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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas alkherallah Menhal Kamel al-Bohassan Mustafa Mohammed alawami Hayder ali alsebaa Jawad ali alnemer ghazi fayez al-mutairi May Issa aldossary Dalal A.aldowaihi Fahd alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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Beta-endorphin in serum and seminal plasma in infertile men

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Asian Journal of Andrology 2006年第6期8卷 709-712页

作者： Shawky El-Haggar Salah El-Ashmawy Ahmed Attia Taymour Mostafa M.M.Farid Roaiah Ashraf fayez Sherif ghazi Wael Zohdy Nagwa Roshdy Andrology Department Clinical Pathology Department Medical Biochemistry Department Faculty of MedicineCairo UniversityCairo 12311Egypt

Aim： To access beta-endorphin levels in serum as well as seminal plasma in different infertile male groups. Methods： Beta-endorphin was estimated in the serum and seminal plasma by enzyme-linked immunosorbent assay （ELISA） method in 80 infertile men equally divided into four groups： non-obstructive azoospermia （NOA）, obstructive azoospermia （OA）, congenital bilateral absent vas deferens （CBVAD） and asthenozoospermia. The results were compared to those of 20 normozoospermic proven fertile men. Results： There was a decrease in the mean levels of betaendorphin in the seminal plasma of all successive infertile groups （mean ± SD： NOA 51.30 ± 27.37, OA 51.88 ± 9.47, CBAVD 20.36 ± 13.39, asthenozoospermia 49.26 ± 12.49 pg/mL, respectively） compared to the normozoospermic fertile control （87.23 ± 29.55 pg/mL）. This relation was not present in mean serum level of beta-endorphin between four infertile groups （51.09 ± 14.71, 49.76 ± 12.4, 33.96 ± 7.2, 69.1 ± 16.57 pg/mL, respectively） and the fertile control group （49.26 ± 31.32 pg/mL）. The CBVAD group showed the lowest seminal plasma mean level of beta-endorphin. Testicular contribution of seminal beta-endorphin was estimated to be approximately 40%. Seminal beta-endorphin showed significant correlation with the sperm concentration （r = 0.699, P = 0.0188） and nonsignificant correlation with its serum level （r = 0.375, P = 0.185） or with the sperm motility percentage （r = 0.470, P = 0.899）. Conclusion： The estimation of beta-endorphin alone is not conclusive to evaluate male reproduction as there are many other opiates acting at the hypothalamic pituitary gonadal axis.

关键词： azoospermia beta-endorphin male infertility opioid peptides semen seminal plasma

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