Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140313
Roopa N K, M. S
—Machine Learning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.
{"title":"An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images","authors":"Roopa N K, M. S","doi":"10.14569/ijacsa.2023.0140313","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140313","url":null,"abstract":"—Machine Learning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"24 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83290380","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140150
Ankit Srivastava, Narander Kumar
—Cloud computing has become a growing technology and has received wide acceptance in the scientific community and large organizations like government and industry. Due to the highly complex nature of VM virtualization, lightweight containers have gained wide popularity, and techniques to provision the resources to these containers have drawn researchers towards themselves. The models or algorithms that provide dynamic scalability which meets the demand of high performance and QoS utilizing the minimum number of resources for the containerized cloud have been lacking in the literature. The dynamic scalability facilitates the cloud services in offering timely, on-demand, and computing resources having the characteristic of dynamic adjustment to the end users. The manuscript has presented a technique which has exploited the queuing model to perform the dynamic scalability and scale the virtual resources of the containers while reducing the finances and meeting up the user’s Service Level Agreement (SLA). The paper aims in improving the usage of virtual resources and satisfy the SLA requirements in terms of response time, drop rate, system throughput, and the number of containers. The work has been simulated using Cloudsim and has been compared with the existing work and the analysis has shown that the proposed work has performed better.
{"title":"Queueing Model based Dynamic Scalability for Containerized Cloud","authors":"Ankit Srivastava, Narander Kumar","doi":"10.14569/ijacsa.2023.0140150","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140150","url":null,"abstract":"—Cloud computing has become a growing technology and has received wide acceptance in the scientific community and large organizations like government and industry. Due to the highly complex nature of VM virtualization, lightweight containers have gained wide popularity, and techniques to provision the resources to these containers have drawn researchers towards themselves. The models or algorithms that provide dynamic scalability which meets the demand of high performance and QoS utilizing the minimum number of resources for the containerized cloud have been lacking in the literature. The dynamic scalability facilitates the cloud services in offering timely, on-demand, and computing resources having the characteristic of dynamic adjustment to the end users. The manuscript has presented a technique which has exploited the queuing model to perform the dynamic scalability and scale the virtual resources of the containers while reducing the finances and meeting up the user’s Service Level Agreement (SLA). The paper aims in improving the usage of virtual resources and satisfy the SLA requirements in terms of response time, drop rate, system throughput, and the number of containers. The work has been simulated using Cloudsim and has been compared with the existing work and the analysis has shown that the proposed work has performed better.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"4 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83386027","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01407102
Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui
—Bird detection and species classification are important tasks in ecological research and bird conservation efforts. The study aims to address the challenges of accurately identifying bird species in images, which plays a crucial role in various fields such as environmental monitoring, and wildlife conservation. This article presents a comprehensive study on bird detection and species classification using the YOLOv5 object detection algorithm and deep transfer learning models. The objective is to develop an efficient and accurate system for identifying bird species in images. The YOLOv5 model is utilized for robust bird detection, enabling the localization of birds within images. Deep transfer learning (TL) models, including VGG19, Inception V3, and EfficientNetB3, are employed for species classification, leveraging their pre-trained weights and learned features. The experimental findings show that the proposed approach is effective, with excellent accuracy in both bird detection and tasks for species classification. The study showcases the potential of combining YOLOv5 with deep transfer learning models for comprehensive bird analysis, opening avenues for automated bird monitoring, ecological research, and conservation efforts. Furthermore, the study investigated the effects of optimization algorithms, including SGD, Adam, and Adamax, on the performance of the models. The findings contribute to the advancement of bird recognition systems and provide insights into the performance and suitability of various deep transfer learning architectures for avian image analysis.
{"title":"Bird Detection and Species Classification: Using YOLOv5 and Deep Transfer Learning Models","authors":"Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui","doi":"10.14569/ijacsa.2023.01407102","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01407102","url":null,"abstract":"—Bird detection and species classification are important tasks in ecological research and bird conservation efforts. The study aims to address the challenges of accurately identifying bird species in images, which plays a crucial role in various fields such as environmental monitoring, and wildlife conservation. This article presents a comprehensive study on bird detection and species classification using the YOLOv5 object detection algorithm and deep transfer learning models. The objective is to develop an efficient and accurate system for identifying bird species in images. The YOLOv5 model is utilized for robust bird detection, enabling the localization of birds within images. Deep transfer learning (TL) models, including VGG19, Inception V3, and EfficientNetB3, are employed for species classification, leveraging their pre-trained weights and learned features. The experimental findings show that the proposed approach is effective, with excellent accuracy in both bird detection and tasks for species classification. The study showcases the potential of combining YOLOv5 with deep transfer learning models for comprehensive bird analysis, opening avenues for automated bird monitoring, ecological research, and conservation efforts. Furthermore, the study investigated the effects of optimization algorithms, including SGD, Adam, and Adamax, on the performance of the models. The findings contribute to the advancement of bird recognition systems and provide insights into the performance and suitability of various deep transfer learning architectures for avian image analysis.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"56 7 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83481543","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01403104
D. Nc, K. Suresh, Chandrasekhar V, D. R
—Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications. Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system. This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background. In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data. Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion. Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.
手势识别(hand Gesture Recognition, HGR)是指识别在手语识别(Sign Language Recognition, SLR)和人机交互(Human Computer Interaction, HCI)应用中使用的各种手势。在不可控的环境条件下，复杂背景是影响HGR系统识别精度的主要挑战。这可以通过使用合适的语义分割方法来有效地解决，该方法将手部区域像素预测到前景，其余像素预测到背景。在本文中，我们分析和评估了使用RGB和深度数据进行手部区域分割的知名语义分割架构。进一步，通过概率分数融合，将分割后的RGB流和深度流集成到手势分类中。实验结果表明，本文提出的语义分割与集成分类(SSEC)框架适用于静态手势识别，在OUHANDS测试数据集上获得了88.91%的f1分。
{"title":"SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data","authors":"D. Nc, K. Suresh, Chandrasekhar V, D. R","doi":"10.14569/ijacsa.2023.01403104","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01403104","url":null,"abstract":"—Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications. Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system. This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background. In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data. Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion. Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"39 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84536312","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140787
Dou Lei, W. S. A. W. M. Daud
—This study investigates the fusion of augmented reality (AR) and body painting as a novel concept for artistic expression. By combining the immersive capabilities of AR with the creative potential of body painting, this research explores individuals' perceptions and attitudes towards this innovative artistic approach from an HCI perspective. Drawing upon the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DIT), the study examines the factors influencing individuals' acceptance and intention to engage in AR-integrated body painting. Additionally, the research explores the mediating role of artistic expression in understanding the impact of these factors on the actual outcomes of this merged concept. A sample of 212 respondents participated in an online survey to accomplish the research objectives. The survey comprehensively measured participants' perceptions of innovativeness, social system support, perceived usefulness, perceived ease of use, artistic expression, and behavioral intention towards AR-integrated body painting. Rigorous data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the intricate relationships between the variables. The findings underscore the significant impact of factors such as Innovativeness, social system support, perceived usefulness, and perceived ease of use on individuals' acceptance and intention to engage in AR-integrated body painting from an HCI perspective. Moreover, the study reveals the mediating role of artistic expression in connecting these influential factors with the actual outcomes of this merged concept. These empirical insights substantially contribute to our understanding of the fundamental mechanisms driving the adoption and utilization of AR in artistic practices, particularly within the domain of body painting, from both an artistic and HCI standpoint.
