Volume 10 | Issue 6

 Abstract

In order to achieve financial soundness banks should manage the risk effectively. Credit risk is banker's loss arising from a borrower who fails to keep up his promise of repayment well in time. The bank's profitability is impacted by the level of loans and advances, non-performing loans and deposits. In this 'market driven economy' globalization trends coupled with intensive competition within the country and outside the country are making the banks to frame suitable risk management strategies. Bank frauds, failures, economic sluggishness, global financial crisis impactness, large sized money transfer from one to another country, taking shelter in other countries and possible legal delays etc., are some of the examples where in the commercial banks should be careful and take extra care to measure the quantum of associate risks. Credit risk causes economic downturn as banks fail due to default risk from customers, which has had a negative impact on the economic development of many nations (Reinhart &Regoff, 2008). In 2008 at global level credit crises emerged due to mass issuing of sub-prime mortgages to individuals in the US leading to default which caused problems around the world. Subprime mortgages and other loans with less restrictions can generate remarkable losses including corporate failures and bankruptcy of financial institutions (Brown et al. 2014). The present research paper reveals data that socio-economic characteristics are impacting on CRM, respondents are well aware of objectives behind CRM. Further, the respondents clearly expressed about the causes of risk management and also ranked the factors impacting credit risk mitigation. The study analyzed the data with the help of quantitative techniques like chi-square, contingency co-efficient, ANOVA, Kendall's concordance and Garrette ranking technique.

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Competition, relationship, variation, risk mitigation, objectives Basel III, banking, corporate failures, deposits, interests

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In the two decades, there has been continuous change in Scheduled Castes and Scheduled Tribes population in Sindhudurg District. SC and ST mean such castes, races or tribes or parts of or groups within castes, races or tribes as are deems under as per the Constitution of India since independence the Indian Government rehabilitated the Scheduled Castes and Scheduled Tribes population. The Government has made large-scale provisions of opportunities for social, cultural and educational up-lift meant of these castes. This social group is among the most subordinated and poorest in India. They are now participating in all activities. But these welfare programmers could yield to a very small segment of the society and after the independence, the socio-economic conditions of the rural SC and ST population is more or less. In this context present investigates is also useful for developmental planning of SC and ST population in Sindhudurg District. If we come the main theme of the paper that the decadal variation of SC and ST population in Sindhudurg District between on decade 1991-2001. Overall the SC population in Sindhudurg District is decreased and ST population is increased in the decade 1991-2001. As per 2001 census the SC population is 38536 and ST population is 4952, it means the proportion of SC population is 4.4 percent and ST population is 0.6 percent to the total population in the Sindhudurg District

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(SC) Scheduled Castes, (ST) Scheduled Tribes, Decades

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The Forward Contracts (Regulation) Act (FCRA), enacted in 1952, aimed to regulate forward trading, with the Forward Market Commission (FMC) established as its regulatory body in 1953. Despite intentions for autonomy, FMC faced limitations due to government interference, restricted powers, and inadequate infrastructure, leading to inefficiencies in commodity market regulation. The merger of FMC with SEBI in 2015 under the Securities Contract Regulation Act (SCRA) marked a significant regulatory shift. This article explores the shortcomings of FMC, the rationale behind the merger, and the potential for SEBI to enhance market integrity, efficiency, and innovation while addressing challenges in spot market integration and pricing mechanisms.

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FCRA, FMC, SEBI, Commodity derivative market, SCRA

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Abstract: Medical tourism involves crossing international borders to avail medical treatment and facilities in another country and enjoy the tourism facilities there. Medical tourism is an important sector in stimulating the economic growth of Kerala. The growth of medical tourism in Kerala is a multifaceted phenomenon driven by the state's high-quality healthcare services, affordability, and integration of traditional and modern medicine. The sector significantly contributes to Kerala's economic growth through revenue generation, job creation, and infrastructure development. More than five lakh foreign patients come to the state for treatment every year. About 25% to 30% of major hospitals' revenue comes from medical tourism (Confederation of Indian Industry). Government initiatives play a crucial role in supporting and enhancing this growth by implementing supportive policies, promoting the state internationally, and investing in healthcare infrastructure. This study explores how Kerala is progressing in medical tourism, why people from other countries choose Kerala as their favorite medical tourism destination and the role of government in promoting medical tourism.

