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MXenes and heterostructures-based electrocatalysts for hydrogen evolution reaction:Recent developments and future outlook

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Journal of Energy Chemistry 2024年第5期92卷 176-206页

作者： Abdul hanan Hafiz Taimoor ahmed Awan Faiza Bibi Raja Rafidah Raja Sulaiman Wai Yin Wong Rashmi Walvekar Seema Singh Mohammad Khalid Sunway Centre for Electrochemical Energy and Sustainable Technology(SCEEST) School of Engineering and TechnologySunway UniversityNo.5 Jalan UniversitiBandar Sunway47500 Petaling JayaSelangorMalaysia Fuel Cell Institute Universiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia Faculty of Innovation and Technology School of EngineeringChemical Engineering ProgrammeNo.1 Jalan Taylor’sTaylor’s University Malaysia47500 Subang JayaSelangorMalaysia Chitkara Centre for Research and Development Chitkara UniversityHimachal Pradesh 174103India School of Applied and Life Sciences Uttaranchal UniversityDehradunUttarakhand 248007India Manipal Institute of Technology Manipal Academy of Higher EducationManipal 576104KarnatakaIndia Centre of Research Impact and Outcome Chitkara UniversityPunjab 140401India

The increasing focus on electrocatalysis for sustainable hydrogen(H_(2))production has prompted significant interest in MXenes,a class of two-dimensional(2D)materials comprising metal carbides,carbonitrides,and *** materials exhibit intriguing chemical and physical properties,including excellent electrical conductivity and a large surface area,making them attractive candidates for the hydrogen evolution reaction(HER).This scientific review explores recent advancements in MXene-based electrocatalysts for HER *** discusses various compositions,functionalities,and explicit design principles while providing a comprehensive overview of synthesis methods,exceptional properties,and electro-catalytic approaches for H_(2) production via electrochemical ***,challenges and future prospects in designing MXenes-based electrocatalysts with enhanced kinetics are highlighted,emphasizing the potential of incorporating different metals to expand the scope of electrochemical *** review suggests possible efforts for developing advanced MXenes-based electrocatalysts,particularly for efficient H_(2) generation through electrochemical water-splitting reactions..

关键词： MXenes Electrocatalyst Water Splitting Hydrogen Generation Clean Energy

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus

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World Journal of Diabetes 2024年第3期15卷 331-347页

作者： Saleh Fahad alqifari Omar alkomi Abdullah Esmail Khadijeh alkhawami Shahd Yousri Mohamad Ayham Muqresh Nawwarah alharbi Abdullah A Khojah ahmed aljabri Abdulrahman allahham Kousalya Prabahar hanan alshareef Mohammed aldhaeefi Tariq alrasheed ali alrabiah Laila A alBishi Department of Pharmacy Practice Faculty of PharmacyUniversity of TabukTabuk 71491Saudi Arabia Department of Clinical Sciences Sulaiman Alrajhi University-College of MedicineAl Bukayriyah 52726Saudi Arabia Department of Family Medicine Dr.Soliman Fakeeh Hospital DSFHJeddah 21461Saudi Arabia Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah 22252Saudi Arabia Clinical and Administrative Pharmacy Sciences College of PharmacyHoward UniversityWashingtonDC 20059United States Department of Internal Medicine Faculty of MedicineUniversity of TabukTabuk 71491Saudi Arabia Department of Pharmacy Practice Raabe College of PharmacyOhio Northern UniversityOhioOH 45810United States Department of Pediatric Faculty of MedicineUniversity of TabukTabuk 71491Saudi Arabia

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus common second-line choice after metformin for treating *** considerations can make selecting and switching between different GLP-1 RAs *** study aims to provide a comprehensive guide for the usage of GLP-1 RAs and dual GIP and GLP-1 RAs for the management of T2DM.

关键词： Glucagon-like peptide-1 receptor agonist Diabetes mellitus Metabolic syndrome Dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist Clinical practice Endocrinology

A Dual Attention Encoder-Decoder Text Summarization Model

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Computers, Materials & Continua 2023年第2期74卷 3697-3710页

作者： Nada ali Hakami hanan ahmed Hosni Mahmoud Jazan University Computer Science DepartmentCollege of Computer Science and Information TechnologyJazanSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

A worthy text summarization should represent the fundamental content of the *** studies on computerized text summarization tried to present solutions to this challenging *** models are employed extensively in text summarization *** attention techniques are utilized to acquire the context data in the decoding ***,without real and efficient feature extraction,the produced summary may diverge from the core *** this article,we present an encoder-decoder attention system employing dual attention *** the dual attention mechanism,the attention algorithm gathers main data from the encoder *** the dual attentionmodel,the system can capture and producemore rational main *** merging of the two attention phases produces precise and rational text *** enhanced attention mechanism gives high score to text repetition to increase phrase *** also captures the relationship between phrases and the title giving them higher *** assessed our proposed model with or without significance optimization using ablation *** model with significance optimization achieved the highest performance of 96.7%precision and the least CPU time among other models in both training and sentence extraction.

