Browsing by Author "Vivek Kumar Singh"

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6G networks for artificial intelligence-enabled smart cities applications: A scoping review
(Elsevier B.V., 2023) Prabhat Ranjan Singh; Vivek Kumar Singh; Rahul Yadav; Sachchida Nand Chaurasia
Due to the increasing need for time with respect to industrial growth and the speeding up of human day-to-day work, network evolution is always the center of focus for research organizations. Artificial intelligence (AI), on the other hand, is playing an increasingly important role in automating systems, increasing efficiency, and ensuring high dependability in complicated activities. Network and AI research are generating a boom in IoT applications, as firms strive to serve their consumers with ever-improving features and functionality. In this article, we look at the evolution of network technology across time as well as the role of AI in the next generation of networks. First, we will introduce the reader to the features of different network generations, technology used in. Second in the context of network evolution and AI, we will define the scope of IoT applications and their need in order to alleviate user demand. Consequently, we provide the requirements of advanced models in smart cities applications and insights on the development of future network for those applications. Our study showed that, though important features were introduced in the advanced automated networks, these are still to be tested under for automated applications that have strict requirements in terms of low latency, high reliability, and fast transmission. © 2023
A Bibliometric Analysis of Research on Sustainable Development Goals by the G20 Countries
(National Institute of Science Communication and Policy Research, 2024) Aakash Singh; Anurag Kanaujia; Vivek Kumar Singh
United Nations Sustainable Development Goals (SDGs) and their targets are highly interconnected and require international collaboration. In this context, the G20 organization of 20 countries/units, founded in 1999 holds an important position being an important political and economic platform for addressing various developmental concerns. With 75% of global population, the Group Nations accounts for 85% of the global GDP and about 75% of the global trade. Considering the strength, resources and representation of these countries, they hold the major part of the responsibility towards achieving the SDGs. Scientific and technological research is a major requirement for achieving the SDGs. Given that the G20 has about 88.8% of the world’s researchers, 93.2% of research spending and produce about 90.6% of scientific publications, it would be interesting to analyse what quantum of this research is focused on achieving the SDGs. However, there are no existing studies on this aspect. This study, therefore, attempts to bridge this research gap by presenting a quantitative analysis of research on SDGs by the G20 member countries. Important patterns are identified, which can be useful for different policy perspectives. © 2024 National Institute of Science Communication and Policy Research. All rights reserved.
A Bibliometric Analysis of Research Output from Indian Institutes of Management
(Defense Scientific Information and Documentation Centre, 2022) Prashasti Singh; Abhirup Nandy; Vivek Kumar Singh
Indian Institutes of Management (IIMs) are among the most prestigious business schools in India, mainly offering postgraduate, doctoral and executive education programmes in the fields of Management and Business Education. They also contribute significantly to research in the area. This article attempts to analyse the bibliometric patterns in research output of IIMs. The data for research publications indexed in Scopus during 2010-19 is downloaded and analysed to identify important patterns and trends of research output, citations, international collaboration, open access, gender distribution and social media visibility. The results are also compared with three top internationally renowned business schools (Harvard Business School, MIT Sloan School of Management and NUS Business School). Results indicate that the older IIMs like Ahmedabad and Bangalore are placed at the top in terms of publication counts and citations. Newer IIMs like Rohtak and Raipur are found to be doing well in publications as compared to other IIMs of their generation. IIM Udaipur has more than 40 % of its research output internationally collaborated and also highest citations per paper value amongst all the IIMs. However, when the IIMs are compared with three well-known international schools (two of which have mentored the initial two IIMs), there appears a large gap in several indicators, such as h-index. The paper, thus, indicates that IIMs need to improve their research output and quality to be at par with the top business schools of the world. Research themes like ‘sustainability’, ‘emerging markets’ and ‘supply chain management’ are the most prominent thematic areas observed in the research output from IIMs, which indicates that IIMs are working on research topics of contemporary relevance. © 2022, DESIDOC.
A clustering and opinion mining approach to socio-political analysis of the blogosphere
(2010) Vivek Kumar Singh; Rakesh Adhikari; Debanjan Mahata
Blogosphere is the name associated to universe of all the blog sites. A blog is a website that allows people to write about topics they want to share with others. The ease & simplicity of creating blog posts and their free form and unedited nature have made blogging a happening thing. The blogosphere today contains a large number of posts on virtually every topic of interest. This rich and unique source of data has attracted people and companies across disciplines to exploit it for varied purposes. However, it not only provides a rich data source for commercial exploitation but also for psychological & socio-political analysis purposes. This paper tries to identify the methodology and technical framework required for an analytical experiment of this kind, and demonstrates the plausibility of this idea through our clustering & opinion mining experiment on analysis of blog posts about a recent socio-political issue. © 2010 IEEE.
A fuzzy rule-based system with decision tree for breast cancer detection
(John Wiley and Sons Inc, 2023) Vedika Gupta; Harshit Gaur; Srishti Vashishtha; Uttirna Das; Vivek Kumar Singh; D. Jude Hemanth
Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human-understandable terms-linguistic variables. The Mamdani Fuzzy Rule-Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels- benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state-of-the-art dataset. © 2023 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
A hybrid transformer–sequencer approach for age and gender classification from in-wild facial images
(Springer Science and Business Media Deutschland GmbH, 2024) Aakash Singh; Vivek Kumar Singh
The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, human–computer interaction, etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed model’s performance is compared with several state-of-the-art models proposed so far. An improvement of approximately 10% and 6% over the state-of-the-art implementations for age and gender classification, respectively, is noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
A large-scale comparison of coverage and mentions captured by the two altmetric aggregators: Altmetric.com and PlumX
(Springer Science and Business Media B.V., 2021) Mousumi Karmakar; Sumit Kumar Banshal; Vivek Kumar Singh
The increased social media attention to scholarly articles has resulted in creation of platforms & services to track the social media transactions around them. Altmetric.com and PlumX are two such popular altmetric aggregators. Scholarly articles get mentions in different social platforms (such as Twitter, Blog, Facebook) and academic social networks (such as Mendeley, Academia and ResearchGate). The aggregators track activity and events in social media and academic social networks and provide the coverage and transaction data to researchers for various purposes. Some previous studies have compared different altmetric aggregators and found differences in the coverage and mentions captured by them. This paper attempts to revisit the question by doing a large-scale analysis of altmetric mentions captured by the two aggregators, for a set 1,785,149 publication records from Web of Science. Results obtained show that PlumX tracks more altmetric sources and captures altmetric events for a larger number of articles as compared to Altmetric.com. However, the coverage and average mentions of the two aggregators, for the same set of articles, vary across different platforms, with Altmetric.com recording higher mentions in Twitter and Blog, and PlumX recording higher mentions in Facebook and Mendeley. The article also analysed coverage and average mentions captured by the two aggregators across different document types, subjects and publishers. © 2021, Akadémiai Kiadó, Budapest, Hungary.
A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews
(Springer Verlag, 2017) Rajesh Piryani; Vedika Gupta; Vivek Kumar Singh; Udayan Ghose
Aspect-level sentiment analysis refers to sentiment polarity detection from unstructured text at a fine-grained feature or aspect level. This paper presents our experimental work on aspect-level sentiment analysis of movie reviews. Movie reviews generally contain user opinion about different aspects such as acting, direction, choreography, cinematography, etc. We have devised a linguistic rule-based approach which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches. The system generates an aspect-level opinion summary. The experimental design is evaluated on datasets of two movies. The results achieved good accuracy and shows promise for deployment in an integrated opinion profiling system. © Springer Nature Singapore Pte Ltd. 2017.
A quantitative and text-based characterization of big data research
(IOS Press, 2019) Vedika Gupta; Vivek Kumar Singh; Udayan Ghose; Pankaj Mukhija
This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus.We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it's a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. © 2019 - IOS Press and the authors. All rights reserved.
A Sciento-text framework to characterize research strength of institutions at fine-grained thematic area level
(Springer Netherlands, 2016) Ashraf Uddin; Jaideep Bhoosreddy; Marisha Tiwari; Vivek Kumar Singh
This paper presents a Sciento-text framework to characterize and assess research performance of leading world institutions in fine-grained thematic areas. While most of the popular university research rankings rank universities either on their overall research performance or on a particular subject, we have tried to devise a system to identify strong research centres at a more fine-grained level of research themes of a subject. Computer science (CS) research output of more than 400 universities in the world is taken as the case in point to demonstrate the working of the framework. The Sciento-text framework comprises of standard scientometric and text analytics components. First of all every research paper in the data is classified into different thematic areas in a systematic manner and then standard scientometric methodology is used to identify and assess research strengths of different institutions in a particular research theme (say Artificial Intelligence for CS domain). The performance of framework components is evaluated and the complete system is deployed on the Web at url: www.universityselectplus.com. The framework is extendable to other subject domains with little modification. © 2016, Akadémiai Kiadó, Budapest, Hungary.
A Scientometric analysis of computer science research in India
(Institute of Electrical and Electronics Engineers Inc., 2015) Khushboo Singhal; Sumit Kumar Banshal; Ashraf Uddin; Vivek Kumar Singh
This paper presents results of our Scientometrics and text-based analysis of computer science research output from India during the last 25 years. We have collected the data for research output indexed in Scopus and performed a detailed computational analysis to obtain important indicators, such as total research output, citation impact, collaboration patterns, top institutions/authors/publication sources. We also performed a text-based analysis on keywords of all papers indexed in Scopus to identify thematic trends during the period. The analytical results present a detailed and useful picture of status and competence of CS domain research in India. © 2015 IEEE.
