Applications of Machine Learning in Analysis of Citation Network

Pradhan, Dinesh; Chakraborty, Joyita; Nandi, S.

doi:10.1145/3297001.3297053

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Rigorous efforts of the research community in recent years have created a general awareness about the common citation malpractices at least. Some of the important papers include Franck (1999), Krell (2010), Lynch (2012), Wilhite and Fong (2012), Van Noorden (2013), Heneberg (2014), Chorus and Waltman (2016), Heneberg (2016), Fister Jr et al (2016), Fong and Wilhite (2017), Bai et al (2016), Racz and Marković (2018), Chakraborty et al (2018), Wang et al (2019), Pradhan et al, 2019 and blog posts, reports and news include Tho (2009), Mongeon et al (2016), Arnold (2009), Martin (2013), Davis (2012), Oransky and Marcus (2017). However, in this paper, we define a generalized feature set (or rules) that can approximately identify the outlier class.…”

Section: Dataset and Methodologymentioning

confidence: 99%

“…However, in recent years “Clarivate Analytics” suppress journal from ranking due to Impact Factor biased excessive self‐citation. Prior study of this dataset shows that during the 1990–2000 period, self‐citation has abruptly increased due to the expansion of research in the Computer Science domain (Chakraborty, Kumar, Goyal, Ganguly, & Mukherjee, 2015; Pradhan et al, 2019). Normalized count of journals along with varying self‐citation (in percentage) along the x ‐axis is shown in Figure 2.…”

Section: Dataset and Methodologymentioning

confidence: 99%

“…Past literature has identified individual cases of unethical citation practices. A significant drawback occurs due to exponential growth in all scientific entities and the evolving dynamics of citation networks (Pradhan, Chakraborty, & Nandi, 2019). Individually detecting anomalous instances from this vast network is not efficient as well as time‐consuming.…”

Section: Introductionmentioning

confidence: 99%

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On the identification and analysis of citation pattern irregularities among journals

Chakraborty

Pradhan²,

Nandi

2020

Expert Systems

Self Cite

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Recent studies report that few journals are adopting unethical citation practices to inflate Impact Factor (IF) artificially. "Clarivate Analytics" has started to blacklist such journals since 2006. As reported in the literature, evaluation of journals individually, to detect anomalies from vast and dynamically changing citation network is not efficient. The primary purpose of this work is to define a diverse feature set that can identify such cases of extreme outliers and reason them. The sample size is narrowed down using an unsupervised clustering algorithm in the absence of a labeled training dataset. Next, time-series IF data is analyzed to detect point outliers. Furthermore, microscopic features are identified to reason them. Results reflected from the F-value after ANOVA analysis reveals that geometrical patterns (self-loop, pairwise and group mutual-citation) among journals, an abrupt increase in the paper count of donor and corresponding IF inflation of recipient are some of the essential features. Microscopic features include social factor (calculation of revised IF after removing directed self or mutual-citation), impact of the field of study, impact of publication house and author factor that includes author self and mutual-citation. The significance of this work is to ensure that the quality of a journal is withheld without compromising research integrity by controlling or auditing individual features periodically.

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Section: Dataset and Methodologymentioning

confidence: 99%

Section: Dataset and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the identification and analysis of citation pattern irregularities among journals

Chakraborty

Pradhan²,

Nandi

2020

Expert Systems

Self Cite

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“…In the industrial domain, deep learning and machine learning algorithms such as Random Forest, Deep Neural Network, Multilayer Feedforward Neural Network, t-Distributed Stochastic Neighbor Embedding (t-SNE), faster region-based convolutional neural networks, and region-based fully convolutional networks have been used to improve the safety of workers around heavy machinery by automatically detecting when people or objects are within an unsafe distance from machines or for risk detection and trajectory tracking at construction sites (we need to verify the identity of the workers and track their walking paths). [71][72][73] Construction worker detection has been proposed for construction safety, 74,75 worker behavior analysis 76,77 and productivity analysis. 78,79 The results obtained have shown a 98% prediction accuracy for work zone events, thus using prevent variables can be a viable proposal to predict the occurrence of a safety-critical event using those models.…”

Section: Manufacturingmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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“…Absenteeism at workplace does have adverse impact on organizational environment and productivity. In recent years, researchers and scientists are getting more interest to model these real-world problems using artificial intelligence-based techniques [8][9][10][11]. More recently in 2018, Gayathri conducted a research study for absenteeism at workplace [12] and proposed a model which can predict the number of hours an employee is absent from work.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

et al. 2020

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Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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Applications of Machine Learning in Analysis of Citation Network

Cited by 11 publications

References 6 publications

On the identification and analysis of citation pattern irregularities among journals

On the identification and analysis of citation pattern irregularities among journals

Deep learning support for intelligent transportation systems

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

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