关键词： Text summarization attention model phrase significance

A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

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Computers, Materials & Continua 2022年第12期73卷 5655-5669页

作者： Abeer Abdulaziz alArfaj hanan ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Lie detection test is highly significant task due to its impact on criminology and *** lie detection test model using electroencephalogram(EEG)signals is studied in *** this paper we studied deep learning framework in lie detection test ***,we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole *** model describes a temporal feature map of the EEG signals measured during the lie detection test.A deep learning attention model(V-TAM)extracts the temporal map vector during the learning *** technique reduces computational time and lessens the overfitting in Deep Learning *** propose a Cascading attention model with a deep learning convolutional neural network(CNN).V-TAM model extracts local features and global features in separate paths spatial and ***,to enhance the EEG segmentation precision,a novel Visual-Temporal AttentionModel(V-TAM)is *** accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection *** compared our model with three recent published *** proposed model attained the highest performance of(98.5%)with(pal-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time *** investigationwere performed to prove the correctness and reliability of the proposed method through sizing of the input data,proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.

关键词： Lie detection EEG image deep learning machine learning

An Intelligent Tree Extractive Text Summarization Deep Learning

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Computers, Materials & Continua 2022年第11期73卷 4231-4244页

In recent research,deep learning algorithms have presented effective representation learning models for natural *** deep learningbased models create better data representation than classical *** are capable of automated extraction of distributed representation of *** this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses *** proposed model employs a tree structured mechanism to generate the phrase and text *** proposed architecture mimics the tree configuration of the text-texts and provide better feature *** also incorporates an attention mechanism that offers an additional information source to conduct better summary *** novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary *** model classification is divided into multiple features recognition such as information entropy,significance,redundancy and *** model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)*** experimental results prove that our proposed model has better summarization precision *** models by a considerable margin.

关键词： Neural network architecture text structure abstractive summarization

Deep Learning Prediction Model for Heart Disease for Elderly Patients

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Intelligent Automation & Soft Computing 2023年第2期35卷 2527-2540页

作者： Abeer Abdulaziz alArfaj hanan ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The detection of heart disease is a problematic task in medical *** diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical *** massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things ***,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart *** proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning *** mod-el performance is veriﬁed with a full and minimum set of *** features enhance the processing time of the classiﬁcation process while the accuracy is *** performance of classiﬁers with less features has been analyzed with experimental *** proposed system is built on the Internet of Things Platform for medical data for the classiﬁcation process which aids medical profes-sionals to detect heart diseases through cloud *** results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble *** analysis of the proposed diagnostic system can determine the heart disease risks efﬁ*** results demonstrate thatﬂexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.

关键词： Heart disease internet of things deep learning

Automated Patient Discomfort Detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 2559-2577页

作者： Imran ahmed Iqbal Khan Misbah Ahmad Awais Adnan hanan aljuaid Center of Excellence in Information Technology Institute of Management SciencesPeshawarPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)RiyadhSaudi Arabia

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been *** IoTbased sensors are used in the healthcare sector and offer quality and safe care to *** work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively *** do this,the overhead view patients’data set has been *** testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based *** model uses confidence maps and detects 18 different key points at various locations of the body of the *** association rules and part affinity fields,the detected key points are later converted into six main body ***,the distance of subsequent key points is measured using coordinates ***,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal *** accuracy of the proposed system is assessed on various test *** experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

关键词： Artificial intelligence patient monitoring discomfort detection deep learning

Predicting Violence-Induced Stress in an Arabic Social Media Forum

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Intelligent Automation & Soft Computing 2023年第2期35卷 1423-1439页

作者： Abeer Abdulaziz alArfaj Nada ali Hakami hanan ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia

Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over *** interaction analysis aids in deﬁning people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and *** this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social *** searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on *** applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social *** extracted a set of tex-tual,spatial,and interaction attributes from various ***,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress *** depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in ***,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.

关键词： Arabic language analysis violence-induced stress detection hybrid model deep learning

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

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Computer Systems Science & Engineering 2023年第5期45卷 1393-1407页

作者： hanan Abdullah Mengash Jaber S.alzahrani Majdy M.Eltahir Fahd N.al-Wesabi Abdullah Mohamed Manar ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN *** presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-MLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM *** experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization