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Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2291-2319页

作者： Abdullah Ahmed Al-Dulaimi Muhammet Tahir Guneser Alaa Ali Hameed mohammad shukri salman Department of Electrical and Electronics Engineering Karabuk UniversityKarabuk78100Turkey Department of Computer Engineering Istinye UniversityIstanbul34303Turkey College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait

Recently,the demand for renewable energy has increased due to its environmental and economic *** panels are the mainstay for dealing with solar energy and converting it into another form of usable *** panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these ***,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low ***,it causes an insulating layer on solar panels and the inability to produce electrical *** detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper *** paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel *** this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion ***,the dataset was replicated using the upsampling technique in order to handle the unbalancing *** conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial *** fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both *** accuracy of the models has been compared and verified to distinguish and classify the processed *** accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives *** the second case,the models outperformed their counterparts in the first case by evaluating performan

关键词： Deep learning CNN models image classification solar panels solar panel defect detection

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： mohammad Mehedi Hassan salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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Post-discharge follow-up of patients with spine trauma in the National Spinal Cord Injury Registry of Iran during the COVID-19 pandemic:Challenges and lessons learned

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Chinese Journal of Traumatology 2024年第3期27卷 173-179页

作者： Zahra Azadmanjir Moein Khormali Mohsen Sadeghi-Naini Vali Baigi Habibollah Pirnejad mohammad Dashtkoohi Zahra Ghodsi Seyed Behnam Jazayeri Aidin Shakeri Mahdi mohammadzadeh Laleh Bagheri mohammad-Sajad Lotfi salman Daliri Amir Azarhomayoun Homayoun Sadeghi-Bazargani Gerard O'reilly Vafa Rahimi-Movaghai Health Information Management Department School of Allied Medical SciencesTehran University of Medical SciencesTehranIran Sina Trauma and Surgery Research Center Tehran University of Medical SciencesTehranIran Department of Neurosurgery Lorestan University of Medical SciencesKhoram-AbadIran Department of Epidemiology and Biostatistics School of Public HealthTehran University of Medical SciencesTehranIran Patient Safety Research Center Clinical Rsearch Institute Urma Universty of Medical SciencesUrmaIran Erasmus School of Health Policy&Management(ESHPM) Erasmus University RotterdamRoterdamthe Netherlands Brain and Spinal Cord Injury Research Center Neuroscience InstituteTehran University of Medical SciencesTehranIran Students Scientifc Research Center(SSRC) Tehran University of Medical SciencesTehranIran Department of Neurosurgery Arak University of Medical SciencesArakIran Trauma Research Center Kashan University of Medical SciencesKashanIran Shahid Rahnemoun Hospital Shahid Sadoughi University of Medical SciencesYazdIran Trauma Nuring Rearch entre Faculty of Nursing and Midwifry Kahan Universty of Medical Sciences Kahan Iran Clinical Research Development Unit Imam Hossein HospitalShahroud University of Medical SciencesShahroudIran Research Center for Evidence Based Medicine Faculty of MedicineTabriz University of Medical SciencesTabrizIran Road Trafic Injury Research Center Tabriz University of Medical SciencesTabrizIran School of Public Health and Preventive Medicine Monash UniversityMelbourneAustralia National Trauma Research Institute The AlfredMelbourneAustralia Department of Neurosurgery Shariati HospitalTehran University of Medical SciencesTehranIran Universal Scientific Education and Research Network(USERN) TehranIran Institute of Biochemistry and Biophysics University of TehranTehranIran

Purpose:The purpose of the National Spinal Cord Injury Registry of Iran(NSCIR-IR)is to create an infrastructure to assess the quality of care for spine trauma and in this study,we aim to investigate whether the NSCIR-IR successfully provides necessary post-discharge follow-up data for these ***:An observational prospective study was conducted from April 11,2021 to April 22,2022 in 8 centers enrolled in NSCIR-IR,respectively Arak,Rasht,Urmia,Shahroud,Yazd,Kashan,Tabriz,and *** were classified into 3 groups based on their need for care resources,respectively:(1)non-spinal cord injury(SCI)patients without surgery(group 1),(2)non-SCI patients with surgery(group 2),and(3)SCI patients(group 3).The assessment tool was a self-designed questionnaire to evaluate the care quality in 3 phases:pre-hospital,in-hospital,and *** data from the first 2 phases were collected through the *** post-hospital data were collected by conducting follow-up *** follow-ups were conducted for groups 1 and 2(non-SCI patients),while group 3(SCI patients)had a face-to-face *** study took place during the COVID-19 *** on age and time interval from injury to follow-up were expressed as mean±standard deviation(SD)and response rate and follow-up loss as a ***:Altogether 1538 telephone follow-up records related to 1292 patients were registered in the *** the total calls,918(71.05%)were related to successful follow-ups,but 38 cases died and thus were excluded from data *** the end,post-hospital data from 880 patients alive were *** success rate of follow-ups by telephone for groups 1 and 2 was 73.38%and 67.05%respectively,compared to 66.67%by face-to-face visits for group 3,which was very hard during the COVID-19 *** data completion rate after discharge ranged from 48%-100%,22%-100%and 29%-100%for groups ***:To improve patient accessibility,NSCIR-IR should take me