Abusive comment detection in Tamil using deep learning
(Elsevier, 2024) Deepawali Sharma; Vedika Gupta; Vivek Kumar Singh
During the recent years, online social media have expanded in volume and coverage and have become a significant source of information for different groups of people. The comments posted on social media can be emotion-laden and hence can create an impact on mental health of an individual or a group of individuals. One such category of posts includes comments that are abusive or hateful in nature. The comments that spread hate and are abusive in nature usually target certain individuals or some specific communities. It is, therefore, very important to know about them and perhaps be able to detect such content in time. While there exist methods for automated detection of hate speech from posts in English language, there is relatively less research done on other low-resource languages, such as Tamil. This chapter presents an overview of research on detecting hate speech in low-resource languages and explores application of various deep learning models for the task. The abusive comments are classified in different categories: Homophobia, Xenophobia, Transphobic, Misandry, Misogyny, Counter-speech, and Hope speech, from Tamil and Tamil–English code-mixed language. Those comments that are not in the Tamil language are categorized as “Not-Tamil.” The following deep learning models: recurrent neural network, long-short term memory (LSTM), and bidirectional LSTM, are applied to the task. Experimental results are presented along with an analysis of the quality of results. © 2024 Elsevier Inc. All rights reserved.
Agent based modeling of individual voting preferences with social influence
(2011) Vivek Kumar Singh; Swati Basak; Neelam Modanwal
Agent Based Modeling (ABM) is usually referred to as the third way of doing modeling & simulation in contrast to the historically popular equation-based macrosimulations and microsimulations. In ABM a system is modeled as a set of autonomous agents who interact with each other and also with the environment. The agents represent various actors in the system and the environment represents agent's surroundings and the overall context. ABM has been successfully applied to model and analyze a number of complex social processes and structures, ranging from habitation patterns to spread of culture. In this paper, we present our experimental work on applying ABM to model individual voting preferences. The model explores process of formation of voting preferences, the factors governing it and its effect on the final voting pattern. We have incorporated social influence theory in our model and experimented with various settings. The paper concludes with a short discussion of the results and their relevance. © 2011 Springer-Verlag Berlin Heidelberg.
Agent based models of social systems and collective intelligence
(2009) Vivek Kumar Singh; Ashok K. Gupta
Agent-based modeling and simulation techniques have now become a suitable and popularly used approach to build useful models of social systems, which not only helps to get better understanding of various social phenomena but also enriches the agent-based computing paradigm in return. Agent-based models allow simulating social units such as individuals, households, organizations or nations and their direct or indirect interactions. These models demonstrate how global order and collective intelligence can emerge from relatively simple local interactions and can explain the dynamics of the emergent behaviour. The Agent-based modeling approach has provided the bridging link between psychological & sociological analysis of individual and social behaviours respectively, which was otherwise missing. This generative, proof-by- construction approach has also complemented the individual-centered research in cognitive science by showing that individual alone is not the crucial unit of cognition but is affected by environment and society besides affecting them as well. In this paper, we have given an analytical account of Agent-based modeling of emergent collective social behaviours, on these lines, along with relevant theoretical & experimental outcomes and their implications for multi-agent systems. ©2009 IEEE.
Agent-based computational modeling of emergent collective intelligence
(Springer Verlag, 2009) Vivek Kumar Singh; Divya Gautam; Rishi Raj Singh; Ashok K. Gupta
Collective Intelligence is a form of intelligence which emerges out of collaboration and coordination of many individual agents. A group of actors performing simple behaviours and interacting with fellow group members & the environment often produce global behaviours which seems intelligent. Understanding the emergence of intelligent collective behaviours in social systems, such as norms & conventions, higher level organizations, collective wisdom and evolution of culture from simple and predictable local interactions; has been an important research question since decades. Agent-based modeling of complex social behaviours by simulating social units as agents and modeling their interactions; provides a new generative approach to understanding the dynamics of emergence of collective intelligence behaviours. In this paper, we have presented an analytical account of nature, form and dynamics of collective intelligence, followed by some of our experimental work on evolution of collective intelligence. The paper concludes with a short discussion of the results and relevant implications for designing systems for achieving desired collective intelligence. © 2009 Springer Berlin Heidelberg.
Altmetric data quality analysis using Benford’s law
(Akademiai Kiado ZRt., 2024) Solanki Gupta; Vivek Kumar Singh; Sumit Kumar Banshal
Altmetrics, or alternative metrics, refer to the newer kind of events around scholarly articles, such as the number of times the article is read, tweeted, mentioned in blog posts etc. These metrics have gained a lot of popularity during last few years and are now being collected and used in several ways, ranging from early measure of article impact to a potential indicator of societal relevance of research. However, there are several studies which have cautioned about use of altmetrics on account of quality and reliability of altmetric data, as they may be more prone to manipulations and artificial inflations. This study proposes a framework based on application of Benford’s Law to evaluate the quality of altmetric data. A large sized altmetric data sample is considered and the fits with Benford’s Law are computed. The analysis is performed by doing plots of the empirical data distributions and the theoretical Benford's, and by employing relevant statistical measures and tests. Results for fit on first and second leading digit of altmetric data show conformity to Benford's distribution. To further explore the usefulness of the framework, the altmetric data is subjected to artificial manipulations through a systematic process and the fits to Benford’s law are reassessed to see if there are distortions. The results and analysis suggest that Benford’s Law based framework can be used to test the quality of altmetric data. Relevant implications of the research are discussed. © Akadémiai Kiadó, Budapest, Hungary 2024.
An altmetric analysis of scholarly articles from India
(IOS Press, 2018) Sumit Kumar Banshal; Vivek Kumar Singh; Golam Kaderye; Pranab Kumar Muhuri; Belém Priego Sánchez
Scholarly articles are considered one of the primary medium for dissemination of inventions and discoveries. Traditionally, usefulness and popularity of a scholarly article has been measured in terms of citations it receives. However, in the changed research publishing landscape, where most of the publications are now available in digital form accessible through various digital libraries; new measures of measuring usefulness of scholarly articles have emerged. Nowadays, scholarly articles are easily available for access and download from various digital access portals. The use and popularity of these digital access portals has also made it possible to integrate various social media platforms with journal access and use. Most of the journals now maintain statistics about reads, number of downloads, social profile shares etc. Several newer platforms like ResearchGate, Academia and Mendeley have also become popular. Researchers now often share their articles on various such platforms and also use social media channels to disseminate their article to a wider audience. This transformed environment has allowed to track and measure usefulness and popularity of scholarly articles through alternative metrics (now popularly known as Altmetrics) as compared to traditional citation impact measures. Altmetrics attempts to derive impact of a scholarly article by using data from different kinds such as social network share, mentions, tweets etc. The use of Altmetrics varies widely from country to country and discipline to discipline. This paper attempts to present findings of an exploratory analysis of relevance of Altmetrics data through a case study of scholarly articles from India published during 2016 and indexed in Web of Science and also updated on ResearchGate. The results obtained provide an interesting insight on relatedness and correlation of presence of scholarly articles in Web of Science and ResearchGate. It is observed that about 61% papers indexed inWeb of Science have an entry in ResearchGate. There are, however, disciplinary variations in presence of articles in ResearchGate. Only about 61% of the total disciplines in Web of Science are found to be covered in ResearchGate. © 2018-IOS Press and the authors. All rights reserved.
An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning
(Springer Science and Business Media B.V., 2023) Nirmal Marwah; Vivek Kumar Singh; Gautam Siddharth Kashyap; Samar Wazir
Agriculture is a vital sector in developing nations such as India, and the use of autonomous vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming practices. Unmanned Aerial Vehicles (UAVs) are becoming increasingly important in agriculture, as they can provide valuable data for crop monitoring and pest control. In this study, we investigate the reliability of a Multi-Agent Reinforcement Learning (MARL) method for UAV field coverage. The algorithm enables a group of UAVs equipped with ground-facing cameras to learn how to provide complete coverage of an unknown Field of Interest (FoI) while minimizing camera view overlap. We test the algorithm in scenarios where the FoI and camera Field of View (FoV) are dynamically updated in the environment, to evaluate its performance under more dynamic conditions. Our results demonstrate the effectiveness and resilience of the proposed method in varying environmental conditions, highlighting its potential for Precision Agriculture (PA) applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
An empirical analysis of existence of power laws in social media mentions to scholarly articles
(International Society for Scientometrics and Informetrics, 2021) Sumit Kumar Banshal; Aparna Basu; Vivek Kumar Singh; Hiran H. Lathabai; Solanki Gupta; Pranab K. Muhuri
Power laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit determined using least squares regression. We also explore fit to other distributions like the log-normal and Hooked Power Law. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles and we conclude that altmetric distributions also follow power law with a fairly good fit over a wide range of values. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved.
Analysing author name mentions in citation contexts of highly cited publications
(CEUR-WS, 2019) Rajesh Piryani; Wolfgang Otto; Philipp Mayr; Vivek Kumar Singh
In this paper, we are analysing author name mentions in citation contexts of highly cited articles in a PLOS ONE corpus. First, we have identified author mentions in our corpus of citation contexts. Then, we examined frequent nouns and verbs in the neighbourhood of the identified author mentions using n-grams and utilized these top nouns and verbs to identify the most frequent patterns. We observed that most frequent patterns are associated with the methods which are proposed in the corresponding highly cited references. © 2019 CEUR-WS. All rights reserved.