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Keywords: Medical tourism, Market Development Assistance (MDA) Scheme, Indian Healthcare Federation (IHF), Medical Visa, Heal in India Initiative, Kerala Medical Value Travel Society

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A number of methods for solving differential equations are discussed in this article along with the many fields of science and engineering that make use of them. To comprehend both naturally occurring and artificially created systems one must be familiar with differential equations which are mathematical models that depict the change of a quantity over time or space. This article will go over what a differential equation are, the various kinds of differential equations, how to solve them, the order and degree of the differential equation, and some instances of ordinary differential equations with real-world problems and a solved issue. Variable separation, integration factors, homogeneous equations and replacements, the characteristics approach, numerical methods, Laplace and Fourier transforms, and symmetry methods are the main techniques covered. Various methods are applicable to various kinds of differential equations; together they provide a toolbox full of methods for solving these problems either analytically or with reasonable approximations. Various areas rely on differential equations as shown by the highlighted applications. These include engineering, biology, economics, environmental science, technology, medicine, and physics. This study strives to provide a thorough grasp of the techniques used to solve differential equations and their significance in addressing real-world situations by giving both the theoretical foundations and practical implementations.

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differential equations, applications, techniques

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Supply chain management (SCM) that makes use of BDA is becoming more prevalent. The wide range of SCM applications of BDA, including demand prediction, trend analysis, and consumer behaviour, is the reason for this. This study aims to enhance precision and efficiency in demand forecasting and SCM through the application of BDA. Despite advancements, challenges persist such as reliance on isolated datasets, underutilisation of advanced machine learning models, and limited incorporation of external variables. Demand forecasting, crucial for supply chain decisions like production planning and inventory control, is hindered by demand volatility influenced by factors such as promotions and market trends. This study leverages integrated, real-time data sources and sophisticated analytics to improve prediction accuracy and operational efficiency, thereby enabling businesses to better manage uncertainties in demand, supply, and costs. The research also identifies gaps in current literature, including the need for more robust models, comprehensive external variable analysis, and broader validation across industries. By addressing these gaps, the research contributes to a development of more resilient and responsive supply chains

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Big data analytics for supply chain, supply chain through demand forecasting, demand forecasting using supply chain.

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This study explores integrating artificial intelligence (AI) and machine learning (ML) techniques into cloud automation processes, focusing on enhancing auto-scaling mechanisms. Auto-scaling is a critical cloud management component, dynamically adjusting resources to meet fluctuating demands. Traditional auto-scaling methods often rely on static thresholds and reactive policies, which can lead to inefficiencies such as over-provisioning or resource shortages. This research addresses these limitations by employing predictive analytics and machine learning algorithms to create a more adaptive, intelligent, and proactive auto-scaling system. The research utilizes a combination of supervised and unsupervised machine learning models to predict workload patterns and optimize resource allocation in real time. Historical data from cloud infrastructure, including CPU usage, memory consumption, and network traffic, are analyzed to train these models. The study implements various algorithms, such as decision trees, neural networks, and reinforcement learning, to enhance the auto-scaling mechanisms' predictive accuracy and decision-making capabilities. A simulated cloud environment tests and validates the proposed system, ensuring its robustness and scalability. The findings demonstrate that AI-driven auto-scaling mechanisms significantly outperform traditional methods regarding resource utilization, cost efficiency, and response time. The predictive models successfully anticipate workload surges and optimize resource allocation before bottlenecks occur, leading to a smoother and more efficient cloud operation. Additionally, integrating machine learning into the auto-scaling process reduces the reliance on manual configurations and static policies, allowing for more dynamic and flexible cloud management. The implications of this research are far-reaching for cloud service providers and enterprises relying on cloud infrastructure. By leveraging AI and machine learning, organizations can achieve more efficient resource management, leading to cost savings, enhanced performance, and improved user experiences. The study also sets the stage for future advancements in cloud automation, where AI-driven approaches could become the norm, further pushing the boundaries of what cloud computing can achieve. This study discusses the role and capability of AI and machine learning in scaling clouds, focusing on improving auto-scaling dynamic attributes. Therefore, the switch from reactive resource management to predictive and proactive resource management is a welcome publication that provides new angles for increasing the smartness of cloud structures to meet the continuously growing demand.