关键词： Follow-up studies Spinal injuries Spinal cord injuries COVID-19 NSCIR-IR

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Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale

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Journal of Mountain Science 2023年第10期20卷 2975-2992页

作者： mohammad Sajjad GHAVAMI Shamsollah AYOUBI mohammad Reza MOSADDEGHI salman Naimi Department of Soil Science College of AgricultureIsfahan University of TechnologyIsfahan ***Iran

Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the application of this tool,thus urging the need to estimate soil properties and consequently,to perform the spatial *** research attempted to examine the proficiency of three machine learning methods(RF:Random Forest;Cubist:Regression Tree;and SVM:Support Vector Machine)to predict soil physical and mechanical properties,saturated hydraulic conductivity(Ks),Cohesion measured by fall-cone at the saturated(Psat)and dry(Pdry)states,hardness index(HI)and dry shear strength(SS)by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed,central *** determine the best combination of input variables,three scenarios were examined as follows:scenarioⅠ,terrain attributes derivative from a digital elevation model(DEM)+remotely sensed data;scenarioⅡ,covariates of scenarioⅠ+selected climatic data and some thematic maps;scenarioⅢ,covariates in scenarioⅡ+intrinsic soil properties(Clay,Silt,Sand,bulk density(BD),soil organic matter(SOM),calcium carbonate equivalent(CCE),mean weight diameter(MWD)and geometric weight diameter(GWD)).The results showed that for Ks,Psat Pdry and SS,the best performance was found by the RF model in the third scenario,with R2=0.53,0.32,0.31 and 0.41,respectively,while for soil hardness index(HI),Cubist model in the third scenario with R2=0.25 showed the highest *** predicting Ks and Psat,soil characteristics(*** and soil SOM and BD),and land use were the most important *** predicting Pdry,HI,and SS,some topographical characteristics(Valley depth,catchment area,mltiresolution of ridge top flatness index),and some soil characteristics(***,SOM and MWD)were the most important input *** results of this research present moderate accuracy,however,the methodology em

关键词： Machine learning Soil physical property Soilmechanical property Saturatedhydraulic conductivity Soil cohesion Soil shear strength.

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Outpatient insulin use in type 2 diabetes mellitus and acute respiratory distress syndrome outcomes:A retrospective cohort study

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World Journal of Clinical Cases 2024年第17期12卷 2966-2975页

作者： Georges Khattar Samer Asmar Laurence Aoun Fares Saliba Shaza Almardini Saif Abu Baker Catherine Hong Carolla El Chamieh Fadi Haddadin Toni Habib Omar Mourad Zeina Morcos Fatema Arafa Jonathan Mina Khalil El Gharib mohammad Aldalahmeh salman Khan Elie Bou Sanayeh Department of Medicine Holy Spirit University of KaslikJounieh 00000Lebanon Department of Internal Medicine Staten Island University HospitalStaten IslandNY 10305United States Public Heath and Biostatistics Independent ResearchBeirut 0000Lebanon Department of Medicine Lebanese UniversityBeirut 00000Lebanon Department of Medicine University of BalamandBeirut 00000Lebanon Department of Medicine Lebanese American UniversityBeirut 00000Lebanon Department of Medicine Northwell Health Staten Island University HospitalNew YorkNY 10305United States

BACKGROUND The impact of type 2 diabetes mellitus(T2DM)on acute respiratory distress syndrome(ARDS)is debatable.T2DM was suspected to reduce the risk and complications of ***,during coronavirus disease 2019(COVID-19),T2DM predisposed patients to ARDS,especially those who were on insulin at *** evaluate the impact of outpatient insulin use in T2DM patients on non-COVID-19 ARDS *** We conducted a retrospective cohort analysis using the Nationwide Inpatient Sample *** patients diagnosed with ARDS were stratified into insulin-dependent diabetes mellitus(DM)(IDDM)and non-insulindependent DM(NIDDM)*** applying exclusion criteria and matching over 20 variables,we compared cohorts for mortality,duration of mechanical ventilation,incidence of acute kidney injury(AKI),length of stay(LOS),hospitalization costs,and other clinical *** Following 1:1 propensity score matching,the analysis included 274 patients in each ***,no statistically significant differences emerged between the IDDM and NIDDM groups in terms of mortality rates(32.8%vs 31.0%,P=0.520),median hospital LOS(10 d,P=0.537),requirement for mechanical ventilation,incidence rates of sepsis,pneumonia or AKI,median total hospitalization costs,or patient disposition upon *** Compared to alternative anti-diabetic medications,outpatient insulin treatment does not appear to exert an independent influence on in-hospital morbidity or mortality in diabetic patients with non-COVID-19 ARDS.

