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Optimized synthesis and photovoltaic performance of TiO_2 nanoparticles for dye-sensitized solar cell

Particuology 2013年第6期11卷 753-759页

作者： Siti Nur Fadhilah Zainudin Masturah Markom huda abdullah Renata Adami Siti Masrinda Tasirin Department of Chemical Engineering and Process Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia Department of Electrical Electronic and System EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia Department of Chemical and Food Engineering University of Salerno

This paper presents response surface methodology （RSM） as an efficient approach for modeling and optimizing TiO2 nanoparticles preparation via co-precipitation for dye-sensitized solar cell （DSSC） perfor- mance. Titanium （IV） bis-（acetylacetonate） di-isopropoxide （DIPBAT）, isopropanol and water were used as precursor, solvent and co-solvent, respectively. Molar ratio of water, aging temperature and calcina- tion temperature as preparation factors with main and interaction effects on particle characteristics and performances were investigated, Particle characteristics in terms of primary and secondary sizes, crys- tal orientation and morphology were determined by X-ray diffractometry （XRD） and scanning electron microscopy （SEM）. Band gap energy and power conversion efficiency of DSSCs were used for perfor- mance studies. According to analysis of variance （ANOVA） in response surface methodology （RSM）, all three independent parameters were statistically significant and the final model was accurate. The model predicted maximum power conversion efficiency （0.14%） under the optimal condition of molar ratio of DIPBAT-to-isopropanol-to-water of 1 ： 10：500, aging temperature of 36 C and calcination temperature of 400 ℃. A second set of data was adopted to validate the model at optimal conditions and was found to be 0.14 ± 0.015%, which was very close to the predicted value. This study proves the reliability of the model in identi（ving the optimal condition for maximum performance.

关键词： Titanium dioxide Hydrolysis precipitation Dye-sensitized solar cell Optimization Response surface methodology

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Review:Development of lanthanum strontium cobalt ferrite composite cathodes for intermediate-to low-temperature solid oxide fuel cells

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第1期14卷 11-24页

作者： Nurul Akidah BAHARUDDIN Hamimah Abd RAHMAN Andanastuti MUCHTAR Abu Bakar SULONG huda abdullah Fuel Cell Institute Universiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia86400 BatuPahatJohorMalaysia Department of Mechanical and Materials Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia

Solid oxide fuel cells （SOFCs） offer high energy conversion, low noise, low pollutant emission, and low processing cost. Despite many advantages, SOFCs face a major challenge in competing with other types of fuel cells because of their high operating temperature. The necessity to reduce the operational temperature of SOFCs has led to the development of research into the materials and fabrication technology of fuel cells. The use of composite cathodes significantly reduces the cathode polarization resistance and expands the triple phase boundary area available for oxygen reduction. Powder preparation and composite cathode fabrication also affect the overall performance of composite cathodes and fuel cells. Among many types of cathode materials, lanthanum-based materials such as lanthanum strontium cobalt ferrite （Lal_xSrxCOl_yFey03_~） have recently been discovered to offer great compatibility with ceria-based electrolytes in performing as composite cathode materials for intermediate- to low-temperature SOFCs （IT-LTSOFCs）. This paper reviews various ceria-based composite cathodes for IT-LTSOFCs and focuses on the aspects of progress and challenges in materials technology.

关键词： Ceria-based composite cathode Lanthanum strontium cobalt ferrite Intermediate- to low-temperature solid oxidefuel cells (IT-LTSOFCs)

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Text Extraction with Optimal Bi-LSTM

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Computers, Materials & Continua 2023年第9期76卷 3549-3567页

作者： Bahera H.Nayef Siti Norul huda Sheikh abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed Computer Techniques Engineering Department Ibn Khaldun University CollegeBaghdad10011Iraq Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University MalaysiaSepang43900Malaysia

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the *** Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant *** study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain *** pass them to deep learning layers to extract feature maps and reform the predicted *** Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training *** Connectionist Temporal Classification(CTC)function calcifies the training and validation loss *** addition to decoding the extracted feature to reform characters again and linking them according to their time *** proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)*** result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and *** the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

关键词： Deep neural network text features dual max-pooling concatenating convolution neural networks bidirectional long short memory text connector characteristics

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Novel Adaptive Binarization Method for Degraded Document Images

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Computers, Materials & Continua 2021年第6期67卷 3815-3832页

作者： Siti Norul huda Sheikh abdullah Saad M.Ismail Mohammad Kamrul Hasan Palaiahnakote Shivakumara Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaSelangor43600Malaysia The Faculty of Computer and Information Sciences Aljouf UniversitySaudi Arabia Faculty of Computer Systems and Information Technology University of MalayaMalaysia

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast,bleedthrough,and nonuniform illumination *** the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better *** enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced *** the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic *** dynamic window is derived by exploiting the advantage of Otsu *** assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for *** comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

关键词： Global and local thresholding adaptive binarization degraded document image image histogram document image binarization contest

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IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

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Computers, Materials & Continua 2022年第5期71卷 2579-2597页

作者： Taher M.Ghazal Mohammad Kamrul Hasan Siti Norul huda abdullah Khairul Azmi Abubakkar Mohammed A.M.Afifi Faculty of Information Science and Technology University Kebangsaan MalaysiaUKM43600SelangorMalaysia Skyline University College SharjahUnited Arab Emirates

Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring *** systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic *** collection of WS data and integration of that data for diagnostic purposes is a difficult *** paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition *** research is based on a case study in a health *** the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized *** a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic *** breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect *** paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance *** of WS data are examined extensively using active and iterative learning to identify errors in specific *** technology improves position detection accuracy,analysis duration,and error rate,regardless of user *** devices play a critical role in the management and treatment of *** can ensure that patients are provided with a unique treatment for their medical *** paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature *** first,the patients’walking patterns are tracked at various time *** characteristics are then evaluated in relation to the stored data using a random forest classifier.

关键词： Data fusion(DF) posture recognition healthcare systems(HCS) wearable sensor(WS) medical data errorless data fusion(EDF)

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah abdullah Al-Johany Sanaa abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Potential role of tubulin glutamylation in neurodegenerative diseases

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Neural Regeneration Research 2024年第6期19卷 1191-1192页

作者： abdullah Md.Sheikh Shatera Tabassum Department of Laboratory Medicine Shimane University Faculty of MedicineShimaneJapan

The differentiation of neuronal stem cells into mature neurons is a complex process that involves both structural and functional *** cells undergo differentiation,there are notable functional changes,including the expression of various transcription factors,cytokines,and ***,structural changes occur as the cells develop various processes from the cell body and establish synaptic contacts with other cells.

关键词： diseases degenerative cytokines

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Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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Computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

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Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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Computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz abdullah Alsaeedi Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

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A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

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Computer Systems Science & Engineering 2024年第2期48卷 431-449页

作者： abdullah Alsaleh Department of Information Engineering Florence UniversityFlorenceItaly Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also *** intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious *** is important in developing advanced security *** study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention ***,it focuses on machine learning(ML)and deep learning(DL)techniques for *** paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of *** speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning *** nearest class mean classifier is applied during the testing phase to identify new *** results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

关键词： Internet of Vehicles intrusion detection machine learning unknown attacks data processing layer

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