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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

Self-supported copper-based gas diffusion electrodes improve the local CO_(2)concentration for efficient electrochemical CO_(2)reduction

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Frontiers of Chemical Science and Engineering 2024年第3期18卷 51-62页

作者： Azeem Mustafa Bachirou Guene Lougou Yong Shuai Zhijiang Wang Haseeb-ur-Rehman samia Razzaq Wei Wang Ruming Pan Jiupeng Zhao Key Laboratory of Aerospace Thermophysics of MIIT Harbin Institute of TechnologyHarbin150001China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin150001China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin150001China Mechanical Engineering Department University of Engineering and TechnologyTaxila47050Pakistan School of Aerospace Mechanical and Mechatronics EngineeringUniversity of SydneySydney2006Australia

Electrochemical CO_(2)reduction is a sustainable approach in green chemistry that enables the production of valuable chemicals and fuels while mitigating the environmental impact associated with CO_(2)*** its several advantages,this technology suffers from an intrinsically low CO_(2)solubility in aqueous solutions,resulting in a lower local CO_(2)concentration near the electrode,which yields lower current densities and restricts product *** diffusion electrodes(GDEs),particularly those with tubular architectures,can solve these issues by increasing the local CO_(2)concentration and triple-phase interface,providing abundant electroactive sites to achieve superior reaction *** this study,robust and self-supported Cu flow-through gas diffusion electrodes(FTGDEs)were synthesized for efficient formate production via electrochemical CO_(2)*** were further compared with traditional Cu electrodes,and it was found that higher local CO_(2)concentration due to improved mass transfer,the abundant surface area available for the generation of the triple-phase interface,and the porous structure of Cu FTGDEs enabled high formate Faradaic efficiency(76%)and current density(265 mA¸cm^(−2))at–0.9 V *** hydrogen electrode(RHE)in 0.5 mol·L^(−1)*** combined phase inversion and calcination process of the Cu FTGDEs helped maintain a stable operation for several *** catalytic performance of the Cu FTGDEs was further investigated in a non-gas diffusion configuration to demonstrate the impact of local gas concentration on the activity and performance of electrochemical CO_(2)*** study demonstrates the potential of flow-through gas-diffusion electrodes to enhance reaction kinetics for the highly efficient and selective reduction of CO_(2),offering promising applications in sustainable electrochemical processes.

关键词： CO_(2)electroreduction flow-through delivery hollow fiber structure local concentration formate

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

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Computers, Materials & Continua 2024年第6期79卷 4845-4861页

作者： samia Allaoua Chelloug Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Intrusion detection is a predominant task that monitors and protects the network ***,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and *** particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic *** is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new *** has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection *** that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD *** base model is trained on the NSL-KDD dataset to extract significant *** the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base *** combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection *** experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion *** performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual *** stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification ***,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficien

关键词： Intrusion detection multi classification deep learning stacking NSL-KDD

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

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Computers, Materials & Continua 2024年第1期78卷 873-897页

作者： Shereen K.Refaay samia A.Ali Moumen T.El-Melegy Louai A.Maghrabi Hamdy H.El-Sayed Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Engineering Assiut UniversityAssiut711745Egypt College of Engineering University of Business and TechnologyJeddah23411Saudi Arabia

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s *** studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor *** paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial *** proposed algorithms lack algorithm-specific parameters and metaphorical *** proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned *** proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing *** compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)*** efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base *** experimental results were compared with those obtained using the Jaya optimization *** proposed algorithms exhibited the best *** proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base sta

关键词： Wireless sensor networks Rao algorithms optimization LEACH PEAGSIS

Impact of the COVID-19 Pandemic on Cancer Patients: Experience of the medical oncology department of CHU HASSAN II Fez.

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Journal of Cancer Therapy 2024年第3期15卷 83-91页

作者： Khadija Hinaje Oumaima Talbi Lamiae Amaadour Karima Oualla Zineb Benbrahim samia Arifi Nawfel Mellas Department of Medical Oncology Hassan II University Hospital University Sidi Mohamed Ben Abdellah Fez Morocco

Introduction: In December 2019, COVID 19 spread worldwide, but did not officially start in MOROCCO until March 02, 2020. Since then, this pandemic has significantly impacted the health status of patients in general and cancer patients in particular. The main objective of our study is to evaluate the prognosis of patients treated for cancer and infected with COVID-19. Material and method: A descriptive study with prospective collection was carried out at the medical oncology department of CHU Hassan II in FEZ over a period of two years, from March 2020 to March 2022. Data was carried out on the software SPSS. Results: One hundred cancer patients tested positive for COVID-19 infection and were collected within our department. The average age was 56 years [22-91]. The sex ratio was 1.2. Patients with breast cancer were the most affected by this infection (34%). The clinical symptomatology was dominated by the respiratory syndrome (45%).The diagnosis was made through thoracic CT scan in 62% of cases. 76% of patients were in a metastatic stage. 96% of patients were undergoing oncological treatment. For symptomatique patients, the standard treatment approach involved using antibiotics in 76% of cases. Evolution was marked by recovery in 79% of patients, with a death rate of 12% in this cancer patient population. Conclusion: COVID-19 infection is particularly severe in cancer patients. Mortality among these patients remains high and is associated with overall patient characteristics. However, anticancer treatments have not shown deleterious effects on the course of COVID-19.