{"title":"Innovating Art with Augmented Reality: A New Dimension in Body Painting","authors":"Dou Lei, W. S. A. W. M. Daud","doi":"10.14569/ijacsa.2023.0140787","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140787","url":null,"abstract":"—This study investigates the fusion of augmented reality (AR) and body painting as a novel concept for artistic expression. By combining the immersive capabilities of AR with the creative potential of body painting, this research explores individuals' perceptions and attitudes towards this innovative artistic approach from an HCI perspective. Drawing upon the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DIT), the study examines the factors influencing individuals' acceptance and intention to engage in AR-integrated body painting. Additionally, the research explores the mediating role of artistic expression in understanding the impact of these factors on the actual outcomes of this merged concept. A sample of 212 respondents participated in an online survey to accomplish the research objectives. The survey comprehensively measured participants' perceptions of innovativeness, social system support, perceived usefulness, perceived ease of use, artistic expression, and behavioral intention towards AR-integrated body painting. Rigorous data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the intricate relationships between the variables. The findings underscore the significant impact of factors such as Innovativeness, social system support, perceived usefulness, and perceived ease of use on individuals' acceptance and intention to engage in AR-integrated body painting from an HCI perspective. Moreover, the study reveals the mediating role of artistic expression in connecting these influential factors with the actual outcomes of this merged concept. These empirical insights substantially contribute to our understanding of the fundamental mechanisms driving the adoption and utilization of AR in artistic practices, particularly within the domain of body painting, from both an artistic and HCI standpoint.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"33 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84552468","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140750
Sarah N. Abdulkader, Taha M. Mohamed
—Electromyogram (EMG) signals are used to recognize gestures that could be used for prosthetic-based and hands-free human computer interaction. Minimizing calibration times for users while preserving the accuracy, is one of the main challenges facing the practicality, user acceptance and spread of upper limb movements’ detection systems. This paper studies the effect of minimized user involvement, thus user calibration time and effort, on the user-independent system accuracy. It exploits time based features extracted from EMG signals. One-versus-all kernel based Support Vector Machine (SVM) and K Nearest Neighbors (KNN) are used for classification. The experiments are conducted using a dataset having five subjects performing six distinct movements. Two experiments performed, one with complete user dependence condition and the other with the partial dependence. The results show that the involvement of at least two samples, representing around 2% of sample space, increase the performance by 62.6% in case of SVM, achieving accuracy result equal to 89.6% on average; while the involvement of at least three samples, representing around 3% of sample space, cause the increase by 50.6% in case of KNN, achieving accuracy result equal to 78.2% on average. The results confirmed the great impact on system accuracy when involving only small number of user samples in the model-building process using traditional classification methods.
{"title":"Enhancing User Experience Via Calibration Minimization using ML Techniques","authors":"Sarah N. Abdulkader, Taha M. Mohamed","doi":"10.14569/ijacsa.2023.0140750","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140750","url":null,"abstract":"—Electromyogram (EMG) signals are used to recognize gestures that could be used for prosthetic-based and hands-free human computer interaction. Minimizing calibration times for users while preserving the accuracy, is one of the main challenges facing the practicality, user acceptance and spread of upper limb movements’ detection systems. This paper studies the effect of minimized user involvement, thus user calibration time and effort, on the user-independent system accuracy. It exploits time based features extracted from EMG signals. One-versus-all kernel based Support Vector Machine (SVM) and K Nearest Neighbors (KNN) are used for classification. The experiments are conducted using a dataset having five subjects performing six distinct movements. Two experiments performed, one with complete user dependence condition and the other with the partial dependence. The results show that the involvement of at least two samples, representing around 2% of sample space, increase the performance by 62.6% in case of SVM, achieving accuracy result equal to 89.6% on average; while the involvement of at least three samples, representing around 3% of sample space, cause the increase by 50.6% in case of KNN, achieving accuracy result equal to 78.2% on average. The results confirmed the great impact on system accuracy when involving only small number of user samples in the model-building process using traditional classification methods.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"20 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84649548","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01406143
