International Journal of Advanced Computer Science and Applications最新文献_第2页

A multigrid technique for Lagrange multiplier-based unfitted Finite Element contact issues. 基于拉格朗日乘法器的非拟合有限元接触问题的多重网格技术。

IF 0.9

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-27 DOI: 10.47679/ijasca.v2i2.22

Kothari Rolf, Krause Hardik

{"title":"A multigrid technique for Lagrange multiplier-based unfitted Finite Element contact issues.","authors":"Kothari Rolf, Krause Hardik","doi":"10.47679/ijasca.v2i2.22","DOIUrl":"https://doi.org/10.47679/ijasca.v2i2.22","url":null,"abstract":"Internal interfaces in a domain could exist as a material defect or they can appear due to propagations of cracks. Discretization of such geometries and solution of the contact problem on the internal interfaces can be computationally challenging. We employ an unfitted Finite Element (FE) framework for the discretization of the domains and develop a tailored, globally convergent, and efficient multigrid method for solving contact problems on the internal interfaces. In the unfitted FE methods, structured background meshes are used and only the underlying finite element spaces are modified to incorporate the discontinuities. The non-penetration conditions on the embedded interfaces of the domains are discretized using the method of Lagrange multipliers. We reformulate the arising variational inequality problem as a quadratic minimization problem with linear inequality constraints. Our multigrid method can solve such problems by employing a tailored multilevel hierarchy of the FE spaces and a novel approach for tackling the discretized non-penetration conditions. We employ pseudo-L2 projection- based transfer operators to construct a hierarchy of nested FE spaces from the hierarchy of non-nested meshes. The essential component of our multigrid method is a technique that decouples the linear constraints using an orthogonal transformation. The decoupled constraints are handled by a modified variant of the projected Gauss–Seidel method, which we employ as a smoother in the multigrid method. These components of the multigrid method allow us to enforce linear constraints locally and ensure the global convergence. We will demonstrate the robustness, efficiency, and level independent convergence property of the proposed method for Signorini’s problem and two-body contact problems","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"43 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89346474","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}

引用次数: 0

Polarimetric SAR Characterization of Mangrove Forest Environment in the United Arab Emirates (UAE) 阿拉伯联合酋长国红树林环境的极化SAR特征

IF 0.9

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140380

Soumaya Fatnassi, M. Yahia, Tarig Ali, M. Mortula

引用次数: 0

Dimensionality Reduction with Truncated Singular Value Decomposition and K-Nearest Neighbors Regression for Indoor Localization 基于截断奇异值分解和k近邻回归的室内定位降维方法

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141034

Hang Duong Thi, Kha Hoang Manh, Vu Trinh Anh, Trang Pham Thi Quynh, Tuyen Nguyen Viet

{"title":"Dimensionality Reduction with Truncated Singular Value Decomposition and K-Nearest Neighbors Regression for Indoor Localization","authors":"Hang Duong Thi, Kha Hoang Manh, Vu Trinh Anh, Trang Pham Thi Quynh, Tuyen Nguyen Viet","doi":"10.14569/ijacsa.2023.0141034","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141034","url":null,"abstract":"—Indoor localization presents formidable challenges across diverse sectors, encompassing indoor navigation and asset tracking. In this study, we introduce an inventive indoor localization methodology that combines Truncated Singular Value Decomposition (Truncated SVD) for dimensionality reduction with the K-Nearest Neighbors Regressor (KNN Regression) for precise position prediction. The central objective of this proposed technique is to mitigate the complexity of high-dimensional input data while preserving critical information essential for achieving accurate localization outcomes. To validate the effectiveness of our approach, we conducted an extensive empirical evaluation employing a publicly accessible dataset. This dataset covers a wide spectrum of indoor environments, facilitating a comprehensive assessment. The performance evaluation metrics adopted encompass the Root Mean Squared Error (RMSE) and the Euclidean distance error (EDE)—widely embraced in the field of localization. Importantly, the simulated results demonstrated promising performance, yielding an RMSE of 1.96 meters and an average EDE of 2.23 meters. These results surpass the achievements of prevailing state-of-the-art techniques, which typically attain localization accuracies ranging from 2.5 meters to 2.7 meters using the same dataset. The enhanced accuracy in localization can be attributed to the synergy between Truncated SVD's dimensionality reduction and the proficiency of KNN Regression in capturing intricate spatial relationships among data points. Our proposed approach highlights its potential to deliver heightened precision in indoor localization outcomes, with immediate relevance to real-time scenarios. Future research endeavors involving comprehensive comparative analyses with advanced techniques hold promise in propelling the field of accurate indoor localization solutions forward.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134882317","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}

