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Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

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Computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： Ahmed S.Almasoud Siwar Ben Haj Hassine Fahd N.Al-Wesabi Mohamed K.nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially *** handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative ***,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated *** paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text *** proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further ***,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical *** order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is *** experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed *** comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

Clinical spectrum of ocular manifestations in COVID-19:a case series

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Infectious Medicine 2023年第2期2卷 148-152页

作者： Souheil Zayet Ayoub Mihoubi Marion Chatain nour Sreiri Malek Bouzaien Trimech Vincent Gendrin Fatiha Benjelloun Timothée Klopfenstein Department of Infectious Diseases Nord Franche-Comte Hospital90400 TrevenansFrance Department of Ophthalmology Nord Franche-Comte Hospital90400 TrevenansFrance

In this current case series,all Coronavirus disease 2019 patients had predominant ophthalmological *** one patient sough care for concomitant respiratory *** reported herein 2 cases with cranial oculomotor nerve palsy,one patient with confirmed diagnosis of branch retinal vein occlusion,and the last one patient presenting for acute kareto-conjunctivitis with several recurrences,which was unsuccessfully treated with steroids and requiring *** case series highlights the importance of collecting a careful history of ocular presentation,including exposures to possible infected patients with *** this will lead to an early diagnosis and treatment and to make appropriate infection control measures.

关键词： SARS-CoV-2 COVID-19 Diplopia Ophtalmoplegia Neuro-ophtalmology

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

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Computers, Materials & Continua 2022年第8期72卷 3055-3071页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Mohamed K.nour Fahd N.Al-Wesabi Abdelwahed Motwakel Anwer Mustafa Hilal Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Modulation signal classification in communication systems can be considered a pattern recognition *** works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,*** recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation *** this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication *** proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated *** addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)***,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO *** order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried *** experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

关键词： Complex systems communication system signal processing modulation signals classification artificial intelligence deep learning parameter tuning

Vendor Selection,Using Fuzzy Approach

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Vendor Selection,Using Fuzzy Approach

Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

2011 International Conference on Mechanical, Industrial, and Manufacturing Engineering (MIME 2011)

作者： nour Mohammad Yaghoubi Hamid Hajihosseini Department of Nuclear Power Engineering University of Sistan & Baluchestan Zahedan Department of Industrial Engineering Islamic Azad University Zahedan Branch Zahedan

Vendor selection is an evaluation process that is based on many criteria that uses inaccurate or uncertain *** while the criteria are often numerous and the relationships between higher-level criteria and lower-level sub-criteria are complex,most conventional decision models cannot help us clarify the interrelationships among the *** proposed integrated fuzzy multiple criteria decision making(MCDM) method addresses this issue within the context of the vendor selection ***,we use triangular fuzzy numbers to express the subjective preferences of ***,using Fuzzy TOPSIS written in C++ language,the ranking of the vendors for a manufacturing company in supply chain and the consequences of the mentioned above were discussed and investigated.

关键词： Chain management,Vendor evaluation and selection,Multiple criteria decision making(MCDM),Fuzzy logic,FUZZY TOPSIS

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Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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Computer Systems Science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

化学吸附法脱除FCC汽油中含硫化合物的研究

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化学吸附法脱除FCC汽油中含硫化合物的研究

作者： Salissou Zakary Mahaman nour 华东理工大学

学位级别：硕士

虽然流化催化裂化（FCC）工艺已经商业化超过60年,但是该技术仍在不断的发展以适应新的挑战。本研究提出并描述了FCC汽油吸附脱硫用的沸石吸附剂及其工艺的研究进展。为了减少液体碳氢燃料中的硫含量以加强环境保护,在镍锌脱硫吸附剂上... 详细信息

虽然流化催化裂化（FCC）工艺已经商业化超过60年,但是该技术仍在不断的发展以适应新的挑战。本研究提出并描述了FCC汽油吸附脱硫用的沸石吸附剂及其工艺的研究进展。为了减少液体碳氢燃料中的硫含量以加强环境保护,在镍锌脱硫吸附剂上对模拟FCC汽油进行了S-Zorb吸附。首先制备了一系列沸石吸附剂,并对它们的噻吩吸附脱硫能力进行了测试。这些吸附剂对噻吩脱除具有显著的选择性。在进一步的工作中,为筛选更好的吸附剂,用氮气吸附法对吸附剂的比表面积,总孔体积以及孔大小和平均孔径进行了分析。同时用X-射线衍射,热重分析对催化剂进行了表征。X-射线衍射图表明,ZnO颗粒与氧化镍颗粒按一定比例很好地分散在了HY沸石上,并表现出较高的结晶度。TPR等分析还显示,在350℃以上时,由于负载的Ni2+减少而减少了氢气的消耗量。在活性评价时,反应温度,压力和氢油比都在脱硫中起到了关键作用,并且吸附剂脱硫率保持在90%以上。

关键词： FCC汽油脱硫吸附 S-Zorb 噻吩分子筛 Ni2O3 ZnO

Evaluating Banking Efficiency in Kuwait --A Pre and Post Recession Perspective

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Economics World 2014年第5期2卷 333-346页

作者： Mansour Al-Shamali nour AI-Shamali Pierre AI-Khoury Public Authority for Education and Training Safat Kuwait Lebanese German University Jounieh Lebanon

This paper studies banking efficiency from a Kuwaiti banking sector perspective. This will be accomplished by comparing and analysing the efficiency of Kuwaiti banking operations before and after the International Financial Crisis. Traditional and Islamic banks will be taken into consideration. The overall goal of this inquiry is to determine whether the efficiency of Kuwaiti banks has improved and why. Internal and external influences on Kuwaiti banking efficiency will be examined as well. Methods of work performed by bank managers and policy makers in Kuwait contributing to banking operations efficiency will be considered as well.

关键词： banks banking sector efficiency of banks banking recession banking system banking operations banking in Kuwait

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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Computer Systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer