Volume 11 | Issue 5 |

 Abstract

Because of the intricacy, unconventionality, and nonlinearity of monetary frameworks, it is challenging to anticipate stock costs precisely. The PCC-BLS structure, a stock expense deciding multi-pointer feature decision method considering the Pearson connection coefficient (PCC) and Broad Learning System (BLS), was presented in this brief. PCC chose the initial stock price, technical indicators, and financial indicators as input characteristics from a list of 35 options. Second, a BLS was trained with these filtered input characteristics and information features were quickly extracted. The proposed strategy's performance was evaluated using four stocks that are either listed on the Shanghai Stock Exchange or the Shenzhen Stock Exchange. In addition, the forecasting results were compared to those of the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Broad Learning System (BLS), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron (MLP), and Support Vector Regression (SVR). The highest modelfitting capability was demonstrated by the proposed model, which outperformed all other algorithms in this brief.

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 Keywords

Broad learning system, machine learning, Pearson correlation coefficient, time series forecasting, complex system.

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The present study examined the relationship between workplace procrastination, work load and work performance among working individuals in India; the difference in procrastination, work load and work performance between males and females along with understanding the impact of procrastination on work load and work performance. The data was collected in hybrid mode within the age group of 20- 40 years from 202 working individuals employed in IT sector and involved in software development domain, residing in India using Procrastination at Workplace scale, Quantitative Workload Inventory and Individual Work performance Questionnaire. Statistical analysis of correlation, independent sample t test and linear regression was done to test the hypothesis. The results indicated that the variables, procrastination, workload and work performance were correlated to each other; independent variable procrastination influenced the dependent variables work load and work performance; and these variables do not have difference among males and females and different age bracket. Implications, limitations and future suggestions of the study were also stated.

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 Keywords

Workplace Procrastination, Work load, Work performance, Working Individuals

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An approach to accurately acknowledge various Yoga pose Assess deep learning algorithms has been presented during this work. During system, our purpose to access a Yoga pose assessment technique using pose detection to assist the self-learning of Yoga. The system first detects a Yoga pose using multi human joint parts detection only with laptop camera. During this system, we have a tendency to additionally propose AN improved formula to calculate scores which will of all poses. Our application is evaluated on completely different Yoga poses. A hybrid Machine learning model is proposed using Logistic Regression algorithm for Yoga recognition on real-time videos, where Logistic regression is used to extract features from key-points of each frame obtained from Open-Pose. LR algorithm would be trained on a labelled dataset of correct and incorrect yoga poses, and its output could be used to provide real-time feedback to the practitioner.

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 Keywords

Logistic Regression, Anaconda navigator platform, Deep Learning , Yoga Pose detection, etc.

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The issue of work-related stress has become increasingly prevalent, as it not only affects the health and well-being of employees but also impacts the productivity of organizations. To delve deeper into this problem, a study was conducted to examine how work-related stress influences job performance and which gender is more susceptible to its effects. The research design implemented a quantitative approach, with a sample size of 100 individuals (50 men and 50 women) selected from a reputable technology company based in Mumbai. The results indicated that work-related stress has an adverse effect on job performance, with male and female employees experiencing a similar impact. These findings underscore the importance of addressing work-related stress to enhance job performance and prevent negative consequences

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work-related stress, job performance, gender

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Cryptocurrency becomes more popular and merchant accept it, it is playing an increasingly vital role in reshaping the financial system. While many people are forming investments in Cryptocurrency, the vital features, distrust, the predictability of Cryptocurrency are quiet basically unknown, which dramatically risk the investments. It's a business to strain to conclude the procurators that impact the value formation. Here we apply advanced artificial intelligence frameworks of completely connected Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) periodical Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to calculate more on long- term chronicle while LSTM tends to calculate more on short- term dynamics, which indicate the effectiveness of LSTM to exploit useful data hidden in literal memory is stronger than ANN. still, given enough historical data ANN can attain an analogous accurateness equated with LSTM. This reverie provides a unique demo that Cryptocurrency market price is predictable. Still, the explanation of the predictability could differ depending on the complexion of the detailed machine- learning model.

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 Keywords

Artificial Intelligence, ANN, LSTM, Block Chain.

