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Empirical analysis of borrowers' motivation to use online peer-to-peer lending platforms in India
Established on the technology acceptance model, this paper puts forward a model to understand the borrowers' motivation to use (MU) peer-to-peer (P2P) lending platforms. Data from 362 Indian users were employed to test the research model by applying structural equation modelling. The results show that perceived intention, ease of use, and usefulness have significant relation in motivating borrowers to use P2P lending platforms. However, borrowers' perceptions of trust had an insignificant impact on MU the P2P lending platform. When compared to the individual technology acceptance model, the integrated model provides further explanation regarding the motivation of borrowers to use P2P lending platforms. The study contributes to the theoretical area by identifying the factors that motivate borrowers to use P2P lending platforms for their short-term financial requirements, from a unified perspective. In addition, this research provides insights about borrowers' MU P2P lending platforms in India. Copyright 2023 Inderscience Enterprises Ltd. -
Sentiment Analysis of Lenders Motivation to Use a Peer-To-Peer (P2P) Lending Platform: LenDenClub.Com
Peer-To-Peer lending platforms are becoming a good investment avenue for lenders to invest their money in borrowers, who need money for a different purpose. As lending and borrowing of money is facilitated by the P2P lending platform, it becomes necessary for the platform to understand the users and accordingly fine tune the 'User Interface' (UI) and 'User Experience' (UX) of the platform. For lending and borrowing to take place through a platform it is necessary to have an 'n' number of lenders who are ready to lend money to an 'x' number of borrowers. This study is specifically done to understand lenders' motivation to use P2P lending platforms. This is a unique research work as sentiment analysis of lenders' motivation to use these platforms has not been explored earlier. The sentiment analysis technique was used to examine lenders' sentiments towards the use of P2P lending platforms. The research results show that, ~ 70 percent of lenders showed motivation to use P2P lending platforms as an investment avenue in the future. As the P2P lending platforms are relatively new more research can be carried out in future. 2024 IEEE. -
Measuring Consumer Perception for P2P Platform: NLP Approach
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik's wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower's., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear. 2022 IEEE. -
Promoting photocatalytic hydrogen evolution rates in layered graphitic carbon nitride through integrated non-noble CoB co-catalyst
Despite being one of the most widely studied metal-free semiconductors, graphitic carbon-nitride (gC3N4) shows meaningful photocatalytic activities only when loaded with noble-metal co-catalysts. The present work reports an alternative to noble metals in the form of cobalt boride (CoB) co-catalyst that can be easily integrated within the gC3N4 framework with facile fabrication strategies. The optimized CoB-gC3N4 composite showed ?60 times higher hydrogen generation rate compared to bare gC3N4 nanosheets, with good stability. Detailed morphological, structural, chemical, electrochemical and spectroscopic investigations revealed the key aspects of CoB-gC3N4 composite that unanimously led to higher photocatalytic activity. Computational investigations not only corroborated the experimental results but also established that the surface Co and B sites in CoB provided the most energetically favoured sites for hydrogen evolution reaction. Based on the experimental and computational investigations, a generic reaction mechanism was formulated that will prove as a guiding light for future studies on similar photocatalytic systems. 2024 The Authors -
Survey of prevalence of anxiety and depressive symptoms among 1124 healthcare workers during the coronavirus disease 2019 pandemic across India
Background: A prospective study was conducted during the second phase of the coronavirus disease 2019 (COVID-19) pandemic in India to assess the prevalence of anxiety and depressive symptoms among healthcare workers (HCWs) and factors that influence the outcome. Methods: A self-administered questionnaire was completed by 1124 HCWs during the COVID-19 pandemic (March 30, 2020, to April 2, 2020). Demographic data, questions on COVID-19 and scores of the Hospital Anxiety and Depression Scale were analysed using the chi-square test (Bonferroni correction) and binary logistic regression. Results: The study consists of 1124 HCWs, including 749 doctors, 207 nurses, 135 paramedics, 23 administrators and ten supporting staff members. The prevalence of anxiety and depressive symptoms were reported as 37.2% and 31.4%, respectively. The risk factors for anxiety were female gender (30.6% vs 45.5%), age group (2035 years) (50.4% vs 61.2%), unmarried (21.2% vs 30.6%) and job profile (nurse) (14.7% vs 26.4%). The protective factor was having service of more than 20 years (23.4% vs 14.8%). The risk factors for depression were age group (20-35 years) (51.3% vs 61.3%) and employed at a primary care hospital (16.2% vs 23.4%). The protective factors were job profile (doctor) (69.9% vs 59.6%) and having service of more than 20 years (22.3% vs 15.5%). Conclusion: Approximately one-third of the HCWs reported anxiety and depressive symptoms. The risk factors for anxiety symptoms were female gender, younger age and job profile (nurse) and for depressive symptoms were younger age and working at a primary care hospital. Future research studies should identify strategies for providing a safer and supportive work environment for HCWs to face epidemics/pandemics. 2020 -
