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| 1 | Protective Effect of Solanum nigrum Leaves Extract on Immobilization Stress Induced Changes in Rat’s Brain显示文摘 | Syed Kashif Zaidi Md. Nasrul Hoda Shams Tabrez Shakeel Ahmed Ansari Mohammad Alam Jafri Mohd Shahnawaz Khan Shirin Hasan Mohammed H. Alqahtani Adel Mohammed Abuzenadah Naheed Banu Andrew Newberg | 2014 | Evidence-Based Complementary and Alternative Medicine2014，，： | 1 |
| 2 | Australian Hajj pilgrims' knowledge about MERS-CoV and other respiratory infections显示文摘Dear Editor,With the intense crowding in mass gatherings such as Hajj,there is a high risk of acquisition of airborne in-fections with the potential for its transmission in the pilgrims’country of origin(Memish Z A,et al.,2014).The risk of importing serious infections from Hajj has escalated since the emergence of the Middle East respiratory syndrome coronavirus(MERS-CoV)in Saudi Arabia and other neighbouring countries from September2012.Active surveillance of Hajj pilgrims in 2012 and | Mohamed Tashani Mohammad Alfelali Osamah Barasheed Fayeza Nusrat Fatema Amani Alqahtani Harunor Rashid Robert Booy | 2014 | Virologica Sinica2014，29，5： | 1 |
| 3 | Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication显示文摘With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches. | Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，6： | 1 |
| 4 | Improved Whale Optimization with Local-Search Method for Feature Selection显示文摘Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks. | Malek Alzaqebah Mutasem KAlsmadi Sana Jawarneh Jehad Saad Alqurni Mohammed Tayfour Ibrahim Almarashdeh Rami Mustafa A.Mohammad Fahad A.Alghamdi Nahier Aldhafferi Abdullah Alqahtani Khalid A.Alissa Bashar A.Aldeeb Usama A.Badawi Maram Alwohaibi Hayat Alfagham | 2023 | Computers, Materials & Continua2023，，4： | 1 |
| 5 | Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model显示文摘Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction withothers.Advanced information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern detection.Recent advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the disease.This study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches. | Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 6 | Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0显示文摘Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches. | Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 7 | IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning显示文摘Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to cyberattacks.It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks.Botnet is one of the dreadfulmalicious entities that has affected many users for the past few decades.It is challenging to recognize Botnet since it has excellent carrying and hidden capacities.Various approaches have been employed to identify the source of Botnet at earlier stages.Machine Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection methodology.In spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset.The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)model.The presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter tuning.Initially,the network data is pre-processed to make it compatible for further processing.Besides,RSO algorithm is exploited for effective selection of subset of features.Additionally,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of botnets.Finally,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM model.In order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was conducted.The obtained results confirmed the supremacy of BDCRSODL model over recent approaches. | Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 8 | Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment显示文摘Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection methods.Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical systems.Malicious software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the information.The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)model.The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform.To attain this,Zscore data normalization is utilized to scale the data into a uniform format.In addition,BDDBN model is also exploited for recognition and categorization of malware.To effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is applied.This scenario enhances the classification results and also shows the novelty of current study.The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches. | Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 9 | Approximation algorithms for nonnegative polynomial optimization problems over unit spheres显示文摘 | Xinzhen ZHANG Guanglu ZHOU Louis CACCETTA Mohammed ALQAHTANI | 2017 | Frontiers of Mathematics in China2017，12，6： | 0 |
| 10 | PBL Quizzes and Their Effects on Student Performance显示文摘 | Mohand Alzughaibi Mohammed Alotaibi Faris Ahmed Bader Alqahtani Mohammed Bargo | 2016 | Journal of US-China Medical Science2016，12，2： | 0 |
