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Effect of Work Experience on Psychological Capital and Job Satisfaction among Employees
In todays fast-paced workplaces, where technology is evolving at a dizzying rate, professionals face a myriad of problems. Their inability to strike a healthy work-life balance may lead to feelings of dissatisfaction with their job. Consequently, in order to achieve flexible, long-term growth and job happiness, businesses should support their employees good psychological development. Primary data was acquired from employees in the automotive manufacturing company, totalling 95 individuals, using standardized questionnaires that had a good level of reliability and validity. The results indicated that there is no significant effect of work experience on the psychological capital of employees (F = 1.21; p < 0.30) and their job satisfaction (F = 0.35; p < 0.70). The major findings indicate that regardless of an employees level of experience, there is no substantial variation in the psychological capital and job satisfaction of the employees. This variation may also arise because of other specific factors. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
Precision agriculture is the process that uses information and communication technology for farming and cultivation to improve overall productivity, efficient utilization of resources. Soil prediction is one of the primary phases in precision agriculture, resulting in good quality crops. In general, farmers perform the soil prediction manually. However, the efficiency of soil prediction may be enhanced by using current digital technologies. One effective way to automate soil prediction is image processing techniques in which soil images may be analyzed to determine the soil. This paper presents an efficient image analysis technique to predict the soil. For the same, a robust feature selection technique has been incorporated in the image analysis of soil images. The developed feature selection technique uses a new oscillating spider monkey optimization algorithm (OSMO) for the selection of features that are relevant and non-redundant. The new oscillating spider monkey optimization algorithm increases precision and convergence behavior by using an oscillating perturbation rate. A set of standard benchmark functions was deployed to visualize the performance of the new optimization technique (OSMO), and results were compared based on mean and standard deviation. Furthermore, the soil prediction approach is validated on a soil dataset, having seven categories. The proposed feature selection method selects the 41% relevant features, which provide the highest accuracy of 82.25% with 2.85% increase. 2013 IEEE. -
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE. -
Implementing Quality Healthcare Strategies for Improving Service Delivery at Private Hospitals in India
Healthcare is becoming the largest growing sector of India because of its huge coverage, providing services and investment by public and private players. In India growth of private hospitals have totally changed the scenario of health care delivery. This study explores the effectiveness of the strategies to provide quality health care and thereby improving the service delivery in Private Hospitals. In total 122 responses were collected after administering the questionnaires. The findings of this study reveals that quality health care strategies has positive impact on service delivery. Quality health care strategies showed a different kind of associations with three measures of quality namely structure, process and outcome measures. The implications from the study provides the need of multifaceted approach for implementing quality improvement strategies and adoption of the model for the same. This study recommends a blend of quality improvement programs with increased ICT (Information and Communication Technology) applications for enhancing the turnaround time. Further study can be conducted on other healthcare quality dimensions and strategic interventions that can enhance the quality of health care and clinical outcomes in Private Hospitals in India. 2017 Indian Institute of Health Management Research. -
Standards of human rights to palliative care: gaps and trends
Purpose: The purpose of this paper is to investigate key milestones in development of standards of human rights to health care in particular context of addressing palliative care, relevant efforts of advocacy in past decade and future area of growth. Design/methodology/approach: In this study, analysis of human rights and its standards in context of palliative care has been provided through the lens of freedom from ill treatment and torture, right to health care and older persons and childrens rights. Findings: Findings of this study highlighted significant developments in this area which include following: first treaty of human rights which explained right to palliative care; first resolution on palliative care by World Health Assembly; special rapporteurs report focussed on denial of pain; and addressing issue of controlled medicine availability in special session of UN General Assembly. Originality/value: Human rights standards and their development in context of palliative care have been most significant in relation to freedom from ill treatment and torture, right to health care and older persons rights. Further work is required in context of childrens rights and treaty bodies of human rights need to consistently address state obligations towards palliative care. 2020, Emerald Publishing Limited. -
Comprehensive study of the relationship between multiverse and big data
Studies linking two broader spectra of topics have fascinated scholars in many aspects. Here we tried associating two such far-reaching aspects which have finite connectivity between them. Multiverse has been the talk of the hour which explains the theory of multiple universes which exist in parallel. This is a topic in physics concerned with many relative matters. On the other side, Big data is the subject in computing and information science describing the volume, velocity, and variability of the data hitting computer-connected systems. Big data can only be handled with newer architectures, algorithms, and methodologies as its features are contradicting regular computer systems and networks. It is well known that multiple processors are required to handle big data existing in parallel performing a single job given by the data analyst. So as we know, multiverse consist of hypothetical concepts of several parallel universe having everything like information, energy, and time. However, we see this situation to draw an association connecting parallel universes of the multiverse with parallel processors of big data by incorporating the concepts of working of parallel universe in the processing of Big Data. We provide a comprehensive observation on both the topics and take positive lenience on bringing a newer terminology in data science. History of multiverse along with big data structures are brought in with related parameters. This aspect is novel in its nature and we complement the literature carried out by the researchers and scholars appropriate analysis. We also showcase a model of the school of thought mentioned above in drawing conclusions. 2023 The Authors -