Positive People and Confident Competitors: Resilient Youth Development Through Sport and Physical Activity
In the altering world scenario, there is a necessity to plan, prepare and progress with youth development. Research has associated positive youth development with the 5Cs model (competence, confidence, connection, character and caring) (Lerner et al., The Journal of Early Adolescence 25:17-71, 2005) to build resilience in youth. Over the past 35 years, sport psychology has established that sport helps in developing necessary psychological skills and attributes among youth. Youth sport is an extracurricular activity that provides young people with unique negative and positive experiences. Within these experiences, the individual goes beyond the self and has to work with a diverse group of others for self-development and achievement of shared goals. In this chapter, our primary objective is to review the foundations of literature concerning confidence, resilience and identity as corner-stones for positive youth development through sport. To achieve this objective, we adopt a global approach blending field experience from participatory sport, developmental sport and elite sport to provide an intervention framework grounded in applied sport psychology. Intervention framework provided is aligned to the COM-B behaviour change model (Michie et al. 2011) for sustainable change. The focus is on a balance between developing stable protective factors for mental health and positive youth development to ensure appropriate cognitive, social, emotional and behaviour skills to thrive in an evolving world. Implications for transferring this learning cross-culturally and in non-sport contexts such as schools and grass-root programs are discussed with recommendation for good practice. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
An effective Approach for Pneumonia Detection using Convolution Vision Transformer
Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. 2022 IEEE. -
EmploChain: A Blueprint for Blockchain-Driven Transformation in Employee Life Cycle Management
Integrating blockchain technology into human resource management presents both transformative opportunities and implementation challenges that need to be addressed. This paper proposes a blockchain-based EmploChain Framework, a decentralized ledger approach specifically designed to enable Employee Life Cycle Management by harnessing the potential of blockchain technology. The study looks at the potential benefits of the proposed framework, including increased security, transparency, and automation. The paper also looks at potential limitations like scalability concerns and implementation costs and explores the possible solutions to overcome them. The aim of this research is to provide a thorough understanding of the framework's implications, thereby facilitating informed decisions to implement EmploChain Framework for managing the Employee Life Cycle of an organization.. 2024 IEEE. -
Blockchain-Enabled Resume Verification: Architectural Innovations for Secure Credential Authentication in the Digital Era
In the contemporary digital landscape, the verification of resume credentials poses a significant challenge, with the integrity of such information being crucial for job seekers and employers alike. This paper presents an avant-garde architectural framework that utilizes blockchain technology to revolutionize the storage, verification, and sharing of resume information, thus ensuring an unparalleled level of security and reliability. Through the implementation of a decentralized ledger that is both immutable and tamper-evident, this innovative architecture facilitates the permanent recording of academic credentials, employment history, and professional accomplishments, thereby enabling immediate and verifiable access for potential employers and educational institutions 2024 IEEE. -
Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model
In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models. 2024 River Publishers. -
Moving Towards Responsible Consumption: The Road Ahead for Sustainable Marketing
The fundamental tenet of consumerism revolves around the belief that the burgeoning consumption of goods is favourable for the economy. Since the dawn of the Industrial Revolution, humanity has witnessed an exponential upsurge in consumerism. It has been related both to the increase in the population size as well as an increase in our demands due to constant changes in lifestyle. Multiple sources have corroborated the fact that if this consumption behaviour continues unabated, we will soon face an acute shortage of resources of all kinds. Both consumer behaviour patterns such as addictive consumption and conspicuous consumption can be attributed to this. Amongst the solutions available, 'Demarketing' is one. It is a type of marketing when a brand wants to discourage you from buying its product. The paper is descriptive in nature and is based on secondary data which has been collected from journals, blogs, websites, magazines, books, etc. The paper intends to explore the theme of demarketing vis-vis the materialistic purchase behaviour of a modern-day consumer and green demarketing strategies that companies are adopting by way of sustainable marketing. The Electrochemical Society -
Emotional Maturity and Self-Perception Among Adolescents Living With HIVNeed for Life-Skills Intervention
