Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

Gilal, Abdul Rehman; Jaafar, Jafreezal; Omar, Mazni; Basri, Shuib; Aziz, Izzatdin Abdul; Khand, Qamar Uddin; Hasan, Mohd Hilmi

doi:10.14569/ijacsa.2017.080820

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Clustering. As a distancebased partition clustering algorithm, the K-means clustering algorithm has the advantages of simple algorithm structure, high running efficiency, and wide application range [22][23][24]. e K-means clustering algorithm is generally optimized by the objective function shown as follows:…”

Section: Basic Principles Of K-meansmentioning

confidence: 99%

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Heng-chuan

Liu

Zhen

2022

Security and Communication Networks

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Football is one of the most popular sports in the world. As the popularity of football continues to grow worldwide, so does the number of incidents of violence on the pitch. Today, doping, match fixing, black whistles, and football hooliganism are ranked as the four most toxic aspects of sport. How to study the factors that cause aggressive behaviour of fans from a psychological perspective has become a key issue in the field of sports. Therefore, this study proposes a method for mining the psychological factors of sport fan community members based on machine learning clustering. Firstly, three different members of a large fan community, i.e., university students, office workers, and unemployed people, are used as research subjects to investigate the psychological factors influencing fans’ aggressive behaviour using a questionnaire method. Secondly, the data obtained were mined and analysed using the K-means clustering algorithm in machine learning techniques. At the same time, a K-means initial clustering centre optimization algorithm based on principal component analysis (PCA) was proposed for the data characteristics of the interaction of psychological factors. The results show that the new algorithm significantly improves the quality of clustering compared with other optimization algorithms and accurately identifies the multiple factors that contribute to the occurrence of fan attacks.

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Section: Basic Principles Of K-meansmentioning

confidence: 99%

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Heng-chuan

Liu

Zhen

2022

Security and Communication Networks

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“…Personality traits can foster positive employee behavior, ultimately leading to successful project outcomes (Gilal et al, 2019c;Gilal et al, 2017a). However, research has shown that personality traits such as stress tolerance, flexibility, and motivation can significantly impact the success of a software development project (Gilal et al 2019a;Gilal et al 2018;Gilal et al, 2017b). In particular, how software developers interact with each other and handle stress can have a major effect on the outcome of a project.…”

Section: Discussionmentioning

confidence: 99%

The key factors contribute to time pressure in software development projects: A review

Gilal,

Omar,

Rejab

2023

Int. j. adv. appl. sci.

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The success of software development projects is often hindered by time pressure (TP), leading to decreased productivity, compromised quality, and increased risk of failure. To address this issue, it is crucial to understand the key factors contributing to TP in software development projects. In line with the study's objectives, the review methodology followed the Kitchenham and Charters criteria, and a search strategy encompassed four primary digital databases, namely IEEE, ACM Digital Library, Science Direct, and Springers, resulting in 4,500 relevant sources. After applying inclusion and exclusion criteria, a total of 128 papers were selected for analysis. This paper offers a comprehensive overview of the factors contributing to TP in software development. This study synthesizes the findings from multiple studies to guide practitioners in improving their project management approaches and highlights the significance of enhancing various aspects of the development process. The findings highlight the importance of improving project management, estimation techniques, knowledge, and skills to effectively manage TP. Additionally, managing requirements volatility, setting clear goals and objectives, and reducing distractions and interruptions emerge as crucial strategies for mitigating TP and enhancing project success. Furthermore, selecting software developers based on their personality traits is recommended to foster a work environment conducive to reduced TP and improved software development outcomes. By understanding and addressing these factors, software development teams can alleviate TP and increase the likelihood of successful software products. Implementing these recommendations can contribute to reduced TP, improved project outcomes, and enhanced overall success in software development.

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“…The three key dictionaries in this model are generic attribute dictionary, personalized attribute dictionary, and sentiment tendency dictionary. Rehman et al (2017) proposed a set of rules to identify spam reviews and combined this rule with a time series approach to identify the sentiment polarity and sentiment intensity of spam reviews. In addition, some other researchers have utilized sentiment analysis techniques in extracting feature metrics.…”

Section: Related Researchmentioning

confidence: 99%

A Deep Learning-Based Sentiment Classification Model for Real Online Consumption

Yang

Shen

2022

Front. Psychol.

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Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers’ product experience and contain a lot of important information, but inevitably there are malicious or irrelevant reviews. It is especially important to discover and identify the real sentiment tendency in online reviews in a timely manner. Therefore, a deep learning-based real online consumer sentiment classification model is proposed. First, the mapping relationship between online reviews of goods and sentiment features is established based on expert knowledge and using fuzzy mathematics, thus mapping the high-dimensional original text data into a continuous low-dimensional space. Secondly, after obtaining local contextual features using convolutional operations, the long-term dependencies between features are fully considered by a bidirectional long- and short-term memory network. Then, the degree of contribution of different words to the text is considered by introducing an attention mechanism, and a regular term constraint is introduced in the objective function. The experimental results show that the proposed convolutional attention–long and short-term memory network (CA–LSTM) model has a higher test accuracy of 83.3% compared with other models, indicating that the model has better classification performance.

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Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

Cited by 5 publications

References 36 publications

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

The key factors contribute to time pressure in software development projects: A review

A Deep Learning-Based Sentiment Classification Model for Real Online Consumption

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