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COVID19 is a dreadful infectious disease, morphed into an economic crisis causing extensive and longstanding ramifications across global markets. Investors continue to hear about COVID-19 and its impact in one corner of the globe or the other for a long time. Though the effects of COVID19 started in December 2019 in Wuhan, China, global markets did not respond actively till W.H.O officially declared on March 11, 2020, that the COVID19 outbreak is a global pandemic. These multi-channel events have eroded investor sentiment, tanking the global stock markets. This article uses a machine learning approach to Twitter to analyze and follow investor sentiment that has guided the market to the new low during the first 150 days of the COVID-19 era. The only respite for recovery of financial markets is the lowering of COVID-19 infected cases for the time being till a vaccine is developed for the virus. © The Authors 2021.

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack of them disables the victim’s online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.

Purpose Self-help groups (SHGs) have widely been accepted by developing nations for their contribution to inclusive development. Despite its importance and the recognition of the same, SHGs and their members face several challenges. The purpose of this paper is to comprehensively investigate the inhibiting factors affecting the effectiveness of women SHGs. The paper also proposes a roadmap for all the self-help promoting institutions, such as banks, state and central government, and non-government organizations to address the challenges. Design/methodology/approach The study adopts an exploratory research design to undertake this applied research. It uses phenomenographic and interpretative approaches. Qualitative data have been collected from 24 members belonging to eight SHGs from the eastern Indian state of Odisha with the help of a semi-structured interview schedule. The result has been analyzed by using content analysis. Findings The study finds that SHG members in Odisha face several challenges in the form of financial bottlenecks, entrepreneurial obstacles, capacity-building impediments, mentor-ship challenges, socio-structural challenges and group dynamics. Research limitations/implications The study finds many unique and contextual inhibiting factors, which are valuable additions to the existing literature. At the same time, the result may not be applicable to other kinds of SHGs, such as Alcoholic Anonymous, as their nature, composition and deliverables are quite different from the SHG that the current study has taken into consideration. Practical implications The proposed roadmap would be useful to policymakers, non-government organizations and other agencies involved in the promotion and capacity building of SHGs operating in Odisha in particular and India in general. Originality/value The literature is mainly concerned with the impact evaluation of SHG programs. This study investigates the challenges faced by SHGs in a systematic way and provides a roadmap to address the same in a comprehensive way.

This article describes how knowledge discovery is a frontier research issue of knowledge management, and social media provides an opportunity for knowledge discovery that was at no other time as virtuous as the present. Despite the fact that, the articulations in national dialects via web-based networking media is mounting day by day. This discovery endeavor in regional languages is rare. The usage of Hindi, the Indian National language, is also observing the similar trend. Any expression in social media contains multiple features. Discovering the hidden sentiments behind these features have wider functions. This article is the first attempt to mine the opinion at the features level in the Hindi language. The principle contribution of this article is the development of context specific corpus in the Hindi language. Based on this corpus authors extract the sentiments on one of the prominent leader of India at the feature level. Opinion mining conclusion based on present work is reproduced likewise in the subsequent election results.

One of the most critical and important aspects of any software development project is the estimation of cost and effort, as the success or failure of the entire project is largely dependent on the accuracy of these estimations. For any software development project, several methods such as waterfall, prototyping etc. exist, but the agile methods have prevailed in terms of its efficiency and implementation in solving the problems related to the projects thus substituting the traditional methodologies. Agile methods have become much popular recently because of its ability to adopt to the changing dynamics (requirements) of software projects. This dynamic nature makes the task of estimation even more challenging than the traditional methodologies present. Thus, it becomes convenient to accurately estimate the effort and cost while adopting the agile methods, for which various techniques have already been proposed such as analogy, dis-aggregation, expert opinion etc, but none among the same have a proper mathematical model. This work has presented a novice method from the domain of evolutionary algorithms. The work is based on mathematical morphology (MM) consisting of a hybrid-artificial neuron (Dilation-Erosion perceptron (DEP)) extended from the concept of complete lattice theory (CLT). Authors have presented a chaotically modified genetic algorithm (CMGA) to build the DEP-CMGA model for solving the software development effort estimation (SDEE) problem. Calibration of the proposed model was done using data collected from 21 software projects based on agile software development (ASD). Four different statistics were used for determining the precision of the model and the results were compared with the one’s obtained using the best available model in literature.

Microfinance as a tool for poverty alleviation has attracted much attention especially after Grameen Bank of Bangladesh was awarded the Noble peace prize. Self-help groups (SHGs) in India, which originated during the same time as Grameen Bank, are considered to be an efficient mechanism for delivering microfinance to the poor. Apart from providing economic benefits, participation in SHG also brings for its members social and political empowerment. This paper examines the impact of the level of participation in SHGs on the empowerment (economic, social and political) of women SHG members. The study also investigates the impact of moderating variables, such as socio-economic status, age, and place of residence, on the relationship between the level of participation and empowerment. As we were unable to find suitable scales in the existing literature to measure the level of participation, empowerment (economic, social and political) and socio-economic status, we developed new scales to measure these constructs. Using an interview schedule in a survey method, we interviewed 353 women SHG members from the eastern Indian state of Odisha to collect primary data. Using partial least squares structural equation modeling (PLS-SEM), the study explicates the relationship between level of participation and women empowerment. The findings suggest that an increased level of participation in SHGs results in enhanced economic empowerment of its members by bringing in employment opportunities, higher income, stability in current jobs, and improved entrepreneurial abilities. Besides, higher participation also brings in higher social empowerment of members in the form of decision-making capability in households, access to health care, selfconfidence, heightened self-esteem, communication skills, and the ability to transact with banks and NGOs. In addition, increased participation in SHGs leads to higher political empowerment in the form of higher political participation and better community mobilization. Socio-economic status and place of operation significantly moderate the effect of the level of participation on women (SHG member) empowerment. Insights from this study are helpful for the government and non-government agencies working towards empowering women in developing regions. They may take note of the different indicators of participation and role of the significant moderators while implementing similar programs.

Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English language cannot be applied directly to text in Hindi languages. The objective of this paper is to extract the political sentiment at the document-level from Hindi blogs. The authors could not find any literature about extracting sentiments at the document-level from Hindi blogs. They extracted opinion about one of India's very famous leaders who was a prominent face in the national election of 2014. They prepared the datasets from Hindi blogs reviews. They purposed the lexicon and machine learning technique to classify the sentiment. Their purposed method used four steps: (1) Crawling and preprocessing the blog reviews; (2) Extracting reviews relevant to the query using the Vector Space Model (VSM); (3) Identifying sentiment at the document level using the Lexicon method, and (4) Measuring the result using the Machine learning technique. Their experimental result demonstrates the effectiveness of our algorithms.

Purpose The paper explores the importance of mobile technology to enable diffusion of agriculture-related knowledge among farmers in India. The purpose of the paper is to evaluate the current socio-economic factors and challenges that impact the feasibility of m-governance project. The authors intend to explore different behavioral aspects of farmers, specifically their information seeking behavior to understand their communication ecosystem. Design/methodology/approach The authors have used multiple methods to analyze the significance of m-governance in current social dynamics. To achieve in depth understanding of farmer’s attitudes and opinion, the authors have conducted semi-structured interviews with farmers. The authors have also applied experimental observations to evaluate the actual effectiveness of information dissemination and the social dynamics behind the process. The secondary/archival data was also collected from the government offices and non-governmental organizations. Findings Findings explore the pattern of mobile usage among the farmers which could lead to interesting implications for the design and implementation of future m-governance projects. The research has also drawn some interesting implication on the feasibility of m-governance project. Research limitations/implications Because the findings are co-related with the prevalent socio-cultural dynamics, testing the findings in different context might add value to the proposed theory and its implications. Originality/value Considering the need and significance of agriculture-based reforms in rural India, present study offers guidance in devising an efficient communication medium among farmers and government. The authors infer from our field observations that the communication platform is vital for successfully reaching farmers for their overall welfare. The present work is based on findings which are drawn from the ground reality which helps in explicating inferences which are useful for implementation purpose.

Purpose – The purpose of this paper is to propose a tool in the form of a roadmap for planning and implementation of interoperability capabilities. The issue of interoperability is gaining importance for offering services as “one-stop shop” due to rapid developments in the field of e-government in recent times. Several complexities are associated with this important dimension, and there is an urgent need for developing a tool to facilitate rollout of e-government projects with interoperability capabilities. The proposed roadmap can enable knowledge sharing among key stakeholders for building a common vision and contribute to planning and implementation of interoperability features of e-government. Design/methodology/approach – The paper is an exploratory study using the phenomenographic interpretive approach. The literature review identifies the facets and the issues that are crucial for facilitating interoperability. The status of interoperability in some of the developed countries is reviewed where e-government is in a relatively mature stage. Based on the review, this paper identifies an initial set of issues that can guide semi-structured interviews with policymakers, partners and the government agencies implementing e-government projects. The views of these key stakeholders are then analyzed and used for developing the roadmap for implementation of interoperability of e-government services. The findings are then mapped in India’s context for triangulation purpose. Findings – The paper provides insights into various planning and implementation issues that have to be addressed for building interoperability capability. A roadmap is proposed encompassing the essential legal, regulatory, organizational and technical aspects that are deemed essential for successful planning and implementation of interoperability capability. Research limitations/implications – The study has the limitation that the exploratory part of the study is based on inputs from key actors in the Indian context only. It needs to be validated by empirical studies in various contexts. Practical implications – The roadmap proposed will be useful for the policymakers, as it can guide them for planning and implementing e-government systems with interoperability capabilities. It can be used as a knowledge-sharing tool for spreading awareness of steps required for designing and implementing interoperability capabilities. Originality/value – The paper contributes by providing a tool in the form of a roadmap that can be used by the policymakers and implementers to take a holistic view of requirements for development of interoperability capabilities. The paper can form the basis for future research in specific issues of interoperability.

This study seeks to deepen the understanding of research in business value of information technology by examining the role of contextual factors such as firm age and export activity in information technology impact following contingency theory perspective. The study is set in India and provides generalization in a new setting of emerging economy. An empirical study was conducted using data from 320 firms between 2007 and 2010. The results indicate that information technology investments enable firms to reduce their operational costs without contingencies related to firm age and export activity. However, the impact of information technology investment for improving profit is contingent on export levels such that the impact is higher for high exports firms. This work contributes to the literature by examining firm age and export activity as contextual factors and reveals the impact of information technology might be either contingent or non-contingent, subject to specific organizational outcome.

Purpose Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost). Design/methodology/approach Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method. Findings Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails. Research limitations/implications This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study. Practical implications In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers. Originality/value A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

Path-finding is a fundamental problem, which involves for solving a planning problem seeking optimal paths from start state to a goal state. This review paper focused on usage of Radio Frequency Identification (RFID) technology in path-finding and navigation for an autonomous mobile robot. The RFID tags (IC tags) are used for the purpose of tracing the current co-ordinate/location of the Robot. Various searching algorithms are analyzed for the purpose towards navigation of mobile robot. Different soft-computing techniques like Genetic Algorithm, Neural Network, and Fuzzy Logic are considered. This research also focused on some hybrid algorithm for path-finding and navigation of mobile robot. Different methods like cellular automata, Network Simplex Method, Complete Coverage Navigation etc. are also studied.