Among Adolescents Living With HIV (hereby referred to as ALWH), emotional maturity and self-perception gain importance because they play a decisive role in the overall development into adulthood. This study examines the relationship between self-perception and emotional maturity among ALWH. The sample comprised of 92 male and female ALWH, aged 13 to 18 years. Self-Perception Questionnaire and Emotional Maturity Scale were the tools used. Descriptive and inferential statistics were used for analysis. Correlation analysis showed a negative moderate relationship between Physical Competence and Global Self-Worth dimensions of self-perception and emotional maturity, with regression analysis confirming their predictive abilities. Discussions are focused on the relevance of the findings in terms of a need for a psychosocial life-skills intervention for the population. The Author(s) 2019. -
Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm
UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This studys principal objective is to further develop UAV network routing decisions by using CNNs ability for design recognition and the Modified SBOs optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAVs, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. 2024 River Publishers. -
Analysing the Impact of Perceived Risk, Trust and Past Purchase Satisfaction on Repurchase Intentions in Case of Online Grocery Shopping in India
The Indian online grocery market has been propelling since last few years. The size of online grocery market in 2020 was estimated as $2.9 billion and it is further anticipated to reach at the compound annual growth rate (CAGR) of 37.1% during 2021 to 2028. Companies such as Amazon, Flipkart grocery, BigBasket, Grofers and Jiomart have been coming up with new attractions for consumers such as providing timely no contact delivery, accepting various digital modes of payment and offering several discounts which have fascinated consumers towards buying their regular grocery from various online platforms. Corona virus has also fuelled up the safety concerns of people; due to which a large section of the citizens are working from home and are dependent on the online platform for various purposes including grocery shopping. This has provided several growth opportunities to the online grocery market. This research investigates about the purchase behaviour of customers towards online grocery shopping. The study aims to understand the purchase behaviour of e-grocery shoppers of India and to examine the association between satisfactions with online purchase, trust on online grocers, perceived risk and online repurchase intention of grocery items. The study uses primary data collected from 555 online grocery buyers. The findings of the study indicate that online customer satisfaction is a significant factor that influences repurchase intentions of online grocery shopping. Perceived risk negatively influence trust as well as repurchase intentions. Trust is found to be a mediating factor between shopping satisfaction and repurchase intentions. The study also builds and tests an online customer behavioural model with actual purchasing behaviour and identifies the continued presence of perceived risk, shopping satisfaction and trust in grocery e-retailing. 2023 IMI. -
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork
The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %. 2022 IEEE. -
A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research. 2013 IEEE. -
Social Media and Steganography: Use, Risks and Current Status
Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People's actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper's concept's future scope is explored. 2013 IEEE. -
Advances in text steganography theory and research: A critical review and gaps
There is an immense advancement in science and technology, and computing systems with the highest degree of security are the present hot topic; however, the domination of hackers and espionage in terms of disclosing the sensitive information are steadily increasing. This chapter presents a theoretical view and critical examination of the few text steganography methods in the contemporary world. It tells the direction in which research has developed over the past few years. Cryptography, the encipherment to a certain extent, protects the data by making it unreadable but not safe. Improvisation of the same can be done using another layer of protection that is steganography in which the secret embedded inside the cover text will not be revealed. 2021, IGI Global. -
Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
Sustainable luxury tourism: Promises and perils
Recent decades have witnessed a rising concern regarding the prosperity of the environment and utilisation of resources. A sustainable approach is being promoted in all sectors. In the field of tourism, sustainable tourism is widely discussed among researchers and practitioners. On the other hand, luxury tourism is criticised for lavish resource utilisation to serve the few luxury tourists. There is a need to include sustainability in luxury tourism to benefit the environment, local communities, tourist destination and luxury tourists. However, sustainable luxury tourism is an emerging concept and needs more investigation. This chapter attempts to present the existing knowledge about sustainable luxury tourism by implementing a systematic literature review. Further, the opportunities and challenges associated with sustainable luxury tourism are being highlighted. This study has identified the factors that need to be considered to promote sustainable luxury tourism. Moreover, suggestions of the researchers are being presented to serve as guidelines. This study includes an example of the Diphlu river lodge, which has practised sustainable luxury tourism for many years. The viewpoint of luxury tourists are being understood by analysing the reviews of tourists from TripAdvisor using NVIVO-12 qualitative data analysis software. The combination of literature review and practical information provides insight into sustainable luxury tourism. 2022 by Emerald Publishing Limited. All rights reserved.