引用次数: 0

Cyberbullying Detection using Machine Learning and Deep Learning 利用机器学习和深度学习检测网络欺凌

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141045

Aljwharah Alabdulwahab, Mohd Anul Haq, Mohammed Alshehri

{"title":"Cyberbullying Detection using Machine Learning and Deep Learning","authors":"Aljwharah Alabdulwahab, Mohd Anul Haq, Mohammed Alshehri","doi":"10.14569/ijacsa.2023.0141045","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141045","url":null,"abstract":"—With the human passion for gaining knowledge, learning new things and knowing the news that surrounds the world, social networks were invented to serve the human need, which resulted in the rapid spread and use among people, but social networks have a dark and bright side. The dark side is that strangers or anonymous people harass some users with obscene words that the user feels wrong about, which leads to psychological harm to him, and here we try to discover how to discover electronic bullying to block this alarming phenomenon. In this context, the utility of Natural Language Processing (NLP) is employed in the present investigation to detect electronic bullying and address this alarming phenomenon. The machine learning (ML) method is moderated based on specific features or criteria for detecting cyberbullying on social media. The collected characteristics were analyzed using the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), and Random Forest (RF) methods. Naturally, there are test results that use or operate on the proposed framework in a multi-category setting and are encouraged by kappa, classifier accuracy, and f-measure standards. These apparent outcomes show that the suggested model is a valuable method for predicting the behavior of cyberbullying, its strength, and its impact on social networks via the Internet. In the end, we evaluated the results of the proposed and basic features with machine learning techniques, which shows us the importance and effectiveness of the proposed features for detecting cyberbullying. We evaluated the models, and we got the accuracy of the KNN (0,90), SVM (0,92), and Deep learning (0,96)","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134882328","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}

引用次数: 0

Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm 基于元启发式算法优化黑色素瘤分类的超参数

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141057

Shamsuddeen Adamu, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Ayed Alwadin, Abdullahi Abubakar Imam, Aliyu Garba, Yahaya Saidu

{"title":"Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm","authors":"Shamsuddeen Adamu, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Ayed Alwadin, Abdullahi Abubakar Imam, Aliyu Garba, Yahaya Saidu","doi":"10.14569/ijacsa.2023.0141057","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141057","url":null,"abstract":"Melanoma, a prevalent and formidable skin cancer, necessitates early detection for improved survival rates. The rising incidence of melanoma poses significant challenges to healthcare systems worldwide. While deep neural networks offer the potential for precise melanoma classification, the optimization of hyperparameters remains a major obstacle. This paper introduces a groundbreaking approach that harnesses the Manta Rays Foraging Optimizer (MRFO) to empower melanoma classification. MRFO efficiently fine-tunes hyperparameters for a Convolutional Neural Network (CNN) using the ISIC 2019 dataset, which comprises 776 images (438 melanoma, 338 non-melanoma). The proposed cost-effective DenseNet121 model surpasses other optimization methods in various metrics during training, testing, and validation. It achieves an impressive accuracy of 99.26%, an AUC of 99.56%, an F1 score of 0.9091, a precision of 94.06%, and a recall of 87.96%. Comparative analysis with EfficientB1, EfficientB7, EfficientNetV2B0, NesNetLarge, ResNet50, VGG16, and VGG19 models demonstrates its superiority. These findings underscore the potential of the novel MRFO-based approach in achieving superior accuracy for melanoma classification. The proposed method has the potential to be a valuable tool for early detection and improved patient outcomes.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318205","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}