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 Keywords

AI-powered cloud automation, Auto-scaling mechanisms, Predictive analytics, Machine learning, Cloud infrastructure

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 Abstract

This research aims to examine the effect of a weight training program on the serving performance of high school volleyball players. Twenty-seven players from Vijayapur, Karnataka, India, who were also high school volleyball players, were randomly assigned to three equal groups of nine. The groups included: Experimental Group I (WR), which engaged in resistance exercises using their own body weight; Experimental Group II (WRS), which combined resistance exercises with weights and specific volleyball skills; and Group III (CG), which served as the control group with no additional training. The participants underwent pre-test evaluations of their serving abilities using the AAHPER Volleyball Serving Test, with performance measured in points. Over twelve weeks, the experimental groups followed their respective weight training programs. Post-test serving performance scores were collected after the training period. Statistical analyses using ANOVA and ANCOVA were conducted to determine significant differences in serving skills among the groups. The LSD post hoc test was employed when the ANOVA F-value indicated significance, with a 0.05 level of confidence set for all statistical tests. The findings highlight the value of combining resistance training with skill-specific practice to enhance serving performance in volleyball. Coaches and trainers are encouraged to integrate both resistance training and skill drills into their programs to achieve optimal performance improvements. Future research could explore the long-term effects of these training methods and their applicability to actual game performance.

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Serving, Weight training, Resistance exercises, Volleyball, Skill performance, High school players

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 Abstract

Power quality is one of major concerns in the present era. It has become important, with the introduction of sophisticated devices, whose performance is very sensitive to the quality of power supply that results in a failure of end user equipment's. One of the major problems dealt here is the voltage sag. To solve this problem, custom power devices are used. One of those devices is the Dynamic Voltage Restorer (DVR), which is the most efficient and effective modern custom power device used in power distribution networks. Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbance. It can provide the most commercial solution to mitigation voltage sag by injecting voltage as well as power into the system. This paper presents modeling, analysis and simulation of a Dynamic Voltage Restorer (DVR) using MATLAB. The efficiency of the DVR depends on the performance of the efficiency control technique involved in switching the inverters. In this model a PI controller and Discrete PWM pulse generator is used.

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Dynamic Voltage Regulator & Power Quality Distribution System

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This study focuses on the impact of private schools in improving educational opportunities for tribal children in the Palamu region of Jharkhand, India. The research highlights the key role played by specific private schools in increasing enrollment and reducing dropout rates among Scheduled Tribe students. These schools, such as St. Joseph School, St Teresa School and St. Xavier School, Mahuadanr of Palamu division have significantly improved access to quality education for tribal communities, contributing to their socio-economic advancement. The study recommends concerted efforts to address educational disparities faced by Scheduled Caste, Scheduled Tribe, and tribal children, emphasizing the need for tailored interventions to support educational equity in rural India. Overall, the research provides insights into effective strategies for promoting educational equity and empowering tribal communities in the Palamu region.

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quality, educational opportunity, private schools, enrolment, drop-out

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 Abstract

????? ??? ???? ?? (Goods and Service Tax- GST) ?? ?? ?????????? ?? ??? ?? ?????? 1 ???? 2017 ???? ???? ??????? ??? ????. ?????? ????? ??? ???? ?? ???? ???? ?? ?? ???? ???? ?????????? ??? ????????? ?? ????????? ???????? ?????????? ?????? ?? ???????????? ???? ????. ?????, ????? ??? ???? ?? ?? ?????????? ?????? ???? ???? ??? ????? ??? ???? ?? ?????? ????? ?????? ????? ??? ???? ?? ?????? ?????? ???? ??????? ??? ????????? ????? ????? ?????. ???? ?? ????, ?????????????? ??????, ???? ??????? ????, ITC ??????, ????? ??? ???? ?? ?? ??? ?-?? ??? ??????? ????????? ???????????, ??????? SMEs ?? ??????? ?????? ???? ???. ?? ????? ?????? ?????? ??? ??????? ??????????? ?????? ?????? ???? ????? ??????? ??????? ???? ???.

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GST Good and Services Tax

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The evolution and growth of social networking and modern web technology have made an individual's online presence permanent. People frequently express their thoughts, ideas, and emotions through social networking links, with the most popular activity being the discussion of everyday events, which may include private or public conversations. Bullying that involves technology is referred to as cyber-bullying. Bullying attacks target teenagers and young people who spent lot of time on social networking sites. The rise of social media, especially Twitter, has caused confusion about the meaning of free expression, which has given rise to a number of worries. Cyber-bullying is one of these issues, a severe global problem that has an impact on both people and societies. There have been numerous reported attempts to intervene, prevent, or lessen cyber-bullying; however, these efforts are unworkable since they depend on the victims' interactions. Victims of this behavior may experience hopelessness and other potentially fatal issues. It is necessary to create monitoring and detection procedures for potentially hazardous Internet behavior. By using machine learning, we can create algorithms to automatically identify cyber-bullying content and recognize language patterns used by bullies and their victims. Therefore, it is crucial to identify cyber-bullying without the victims' participation. Additionally, numerous machine learning classifiers were applied, including the K-nearest neighbor technique, linear regression, decision trees, and the Support Vector Machine classifiers, Random Forest, Naive Bayes, and AdaBoost. The most precise supervised learning algorithm was used to identify communications that contained cyber-bullying.

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cyber-bully, cybercrime, social media, traditional bullying, social Networking sites, Harassment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

?????? ????? ???? ?? ???? ?? ?? ?????? ?? ???? ?? ????? ???? ??? ?????????? ?????? ?????? ???? ???? ??? ?????? ??????? ??????? ?????? ?? ?????????? ??? ??????? ???? ??, ???? ?? ??????? ?? ?????? ????????? ???????? ?????? ????? ?????? ?? ?????????? ??? ??? ???? ???? ???????? ?? ????? ???????? ?? ????? ?? ????? ???? ?? ?????? ???? ??? ???????? ??? ???? ?? ???????? ??????? ??? ?????? ?????????? ??? ??????? ???????? ?? ????? ???????? ?? ????????? ?????? ???? ??? ?????? ??? ?????????? ????????? ???????? ???? ?? ?????? ???? ???? ????????? ?? ??? ??? ????? ???? ?? ??????? ??? ?????? ?????????? ?? 60 ?????? ??? ?????????? ?? ??? ?????????? ?????????? ???? ?????? ???? ???? ??????? ??????? ???? ?? ??0 ??????? ?????? ??????? "??????? ????? ??????? ?????" ?? ?????? ???? ???? ??????? ???????? ???? ???????, ???? ????? ??? ???????? ?????? ?? ?????? ???? ???? ???????? ??? ??? ???? ??? ?? ?????? ?????????? ?? ?????? ??????? ?????????? ?? ???????? ?? ??????? ???? ????? ?? ???? ??????? ??? ?? ?????? ?? ?????? ?????? ????????? ??????? ?? ???????? ?? ???????? ?? ????? ?? ??? ?????????? ??????? ???? ??? ??? ?? ???? ??? ???? ?? ?????? ??????? ?? ?? ?? ????? ?? ????? ??? ?????

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?????? ????????, ??????? ????????, ???????? ????, ????? ????????

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 Abstract

PWM Techniques for the Reduction of Harmonic Magnitudes at Multiples of Switching Frequencies

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PWM Techniques for the Reduction of Harmonic Magnitudes at Multiples of Switching Frequencies

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We're entering a new era of computing technology that many are calling the Internet of Things (IoT). Machine to machine, machine to infrastructure, machine to environment, the Internet of Everything, the Internet of Intelligent Things, intelligent systems--call it what you want, but it's happening, and its potential is huge. We see the IoT as billions of smart, connected "things" (a sort of "universal global neural network" in the cloud) that will encompass every aspect of our lives, and its foundation is the intelligence that embedded processing provides. The IoT is comprised of smart machines interacting and communicating with other machines, objects, environments and infrastructures. As a result, huge volumes of data are being generated, and that data is being processed into useful actions that can "command and control" things to make our lives much easier and safer--and to reduce our impact on the environment. The creativity of this new era is boundless, with amazing potential to improve our lives. The following thesis is an extensive reference to the possibilities, utility, applications and the evolution of the Internet of Things. The Internet of Things (IOT) describes a kind of network which interconnects various devices with the help of internet. IOT assists to transmit data with among devices, tracing and monitoring devices and other things. IOT make objects 'smart' by allowing them to transmit data and automating of tasks, without lack of any physical interference. A health tracking wearable device is an example of simple effortless IOT in our life. A smart city with sensors covering all its regions using diverse tangible gadgets and objects all over the community and connected with the help of internet. Thus, IoT is paving the way for new dimensions of research to be carried out. This paper presents the recent development of IoT technologies and discusses future applications and research challenges.