Impact of COVID-19 on the mental health among children in China with specific reference to emotional and behavioral disorders
Purpose: This paper aims to investigate impact of coronavirus COVID-19 on childrens mental health specifically emotional and behavioral disorders. It aims at identifying the main disorders faced by children during epidemics and suggests recommendations to nurture resilience among children and involving them in various positive activities. Design/methodology/approach: This study is based on review of literature focused on COVID-19. Recent articles related to coronavirus or COVID-19 and psychological distress among children were included to draw conclusion and impact of COVID-19 on mental health of children. Due to the limited availability of studies on CONID-19 impact on mental health of children, studies focused on recent pandemic were focused. Findings: The identified literature reports a negative impact of COVID-19 on individuals mental health. Relatives health, poor appetite, fear of asking questions about epidemics, agitation, clinginess, physical discomfort, nightmares and poor sleep, inattention and separation issues were among the major psychological conditions analyzed. Personal attributes such as resilience, should be nurtured so that children will be empowered to manage difficult situations such as traumas and disappointments. Several measures were suggested by pediatricians in China to family members and parents such as playing games with children to reduce feeling of loneliness, increased communication to address their concerns and fears, promoting and encouraging physical activities and involving in musical activities to reduce fear, worry and stress among children. Originality/value: Coronavirus is new pandemic and growing rapidly. most of the research studies are focused on physical health of individuals, but mental health concept has bene overlooked. This study helps to broaden the scope of research on children's mental health by examining the impact of COVID-19. 2020, Emerald Publishing Limited. -
Crowdsourcing in Higher Education: Theory and Best Practices
The widespread use of crowdsourcing strategies in higher education institutions improves the performance of students by using collective initiatives to enhance the skills of each student, efficiently optimizes the lecturing process by exchanging and pooling research materials, and also improves the financial situation of alumni by encouraging crowdfunding of tuition. We identify four main areas in this study where the use of crowdsourcing strategies plays an important role in the success of alumni in institutions of higher education. The proposed crowd teaching approach optimizes lecturing, allowing lecture notes to be shared and exchanged according to the various curricula of Higher Education Studies. With crowd learning, students learn by execution on collaborative projects in which different students share (effectively) teaching each other under the guidance of the lecturer, learn the necessary skills to achieve the projects goals and solve the proposed issue. In relation to accessing funding, the tuition fees of students can be financed by crowdsourcing approaches through crowd tuition and even crowdfunding can be used to procure laboratory and classroom content or the learning stays of students abroad. Using this crowdsourcing tool, students can find assistance in paying university taxes and also have an interest in further learning with other students. The application of crowdsourcing to education allows for optimization of the institutions budget and a more effective use of learning time, leading eventually to better outcomes for students. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Interconnections of yogic practices with mental health
Yoga, an ancient practise of humankind, attempts to promote a lifestyle that is free of maliciousness with emphasis on inculcating qualities that would aid the individual in living a life that is truly actualizing. Practise of yoga is not limited to holding specific asanas but various components of yoga such as pranayama, pratyahara, etc.; all attempt to enhance an individual's wellbeing. The chapter has contextualized yoga therapy including pranayama, mudras, and chakras to biopsychosocial models, and attempted to identify yogic practises that bring holistic enhancement. Yoga, being cost-effective, and having no side effects, unlike pharmacological treatments, can be used as an adjunctive therapeutic agent in improving symptoms or improving mood and reducing stress. However, it is important to note the feasibility and limitations of yoga interventions, like proper trained professionals to minimize any ill effects. The chapter attempted to promote the practise of yoga as an adjunctive form of treatment which would thereby aid in improving biopsychosocial wellbeing. 2024, IGI Global. All rights reserved. -
A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE. -
A Comprehensive Review of Linear Regression, Random Forest, XGBoost, and SVR: Integrating Machine Learning and Actuarial Science for Health Insurance Pricing
Actuarial science and data science are being studied as a fusion using Industry 4.0 technologies such as the Internet of Things, artificial intelligence, big data, and machine learning (ML) algorithms. When analyzing earlier components of actuarial science, it could have been more accurate and quick, but when later stages of AI and ML were integrated, the algorithms weren't up to the standard, and actuaries experienced some accuracy concerns. The company requires actuaries to be precise with analysis to acquire reliable results. As a result of the large amount of data these companies collect, a choice made manually may turn out to be incorrect. We will, therefore, examine alternative models in this article as part of the decision-making process. Once we have chosen the best path of action, we will use our actuarial expertise to evaluate the risk associated with specific charges features. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE. -
Recommender system for surplus stock clearance
Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend those which require clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
MVTamperBench: Evaluating Robustness of Vision-Language Models
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerability to adversarial tampering and manipulations remains under-explored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping; based on real-world visual tampering scenarios such as surveillance interference, social media content edits, and misinformation injection. MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks. This benchmark challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families. We reveal substantial variability in resilience across tampering types and show that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code, data, and benchmark to foster open research in trustworthy video understanding. 2025 Association for Computational Linguistics. -
1,8-Naphthyridinone compounds and uses thereof /
Patent Number: WO 2019/018583, Applicant: NUVATION BIO INC.
