with reference to gender and area. The Suicidal Tendency Scale (Revised) (STS-R) by Rasik G. Meghnathi (2020) was used. The sample constituted total 120 college students out of which 60 were from boys students (30 urban area and 30 rural area) and 60 from girl students (30 urban area and 30 rural area). The data was collected from normal population of Ahmedabad District. The data was scored, analyzed as per the manual. 'F' test was being calculated. The result showed that 1.. The boys college students is having more suicidal tendency than girls college students group, 2.. The college students of urban area is having more suicidal tendency than college students of rural area group and 3.. The college students of rural area is having more suicidal tendency than college students of rural area group.

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Suicidal Tendency, Boys and Girls, Students of Urban Area and Rural Area

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Attracting and retaining high-quality teachers is a primary necessity as well as a challenge for educational institutions. While intrinsic factors play a significant role in motivating individuals to enter the teaching profession, extrinsic conditions can influence their job satisfaction and desire to remain in teaching. In addition, demographic factors and teacher-specific and school-specific characteristics also affect job satisfaction. The present study aimed to know the job satisfaction among government and private school teachers in relation to their type of teachers and gender. The job satisfaction scale prepared by Dr. (Mrs.) Meera Dixit (2013) was used. The sample constituted total 120 school teaches out of which 60 were from government school teachers (30 male and 30 female) and 60 were from private school teachers (30 male and 30 female). The data was collected from various schools of Ahmedabad District. The data was scored, analyzed as per the manual. 'F' test was being calculated. The result showed that, 1. The private school teachers group is having more job satisfaction than government school teachers, 2. There is no significant difference in the mean score of the job satisfaction among male and female school teachers and 3. There is no significant difference in the interactive effect of the mean score of the job satisfaction among type of school teachers and gender

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Job satisfaction, government and private school teachers, male and female school teachers.

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Adjustment is a process that is supposed to lead to a person's happy and contented life. It establishes a balance between requirements and capacity to meet them, persuades those concerned to adapt their lifestyles to the demands of the circumstances, and provides the strength and capability to bring about desired changes in environmental conditions. The present study aimed to know the home adjustment among high school students. It also aimed to check home adjustment with reference to gender and residence area. High School Adjustment Inventory (HSAI) constructed by A.K.Singh and A. Sen Gupta (2011) was used. The sample constituted total 120 high school students out of which 60 were from boys students (30 urban area and 30 rural area) and 60 from girl students (30 urban area and 30 rural area). The data was collected from Ahmedabad District. The data was scored, analyzed as per the manual. 'F' test was being calculated. The result showed that, 1. The boys high school students group is having batter home adjustment than girls high school students group, 2. The high school students of rural area group is having batter home adjustment than high school students of urban area group and 3. There is no significant difference in the interactive effect of the mean scores of home adjustment with regards to the gender and residence area.

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Home Adjustment, boys students and girls students, urban area and rural area

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Road accidents are more frequent today than ever before for a variety of reasons, including poor road conditions, poor weather, driver inexperience, etc. Due to these reasons, a sizable number of individuals in India have died or been hurt. Traffic accidents need to be analyzed to find issues and their influence on death rates and injury severity levels in order to increase traffic safety. In order to investigate the primary causes of accidents in all of India's states, we did cluster analysis using data sets from road accidents that were published by the Indian government. On the Hadoop platform, hierarchical clustering is used to evaluate the traffic data set for every state in India. The main agenda of Hierarchicalclusteranalysis is tofindingdatasetsthatbelongstogetherandat separating them from the other data resulting a cluster ofvariables.

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ACCIDENT DATA ANALYSIS USING HADOOP HIERARCHICAL CLUSTERING

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Agriculture is an important source of livelihood and Indian economy depends on agricultural production. It is important to detect the plant leaf diseases at early stage to increase the crop yield and profit. Image processing technique is used to detect the leaf diseases accurately since naked eye observation of the diseases does not provide accurate result all the time especially during the early stage. It was done in five phases which are image acquisition, preprocessing of the acquired image, feature extraction, classification of the diseases and displaying the result. This paper provides a detailed survey about classification of the agricultural diseases by using Support Vector Machine classifier.

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Accurate,Acquisition, Segmentation, Support Vector Machine.

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In this research, we show how machine learning may be used to make wired and wireless TCP networks more resilient to congestion. Since TCP reacts the same way to losses caused by congestion as it does to losses caused by link faults, it is sub- optimal in hybrid wired/wireless networks. So, we propose simulating various network topologies and then using machine learning to create a loss classifier automatically. Several machine learning methods are compared, and decision tree boosting is found to be the most effective. In comparison to ad hoc classifiers from the networking literature, it performs better.

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Congestion Management in Wireless Networks: A Machine Learning Approach

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Cheque Truncation System is utilized for clearing cheques. Because of the restrictions of this semi-manual procedure, it can take up to three business days for a national interbank check to clear in Sri Lanka. In light of these drawbacks, it is evident that both cheque users and large financial institutions want a more streamlined and protected system, one that can clear a check in under 24 hours while maintaining the system's security and privacy. To address these concerns, this study presents an automated approach that is within the reach of every financial institution in Sri Lanka. To provide its customers with the faster cheque clearance, the suggested system is built on the blockchain, and all banks that are interested in that structure must link to it. A comprehensive framework was proposed, with answers split into four distinct phases: (i) the traditional paper check clearing process, (ii) the digital cheque producing and cleared process, (iii) the detection and prevention of fraudulent cheque transactions, and (iv) the safeguarding of cheque transactions. The main programming languages and frameworks utilized to build the system were Python and Flutter, while the main distributed ledger used was Ethereum. Because Ethereum adds extra security, the suggested system may become very large very quickly. Both the consumer and the bank benefit from the method, since the clearing of cheques is made easier and faster while also being made more secure. Paper check fraud detection is made easier and more accurate as a consequence.

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Online Digital Cheque Clearance and Verification System Using Block Chain

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Due to the broad availability of cameras, photo taking has been becoming increasingly common in recent years. Images play a crucial role in our everyday lives because to the abundance of information they hold, and it is frequently necessary to modify images to get new insights. There are many options for enhancing photos, but they are also regularly exploited to create fake photos that distribute false information. This is really worrisome since it increases the incidence and severity of picture forgeries. Over time, several conventional methods have been developed to identify picture frauds. The topic of visual forgery detection has been inspired by convolutional neural networks (CNNs), which have gained a lot of attention in recent years. While CNN-based picture forgery detection methods do exist, they typically only identify one sort of fraud (such as image merging or copy-move) at a time. Therefore, a method is needed that can effectively andefficiently detect the existence of unnoticed forgeries in a picture. In this research, we provide a powerful deep learning-based approach for spotting fakes in the setting of double compression of pictures. We train our representation on the disparity between the uncompressed and compressed variants of a picture. The suggested model requires fewer computational resources and has been shown to outperform the current best methods. Positively, experimental findings show a validation validity rate of 92.23 percent overall.

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Using a Concatenation of Low-Complexity Deep Learning Models, We Can Spot Fake Images

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There are currently no reliable ways to determine if it will rain today. Estimates made by the meteorological agency themselves may turn out to be inaccurate. In this post, we will learn how to create a machine-learning model that can forecast whether or not it will rain today based on various atmospheric variables. Because machine learning models typically outperform human beings at the previously known task--predicting rainfall--this issue is connected to rainfall prediction using machine learning.

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RAINFALL PREDICTION BASED ON MACHINE LEARNING

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