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journal of Health Science 2018年第6期6卷 439-445页

作者： Najo A.jomaa Marwan Haddad Grace Y.Adwane amira j.zaylaa Abdallah Rahbani Faculty of Medical Sciences Lebanese UniversityHadath Baabda 1003Lebanon Imaging Department Mount Lebanon HospitalHazmieh 1107Lebanon Epilepsy monitoring unit Department of NeurologyLebanese Geitaoui University HospitalAchrafieh Beirut 1100Lebanon Doctoral School of Science and Technology Lebanese UniversityHadath Baabda 1003Lebanon

There have been many advances in the diagnosis and management of focal epilepsies particularly with neuroimaging techniques and magnetic resonance imaging(MRI).Special MRI sequences can be employed to localize and resect the epileptogenic lesions responsible for focal *** study aims to show the benefit of dedicated epilepsy MRI sequences in the diagnosis of focal epilepsies.A general electric 1.5 Tesla MRI machine was used with standard and special ***,a Nihon Kohden electroencephalography(EEG)machine was *** is a prospective observational study that included 51 patients with focal epilepsies who had an initial negative brain *** underwent epilepsy MRI sequences along with a prolonged video EEG monitoring to localize the lesion,and then results were analyzed statistically using SPSS 22 *** majority of patients were males(62.75%)with a mean age of 30 *** grand majority of patients(74.5%,p value of 0.001)had their lesion localized by the epilepsy *** most commonly found pathology was mesial temporal sclerosis.A significant number of patients(23.5%)were sent for an epilepsy surgery(p value 0.002).This study shows the significant impact of dedicated epilepsy MRI sequences on the diagnosis and management of focal epilepsy in the Lebanese population.

关键词： Epilepsy magnetic resonance imaging dedicated sequences focal epileptogenic lesions hippocampal sclerosis cortical dysplasia.

Automatic Optimization of Chirp Setting Parameters in Medical Ultrasound Contrast Imaging

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journal of Life Sciences 2013年第6期7卷 592-598页

作者： amira zaylaa Sebastien Menigot jamal Charara jean-Marc Girault Department of Medical Biophysics and Imaging Universite Franqois Rabelais de Tours UMR-S930 and ERL3106 B.P 3223Tours France INSERM U930 Tours France Department of Physics and Electronics Lebanese University Hadath Campus B.P. 5 Beirut Lebanon IUT Ville d'Avray Universite Paris Ouest Nanterre La Defense B.P. 105 Avray France

Medical ultrasound contrast imaging is a powerful modality undergoing successive developments in the last decade to date Lately, pulse inversion has been used in both ultrasound tissue harmonic and contrast imaging. However, there was a tradeoff between resolution and penetration. Chirp excitations partially solved the tradeoff, but the chirp setting parameters were not optimized. The present work proposes for the first time combining chirp inversion with ultrasound contrast imaging, with the motivation to improve the contrast, by automatically optimizing the setting parameters of chirp excitation, it is thus an optimal command problem. Linear chirps, 5 μm diameter microbubbles and gradient ascent algorithm were simulated to optimize the chirp setting parameters. Simulations exhibited a gain of 5 dB by automatic optimization of chirp inversion relative to pulse inversion. The automatic optimization process was quite fast. Combining chirp inversion with ultrasound contrast imaging led to a maximum backscattered power permitting high contrast outcomes and optimum parameters.

关键词： Medical ultrasound contrast imaging chirp inversion chirp setting parameters automatic technique optimal command.

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

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Computers, Materials & Continua 2021年第12期69卷 3531-3548页

作者： Nora Shoaip amira Rezk Shaker EL-Sappagh Tamer Abuhmed Sherif Barakat Mohammed Elmogy Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes(CiTIUS)Universidade de Santiago de Compostela Santiago de Compostela15782Spain Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Department of Computer Science and Engineering College of ComputingSungkyunkwan UniversityRepublic of Korea Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia *** is a global health problem.99%of clinical trials have failed to limit the progression of this *** risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical *** for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological *** this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive *** an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very ***,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning *** models are random forest(RF),jRip,and j48,which have been used along with the voting *** dataset was used for this *** proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference ***,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

关键词： Mild cognitive impairment Alzheimer’s disease knowledge based semantic web rule language reasoning system ADNI dataset machine learning techniques

Exploring the band structure of Wurtzite InAs nanowires using photocurrent spectroscopy

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Nano Research 2020年第6期13卷 1586-1591页

作者： Seyyedesadaf Pournia Samuel Linser Giriraj jnawali Howard E.jackson Leigh M.Smith amira Ameruddin Philippe Caroff jennifer Wong-Leung Hark Hoe Tan Chennupati jagadish Hannah j.joyce Department of Physics University of CincinnatiCincinnatiOH45221-0011USA Department of Electronic Materials Engineering Research School of PhysicsThe Australian National UniversityCanberraACT2601Australia Department of Engineering University of Cambridge9 JJ Thomson AvenueCambridgeCB30FAUK

Nano Research volume We use polarized photocurrent spectroscopy in a nanowire device to investigate the band structure of hexagonal Wurtzite *** of optical transitions between four valence bands and two conduction bands are observed which are consistent with the symmetries expected from group *** ground state transition energy identified from photocurrent spectra is seen to be consistent with photoluminescence emitted from a cluster of nanowires from the same growth *** the energies of the observed bands we determine the spin orbit and crystal field energies in Wurtzite *** information is vital to the development of crystal phase engineering of this important III-V semiconductor.

关键词： nanowires Wurtzite InAs photocurrent measurement optical selection rules energy band structure

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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Computer Systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala j.Alshahrani Khaled Tarmissi Ayman Yafoz amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi 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 Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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光卤石的重选与浮选

国外建材译丛 1992年第3期4卷 8-13页

作者： amira,j

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala j.Alshahrani Saud S.Alotaibi Khaled M.Alalayah amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

Gravimetric Study of Geological Structures of Teboursouk Area, Northern Tunisia

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International journal of Geosciences 2012年第4期3卷 675-682页

作者： amira Ayed-Khaled Taher Zouaghi Mohamed Ghanmi Fouad Zargouni Université de Carthage Université Tunis El Manar

Gravity data associated with surface geology in the Northern Tunisian Atlas offer better understand to the underlying structures in Teboursouk area and to highlight other deep or unknown structures in surface. The gravity study was based on qualitative and quantitative analysis including the construction of the gravity Bouguer anomaly, upward continuations, residual anomaly, and Horizontal gradient maxima maps. The main results display many positive and negative anomalies as the response of geological structures (j. Cheid Triassic structure, Khalled plain, El Aroussa plain). In addition, the horizontal gradient maxima integrated with geological and structural maps let the identification of major directions of gravimetric lineaments in the study area us NE-SW trending features at the boundaries of j. Cheid structure, NW-SE direction that limit Gaafour plain and Tabet Ech Cherif syncline, and N-S trending that bordered El Aroussa basin. Major results allowed the construction of a new structural map of the study zone.

关键词： Gravimetry Bouguer Anomaly Upward Continuations Gradient Structure Northern Tunisia