Xiaomin Zhao, Xinxin Wang
Intelligent transportation is a system that combines data-driven information with traffic management to achieve intelligent monitoring and retrieval functions. In order to further improve the retrieval accuracy of the system model, a new retrieval model was designed. The functional requirements of the system were summarized, and the three stages of data preprocessing, feature matching, and feature extraction were analyzed in detail. The study adopted preprocessing measures such as equalization and normalization to minimize the negative effects of noise and brightness. Based on the performance of various algorithms, the distance method was selected as the feature matching method, which has a wider applicability and is better at processing bulk data. Next, the study utilizes Euclidean distance method to extract keyframes and divides the feature extraction into three parts: color, shape, and texture. The methods of color moment, canny operator, and grayscale cooccurrence matrix are used to extract them, and ultimately achieve relevant image retrieval. The research conducted multiple experiments on the retrieval performance of the model, and analyzed the results of retrieving single and mixed features. The experimental results showed that the algorithm performed better in the face of mixed feature extraction. Compared with the average value of a single feature, the recall and precision of the three mixed features increased by 13.78% and 15.64%, respectively. Moreover, in the case of a large number of concurrent features, the algorithm also met the basic requirements. When the concurrent number was 100, the average response time of the algorithm is 4.46 seconds. Therefore, the algorithm proposed by the research institute effectively improves the ability of video retrieval and can meet the requirements of timeliness, which can be widely applied in practical applications. Keywords—Matching extraction; feature fusion; image retrieval; intelligent transportation
{"title":"Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm","authors":"Xiaomin Zhao, Xinxin Wang","doi":"10.14569/ijacsa.2023.01406143","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01406143","url":null,"abstract":"Intelligent transportation is a system that combines data-driven information with traffic management to achieve intelligent monitoring and retrieval functions. In order to further improve the retrieval accuracy of the system model, a new retrieval model was designed. The functional requirements of the system were summarized, and the three stages of data preprocessing, feature matching, and feature extraction were analyzed in detail. The study adopted preprocessing measures such as equalization and normalization to minimize the negative effects of noise and brightness. Based on the performance of various algorithms, the distance method was selected as the feature matching method, which has a wider applicability and is better at processing bulk data. Next, the study utilizes Euclidean distance method to extract keyframes and divides the feature extraction into three parts: color, shape, and texture. The methods of color moment, canny operator, and grayscale cooccurrence matrix are used to extract them, and ultimately achieve relevant image retrieval. The research conducted multiple experiments on the retrieval performance of the model, and analyzed the results of retrieving single and mixed features. The experimental results showed that the algorithm performed better in the face of mixed feature extraction. Compared with the average value of a single feature, the recall and precision of the three mixed features increased by 13.78% and 15.64%, respectively. Moreover, in the case of a large number of concurrent features, the algorithm also met the basic requirements. When the concurrent number was 100, the average response time of the algorithm is 4.46 seconds. Therefore, the algorithm proposed by the research institute effectively improves the ability of video retrieval and can meet the requirements of timeliness, which can be widely applied in practical applications. Keywords—Matching extraction; feature fusion; image retrieval; intelligent transportation","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"6 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84665227","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140832
B. Kada, K. Munawar, M. S. Shaikh
—This paper focuses on two aspects of satellite formation flying (SFF) control: finite-time attitude synchronization and stabilization under undirected time-varying communication topology and synchronization without angular velocity measurements. First, a distributed nonlinear control law ensures rapid convergence and robust disturbance attenuation. To prove stability, a Lyapunov function involving an integrator term is utilized. Specifically, attitude synchronization and stabilization conditions are derived using graph theory, local finite-time convergence for homogeneous systems, and LaSalle's non-smooth invariance principle. Second, the requirements for angular velocity measurements are loosened using a distributed high-order sliding mode estimator. Despite the failure of inter-satellite communication links, the homogeneous sliding mode observer precisely estimates the relative angular velocity and provides smooth control to prevent the actuators of the satellites from chattering. Simulations numerically demonstrate the efficacy of the proposed design scheme.