| 11 | Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification显示文摘In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures. | Tariq Mohammed Alqahtani | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 12 | Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia:A multicenter retrospective study显示文摘BACKGROUND Patients with diabetes mellitus(DM)are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe complications.The Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices(CDC/ACIP)issued immunization recommendations to protect this patient population.AIM To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence rate.METHODS An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online survey.RESULTS Among participants,10.01%(n=71)had never received any vaccine,while 85.89%(n=609)received at least one dose of the coronavirus disease 2019(COVID-19)vaccine,and 34.83%(n=247)had received the annual influenza vaccine.Only 2.96%(n=21),2.11%(n=15),and 1.12%(n=8)received herpes zoster,tetanus,diphtheria,and pertussis(Tdap),and human papillomavirus(HPV)vaccines,respectively.For patients with DM in Saudi Arabia,the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,site of care,income level,DM-related hospitalization history,residency site,hemoglobin A1c(HbA1c)level,and health sector type can significantly influence the vaccination rate in patients with DM.Among non-vaccinated patients with DM,the most reported barriers were lack of knowledge and fear of side effects.This signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with DM.CONCLUSION In Saudi Arabia,patients with DM showed higher vaccination rates for annual influenza and COVID-19 vaccines compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,the site of care,income level,DM-related hospitalization history,residency site,HbA1c level,and health sector type can significantly influence the vaccination rate in patients with DM. | Saleh Fahad Alqifari Aya K Esmail Dalal M Alarifi Ghalya Y Alsuliman Maram M Alhati May R Mutlaq Mohammed Aldhaeefi Shaden A Alshuaibi Palanisamy Amirthalingam Abrar Abdallah Afaf S Wasel Heba R Hamad Shoroq Alamin Tasneem H Atia Tariq Alqahtani | 2024 | World Journal of Diabetes2024，15，3： | 0 |
| 13 | Camel exposure and knowledge about MERS-CoV among Australian Hajj pilgrims in 2014显示文摘Dear Editor,Middle East respiratory syndrome coronavirus(MERS-CoV)affected 1,621 patients worldwide,with a 36%mortality rate by the end of 2015.The highest number of cases was recorded in Saudi Arabia,the location for | Amani Salem Alqahtani Kerrie Elizabeth Wiley Mohamed Tashani Anita Elizabeth Heywood Harold Wayne Willaby Nasser Fahad Bin Dhim Robert Booy Harunor Rashid | 2016 | Virologica Sinica2016，31，1： | 0 |
| 14 | Relative Time Quantum-based Enhancements in Round Robin Scheduling显示文摘Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things,Fog computing and Mobile based applications.The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm(RR)has been widely used for it.The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling.We have carried out an experimental study where we have developed four variations of RR,each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm,and results highlighted that these variations performed better than conventional RR algorithm.In the future,we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time.This way the task performance of modern-day systems can be improved to make them more efficient. | Sardar Zafar Iqbal Hina Gull Saqib Saeed Madeeha Saqib Mohammed Alqahtani Yasser A.Bamarouf Gomathi Krishna May Issa Aldossary | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
| 15 | A Fractional Fourier Based Medical Image Authentication Approach显示文摘Patient medical information in all forms is crucial to keep private and secure,particularly when medical data communication occurs through insecure channels.Therefore,there is a bad need for protecting and securing the color medical images against impostors and invaders.In this paper,an optical medical image security approach is introduced.It is based on the optical bit-plane Jigsaw Transform(JT)and Fractional Fourier Transform(FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and security.To confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security metrics.Furthermore,a comparative analysis is introduced between the suggested security approach and other conventional cryptography protocols.The simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly secure.It has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images. | Fayez Alqahtani Mohammed Amoon Walid El-Shafai | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 16 | Automated Speech Recognition System to Detect Babies’ Feelings through Feature Analysis显示文摘Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or speech.Understanding the emotions of babies and their associated expressions during different sensations such as hunger,pain,etc.,is a complicated task.In infancy,all communication and feelings are propagated through cryspeech,which is a natural phenomenon.Several clinical methods can be used to diagnose a baby’s diseases,but nonclinical methods of diagnosing a baby’s feelings are lacking.As such,in this study,we aimed to identify babies’feelings and emotions through their cry using a nonclinical method.Changes in the cry sound can be identified using our method and used to assess the baby’s feelings.We considered the frequency of the cries from the energy of the sound.The feelings represented by the infant’s cry are judged to represent certain sensations expressed by the child using the optimal frequency of the recognition of a real-world audio sound.We used machine learning and artificial intelligence to distinguish cry tones in real time through feature analysis.The experimental group consisted of 50%each male and female babies,and we determined the relevancy of the results against different parameters.This application produced real-time results after recognizing a child’s cry sounds.The novelty of our work is that we,for the first time,successfully derived the feelings of young children through the cry-speech of the child,showing promise for end-user applications. | Sana Yasin Umar Draz Tariq Ali Kashaf Shahid Amna Abid Rukhsana Bibi Muhammad Irfan Mohammed A.Huneif Sultan A.Almedhesh Seham M.Alqahtani Alqahtani Abdulwahab Mohammed Jamaan Alzahrani Dhafer Batti Alshehri Alshehri Ali Abdullah Saifur Rahman | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 17 | A Novel Metadata Based Multi-Label Document Classification Technique显示文摘From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature. | Naseer Ahmed Sajid Munir Ahmad Atta-ur Rahman Gohar Zaman Mohammed Salih Ahmed Nehad Ibrahim Mohammed Imran BAhmed Gomathi Krishnasamy Reem Alzaher Mariam Alkharraa Dania AlKhulaifi Maryam AlQahtani Asiya A.Salam Linah Saraireh Mohammed Gollapalli Rashad Ahmed | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 18 | ILSM:Incorporated Lightweight Security Model for Improving QOS in WSN显示文摘In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead. | Ansar Munir Shah Mohammed Aljubayri Muhammad Faheem Khan Jarallah Alqahtani Mahmood ul Hassan Adel Sulaiman Asadullah Shaikh | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 19 | Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks显示文摘With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games. | A.M.Hafiz M.Hassaballah Abdullah Alqahtani Shtwai Alsubai Mohamed Abdel Hameed | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 20 | Dissipative flow features of Carreau nanofluid with thermal radiation inside plane wall channel:Jeffery-Hamel analysis显示文摘The current article communicates a numerical investigation on laminar flow of dissipative generalized Newtonian Carreau nanofluid flowing through vertical conduit with converging and diverging plane walls.Thermal and concentration characteristics due to enthalpy change,activation energy,and non-linear thermal radiation have been examined in the presence of buoyancy forces.The channel walls for both temperature and volumetric fraction are assumed to be isothermal.The instability mechanism of nanofluids is reported using a two-phase nanofluid model,which works reasonably well for nanoparticle concentrations below a certain threshold.A Jeffery-Hamel(J-H)flow model is developed by assuming an incompressible purely radial flow of Carreau nanofluids with heat and mass transportation.Using the suitable non-dimensional variables,the resulting nonlinear partial differential equations are turned into a system of ordinary differential equations.The modified governing equations are then numerically solved using the built-in boundary value problem solver bvp4c,on the template form of commercial software MATLAB.The impacts of material,geometrical and thermophysical parameters governing the J-H problem are discussed and illustrated.Results indicate that higher buoyance forces incline the velocity profiles in converging enclosure,while a slight reduction is perceived in opposing forces.A significant decrease of wall heat transmission is reflected for larger values of activation energy and radiation parameter.For endorsing this communication,a comparison analysis is established with existing research and noticed a remarkable agreement.Practically,the flow inside converging and diverging channels are deployed in nuclear reactors that use plate-type nuclear energies,high heat-flux condensed heat exchangers,high-performance micro-electronic cooling systems,jets,rockets nozzles,and jet propulsion inlet. | Sohail Rehman Hashim Fuad A.MAl-Yarimi Sultan Alqahtani Mohammed Awade | 2023 | Propulsion and Power Research2023，12，2： | 0 |
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