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Internet of Things (IoT), Architecture of IOT; Implemantation;IOT Appliacations ;future technologies; IoT Challenges ; Conclusion

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Flight of time

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This study aims to analyze the influence of Goods and Services Tax (GST) on the financial performance of selected automobile firms in India. The research objectives are to examine the financial performance of four selected automobile companies in India and to analyze the impact of GST on their financial performance. The selected companies are Eicher Motors Ltd, Mahindra and Mahindra Ltd, Maruti Suzuki India Ltd, and Tata Motors Ltd. The study uses financial statements from 2013-14 to 2016-17 for Pre-GST data and financial statements from 2017-18 to 2020-21 for Post-GST data. Ratio analysis and t-test techniques are used to analyze the financial performance of the selected companies. The study concludes that there is no significant difference in Return On Capital Employed, Net Profit Margin, and Asset Turnover Ratio during Pre and Post-GST periods for the selected automobile companies. This study provides important insights into the impact of GST on the financial performance of selected automobile firms in India and can help policymakers and industry experts in understanding the implications of GST on the automobile industry.

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GST, Financial Performance, Automobile Industry

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There are different categories that fall in the broader category of women headed households. They are widowed, abandoned, separated and single mothers, women whose husbands have migrated for employment for considerable period of time, and women whose husbands have lost their function as economic providers due to unemployment or illness. This typology also includes women who are defacto heads of households and includes women whose husbands are present at home but are non- functional economically. Thus, a women headed household need not necessarily be single parent family, since the basis for the definition of women headed household is "the women roles as the economic provider regardless of the presence or absence of the spouse at home".

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Women Headed Households and Social Stigma: A Study in Jammu District

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In the present globalised and industrial society, human beings are being exposed to various diseases due to pollution. Also various other conditions are prevailing. So to get rid of health risks it is becoming difficult. So to meet the medical expenses for health problems, the concept of health insurance came into existence. Health insurance is a contract between the insurer (the company) and the insured (the customer) such that later will pay an amount at regular intervals known as premium and the former will pay the amount on the happening of the event. The event may be the diseases/surgeries which will be predefined and mentioned in the policy conditions. Consumers need to know about the different policy conditions in addition to the diseases coverage. There exists a difference in the awareness about health insurance among urban and rural consumers. Hence, in this paper an attempt is made to study about the difference in the consumer awareness among urban and rural consumers towards health insurance. It also studies the requirements of the consumers regarding health insurance.

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Health Insurance Company, Premium, Urban and Rural Consumers

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Purpose of Study: The aim of this study is to identify the challenges and implications of using virtual space in the English Language teaching and learning process. The present global scenario is in a dire necessity of teachers to be updated in new innovative methods of teaching learning processes. The teachers are encouraged to develop a virtual class space to harmonize their teaching process and the students are supposed to devote their learning hours in this virtual space where the teachers may render different sorts of activities to enhance their language learning. Methodology:The study was conducted among twenty five English Language teachers using a background information questionnaire to capture the insights and perceptions of teachers about the virtual English classrooms. Main Findings: This study identifies different challenges and implications of teachers towards the use of virtual space in English language teaching and learning process. The study reveals that the virtual learning spacecan be madean effective tool that facilitates the language learners to feel at ease and to create a stress free learning environment by following certain strategies to overcome the challenges. Application of this study: Considering the various technological findings and their effectiveness as a learning platform, this study tries to provide an overview of the challenges of using thevirtual platforms and strategies to overcome those challenges. The strategies to overcome the challenges can enhance both technical skills and language skills of not only the students but also the teaching fraternity. Novelty of the study: The global education system has embarked upon the utilization of the traditional classrooms and also additional virtual classrooms to improve the quality of teaching learning process at all levels.

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English Language Teaching, Language Learning, Virtual Class Space, Learning Platform, Innovative Teaching, Challenges.

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Creative Commons Attribution 4.0 and The Open Definition