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| 1 | Fabrication and characterization of affordable hydrophobic ceramic hollow fibre membrane for contacting processes显示文摘Affordable hydrophobic hollow fibre membranes were prepared using kaolin and alumina based ceramic powders via a combined phase inversion and sintering technique,followed by a grafting with fluoroalkylsilane(FAS).The crux of the matter in this paper is to study the changes in the properties of the hollow fibre membranes(gas permeation,mechanical strength,pore size,porosity,tortuosity,morphology,and contact angle)by the addition of alumina(Al2O3)to the pure kaolin with mono or multiparticle sizes.By varying the overall loading and particle size of alumina addition,different morphologies of the membranes were obtained due to the differences in the path lengths during phase inversion process for each solvent and nonsolvent exchange.The successful grafting with FAS was evidenced by the increase in contact angle from nearly equal to zero degree before grafting to 140°after grafting.Kaolin-alumina-4,one of the hollow fibres fabricated in this work,achieved a mean pore size of 0.25μm with the bending strength of 96.4 MPa and high nitrogen permeance of 2.3×10^(-5) mol·m^(-2)·Pa^(-1)·s^(-1),which makes the hollow fibre most suitable for the membrane contactor application. | Mohammed Abdulmunem ABDULHAMEED Mohd Hafiz Dzarfan OTHMAN Haider Nadhom Azziz Al JODA Ahmad Fauzi ISMAIL Takeshi MATSUURA Zawati HARUN Mukhlis A.RAHMAN Mohd Hafiz PUTEH Juhana JAAFAR | 2017 | Journal of Advanced Ceramics2017，6，4： | 3 |
| 2 | Role of melatonin in ameliorating lead induced haematotoxicity 显示文摘 | Othman AI al Sharawy S el-Missiry MA | 2004 | Pharmacol Res2004，50，3： | 1 |
| 3 | Isolation,characterirzation and significance of papaya β-galactosidases to cell wall modification and fruit softening during ripening显示文摘 | Ali Z M Ng S Y Othman R er al | 1998 | Physiologia Plantarum1998，104，： | 1 |
| 4 | Uncertainty modeling in power system state estimation显示文摘 | AL OTHMAN A K IRVING M R | 2005 | IEE Proceedings: Generation Transmission and Distribution2005，15，23： | 1 |
| 5 | Proteomics of G fade 3 infiltrating ductal carcinoma in Malaysian Chinese breast cancer patients显示文摘 | Othman MI Majid MIA Singh M ee al | 2009 | Biotechnology and Applied Biochemistry2009，52，3： | 1 |
| 6 | Hepatoprotective effects of vitamin E/selenium against mal- athion -induced injuries on the antioxidant status and apopto- sis-related gene expression in rats 显示文摘 | ABOUL -SOUD MA AL -OTHMAN AM EL -DESOKY GE | 2011 | J Toxicol Sci2011，36，3： | 1 |
| 7 | Phenotypic and molecular epidemiology of Acinetobacter baumannli strains isolated in Rabta Hospital, Tunisia 显示文摘 | BEN OTHMAN A ZRIBI M MASMOUDI A ct al | 2007 | Arch Inst Pasteur Tunis2007，84，14： | 1 |
| 8 | Cheong examination of template structural effects on CEC chiral separation performance of molecule imprinted polymers made by a generalized preparation protocol显示文摘 | ZAIDI S A LEE S M AL OTHMAN Z A | 2011 | Chroma- tographia2011，73，5： | 1 |
| 9 | Novel ti~brication technique of hollow fibre support for micro-tubular solid oxide fuel cells 显示文摘 | Othman M H D Droushiotis N Wu Z el al | 2011 | Journal of Power Sources2011，196，11： | 1 |
| 10 | L-Arginine ameliorates oxidative stress in alloxan-induced experimental diabetes mellitus显示文摘 | El-Missiry MA Othman Al Amer MA | 2004 | J Appl Toxicol2004，24，2： | 1 |
| 11 | Role of melatonin in ameliorating lead induced haematotoxieity显示文摘 | Othman AI al Sharawy S el-Missiry MA | 2004 | Pharmaeol Res2004，50，3： | 1 |
| 12 | Patient outcomes and thoracic aortic volume and morphologic changes following thoracic endnvascular aortic repair in patients with complicated chronic type B aortic dissection显示文摘 | Andacheh ID Donayre C Othman F el al | 2012 | J Vasc Surg2012，56，3： | 1 |
| 13 | Bacteremia caused by Rhizobium radiobacter in a preterm neonate显示文摘 | Khan S AlSweih N Othman A H | 2014 | Indian J Pediatr2014，81，2： | 1 |
| 14 | An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques显示文摘Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%. | Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 15 | Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System显示文摘The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models. | Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 16 | Soil Salinization in Some Irrigated Areas of the Kingdom of Bahrain显示文摘 | Asma Ali Abahussain Abdelhadi Abdelwahab Mohamed Ahmed Ali Salih Ahmad Al Safe Nader Abdul Hamed Mosa Yahya Othman | 2014 | Journal of Agricultural Science and Technology(A)2014，4，2： | 0 |
| 17 | High levels of Zinc-α-2-Glycoprotein among Omani AIDS patients on combined antiretroviral therapy显示文摘Objective:To investigate the levels of zinc-α-2-glycoprotein(ZAG) among Omani AIDS patients receiving combined antiretroviral therapy(cART).Methods:A total of 80 Omani AIDS patients(45 males and 33 females),average age of 36 vears.who were receiving cART at the Saltan Qaboos University Hospital(SQUH).Muscat,Oman,were tested for the levels of ZAG.In addition,SO healthy blood donors(46 males and 34 females),average age of 26 years,attending the SOUH Blood Bank,were tested in parallel as a control group.Measurement of the ZAG levels was performed using a competitive enzyme—linked immunosorbent assay and in accordance with the manufacturer's instructions.Results:The ZAG levels were found to he significantly higher among AIDS patients compared to the healthy individuals(P=0.033).A total of 56(70%) of the AIDS patients were found to have higher levels of ZAG and 16(20%) AIDS patients were found to have high ZAG levels,which are significantly(P>0.031) associated with weight loss.Conclusions:ZAG levels are high among Omani AIDS patients on cART and this necessitales the measurement of ZAG on routine basis,as it is associated with weight loss. | Sidgi Syed Anwer Hasson Mohammed Saeed Al-Balushi Muzna Hamed Al Yahmadi Juma Zaid Al-Busaidi Elias Antony Said Mohammed Shafeeq Othman Talal Abdullah Sallam Mohammed Ahmad Idris Ali Abdullah Al-Jabri | 2014 | Asian Pacific Journal of Tropical Biomedicine2014，4，8： | 0 |
| 18 | Deep Transfer Learning Driven Oral Cancer Detection and Classification Model显示文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%. | Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 19 | Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment显示文摘Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures. | Anwer Mustafa Hilal Fahd NAl-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad Almustafa Nadhem Nemri Mesfer Al Duhayyim Manar Ahmed Hamza Abu Sarwar Zamani | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 20 | Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients显示文摘Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of life.By using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery.This study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient outcomes.We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for SAVR.We divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last follow-up.We then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for SAVR.The results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival period.By optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after SAVR.Our study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS. | Salah Alzghoul Othman Smadi Ali Al Bataineh Mamon Hatmal Ahmad Alamm | 2023 | Intelligent Automation & Soft Computing2023，，7： | 0 |
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