Wasit Journal of Computer and Mathematics Science最新文献

{"title":"The Analytical Methods Of Volterra Integral Equations Of The Second Kind","authors":"Issa Hamza, Ahmed Sh. Al-Atabi","doi":"10.31185/wjcm.119","DOIUrl":"https://doi.org/10.31185/wjcm.119","url":null,"abstract":"This paper discussed the analytic methods to solve Second order Volterra integral equations form using different methods. The domain decomposition method is the first technique. Depending on the hypothesis, the solution is by sequence. The second method is the successive approximation technique, which is used Picard iteration method. The third method used Laplace transformation. The modified decomposition technique is used as the fourth method. Depending on the Taylor series, the fifth method is called the series method. The last method solves the VIE using the functional correction technique called the variational iteration method. We introduce some examples to illustrate these methods. ","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer Vision Techniques for Military Surveillance Drones","authors":"H. Ahmad, Muhammad Farhan, Umer Farooq","doi":"10.31185/wjcms.148","DOIUrl":"https://doi.org/10.31185/wjcms.148","url":null,"abstract":"Commercial unmanned aerial vehicles (UAVs), also referred to as drones, have proliferated recently, raising concerns about security threats and the need for effective countermeasures. To address these concerns, various technologies have been explored, including radar, acoustics, and RF signal analysis. However, computer vision, particularly deep learning approaches, has emerged as a robust and widely used method for autonomous drone identification. The goal of this research is to create an autonomous drone identification and surveillance system that makes use of a mix of static wide-angle cameras and a lower-angle camera placed on a revolving turret. To optimize memory and processing time, we suggested a novel multi-frame DL identification model. In this approach, the frames captured by the turret's magnified camera are stacked on top of the frames from the wide-angle still camera. Utilizing this technique, we can create an efficient pipeline that conducts initial identification of small-sized aerial invaders on the primary picture plane and identification on the expanded image plane at the same time. This approach significantly reduces the computational burden associated with detection algorithms, making it more resource-efficient. Furthermore, we present the complete system architecture, which includes DL classification frameworks, tracking algorithms, and other essential components. By integrating these elements, we create a comprehensive solution for drone identification and tracking. The system leverages the power of deep learning to accurately classify and track drones in real-time, enabling prompt response and mitigating potential security threats. Overall, this research offers a novel and effective approach to autonomously identify and track drones using computer vision and deep learning techniques. By combining static and dynamic camera perspectives and employing a multi-frame detection method, we provide a resource-efficient solution for drone identification. This work contributes to the ongoing efforts in enhancing security measures against potential drone-related risks","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114465891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving the coupled Schrödinger -Korteweg- de-Vries system by modified variational iteration method with genetic algorithm","authors":"A. Mustafa, W. Al-Hayani","doi":"10.31185/wjcm.127","DOIUrl":"https://doi.org/10.31185/wjcm.127","url":null,"abstract":"             A system of nonlinear partial differential equations was solved using a modified variational iteration method (MVIM) combined with a genetic algorithm. The modified method introduced an auxiliary parameter (p) in the correction functional to ensure convergence and improve the outcomes. Before applying the modification, the traditional variational iteration method (VIM) was used firstly. The method was applied to numerically solve the system of Schrödinger-KdV equations. By comparing the two methods in addition to some of the previous approaches, it turns out the new algorithm converges quickly, generates accurate solutions and shows improved accuracy. Additionally, the method can be easily applied to various linear and nonlinear differential equations.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125202939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things","authors":"Mohd Ismail, Siti Nur Binti Mustaffa, Munther H. Abed","doi":"10.31185/wjcms.146","DOIUrl":"https://doi.org/10.31185/wjcms.146","url":null,"abstract":"Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramount. This study recommended a real-time IoT system employing ensemble deep TL to enable early identification of infected COVID-19 individuals. The system allows for real-time transmission and identification of COVID-19 suspicious individuals. The suggested IoT model incorporates several DL models, including InceptionResNetV2, VGG16, ResNet152V2, and DenseNet201. These models, stored on a cloud server, are utilized in conjunction with medical sensors to gather chest X-ray data and detect infections. A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. The comparative investigation demonstrates that the suggested approach facilitates swift and effective diagnosis of COVID-19 suspicious patients, providing valuable support to radiologists. This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. The proposed system, incorporating a deep ensemble model, offers a practical solution for assisting radiologists in efficiently diagnosing COVID-19 cases","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126584535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval","authors":"Rohit Sharma, Bipin Kumar Rai, Shubham Sharma","doi":"10.31185/wjcms.147","DOIUrl":"https://doi.org/10.31185/wjcms.147","url":null,"abstract":"The content-based image retrieval (CBIR) method operates on the low-level visual features of the user input query object, which makes it difficult for users to formulate the query and also does not provide adequate retrieval results. In the past, image annotation was suggested as the best possible framework for CBIR, which works on automatically signing keywords to images that support image retrieval. The recent successes of deep learning techniques, especially Convolutional Neural Networks (CNN), in solving computer vision applications have inspired me to work on this paper to solve the problem of CBIR using a dataset of annotated images","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128558940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Deep Learning-Based System for Agri-Food Quality and Safety Detection","authors":"Habib Shah, Harish Kumar, Ali Akgül","doi":"10.31185/wjcms.145","DOIUrl":"https://doi.org/10.31185/wjcms.145","url":null,"abstract":"Deep Learning (DL) has emerged as a highly effective technique for analyzing large volumes of data across various domains, including image processing, speech recognition, and pattern recognition. Recently, DL has also found applications in the field of food science and engineering, a relatively novel area of research. This paper provides a concise introduction to DL and delves into the architecture of a typical Convolution Neural Network (CNN) structure, as well as AI and IoT (Internet of Things) data training methodologies. Our research involved an extensive review of studies that utilized DL as a computational approach to address food-related challenges, such as food recognition, calorie computation, and safety detection of various food types like fruits, potatoes, meats, and aquatic products, as well as food supply chain management and food borne illness detection. Each study examined different problems, datasets, preprocessing techniques, network architectures, and evaluation metrics, comparing their results with alternative solutions. Furthermore, we explored the role of big data in the field of food quality assurance, uncovering compelling trends. Based on our analysis, DL consistently outperforms other approaches, including manual feature extractors and traditional machine learning algorithms. The findings highlight the tremendous potential of DL as a promising technology for food safety inspections and related applications in the food industry","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116514317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"M-dimension hybrid algorithm for scientific workflow in cloud computing","authors":"Zahrra Agheeb, S. M. Mazinani","doi":"10.31185/wjcm.98","DOIUrl":"https://doi.org/10.31185/wjcm.98","url":null,"abstract":"Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. With the emergence cloud computing, can benefit from virtually unlimited resources with minimal hardware investment. Scheduling the submitted Scientific Workflow Application (SWFA) tasks to the available computational resources while optimizing the cost of executing the SWFA is one of the most challenging processes of Workflow Management System (WfMS) in a cloud computing environment. Several cost optimization approaches have been proposed to improve the economic aspect of SWFS in cloud computing. The main goal of the paper is to present a new M-dimension hybrid algorithm, which uses a meta-heuristic algorithm such as Completion Time Driven Hyper-Heuristic (CTDHH), Hybrid Cost-effective Hybrid-Scheduling (HCHS), particle swarm optimization (PSO) and genetic algorithm (GA) and using heuristic algorithms such as the IC-PCPD2 and IC-Loss algorithms. Based on the results of the experimental comparison, the proposed method has proven to yield the most effective performance results for all considered experimental scenarios.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dietary Behavior Based Food Recommender System Using Deep Learning and Clustering Techniques","authors":"Ammar Abdulsalam Al-Asadi, M. Jasim","doi":"10.31185/wjcm.126","DOIUrl":"https://doi.org/10.31185/wjcm.126","url":null,"abstract":"Deep learning algorithms have been highly successful in various domains, including the development of collaborative filtering recommender systems. However, one of the challenges associated with deep learning-based collaborative filtering methods is that they require the involvement of all users to construct the latent representation of the input data, which is then utilized to predict the missing ratings of each user. This can be problematic as some users may have different preferences or interests, which may affect the accuracy of the prediction generation process. The research proposed a food recommender system, which tries to find users with similar dietary behavior and involve them in the recommendations generation process by combining clustering technique with denoising autoencoder to generate a rate prediction model. It is applied to “Food.com Recipes and Interactions” dataset. RMSE score was used to evaluate the performance of the proposed model which is 0.1927. It outperformed the other models that used autoencoder and denoising autoencoder without clustering where the RMSE values are 0. 4358 and 0.4354 consequently.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Performance Analysis of Hybrid Electric Vehicles using Matlab/Simulink","authors":"Yi-Ning Niu, Kai-qing Zhou, V. Abdullayev","doi":"10.31185/wjcms.149","DOIUrl":"https://doi.org/10.31185/wjcms.149","url":null,"abstract":"In this paper introduces an integrated method for the design and performance analysis of hybrid electric vehicles. This method considers a set of parameters that influence the system's performance. This project presents an approach for modelling electric vehicles considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The performance of the hybrid electric vehicle is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new fuzzy logic controller is required to set the rules for better performance. Therefore, in this project fuzzy logic-based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicles with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were conducted, and results were observed","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126702652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning","authors":"Guma Ali, Emre Sadıkoğlu, Hatim Abdelhak","doi":"10.31185/wjcms.151","DOIUrl":"https://doi.org/10.31185/wjcms.151","url":null,"abstract":"The automatic system for classifying traffic signs is a critical task of Advanced Driver Assistance Systems (ADAS) and a fundamental technique utilized as an integral part of the various vehicles. The recognizable features of a traffic image are utilized for their classification. Traffic signs are designed to contain specific shapes and colours, with some text and some symbols with high contrast to the background. This paper proposes a hybrid approach for classifying traffic signs by SIFT for image feature extraction and SVM for training and classification. The proposed work is divided into phases: pre-processing, Feature Extraction, Training, and Classification. MATLAB is used for the implementation purpose of the proposed framework, and classification is carried out by utilizing real traffic sign images","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}