引用次数: 0

An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest 基于用户感兴趣对象的视频摘要深度学习优化方法

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141027

Hafiz Burhan Ul Haq, Watcharapan Suwansantisuk, Kosin Chamnongthai

{"title":"An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest","authors":"Hafiz Burhan Ul Haq, Watcharapan Suwansantisuk, Kosin Chamnongthai","doi":"10.14569/ijacsa.2023.0141027","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141027","url":null,"abstract":"Surveillance video is now able to play a vital role in maintaining security and protection thanks to the advancement of digital video technology. Businesses, both private and public, employ surveillance systems to monitor and track their daily operations. As a result, video generates a significant volume of data that needs to be further processed to satisfy security protocol requirements. Analyzing video requires a lot of effort and time, as well as quick equipment. The concept of a video summary was developed in order to overcome these limitations. To work past these limitations, the concept of video summarization has emerged. In this study, a deep learning-based method for customized video summarization is presented. This research enables users to produce a video summary in accordance with the User Object of Interest (UOoI), such as a car, airplane, person, bicycle, automobile, etc. Several experiments have been conducted on the two datasets, SumMe and self-created, to assess the efficiency of the proposed method. On SumMe and the self-created dataset, the overall accuracy is 98.7% and 97.5%, respectively, with a summarization rate of 93.5% and 67.3%. Furthermore, a comparison study is done to demonstrate that our proposed method is superior to other existing methods in terms of video summarization accuracy and robustness. Additionally, a graphic user interface is created to assist the user with summarizing the video using the UOoI.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318262","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}

引用次数: 0

Human Coach Technology Reactance Factors and their Influence on End-Users' Acceptance of e-Health Applications 人类教练技术的抗拒因素及其对终端用户接受电子健康应用的影响

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141001

Sarah Janböcke, Toshimi Ogawa, Johanna Langendorf, Koki Kobayashi, Ryan Browne, Rainer Wieching, Yasuyuki Taki

{"title":"Human Coach Technology Reactance Factors and their Influence on End-Users' Acceptance of e-Health Applications","authors":"Sarah Janböcke, Toshimi Ogawa, Johanna Langendorf, Koki Kobayashi, Ryan Browne, Rainer Wieching, Yasuyuki Taki","doi":"10.14569/ijacsa.2023.0141001","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141001","url":null,"abstract":"Project e-VITA is a joined research force from Europe and Japan that examines various cutting-edge e-health applications for older adult care. Those specific users do not necessarily feel technology savvy or secure enough to open up for innovative home tech systems. Thus, it is essential to provide the support that is virtual and human beside each other. Human coaches will provide this support to fulfill this role as a mediator between the technological system and the end-user. Reactance towards the system from the mediator's role could lead to the system's failure with the end user, thus failing the development. The effect of technology reactance in the integration process of a technological system can be the decisive factor in evaluating the success and failure of a technological system. We used part-standardized, problem-centered interviews to understand the human coaches’ challenges. The sample included people who act as the mediator role between the user and the technological system in the test application in the study centers. The interviews focused on experienced or imagined hurdles in the communication process with the user and the mediator role as well as the later relationship dynamic between the mediator, end-user, and technological system. The described technological challenges during the testing phase led the human coaches to responsibility, diffusion and uncertainty within their role. Furthermore, they led to a feeling of not fulfilling role expectations, which in the long term could indicate missing self-efficacy for the human coaches. We describe possible solutions mentioned by the interviewees and deepen the understanding of decisive factors for sustainable system integration for e-health applications.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318282","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}

引用次数: 0

A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications 可持续医疗保健应用的普适计算和可穿戴设备模型

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141056

Deshinta Arrova Dewi, Rajermani Thinakan, Malathy Batumalay, Tri Basuki Kurniawan

