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Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

计算机、材料和连续体（英文） 2023年第4期75卷 19-34页

作者： Monirah Al-Ajlan mourad ykhlef Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhKSA

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community *** them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react *** alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection ***,no approach to date has examined the meanings of words and the semantics involved in *** past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this *** eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting *** was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection *** this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to *** parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains *** address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter *** proposed method does not require features for its predictions and its detection of cyberbullying is fully *** firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

关键词： Firefly optimization convolutional neural network(CNN) cyberbullying cyberbullying detection text classification

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Hybrid immunizing solution for job recommender system

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Frontiers of Computer Science 2017年第3期11卷 511-527页

作者： Shaha AL-OTAIBI mourad ykhlef Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Two traditional recommendation techniques, content-based and collaborative filtering （CF）, have been widely used in a broad range of domain areas. Both meth- ods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommen- dation problem from applicant＇s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system （AIS）, which has a powerful explo- ration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors＇ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

关键词： content-based collaborative filtering (CF) hy- bridization computational intelligence (CI) artificial im- mune system (AIS) clonal selection correlation-based simi- larity

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