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| 1 | Machine Learning Approach for COVID-19 Detection on Twitter显示文摘Social networking services(SNSs)provide massive data that can be a very influential source of information during pandemic outbreaks.This study shows that social media analysis can be used as a crisis detector(e.g.,understanding the sentiment of social media users regarding various pandemic outbreaks).The novel Coronavirus Disease-19(COVID-19),commonly known as coronavirus,has affected everyone worldwide in 2020.Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions.This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages(tweets).For this purpose,we propose herein an intelligent model using traditional machine learning-based approaches,such as support vector machine(SVM),logistic regression(LR),naïve Bayes(NB),random forest(RF),and decision tree(DT)with the help of the term frequency inverse document frequency(TF-IDF)to detect the COVID-19 pandemic in Twitter messages.The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories,namely,confirmed deaths,recovered,and suspected.For the experimental analysis,the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches.A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies.The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy,precision,recall,and F1 score between 70%and 80%and the confusion matrix for machine learning approaches(i.e.,SVM,NB,LR,RF,and DT)with the TF-IDF feature extraction technique. | Samina Amin M.Irfan Uddin Heyam H.Al-Baity M.Ali Zeb M.Abrar Khan | 2021 | Computers, Materials & Continua2021，，8： | 1 |
| 2 | Pyranocycloartobiloxanthone A, a novel gastroprotective compound from Artocarpus obtusus Jarret, against ethanol-induced acute gastric ulcer in vivo显示文摘 | Heyam M.A. Sidahmed Najihah Mohd Hashim Junaidah Amir Mahmood Ameen Abdulla A. Hamid A. Hadi Siddig Ibrahim Abdelwahab Manal Mohamed Elhassan Taha Pouya Hassandarvish Xinsheng Teh Mun Fai Loke Jamuna Vadivelu Mawardi Rahmani Syam Mohan | 2013 | Phytomedicine2013，，10： | 1 |
| 3 | α -Mangostin from Cratoxylum arborescens (Vahl) Blume Demonstrates Anti-Ulcerogenic Property: A Mechanistic Study显示文摘 | Heyam M. A. Sidahmed Siddig Ibrahim Abdelwahab Syam Mohan Mahmood Ameen Abdulla Manal Mohamed Elhassan Taha Najihah Mohd Hashim A. Hamid A. Hadi Jamunarani Vadivelu Mun Loke Fai Mawardi Rahmani Maizatulakmal Yahayu Evan Paul Cherniack | 2013 | Evidence-Based Complementary and Alternative Medicine2013，，： | 1 |
| 4 | GC-MS detection and characterization of thebaine as a urinary marker of opium use显示文摘 | Babiker ME Abdelkader MA Heyam SA | 2007 | Forensic Toxicol2007，25，2： | 1 |
| 5 | Emergency Excision of Cardiac Myxoma and Endovascular Coiling of Intracranial Aneurysm after Cerebral Infarction显示文摘 | Youssef Al-Said Heyam Al-Rached Saleh Baeesa Khalil Kurdi Ibrahim Zabani Ahmed Hassan N. S. Litofsky M. Moonis D. J. Rivet I. L. Simone | 2013 | Case Reports in Neurological Medicine2013，，： | 1 |
| 6 | Oripavine as a new marker of opiate product use显示文摘 | Babiker ME Heyam SA Nehad MH | 2011 | Forensic Toxicol2011，29，2： | 1 |
| 7 | Computer Integration Into The Early Childhood Curriculum显示文摘 | MONA HEYAM | | 0，，： | 1 |
| 8 | A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction显示文摘Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process.However,the high dimensionality of genetic data makes the classification process a challenging task.In this paper,we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm(simulated annealing(SA)),which will help select the most informative genes for breast cancer prediction.These optimal genes will then be used to train the classifier to improve its accuracy and efficiency.Three supervised machine-learning algorithms,namely,the support vector machine,the decision tree,and the random forest were used to create the classifier models that will help to predict breast cancer.Two different experiments were conducted using three datasets:Gene expression(GE),deoxyribonucleic acid(DNA)methylation,and a combination of the two.Six measures were used to evaluate the performance of the proposed algorithm,which include the following:Accuracy,precision,recall,specificity,area under the curve(AUC),and execution time.The effectiveness of the proposed classifiers was evaluated through comprehensive experiments.The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time.High accuracy values of 99.77%,99.45%,and 99.45%have been achieved by SA-SVM for GE,DNA methylation,and the combined datasets,respectively.The execution time of the proposed approach was significantly reduced,in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM,which was 0.02,0.03,and 0.02 on GE,DNA methylation,and the combined datasets respectively.In regard to precision and specificity,SA-RF obtained the best result of 100 on GE dataset.While SA-SVM attained the best recall result of 100 on GE dataset. | Heyam H.Al-Baity Nourah Al-Mutlaq | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 9 | Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition显示文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models. | Mesfer Al Duhayyim Hala J.Alshahrani Khaled Tarmissi Heyam H.Al-Baity Abdullah Mohamed Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed IEldesouki | 2023 | Intelligent Automation & Soft Computing2023，37，9： | 0 |
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