关键词： Acute respiratory distress syndrome Type 2 diabetes mellitus Insulin Length of stay Mortality Endotracheal intubation Acute kidney injury Coronavirus disease 2019

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A Novel Beamforming Emulating Photonic Nanojets for Wireless Relay Networks

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Computers, Materials & Continua 2021年第10期69卷 575-588页

作者： Samer Alabed Ibrahim Mahariq mohammad salman Mustafa Kuzuoglu College of Engineering and Technology American University of the Middle EastKuwait Department of Electrical and Electronics Engineering Middle East Technical UniversityAnkara06800Turkey

In this article,a low-cost electromagnetic structure emulating photonic nanojets is utilized to improve the efficiency of wireless relay *** spectral element method,due to its high accuracy,is used to verify the efficiency of the proposed structure by solving the associate field *** application of optimal single-relay selection method shows that full diversity gain with low complexity can be *** this paper,the proposed technique using smart relays combines the aforementioned two methods to attain the benefits of both methods by achieving the highest coding and diversity gain and enhances the overall network performance in terms of bit error rate(BER).Moreover,we analytically prove the advantage of using the proposed *** our simulations,it can be shown that the proposed technique outperforms the best known state-of-the-art single relay selection ***,the BER expressions obtained from the theoretical analysis are perfectly matched to those obtained from the conducted simulations.

关键词： Wireless relay networks cooperative diversity techniques relay selection network beamforming techniques

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Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

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Computers, Materials & Continua 2023年第8期76卷 2145-2177页

作者： Israa K.salman Al-Tameemi mohammad-Reza Feizi-Derakhshi Saeed Pashazadeh mohammad Asadpour Computerized Intelligence Systems Laboratory Department of Computer EngineeringFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran Department of Computer Engineering Faculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran State Company for Engineering Rehabilitation and Testing Iraqi Ministry of Industry and MineralsBaghdad10011Iraq

Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application *** existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple ***,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification *** by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual *** proposed model comprises three deep neural *** different neural networks are proposed to extract the most emotionally relevant aspects of image and text ***,more discriminative features are gathered for accurate sentiment ***,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment ***,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed *** interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and *** proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment Analysis(MVSA)*** results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation cr

关键词： Sentiment analysis multimodal classification deep learning joint fusion decision fusion interpretability

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YOLOv2PD:An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

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Computers, Materials & Continua 2021年第12期69卷 3015-3031页

作者： Chintakindi Balaram Murthy mohammad Farukh Hashmi Ghulam Muhammad salman A.AlQahtani Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Real-time pedestrian detection is an important task for unmanned driving systems and video *** existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such ***,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road *** proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction *** addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection *** proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor *** the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller *** results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian ***,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.

关键词： Computer vision K-means clustering multi-layer feature fusion strategy pedestrian detection YOLOv2PD

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A review of optimization methods for computation offloading in edge computing networks

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Digital Communications and Networks 2023年第2期9卷 450-461页

作者： Kuanishbay Sadatdiynov Laizhong Cui Lei Zhang Joshua Zhexue Huang salman Salloum mohammad Sultan Mahmud College of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China

Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational *** Computing is an emerging computation paradigm that is employed to conquer this *** can bring computation power closer to the end devices to reduce their computation latency and energy ***,this paradigm increases the computational ability of SMDs by collaboration with edge *** is achieved by computation offloading from the mobile devices to the edge nodes or ***,not all applications benefit from computation offloading,which is only suitable for certain types of *** properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading ***,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing *** this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and *** each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed *** discuss a few research problems that are still *** purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

关键词： Edge computing Computation offloading Latency and energy consumption minimization

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Development and characterization of food packaging bioplastic film from cocoa pod husk cellulose incorporated with sugarcane bagasse fibre

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Journal of Bioresources and Bioproducts 2020年第4期5卷 248-255页

作者： Siti Nuurul Hud mohammad Azmin Najah Aliah binti Mohd Hayat Mohd shukri Mat Nor Faculty of Agro-Based Industry Universiti Malaysia Kelantan Jeli CampusLocked Bag 10017600 Jeli KelantanMalaysia Department of Research and Development Jeli Agricultural TechnologyPT7458 Kampung Gemang Baru17700 Ayer LanasJeli KelantanMalaysia

Agricultural wastes,including cocoa pod husk(waste from the chocolate industry)and sugarcane bagasse(waste from the sugar industry),are increasing day by *** development of food packaging biofilms from these two wastes could be beneficial to the environment and ***,this study was conducted to develop biodegradable plastic films by using cocoa pod husk and sugarcane *** and fibre were extracted from cocoa pod husk and sugar-cane bagasse,*** developed bioplastic films were divided into several concentration ratios of cellulose and fibre which are 100꞉0(100%cellulose),75꞉25(cellulose꞉fibre),50꞉50(cel-lulose꞉fibre),25꞉75(cellulose꞉fibre),and 0꞉100(100%fibre).The physicochemical properties for all bioplastic concentration ratios were determined in terms of sensory evaluation,drying time,moisture content,water absorption and water vapor *** the observation and anal-ysis of the physicochemical properties of bioplastic,we found that the most suitable bioplastic film for food packaging goes to the combination of 75%cellulose and 25%fibre bioplastic,as it demonstrated the lowest water absorption percentage and water vapor permeability.

关键词： Agricultural waste Cocoa pod husk Sugarcane bagasse Bioplastic film Water absorption Moisture content

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