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Abstract: Finance and banking is the life blood of trade, commerce and industry. Now-a days, banking sector acts as the backbone of modern business. Development of any country mainly depends upon the banking system. A bank is a financial institution which deals with deposits and advances and other related services. It receives money from those who want to save in the form of deposits and it lends money to those who need it..The banking is one of the most essential and important parts of the human life. In current faster lifestyle peoples may not do proper transitions without developing the proper bank network. The banking System in India is dominated by nationalized banks. The performance of the banking sector is more closely linked to the economy than perhaps that of any other sector. The growth of the Indian economy is estimated to have slowed down significantly. The economic slowdown and global developments have affected the banking sectors' performance in India in FY12 resulting in moderate business growth. It has forced banks to consolidate their operations, re-adjust their focus and strive to strengthen their balance sheets. Here researcher's objective is to study the Indian banking sector and performance of Indian banks.

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Key Words: Banking System, Banking, Indian Economy, Economic Slowdown.

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In the past decade, the interconnectedness, collaboration and dependency of nations, states and the corporate world has increased significantly. Consequently, organizations are now focusing on the imperative value of prioritizing diversity and inclusion strategies to gain a competitive advantage. It is important to investigate diversity perceptions through a positive psychology lens. Thus, the aim of this study is to examine the relationship between perceived workplace diversity climate and psychological capital amongst employees working in the Indian private sector. Participants were selected from a purposive sample of 160 full-time employees (81 males; 79 females) between the ages of 22-44 years. The scales used for the study included Psychological Capital Questionnaire (PCQ-24) by Luthans (2007) and Diversity Perception Scale by Mor Barak (1998). Multiple parametric statistical tests revealed that there is a strong positive relationship between perceived workplace diversity and psychological capital amongst employees. There is also a significant difference in these perceptions on the basis of gender. The practical implications: to improve diversity perceptions, organizations must focus on investing more in diversity and inclusion and enhancing their diversity management policies and practices through regular diversity training, awareness and sensitivity training, responsible recruitment, more nuanced diversity policies. This will result in better work performance, organizational effectiveness and a sustainable future through higher levels of psychological capital.

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 Keywords

Employee perceptions, workplace diversity, psychological capital, diversity management practices and policies, inclusion, fairness, resilience, optimism, hope, self-efficacy

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 Abstract

With the considerable increase in the application of Handwritten text/character recognition in various domains like pattern recognition , image processing , processing application forms , digitizing ancient articles, processing address, bank check processing and many other handwritten hand processing fields are increasing in popularity. Several methods have been suggested since the last three decades. Handwritten text recognition has drawn the attention of researchers , the well known techniques are CNNs and RNNs (Convolutional Neural Network and Recurrent Neural Networks) . Overall , image processing along with Deep Learning techniques have the potential to improve the accuracy and make handwritten text/character recognition more efficient , and have more promising results in different fields.

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Machine Learning , CNN , RNN , Optical Character Recognition

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Artificial intelligence (AI) has emerged as a transformative technology in various industries, and the financial sector is no exception. AI offers unprecedented opportunities to enhance financial decision making by leveraging vast amounts of data and sophisticated algorithms. However, the adoption of AI in financial decision making also presents challenges and ethical considerations that need to be carefully addressed. This research paper explores the role of AI in financial decision making, highlighting the opportunities it provides, discussing the challenges faced, and examining the ethical considerations associated with its implementation. Artificial intelligence (AI) plays a significant role in financial decision making, offering opportunities for improved accuracy, efficiency, and innovation. However, it also presents challenges and raises important ethical considerations.

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 Keywords

Artificial Intelligence (AI), Financial decision making, Opportunities, Challenges, Ethical considerations, Data analysis

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The advancement in modern technology and social communication has been increased in recent times. Because of this, there are many job opportunities available online for job seekers which do include freshers as well. Sometimes, this job opportunities can be fraud. As many job seekers are freshers, so they don't have an idea about this fraud happening online. Hence, we propose a system which can help the job seekers to find out whether the job posting done online is fraud or a legit job posting. This system uses machine learning algorithm to train the detection model. The user has to insert the URL of the job in the search box, with the help of feature extraction, the model will classify the job posting as legit or fraud job posting.

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 Keywords

Job seekers, fraud, legit, machine learning, feature extraction

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Creative Commons Attribution 4.0 and The Open Definition