关键词： COVID-19 Cancer. SARS-CoV-2

Fasting and Cancer: Practice and Patients’ Quality of Life during Ramadan

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Journal of Cancer Therapy 2024年第3期15卷 49-56页

作者： Oumaima Talbi Zineb Benbrahim Khadija Hinaje Imad Chakri Kaoutar Maadin Lamiae Amaadour Karima Oualla samia Arifi Nawfel Mellas Department of Medical Oncology Faculty of Medicine Pharmacy and Dental Medicine of Fez University Sidi Mohamed BenAbdellah Fez Morocco Epidemiology Laboratory Clinical Research and Community Health Faculty of Medicine Pharmacy and Dental Medicine of Fez University Sidi Mohamed BenAbdellah Fez Morocco

Introduction: Ramadan is the ninth lunar month of the Muslim calendar, and fasting during Ramadan is one of the five pillars of Islam. Muslim cancer patients and health professionals are faced with a dilemma between the necessity of fasting and its possible adverse effects during Ramadan on the health status of cancer patients. We believe that addressing the issue of fasting in cancer patients is of global interest. Objectives: Prevalence of fasting in cancer patients during Ramadan;Predictive factors of Ramadan fasting in cancer patients;The effect of fasting during Ramadan on the quality of life of cancer patients. Materials and Methods: A structured questionnaire was used to interview adult cancer patients who received chemotherapy IV—or oral treatment at the medical oncology department at the Hassan II University Hospital in Fez during the month of Ramadan 1443 Hijri (April to May 2022). The quality of cancer patients’ lives during their fasting practice in Ramadan was evaluated via the QLQ-C30 questionnaires. Results: 48% of patients did not fast during Ramadan, 22% and 30% were partial and complete fasters respectively. Patients, with OMS 0 to 1, who receive outpatient treatment, non-metastatic patients and patients on oral treatment were more frequent in the completely fasted group. All these factors showed statistically significant values (p < 0.05). 44% of patients sought advice from the treating oncologist if they could fast, while only 15% of patients during Ramadan asked an imam if they could be excused from fasting. A comparison of the QLQ-C30 data showed a significant improvement in emotional functioning with fasting, however, patients had clinically more but not significantly fatigue after fasting. Conclusion: Female gender, good WHO, non-metastatic disease, and oral or outpatient treatment were more likely to be fasting. This study argues that fasting did not influence the quality of life, and appears to significantly improve emotional functioning in

关键词： Ramadan Fasting Quality of Life Practice

SINGULAR CONTROL OF STOCHASTIC VOLTERRA INTEGRAL EQUATIONS

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Acta Mathematica Scientia 2022年第3期42卷 1003-1017页

作者： Nacira AGRAM Saloua LABED Bernt ФKSENDAL samia YAKHLEF Department of Mathematics KTH Royal Institute of Technology 10044StockholmSweden University Mohamed Khider of Biskra Algeria Department of Mathematics University of OsloPO Box 1053 BlindernN–0316 OsloNorway

This paper deals with optimal combined singular and regular controls for stochastic Volterra integral equations,where the solution X^(u,ξ)(t)=X(t)is given X(t)=φ(t)+∫_(0)^(t) b(t,s,X(s),u(s))ds+∫_(0)^(t)σ(t,s,X(s),u(s))dB(s)+∫_(0)^(t)h(t,s)dξ(s).by Here d B(s)denotes the Brownian motion It?type differential,ξdenotes the singular control(singular in time t with respect to Lebesgue measure)and u denotes the regular control(absolutely continuous with respect to Lebesgue measure).Such systems may for example be used to model harvesting of populations with memory,where X(t)represents the population density at time t,and the singular control processξrepresents the harvesting effort *** total income from the harvesting is represented by J(u, ξ) = E[∫_(0)^(t) f_(0)(t,X(t), u(t))dt + ∫_(0)^(t)f_(1)(t,X(t))dξ(t) + g(X(T))] for the given functions f0,f1 and g,where T>0 is a constant denoting the terminal time of the *** that it is important to allow the controls to be singular,because in some cases the optimal controls are of this *** Hida-Malliavin calculus,we prove sufficient conditions and necessary conditions of optimality of *** a consequence,we obtain a new type of backward stochastic Volterra integral equations with singular ***,to illustrate our results,we apply them to discuss optimal harvesting problems with possibly density dependent prices.

关键词： Stochastic maximum principle stochastic Volterra integral equation singular control backward stochastic Volterra integral equation Hida-Malliavin calculus

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

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Computers, Materials & Continua 2023年第4期75卷 1501-1526页

作者： Samah Ibrahim AlShathri Dina S.M.Hassan samia Allaoua Chelloug Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Task offloading is a key strategy in Fog Computing (FC). Thedefinition of resource-constrained devices no longer applies to sensors andInternet of Things (IoT) embedded system devices alone. Smart and mobileunits can also be viewed as resource-constrained devices if the power, cloudapplications, and data cloud are included in the set of required resources. Ina cloud-fog-based architecture, a task instance running on an end device mayneed to be offloaded to a fog node to complete its execution. However, ina busy network, a second offloading decision is required when the fog nodebecomes overloaded. The possibility of offloading a task, for the second time,to a fog or a cloud node depends to a great extent on task importance, latencyconstraints, and required resources. This paper presents a dynamic service thatdetermines which tasks can endure a second offloading. The task type, latencyconstraints, and amount of required resources are used to select the offloadingdestination node. This study proposes three heuristic offloading *** algorithm targets a specific task type. An overloaded fog node can onlyissue one offloading request to execute one of these algorithms accordingto the task offloading priority. Offloading requests are sent to a SoftwareDefined Networking (SDN) controller. The fog node and controller determinethe number of offloaded tasks. Simulation results show that the average timerequired to select offloading nodes was improved by 33% when compared tothe dynamic fog-to-fog offloading algorithm. The distribution of workloadconverges to a uniform distribution when offloading latency-sensitive nonurgenttasks. The lowest offloading priority is assigned to latency-sensitivetasks with hard deadlines. At least 70% of these tasks are offloaded to fognodes that are one to three hops away from the overloaded node.

关键词： Fog computing offloading algorithm latency-aware software defined networking SDN