{"title":"A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications","authors":"Deshinta Arrova Dewi, Rajermani Thinakan, Malathy Batumalay, Tri Basuki Kurniawan","doi":"10.14569/ijacsa.2023.0141056","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141056","url":null,"abstract":"The user’s demands in the system supported by the Internet of Things are frequently controlled effectively using the pervasive computing system. Pervasive computing is a term used to describe a system that integrates several communication and distributed network technologies. Even so, it properly accommodates user needs. It is quite difficult to be inventive in the pervasive computing system when it comes to the delivery of information, handling standards, and extending heterogeneous aid for scattered clients. In this view, our paper intends to utilize a Dispersed and Elastic Computing Model (DECM) to enable proper and reliable communication for people who are using IoT-based wearable healthcare devices. Recurrent Reinforcement Learning (RRL) is used in the suggested model and the system that is connected to analyze resource allocation in response to requirements and other allocative factors. To provide effective data transmission over wearable medical devices, the built system gives managing mobility additional consideration to resource allocation and distribution. The results show that the pervasive computing system provides services to the user with reduced latency and an increased rate of communication for healthcare wearable devices based on the determined demands of the resources. This is an important aspect of sustainable healthcare. We employ the assessment metrics consisting of request failure, response time, managed and backlogged requests, bandwidth, and storage to capture the consistency of the proposed model.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318420","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}

引用次数: 0

HHO-SMOTe: Efficient Sampling Rate for Synthetic Minority Oversampling Technique Based on Harris Hawk Optimization HHO-SMOTe:基于Harris Hawk优化的合成少数派过采样技术的有效采样率

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0141047

Khaled SH. Raslan, Almohammady S. Alsharkawy, K. R. Raslan

{"title":"HHO-SMOTe: Efficient Sampling Rate for Synthetic Minority Oversampling Technique Based on Harris Hawk Optimization","authors":"Khaled SH. Raslan, Almohammady S. Alsharkawy, K. R. Raslan","doi":"10.14569/ijacsa.2023.0141047","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0141047","url":null,"abstract":"Classifying imbalanced datasets presents a significant challenge in the field of machine learning, especially with big data, where instances are unevenly distributed among classes, leading to class imbalance issues that affect classifier performance. Synthetic Minority Over-sampling Technique (SMOTE) is an effective oversampling method that addresses this by generating new instances for the under-represented minority class. However, SMOTE's efficiency relies on the sampling rate for minority class instances, making optimal sampling rates crucial for solving class imbalance. In this paper, we introduce HHO-SMOTe, a novel hybrid approach that combines the Harris Hawk optimization (HHO) search algorithm with SMOTE to enhance classification accuracy by determining optimal sample rates for each dataset. We conducted extensive experiments across diverse datasets to comprehensively evaluate our binary classification model. The results demonstrated our model's exceptional performance, with an AUC score exceeding 0.96, a high G-means score of 0.95 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.99. These findings collectively establish our proposed approach as a formidable contender in the domain of binary classification models.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318421","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}

引用次数: 0

Image Stitching Method and Implementation for Immersive 3D Ink Element Animation Production 沉浸式3D墨水元素动画制作的图像拼接方法与实现

International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.01410120

Chen Yang, Siti SalmiJamali, Adzira Husain, Nianyou Zhu

{"title":"Image Stitching Method and Implementation for Immersive 3D Ink Element Animation Production","authors":"Chen Yang, Siti SalmiJamali, Adzira Husain, Nianyou Zhu","doi":"10.14569/ijacsa.2023.01410120","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01410120","url":null,"abstract":"As the growth of immersive 3D animation, its application in ink element animation is constantly updating and advancing. However, the current immersive 3D ink element animation production also has the problem of lack of innovation and repeated development, so the research innovatively designs and develops the image stitching method for immersive 3D ink element animation production. The method is designed through stereo matching algorithm and scale-invariant feature transform algorithm, and the stereo matching algorithm is optimized with the weighted median filtering method based on the guide map. In addition, the study also designs the specific implementation of this method from different functional modules. The experimental results show that on four different datasets, the error percentages of the optimized stereo matching algorithm in non-occluded areas are 0.3885%, 0.4743%, 1.6848%, and 1.34%, respectively. The error percentages of all areas are 0.8316%, 0.8253%, 4.3235%, and 4.1760%, respectively. The research and design of image stitching methods can be applied in other fields and has good practical significance.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318479","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}

引用次数: 0