1,8-naphthyridinone compounds as modulators of an adenosine receptor are provided. The compounds may find use as therapeutic agents for the treatment of diseases mediated through a G-protein-coupled receptor signaling pathway and may find particular use in oncology. -
Heterocyclic compounds and uses thereof /
Patent Number: US20190106436, Applicant: NUVATION BIO INC.
Heterocyclic compounds as Weel inhibitors are provided. The compounds may find use as therapeutic agents for the treatment of diseases and may find particular use in oncology. -
Compounds and methods for the treatment of non-alcoholic steatohepatitis /
Patent Number: WO2019111225, Applicant: AVALIV THERAPEUTICS.
Compounds and compositions are provided having the structure of Formula (I) or a pharmaceutically acceptable salt, tautomer, or stereoisomer thereof, wherein T, T', U, U', V, W, R1, R2, R3', n, o, o', o'', and o''' are as defined herein. Suchcompounds are useful for treating liver diseases and abnormal liver conditions, including non-alcoholic steatohepatitis via inhibition of the lysosomal enzyme cathepsin D. -
Financial Analytics AI in Sustainable Innovations
Financial analytics integrates AI, ESG factors, and risk management to drive sustainable investments. It enables data-driven decision-making, optimizing financial and environmental outcomes. AI-powered tools like machine learning and predictive analytics enhance risk assessment and portfolio optimization. ESG integration ensures ethical and impactful investments. Despite challenges like data reliability, financial analytics is key to fostering a resilient, equitable, and sustainable global economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Survey on deep learning techniques used for object identification of underwater forward looking sonar images
Underwater object identification using forward-looking sonar (FLS) images is crucial for autonomous underwater vehicles (AUVs) for navigation and obstacle avoidance. Deep learning techniques have emerged as powerful tools for object recognition in various domains. This paper surveys deep learning approaches employed for object identification in FLS images. We examine the effectiveness of popular deep learning frameworks such as YOLOv5, EfficientDet, and MobileNet, and transfer learning, data enhancements to improve object recognition performance, and the role of adversaries training. We also examine the potential of focusing and lightweight CNN algorithms developed for FLS images despite these advances, challenges still exist due to the limited number of registered cases. The paper analyzes how deep learning methods address these challenges and highlights their effectiveness in object identification. We aim to provide a comprehensive overview of the current state-of-the-art in deep learning for FLS object identification, paving the way for further research and development in this field. Results of this study show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment. 2025 Author(s). -
Regulation and innovation in financial markets: The impact of fintech on traditional banking and financial systems blockchain technology
In this chapter, we present the underlying technical principles of distributed ledger technology (DLT) and blockchain technology and outline their practical applications in FinTech. In the recent years, DLT and blockchain technologies in general and cryptocurrencies, in particular, have attracted substantial attention from both researchers and practitioners due to their unique technological features such as the lack of centralized control and high level of anonymity. Because of the disruptive nature, DLT and blockchain have led to the evolution of decentralized applications in multiple domains such as finance, health care, supply chains etc. In this chapter, we first outline basic principles and foundations underpinning the DLT and blockchain technologies. Second, we discuss several applications in the FinTech domain such as cryptocurrencies, smart contracts, risk management, corporate finance, governance, crowdfunding, and derivative markets. 2025, IGI Global Scientific Publishing. All rights reserved.