{"title":"Attitude Synchronization and Stabilization for Multi-Satellite Formation Flying with Advanced Angular Velocity Observers","authors":"B. Kada, K. Munawar, M. S. Shaikh","doi":"10.14569/ijacsa.2023.0140832","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140832","url":null,"abstract":"—This paper focuses on two aspects of satellite formation flying (SFF) control: finite-time attitude synchronization and stabilization under undirected time-varying communication topology and synchronization without angular velocity measurements. First, a distributed nonlinear control law ensures rapid convergence and robust disturbance attenuation. To prove stability, a Lyapunov function involving an integrator term is utilized. Specifically, attitude synchronization and stabilization conditions are derived using graph theory, local finite-time convergence for homogeneous systems, and LaSalle's non-smooth invariance principle. Second, the requirements for angular velocity measurements are loosened using a distributed high-order sliding mode estimator. Despite the failure of inter-satellite communication links, the homogeneous sliding mode observer precisely estimates the relative angular velocity and provides smooth control to prevent the actuators of the satellites from chattering. Simulations numerically demonstrate the efficacy of the proposed design scheme.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"29 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84701606","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140473
Kabirat Sulaiman Ayomide, T. N. M. Aris, M. Zolkepli
Achieving precise tumor segmentation is essential for accurate diagnosis. Since brain tumors segmentation require a significant training process, reducing the training time is critical for timely treatment. The research focuses on enhancing brain tumor segmentation in MRI images by using Convolutional Neural Networks and reducing training time by using MATLAB's GoogLeNet, anisotropic diffusion filtering, morphological operation, and sector vector machine for MRI images. The proposed method will allow for efficient analysis and management of enormous amounts of MRI image data, the earliest practicable early diagnosis, and assistance in the classification of normal, benign, or malignant patient cases. The SVM Classifier is used to find a cluster of tumors development in an MR slice, identify tumor cells, and assess the size of the tumor that appears to be present in order to diagnose brain tumors. The proposed method is evaluated using a dataset from Figshare that includes coronal, sagittal, and axial views of images taken with a T1-CE MRI modality. The accuracy of 2D tumor detection and segmentation are increased, enabling more 3D detection, and achieving a mean classification accuracy of 98% across system records. Finally, a hybrid approach of GoogLeNet deep learning algorithm and Convolution Neural NetworkSupport Vector Machines (CNN-SVM) deep learning is performed to increase the accuracy of tumor classification. The evaluations show that the proposed technique is significantly more effective than those currently in use. In the future, enhancement of the segmentation using artificial neural networks will help in the earlier and more precise detection of brain tumors. Early detection of brain tumors can benefit patients, healthcare providers, and the healthcare system as a whole. It can reduce healthcare costs associated with treating advanced stage tumors, and enables researchers to better understand the disease and develop more effective treatments. Keywords—MRI brain tumor; anisotropic; segmentation; SVM classifier; convolutional neural network
{"title":"Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks","authors":"Kabirat Sulaiman Ayomide, T. N. M. Aris, M. Zolkepli","doi":"10.14569/ijacsa.2023.0140473","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140473","url":null,"abstract":"Achieving precise tumor segmentation is essential for accurate diagnosis. Since brain tumors segmentation require a significant training process, reducing the training time is critical for timely treatment. The research focuses on enhancing brain tumor segmentation in MRI images by using Convolutional Neural Networks and reducing training time by using MATLAB's GoogLeNet, anisotropic diffusion filtering, morphological operation, and sector vector machine for MRI images. The proposed method will allow for efficient analysis and management of enormous amounts of MRI image data, the earliest practicable early diagnosis, and assistance in the classification of normal, benign, or malignant patient cases. The SVM Classifier is used to find a cluster of tumors development in an MR slice, identify tumor cells, and assess the size of the tumor that appears to be present in order to diagnose brain tumors. The proposed method is evaluated using a dataset from Figshare that includes coronal, sagittal, and axial views of images taken with a T1-CE MRI modality. The accuracy of 2D tumor detection and segmentation are increased, enabling more 3D detection, and achieving a mean classification accuracy of 98% across system records. Finally, a hybrid approach of GoogLeNet deep learning algorithm and Convolution Neural NetworkSupport Vector Machines (CNN-SVM) deep learning is performed to increase the accuracy of tumor classification. The evaluations show that the proposed technique is significantly more effective than those currently in use. In the future, enhancement of the segmentation using artificial neural networks will help in the earlier and more precise detection of brain tumors. Early detection of brain tumors can benefit patients, healthcare providers, and the healthcare system as a whole. It can reduce healthcare costs associated with treating advanced stage tumors, and enables researchers to better understand the disease and develop more effective treatments. Keywords—MRI brain tumor; anisotropic; segmentation; SVM classifier; convolutional neural network","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84729081","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140103
M. Torky, Ali Bakhiet, Mohamed Bakrey, Ahmed Adel Ismail, A. I. E. Seddawy
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{"title":"Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework","authors":"M. Torky, Ali Bakhiet, Mohamed Bakrey, Ahmed Adel Ismail, A. I. E. Seddawy","doi":"10.14569/ijacsa.2023.0140103","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140103","url":null,"abstract":".","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"6 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75623841","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}
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