Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach
- Authors: Acharjee, Uzzal , Arefin, Minhazul , Hossen, Kazi , Uddin, Mohammed , Uddin, Md Ashraf , Islam, Linta
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 26730-26742
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- Description: Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE.
Cyberbullying detection on social networks using machine learning approaches
- Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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- Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.
An efficient hybrid system for anomaly detection in social networks
- Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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- Description: Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s).
CRICRATE : A cricket match conduction and player evaluation framework
- Authors: Uddin, Md Ashraf , Hasan, Mahmudul , Halder, Sajal , Ahamed, Sajeeb , Acharjee, Uzzal
- Date: 2019
- Type: Text , Conference paper
- Relation: International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2018 Vol. 755, p. 491-500
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- Description: Cricket has appeared as one of the most favorite outdoor games in the present world. The cricket players represent a country and create economic, political, and diplomatic relations among nations. The cricket board of a country requires selecting the fittest players for the upcoming team among some good players. We propose an architecture called Cricket Match Conduction and Player Evaluation Framework by developing some algorithms to predict the score of the players as well as the algorithm to evaluate the man of the match in one day or test cricket match. We implemented the framework by Weka and web technology. © Springer Nature Singapore Pte Ltd. 2019.