Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

Combined Application of Various Techniques for Personalized Job Recommendation

The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively. © 2023 IEEE.

INDIA AND THE QUAD: OPPORTUNE GEOPOLITICS OR INCONSEQUENTIAL COOPERATION?

The tragic death of Shinzo Abe and the inception of the Maiden West Asian QUAD, the recent rollout of the Indo-Pacific Maritime Domain Awareness (IPMDA), and the 'Quad Partnership on Humanitarian Assistance and Disaster Relief (HADR) in the Indo-Pacific' and the QUAD's recent announcement of an extension of 50 Billion USD to bridge infrastructural lacunae and debt obligations in the Indo-Pacific have all pummelled the diplomatic alliance under the limelight and back into the microscopic scrutiny of scholars, political thinkers and strategists. Having displayed exceptional collaborative results in the wake of the Covid-19 pandemic, the QUAD is seen reorganising itself, with more structure, a concrete goal, defined ambitions and convergent interests. The alliance, growing in determination, resources and effectiveness has been criticised by being equated to an Asian NATO by a disgruntled China, whose Belt and Road Initiative is being systematically undone and countered by the QUAD and their strategic policies, be it Indo-Pacific monitoring or a competitive module of vaccine diplomacy. However, despite diligent and extensive contribution in the QUAD's undertakings, India has been termed ‘the weakest link' in the QUAD. The purpose of this paper is to understand and analyse whether the QUAD is the future or past of regional geopolitics—whether it has the will, resources, and strength to spearhead international action and cooperation in the pursuit of its agendas—and where the balance of interest lies in India's membership in the QUAD. © 2022, Supporting Academic Initiatives Foundation. All rights reserved.

Rethinking the 'one-stop' neck lump clinic: a novel pathway beyond coronavirus disease 2019.

OBJECTIVES: UK guidelines advocate 'one-stop' neck lump assessment for cancer referrals. This paper reports the pilot of a novel pre-clinic ultrasound pathway, presents outcomes, and discusses strengths and limitations in the context of the coronavirus disease 2019 pandemic. METHODS: Two-week-wait cancer referral patients with a neck lump were allocated a pre-clinic ultrasound scan followed by a clinic appointment. Demographic, patient journey and outcome data were collected and analysed. RESULTS: Ninety-nine patients underwent ultrasound assessment with or without biopsy on average 8 days following referral. Patients were followed up on average 14.1 days (range, 2-26 days) after initial referral. At the first clinic appointment, 45 patients were discharged, 10 were scheduled for surgery, 12 were diagnosed with cancer, 6 were referred to another specialty and cancer was excluded in 19 patients. Retrospectively, four ultrasounds were performed unnecessarily. CONCLUSION: Pre-clinic ultrasound scanning is an alternative to the one-stop neck lump pathway. This study demonstrates fewer clinic visits, faster diagnosis and a low proportion of unnecessary scans, whilst minimising face-to-face consultations and aerosol-generating procedures.

Digital Transformation for Agility and Resilience: An Exploratory Study

In recent times, the concepts of IT-enabled agility and digital resilience have gained increased attention. The COVID-19 pandemic has brought a turbulent environment in its wake, thus providing an opportunity to study organizations' agility and digital resilience. This study examines retailers' strategies and IT's role in facilitating responsiveness to uncertainties in the marketplace. An analysis of the quarterly earnings call transcripts shows the increased reliance of organizations on IT to drive and sustain their strategies. This study supports the oft-made claim that IT capabilities can make an organization more responsive and agile in the face of disruptions. © 2021 International Association for Computer Information Systems.

Study of Marine Natural Products as Anti-SARS-CoV-2 Agents Using Molecular Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a global pandemic. Viral entry into host cells is mediated by spike glycoprotein (SGP) interactions with angiotensin-converting enzyme 2 (ACE2) and heparan sulfate glycosaminoglycans on the cell surface. Carbohydrate small molecules were found to bind to the receptor binding domain (RBD) of SGP, which also interacts with ACE2, forming a ternary complex. Moreover, glycans isolated from sea cucumber and red alga species exhibited anti-SARS-CoV-2 activities, presumably by blocking viral entry mediated through SGP-heparan sulfate interactions. Here we report a collection of computational studies conducted as part of a collaborative effort to investigate the effects of marine natural products (NPs) on the wild-type and N501Y mutant SGP RBD. Starting from an X-ray crystal structure of the RBD-ACE2 complex, a model of SGP RBD was built. To investigate the static and dynamic behavior of RBD-NP interactions, blind and site-targeted molecular docking using diverse docking programs (Glide, AutoDock Vina or ClusPro) was carried out, followed by extensive molecular dynamics simulations with two force fields (CHARMM36 or Glycam06) and binding free energy calculations. Predicted conformations of the NPs varied considerably when modeled in water or in complex with RBD. Five NP binding sites on the RBD were studied. NP binding specificities towards SARS-CoV-2 variants were explained and important RBD residues were identified. Statistical analyses of the stability of various protein-NP complexes during molecular dynamics simulations helped to differentiate pseudo-vs. real-binding sites. Our results provide significant insights into the importance of extensive molecular dynamics calculations in order to move beyond the limitations of molecular docking.

ANALYSING TRADING STRATEGIES AND FORECASTING STOCK PRICES USING LSTM

Algorithmic Stock Trading has been legalized in India since 2008, with SEBI (Securities and Exchange Board of India) regulating the norms governing the same. Earlier, regulations restricted third-party algorithms to be used in circulation with APIs (Application Programming Interface), as these were unregulated by registered brokerages. Owing to the COVID19 pandemic, SEBI relaxed these underlying norms, easing up both algorithm usage for an end-user, as well as the number of trade orders that can be placed per second (which went up from 20 to 120). This article aims to analyze optimal trading strategies for various stocks using both classic mathematical techniques and Recurrent Neural Networks (RNN). Stock prices will be forecasted using data analytics implementing indicators like supertrend and VWAP and machine learning models like LSTM. Upon fine-tuning parameters in both approaches, trend directions and triggers will be plotted (in the case of existing indicators and strategies) and predictions with trade triggers (in the case of LSTM). Furthermore, stocks will be categorized by trading techniques - growth, momentum, and value and further the best strategy with the expected profit percentage in each case will be identified. © 2022 Little Lion Scientific.

Predicting 3D Model of Images for Augmented Reality Applications

Following the COVID-19 Pandemic, traditional offline education has shifted to the online model. Hence, studying anatomy in a 3D view without visiting virtual laboratories can benefit medical students in the online education system. Similarly, a 3D view of furniture items can help enhance customers’ shopping experience while shopping online. Our Android Application will leverage an Augmented Reality Camera to place 3D models in the user's space. To achieve this, we seek to predict the 3D model from a 2D image by using a Differentiable Renderer with PyTorch3D. Previously, Machine Learning Frameworks called Generative Adversarial Networks (3D GAN) used Variational Autoencoder to generate a 3D model corresponding to an input image. This approach was further improved by Hierarchical Surface Prediction (HSP) that used Convolutional Neural Networks (CNN) to predict Volumetric Pixels for the object surface rather than the object volume, reducing the computational power in contrast to 3D GAN. Finally, the Differentiable Renderer came into being and eliminated the drawbacks of 3D GAN and HSP techniques. The latter approaches only considered voxels for 3D Modelling;however, Differentiable Renderer uses Machine Learning to predict the texture and lighting of the model as well. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Leveraging Financial Inclusion Through Technology-Enabled Services Innovation: A Case of Economic Development in India

The majority of the Indian population is not getting the advantages of inclusive growth and development in India, referred to as financial inclusion, which has become a challenge for the Indian economy. The paper aims to investigate the use of available technology-enabled financial services and their role for financial inclusion in the current COVID-19 situation and the reaching of rural and semiurban India. The research is based on the in-depth analysis of the government policies and Fintech in the light of India's situation during COVID-19. The study reveals that the government showed the intent by opening a vast amount of banking accounts (411 million accounts) for financial inclusion in around six years. With radical changes in mobile subscribers and 4G, internet, and smartphone growth, India is close to achieving financial inclusion with full potential. However, significant change and development can be attained only if the government provides and motivates citizens to adopt the innovation services for financial inclusion. Copyright © 2022, IGI Global.

Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model

In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.

Assessing behavioral intention toward green hotels during COVID-19 pandemic: the moderating role of environmental concern

Purpose: The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels. Design/methodology/approach: The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework. Findings: The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention. Research limitations/implications: The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario. Practical implications: The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels. Originality/value: Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern. © 2022, Rajiv Kumar Dwivedi, Manoj Pandey, Anil Vashisht, Devendra Kumar Pandey and Dharmendra Kumar.

Study of morphological nature of Coronavirus: causes and prevention. (Special Issue: Coronaviruses and COVID-19- Past, Present, and Future.)

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarize about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind.

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.

Structural Health Monitoring (SHM) of Machine Using a Combination of Non-Destructive Testing for Heavy Manufacturing Industry

Markets are affected by assorted consumer requirements, which insist on superior quality, shorter delivery time, better customer support, and lower prices. Simultaneously, product life cycles are becoming shorter. Success relies on having either a cost-benefit or a value benefit, or, both in any competitive context. Therefore, non-destructive techniques (NDT) become vital but in the conventional system, the maintenance personnel has to visit the machine that consumes time and energy. In the present COVID-19 situation and to save energy and time, there is a necessity of making condition monitoring contactless as much as possible. Therefore, in this research work, a structural health monitoring analysis presented that covers: firstly, enlisting of the NDT infrastructure commonly available in heavy manufacturing industries;secondly, common causes and reasons of machine failures and finally, discusses need of embedded structural health monitoring (e-SHM) system with the combination of NDT in place of existing monitoring practice. The presented work suggested that a combination of NDT with e-SHM is better for timely fault detection to ensure effective condition monitoring. © 2020 The Authors, published by EDP Sciences.

Study of Morphological Nature of Coronavirus: Causes and Prevention

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarise about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind.