检索结果-南通市图书馆

Role of ZnO-CeO_2 Nanostructures as a Photo-catalyst and Chemi-sensor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Materials Science & Technology 2011年第7期27卷 594-600页

作者： M. Faisal Sher Bahadar Khan Mohammed M. Rahman Aslam Jamal Kalsoom Akhtar M.M. Abdullah Centre for Advanced Materials and Nano-Engineering (CAMNE) and Department of Chemistry Faculty of Sciences and Arts Najran UniversityNajran 11001 Kingdom of Saudi Arabia Division of Nano Sciences and Department of Chemistry Ewha Womans UniversitySeoul 120-750 Korea

ZnO-CeO2 nanostructures were synthesized by simple and effcient low temperature method. The structure and morphology of the ZnO-CeO2 nanostructures were characterized by X-ray powder diffraction （XRD） and field emission scanning electron microscopy （FESEM）, which revealed elongated shaped CeO2 nanoparticles with diameters of 40–90 nm distributed on the surface of elongated ZnO nanostructures with diameters of 50–200 nm （edge–centre）. Further the structure of the synthesized ZnO-CeO2 nanostructure was supported by Raman spectra and Fourier transform infrared spectroscopy （FTIR）. UV-vis absorption spectrum was used to confirm the optical properties of the CeO2 doped ZnO nanostructures. Photo-catalytic activity of CeO2 doped ZnO nanostructure was evaluated by degradation of acridine orange and methylene blue which degraded 84.55% and 48.65% in 170 min, respectively. ZnO-CeO2 nanostructures also showed good sensitivity （0.8331 μA·cm-2·（mol/l）-1） in short response time （10 s） by applying to chemical sensing using ethanol as a target compound by I-V technique. These degradation and chemical sensing properties of ZnO-CeO2 nanostructures are of great importance for the application of ZnO-CeO2 system as a photo-catalyst and chemical sensor.

关键词： ZnO-CeO2 nanostructures Structural properties Optical properties Photocatalytic activity Ethanol sensing

Liver Ailment Prediction Using Random Forest Model

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City university of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the university of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

Th-Shaped Tunable Multi-Band Antenna for Modern Wireless Applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 2517-2530页

作者： Wasi Ur Rehman Khan Muhammad Fawad Khan Muhammad Irfan Sadiq Ullah Naveed Mufti Usman Ali Rizwan Ullah Fazal Muhammad Saifur Rahman Faisal Althobiani Mohammed Alshareef Mohammad E.Gommosani Department of Telecommunication Engineering University of Engineering and TechnologyMardanPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajranSaudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritimes Studies King Abdulaziz UniversityJeddahSaudi Arabia Ports&Maritime Transportation Faculty of Maritimes StudiesKing Abdulaziz UniversityJeddahSaudi Arabia Natural Science Department Faculty of Maritimes StudiesKing Abdulaziz UniversityJeddahSaudi Arabia

A compact,reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this *** was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna,based on use of a single switch within the radiating structure of the *** frequency bands(i.e.,2007–2501 MHz,3660–3983MHz and 9341–1046 MHz)can be operated with the switch in the ON switch *** the OFF state of the switch,the antenna operates within the 2577–3280MHz and 9379–1033MHz *** proposed antenna shows an acceptable input impedance match with Voltage Standing Wave Ratio(VSWR)less than *** peak radiation efficiency of the antenna is 82%.A reasonable gain is obtained from 1.22 to 3.31 dB within the operating bands is *** proposed antenna supports UniversalMobile Telecommunication System(UMTS)-1920 to 2170 MHz,Worldwide Interoperability and Microwave Access(WiMAX)/Wireless Broadband/(Long Term Evolution)LTE2500–2500 to 2690 MHz,Fifth Generation(5G)-2500/3500 MHz,Wireless Fidelity(Wi-Fi)/Bluetooth-2400 to 2480 MHz,and Satellite communication applications in X-Band-8000 to 12000 *** overall planar dimension of the proposed antenna is 40×*** antennawas designed,along with the parametric study,using Electromagnetic(EM)simulation *** antenna prototype is fabricated for experimental validation with the simulated *** proposed antenna is low profile,tunable,lightweight,cheap to fabricate and highly efficient and hence is deemed suitable for use in modern wireless communication electronic devices.

关键词： Antenna design reconfigurable antenna satellite communication frequency bands

Heart Disease Risk Prediction Expending of Classification Algorithms

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf A.Alshamrani City university of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science najran university Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the university of California,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 4885-4895页

作者： Muhammad Irfan Farman Ali Fazal Muhammad Saifur Rahman Ammar Armghan Yousaf Khan Faisal Althobiani Rehan Shafiq Mohammed Alshareef Mohammad E.Gommosani Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Qurtuba University of Science and ITDera Ismail Khan29050Pakistan Department of Electrical Engineering University of Engineering TechnologyMardan23200Pakistan Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Faulty of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering Sarhad University of Science and Information TechnologyPeshawar25220Khyber PukhtunkhwaPakistan Ports&Maritime Transportation Faculty of Maritime Studies King Abdulaziz university JeddahSaudi Arabia Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddahSaudi Arabia

The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input *** channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN ***,addressing of these distortions is the basic objective for the 5G mobile *** this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF *** performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring *** simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.

关键词： Fiber nonlinearity phase noise radio over fiber network advanced modulation system

Cactus-Shaped Frequency Reconfigurable Antenna for Sub 10 GHz Wireless Applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第6期45卷 2693-2704页

作者： Muhammad Irfan Wasi Ur Rehman Khan Sadiq Ullah Naveed Mufti Muhammad Fawad Khan Rizwan Ullah Usman Ali Fazal Muhammad Faisal Althobiani Mohammed Alshareef Shadi Alghaffari Saifur Rehman V.R.Shamji Electrical Engineering Department College of EngineeringNajran UniversitySaudi Arabia Department of Telecommunication Engineering University of Engineering and TechnologyMardanKPPakistan Department of Electrical Engineering University of Engineering and TechnologyMardanKPPakistan Faculty of Maritimes Studies King Abdulaziz UniversityJeddahSaudi Arabia Ports&Maritime Transportation Faculty of Maritimes StudiesKing Abdulaziz UniversityJeddahSaudi Arabia Faculty of Maritime Studies King Abdulaziz UniversityJeddahSaudi Arabia

This paper presents,a novel cactus shaped frequency reconfigurable antenna for sub 10 GHz wireless *** diode is utilized as an electrical switch to achieve reconfigurability,enabling operation in four different frequency *** the switch ON state mode,the antenna supports 2177-3431 and 6301-8467 MHz ***,the antenna resonates within 2329-3431 and 4951-6718 MHz while in the OFF state *** efficiency values,ranging from 68%to 84%,and gain values,ranging from 1.6 to 4 dB,in the operating frequency *** proposed antenna satisfy the practical requirements and *** overall planner dimensions of the proposed antenna model is 40×21 mm^(2).Moreover,the measurement results from the prototype support the simulation *** on the frequency ranges supported by the antenna,it can be used for multiple wireless standards and services,including Worldwide interoperability and Microwave Access(WiMAX),Wireless Fidelity(Wi-Fi),Bluetooth,Long Term Evolution(LTE)and satellite *** increases its applicability for use in mobile terminals.

关键词： Antenna frequency reconfigurable PIN diode switch wireless technology

Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第1期74卷 1333-1349页

作者： Rehana Ghulam Sammar Fatima Tariq Ali Nazir Ahmad Zafar Abdullah A.Asiri Hassan A.Alshamrani Samar M.Alqhtani Khlood M.Mehdar Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Radiological Sciences Department College of applied medical sciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia

Human brain consists of millions of cells to control the overall structure of the human *** these cells start behaving abnormally,then brain tumors *** and initial stage brain tumor detection has always been an issue in the field of medicines for medical *** handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human *** this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 *** initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation *** model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

关键词： U-net brain tumor magnetic resonance images convolutional neural network segmentation

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction