International Journal of Advanced Computer Science and Applications最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Effect of Multi-SVC Installation for Loss Control in Power System using Multi-Computational Techniques","authors":"N. Balasubramaniam, N. A. M. Kamari, I. Musirin, A. A. Ibrahim","doi":"10.14569/ijacsa.2023.01405103","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01405103","url":null,"abstract":"— Flexible AC Transmission Systems (FACTs) play a vital role in minimizing the power losses and improving voltage profile in power transmission system. These increase the real power transfer capacity of the system. However, optimal location of sizing of the FACTs devices determines the extent of benefits provided by the FACTs devices to the transmission system. Non-optimal solution in terms of the location and sizing may possibly lead to under-compensation or over-compensation phenomena. Thus, a robust optimization is a priori for optimal solution achievement. This paper presents a study on the effect on multi static VAR compensators (SVC) installation for loss control in power system using evolutionary programming (EP), artificial immune system (AIS) and immune evolutionary programming (IEP). The objective is to minimize the real power loss transmission and improve the voltage profile of the transmission power system. The study reveals that installation of multi-units SVC significantly reduces the power loss and increases the voltage profile of the system, validated on the IEEE 30-Bus Reliability Test System (RTS).","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":"79757327","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":"Customer Sentiment Analysis in Hotel Reviews Through Natural Language Processing Techniques","authors":"S. Ounacer, Driss Mhamdi, S. Ardchir, A. Daif, M. Azzouazi","doi":"10.14569/ijacsa.2023.0140162","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140162","url":null,"abstract":"—Customer reviews of products and services play a key role in the customers' decision to buy a product or use a service. Customers' preferences and choices are influenced by the opinions of others online; on blogs or social networks. New customers are faced with many views on the web, but they can't make the right decision. Hence, the need for sentiment analysis is to clarify whether opinions are positive, negative or neutral. This paper suggests using the Aspect-Based Sentiment Analysis approach on reviews extracted from tourism websites such as TripAdvisor and Booking. This approach is based on two main steps namely aspect extraction and sentiment classification related to each aspect. For aspect extraction, an approach based on topic modeling is proposed using the semi-supervised CorEx (Correlation Explanation) method for labeling word sequences into entities. As for sentiment classification, various supervised machine learning techniques are used to associate a sentiment (positive, negative or neutral) to a given aspect expression. Experiments on opinion corpora have shown very encouraging performances.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"27 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81957990","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":"Implementation of CNN for Plant Identification using UAV Imagery","authors":"M. A. Haq, Ahsan Ahmed, J. Gyani","doi":"10.14569/ijacsa.2023.0140441","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140441","url":null,"abstract":"Plants are the world's most significant resource since they are the only natural source of oxygen. Additionally, plants are considered crucial since they are the major source of energy for humanity and have nutritional, therapeutic, and other benefits. Image identification has become more prominent in this technology-driven world, where many innovations are happening in this sphere. Image processing techniques are increasingly being used by researchers to identify plants. The capacity of Convolutional Neural Networks (CNN) to transfer weights learned with huge standard datasets to tasks with smaller collections or more particular data has improved over time. Several applications are made for image identification using deep learning, and Machine Learning (ML) algorithms. Plant image identification is a prominent part of such. The plant image dataset of about 300 images collected by mobile phone and camera from different places in the natural scenes with nine species of different plants are deployed for training. A fivelayered convolution neural network (CNN) is applied for largescale plant classification in a natural environment. The proposed work claims a higher accuracy in plant identification based on experimental data. The model achieves the utmost recognition rate of 96% NU108 dataset and UAV images of NU101 have achieved an accuracy of 97.8%. Keywords—Convolutional Neural Networks (CNN); Machine Learning (ML) algorithms; plant image identification; plant image dataset","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":"82027985","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}