N. Venkata Sailaja, L. Padmasree and N. Mangathayaru
Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text…
Abstract
Purpose
Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.
Design/methodology/approach
The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.
Findings
For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.
Originality/value
In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.
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Henrique Ewbank, José Arnaldo Frutuoso Roveda, Sandra Regina Monteiro Masalskiene Roveda, Admilson ĺrio Ribeiro, Adriano Bressane, Abdollah Hadi-Vencheh and Peter Wanke
The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as…
Abstract
Purpose
The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.
Design/methodology/approach
Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.
Findings
A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.
Originality/value
The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.
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Muskan Sachdeva, Ritu Lehal, Sanjay Gupta and Aashish Garg
In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause…
Abstract
Purpose
In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.
Design/methodology/approach
In this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.
Findings
Results of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.
Research limitations/implications
The current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.
Practical implications
Comprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.
Originality/value
The current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.
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I ENTERED the literary world late in the immediate post‐war years when changes of literary taste and loyalty were already in the air. The first broadcast I gave was, I remember…
Abstract
I ENTERED the literary world late in the immediate post‐war years when changes of literary taste and loyalty were already in the air. The first broadcast I gave was, I remember, an attack upon Virginia Woolf. Her books had nurtured me as an adolescent, and I was in reaction against her influence.
Paul R. Carlile, Steven H. Davidson, Kenneth W. Freeman, Howard Thomas and N. Venkatraman
Sonali Bhattacharya and Shubhasheesh Bhattacharya
– The purpose of this paper is to analyse the possible causes of elderly abuse in India and its repercussions for the society, based on the real cases and reports.
Abstract
Purpose
The purpose of this paper is to analyse the possible causes of elderly abuse in India and its repercussions for the society, based on the real cases and reports.
Design/methodology/approach
A multiple case study approach has been used for the study sourced from archival newspaper reports, crime reports, and narration.
Findings
Greater vigilance and more effective legislation would be required to solve the problem related to elder abuse.
Originality/value
There is not much study of causes, consequences, effectiveness of the legal system with respect to elderly abuse in India. In that way, it will be a unique contribution.
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Isla Kapasi, Katherine J.C. Sang and Rafal Sitko
Leadership theories have moved from viewing leadership as an innate trait, towards models that recognise leadership as a social construction. Alongside this theorisation, gender…
Abstract
Purpose
Leadership theories have moved from viewing leadership as an innate trait, towards models that recognise leadership as a social construction. Alongside this theorisation, gender and leadership remain of considerable interest, particularly given the under-representation of women in leadership positions. Methodological approaches to understanding leadership have begun to embrace innovative methods, such as historical analyses. This paper aims to understand how high profile women leaders construct a gendered leadership identity, with particular reference to authentic leadership.
Design/methodology/approach
Thematic analysis of autobiographies, a form of identity work, of four women leaders from business and politics: Sheryl Sandberg, Karren Brady, Hillary Clinton and Julia Gillard.
Findings
Analyses reveal that these women construct gender and leadership along familiar normative lines; for example, the emphasis on personal and familial values. However, their stories differ in that the normative extends to include close examination of the body and a sense of responsibility to other women. Overall, media representations of these “authentic” leaders conform to social constructions of gender. Thus, in the case of authentic leadership, a theory presented as gender neutral, the authenticity of leadership has to some extent been crafted by the media rather than the leader.
Originality/value
The study reveals that despite attempts to “craft” and control the image of the authentic self for consumption by followers, gendered media representations of individuals and leadership remain. Thus, alternative approaches to crafting an authentic leadership self which extend beyond (mainstream) media is suggested.
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Aleem Ansari and Valeed Ahmad Ansari
The purpose of this study is to empirically examine the presence of herding behavior of Indian investors using daily sample data drawn from the Standard and Poor's (S&P) Bombay…
Abstract
Purpose
The purpose of this study is to empirically examine the presence of herding behavior of Indian investors using daily sample data drawn from the Standard and Poor's (S&P) Bombay Stock Exchange-500 Index over the period 2007–2018.
Design/methodology/approach
The study employs the model proposed by Chang et al. (2000), taking stock return dispersion as a measure to capture herding. The empirical results demonstrate the absence of herding behavior in all market states, that is, normal, up and down market conditions for the overall period.
Findings
Contrastingly, the study found negative herding behavior, which underlines that individuals are taking the decision away from the market consensus. The subperiod analysis corroborates the negative herding behavior. The results remain invariant across large, mid and small-capitalization firms except in one year, that is, 2009 for small firms. While using liquidity and sentiment as variables to examine herding, the study finds some evidence of herding behavior for high market liquidity state and sentiment. The findings of negative herding shed new light on herding behavior in the Indian stock market.
Originality/value
This pattern of behavior may indicate irrationality of investor behavior and the presence of noise traders who mistrust market-wide information. Behavioral factors such as overconfidence may explain this pattern of behavior.