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| 1 | Antimicrobial activity of mangrove plant(Lumnitzera littorea)显示文摘Objective:To investigate the antimicrobial activities of n-hexane.ethyl acetate and methanol extracts of the leaves of Lumnitzera littorea(L littorea) against six human pathogenic microbes. Methods:The antimicrobial activity was evaluated using disc diffusion and microdilution methods.Results:The antimicrobial activities of the crude extracts were increased with increasing the concentration.It is clear that n-hexane extract was the most effective extract. Additionally.Gram positive Bacillus cereus(B.cereus) appear to be the most sensitive strain while Pseudomonas aeruginosa(P.aeruginosa) and the yeast strains(Candida albicans(C. albicans) and Cryptococcus neoformans(C.neqformans)) appear to be resistance to the tested concentrations since no inhibition zone was observed.The inhibition of microbial growth at concentration as low as 0.04 mg/ml.indicated the potent antimicrobial activity of L littorea extracts.Conclusions:The obtained results are considered sufficient for further study to isolate the compounds responsible for the activity and suggesting the possibility of finding potent antibacterial agents from L littorea extracts. | Shahbudin Saad Muhammad Taher Deny Susanti Haitham Qaralleh Nurul Afifah Binti Abdul Rahim | 2011 | Asian Pacific Journal of Tropical Medicine2011，4，7： | 7 |
| 2 | Antimicrobial activity and essential oils of Curcuma aeruginosa,Curcuma mangga,and Zingiber cassumunar from Malaysia显示文摘Objective:To analyze the chemical composition of the essential oils of Curcuma aeruginosa (C.aeruginosa),Curcuma mangga(C.mangga),and Zingiber cassumunar(Z.cassumunar). and study their antimicrobial activity.Methods:Essential oils obtained by steam distillation were analyzed by gas chromatography-mass speclrometry(GC-MS).The antimicrobial activity of the essential oils was evaluated against four bacteria:Bacillus cereus(H.cereus).Staphylococcus aureus(S.aureus).Escherichia coli(E.coli).and Pseudomonas aeruginosa(P.aeruginosa);and two fungi:Candida albicans(C.albicans) and Cyptococcus neoformans(C.neoformans),using disc-diffusion and broth microdilution methods.Results:Cycloisolongifolene,8.9-dehydro-9- formyl(35.29%) and dihydrocoslunolide(22.51%) were the major compounds in C.aeruginosa oil; whereas caryophyllene oxide(18.71%) and caryophyllene(12.69%) were the major compounds in C.mangga oil:and 2,6.9,9-tetramethyl-2.6.10-cycloundecatrien-1-one(60.77%) andα-caryophyllene(23.92%) were abundant in Z.cassumunar oil.The essential oils displayed varying degrees of antimicrobial activity against all lested microorganisms.C.mangga oil had the highest and most broad-spectrum activity by inhibiting all microorganisms tested,with C.neoformans being the most sensitive microorganism by having the lowest minimum inhibitory concentration(MIC) and minimum fungicidal concentration(MFC) values of 0.1μL/mL. C.aeruginosa oil showed mild antimicrobial activity,whereas Z.cassumunar had very low or weak activity against the tested microorganisms.Conclusions:The preliminary results suggest promising antimicrobial properties of C.mangga and C.aeruginosa,which may be useful for food preservation,pharmaceutical treatment and natural therapies. | Tg Siti Amirah Tg Kamazeri Othman Abd Samah Muhammad Taher Deny Susanti Haitham Qaralleh | 2012 | Asian Pacific Journal of Tropical Medicine2012，5，3： | 5 |
| 3 | Apoptosis,antimicrobial and antioxidant activities of phytochemicals from Garcinia malaccensis Hk.f显示文摘Objective:To study the chemical constituents of stembark of Garcinia malaccenm(G.malaccenm) together with apoptotic.antimicrobial and antioxidant activities.Methods:Purification and structure elucidation were carried out by chromatographic and spectroscopic techniques, respectively.MTT and trypan blue exclusion methods were performed to study the cytotoxic activity.Antibacterial activity was conducted by dise diffusion and microdilulion methods, whereas antioxidant activities were done by ferric thiocyanate method and DPPH radical scavenging.Results:The phylochemical study led lo the isolation ofα,β-mangostin and cycloarl-24-en-3β-ol.α-Mangostin exhibited cytotoxic activity against HSC-3 cells with an IC50 of 0.33μM.β- andα-mangostin showed activity against K562 cells with IC50 of 0.40μM and 0.48μM,respectively,α-Mangostin was active against Gram-positive bacteria, Staphylococcus aureus(S.aureus) and Bacilus anthracis(B.anthmcis) with inhibition zone and MIC value of(19 mm;0.02S mg/mL) and(20 mm;0.013 mg/mL),respectively.In antioxidant assay,α-mangostin exhibited activity as an inhibitor of lipid peroxidation.Conclusions:G.malaccenm presenceα- andβ-mangostin and cycloart-24-en-3β-ol.β-Mangostin was found very active against H.SC-3 cells and KS62.The results suggest that mangoslins derivatives have the potential to inhibit the growth of cancer cells by inducing apoptosis.In addition,α-andβ-mangostin was found inhibit the growth of Cram-positive pathogenic bacteria and also showed the activity as an inhibitor of lipid peroxidation. | Muhammad Taher Deny Susanti Mohamad Fazlin Rezali Farah Syahidah Ahmad Zohri Solachuddin Jauhari Arief Ichwan Suhaib Ibrahim Alkhamaiseh Farediah Ahmad | 2012 | Asian Pacific Journal of Tropical Medicine2012，5，2： | 5 |
| 4 | Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning显示文摘One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives. | Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 5 | Alzheimer Disease Detection Empowered with Transfer Learning显示文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. | Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 6 | Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication显示文摘Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users. | Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee | 2022 | Computers, Materials & Continua2022，，11： | 1 |
| 7 | Carrier and intermodulation performance of limiters excited by mu]ticartiers显示文摘 | Muhammad Taher Abuelma atti | 1994 | IEEE Trans on Aerospace and Electronic Systems1994，30，3： | 1 |
| 8 | A simple algorithm for fit- ting measured data to Fourier-series models显示文摘 | Abuelma'atti Muhammad Taher | 1993 | International Journal of Mathematical Education in Science and Technolo- gy1993，24，1： | 1 |
| 9 | In vitro antimicrobial activity of mangrove plant Sonneratia alba显示文摘Objective:To investigate the antimicrobial property of mangrove plantSonneratia alba(S. alba).Methods:The antimicrobial activity was evaluated using disc diffusion and microdilution methods against six microorganisms. Soxhlet apparatus was used for extraction with a series of solvents,n-hexane, ethyl acetate and methanol in sequence of increasing polarity.Results:Methanol extract appeared to be the most effective extract whilen-hexane extract showed no activity. The antimicrobial activities were observed against the gram positive bacteria Staphylococcus aureus(S. aureus) and Bacillus cereus(B. cereus), the gram negative Escherichia coli(E. coli) and the yeast Cryptococcus neoformans. Pseudomonas aeruginosa and Candida albicans appeared to be not sensitive to the concentrations tested since no inhibition zone was observed.E. coli(17.5 mm) appeared to be the most sensitive strain followed by S. aureus(12.5 mm)and B. cereus(12.5 mm).Conclusions:From this study, it can be concluded that S. alba exhibit santim icrobial activities against certain microorganisms. | Shahbudin Saad Muhammad Taher Deny Susanti Haitham Qaralleh Anis Fadhlina Izyani Bt Awang | 2012 | Asian Pacific Journal of Tropical Biomedicine2012，2，6： | 1 |
| 10 | An improved approximation of the transmission line parameters of over head wires显示文摘 | Muhammad Taher Abuelmaatti | 1990 | IEEE Transactions on Electromagnetic Compatibility1990，32，4： | 1 |
| 11 | An improved approximation of the transmission line parameters of over head Wires显示文摘 | Muhammad Taher Abuelma'atti | 1990 | IEEE Trans on Electromagnetic Compatibility1990，32，4： | 1 |
| 12 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 13 | Content Based Automated File Organization Using Machine Learning Approaches显示文摘In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user.Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities.Then,the browsing process can be performed quickly and easily.This research aims to design a system to automatically organize files based on their similarities in terms of content.The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system.The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files.The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content.The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows. | Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 14 | Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique显示文摘Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches. | Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 15 | Data Fusion-Based Machine Learning Architecture for Intrusion Detection显示文摘In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’connectivity.To effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be conducted.The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis.This study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DELM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion activity.Data fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks lifespan.We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective.By using the Real Time Sequential Deep Extreme Learning Machine(RTSDELM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished.Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach.Eventually,threats and a more general outlook are explored. | Muhammad Adnan Khan Taher M.Ghazal Sang-Woong Lee Abdur Rehman | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 16 | Certificateless Algorithm for Body Sensor Network and Remote Medical Server Units Authentication over Public Wireless Channels显示文摘Wireless sensor networks process and exchange mission-critical data relating to patients’health status.Obviously,any leakages of the sensed data can have serious consequences which can endanger the lives of patients.As such,there is need for strong security and privacy protection of the data in storage as well as the data in transit.Over the recent past,researchers have developed numerous security protocols based on digital signatures,advanced encryption standard,digital certificates and elliptic curve cryptography among other approaches.However,previous studies have shown the existence of many security and privacy gaps that can be exploited by attackers to cause some harm in these networks.In addition,some techniques such as digital certificates have high storage and computation complexities occasioned by certificate and public key management issues.In this paper,a certificateless algorithm is developed for authenticating the body sensors and remote medical server units.Security analysis has shown that it offers data privacy,secure session key agreement,untraceability and anonymity.It can also withstand typical wireless sensor networks attacks such as impersonation,packet replay and man-in-the-middle.On the other hand,it is demonstrated to have the least execution time and bandwidth requirements. | Bahaa Hussein Taher Muhammad Yasir Abraham Isiaho Judith N.Nyakanga | 2022 | Journal of Computer Science Research2022，4，3： | 0 |
| 17 | Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System显示文摘The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%. | Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 18 | Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM显示文摘Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progression to its last stages makes diagnosis and treatment more difficult.In this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C.The dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C virus.Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main stages.These stages have proven helpful for doctors to analyze the liver’s condition.The traditional way to check the staging is the biopsy,which is a painful and time-consuming process.This article aims to provide an effective and efficient approach to predict hepatitis C staging.For this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results. | Taher M.Ghazal Marrium Anam Mohammad Kamrul Hasan Muzammil Hussain Muhammad Sajid Farooq Hafiz Muhammad Ammar Ali Munir Ahmad Tariq Rahim Soomro | 2021 | Computers, Materials & Continua2021，，10： | 0 |
| 19 | Early Detection of Autism in Children Using Transfer Learning显示文摘Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice. | Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan | 2023 | Intelligent Automation & Soft Computing2023，，4： | 0 |
| 20 | Smart Energy Management System Using Machine Learning显示文摘Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate. | Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
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