Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140809
Izzatul Husna Azman, N. Saad, A. Abdullah, R. A. Hamzah, Adam Samsudin, Shaarmila AP Kandaya
—Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consuming and error-prone. Computer-aided diagnostic (CAD) technologies have emerged as a viable option for early stroke diagnosis in recent years. These systems analyze medical pictures, such as magnetic resonance imaging (MRI), and identify indicators of stroke using modern algorithms and machine learning approaches. The goal of this review paper is to offer a thorough overview of the current state-of-the-art in CAD systems for early stroke detection. We give an examination of the merits and limits of this technology, as well as future research and development directions in this field. Finally, we contend that CAD systems represent a promising solution for improving the efficiency and accuracy of early stroke diagnosis, resulting in better patient outcomes and lower healthcare costs.
{"title":"Automated CAD System for Early Stroke Diagnosis: Review","authors":"Izzatul Husna Azman, N. Saad, A. Abdullah, R. A. Hamzah, Adam Samsudin, Shaarmila AP Kandaya","doi":"10.14569/ijacsa.2023.0140809","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140809","url":null,"abstract":"—Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consuming and error-prone. Computer-aided diagnostic (CAD) technologies have emerged as a viable option for early stroke diagnosis in recent years. These systems analyze medical pictures, such as magnetic resonance imaging (MRI), and identify indicators of stroke using modern algorithms and machine learning approaches. The goal of this review paper is to offer a thorough overview of the current state-of-the-art in CAD systems for early stroke detection. We give an examination of the merits and limits of this technology, as well as future research and development directions in this field. Finally, we contend that CAD systems represent a promising solution for improving the efficiency and accuracy of early stroke diagnosis, resulting in better patient outcomes and lower healthcare costs.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89462380","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.2023.01406139
Yu Wang
Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level. Keywords—Deep learning; calligraphy teaching; BPNN; intelligent evaluation; sparrow search algorithm
{"title":"The Application of Intelligent Evaluation Method with Deep Learning in Calligraphy Teaching","authors":"Yu Wang","doi":"10.14569/ijacsa.2023.01406139","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01406139","url":null,"abstract":"Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level. Keywords—Deep learning; calligraphy teaching; BPNN; intelligent evaluation; sparrow search algorithm","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89786521","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.2023.01408111
Cristian A. Aldana B, Edison F. Montenegro A
.
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{"title":"A Low-Cost Wireless Sensor System for Power Quality Management in Single-Phase Domestic Networks","authors":"Cristian A. Aldana B, Edison F. Montenegro A","doi":"10.14569/ijacsa.2023.01408111","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01408111","url":null,"abstract":".","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89855040","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.2023.0140841
Tamer Bani Amer, Mohammad Ibrahim Ahmed Al-Omar
—As technology advances and cyber threats continue to evolve, cyber security professionals play a critical role in developing and implementing robust security measures, staying ahead of potential risks, and mitigating the impact of cyber incidents. Many studies have examined the impact of cyber security on banks, without focusing on electronic crimes. Despite its importance, to the best of our knowledge, there are no studies on the impact of cyber security on mitigating electronic crimes in the banking sector. Therefore, the purpose of this study is to ascertain how cyber security affects electronic crimes in the Jordanian banking industry. The study sample consisted of 270 senior Jordanian managers and employees who understand the importance of cyber security in the banking sector in 14 Jordanian commercial banks, listed on the Amman stock exchange. The study used SPSS to evaluate how banks can enhance network security infrastructure to prevent unauthorized access and data breaches and also to find out the role of cybersecurity in granting competitive advantage to banks. A relative importance index (RII) was conducted to rank the importance of variables’ statements and test the hypotheses. The results found the most important method through which banks can effectively mitigate the risk of electronic crimes and ensure the security of customers’ financial data is that banks utilize robust encryption technologies to ensure the protection of customer financial data while it is being transmitted and when it is stored (RII=0.740). About 81.5 % of the sample agree, also, banks that have a strong cyber security system provide a secure platform for digital financial services which increases the competitive advantage as they were ranked first for their relative importance at both the category level and overall ranking with (RII=0.754). The study recommended that the banking industry, must consistently educate its customers on information security techniques and how to avoid hacking into their accounts, and develop an alert system that can raise awareness for both banks and bank customers if there is any possible entry or access to the customer's account or organization confidential information.
{"title":"The Impact of Cyber Security on Preventing and Mitigating Electronic Crimes in the Jordanian Banking Sector","authors":"Tamer Bani Amer, Mohammad Ibrahim Ahmed Al-Omar","doi":"10.14569/ijacsa.2023.0140841","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140841","url":null,"abstract":"—As technology advances and cyber threats continue to evolve, cyber security professionals play a critical role in developing and implementing robust security measures, staying ahead of potential risks, and mitigating the impact of cyber incidents. Many studies have examined the impact of cyber security on banks, without focusing on electronic crimes. Despite its importance, to the best of our knowledge, there are no studies on the impact of cyber security on mitigating electronic crimes in the banking sector. Therefore, the purpose of this study is to ascertain how cyber security affects electronic crimes in the Jordanian banking industry. The study sample consisted of 270 senior Jordanian managers and employees who understand the importance of cyber security in the banking sector in 14 Jordanian commercial banks, listed on the Amman stock exchange. The study used SPSS to evaluate how banks can enhance network security infrastructure to prevent unauthorized access and data breaches and also to find out the role of cybersecurity in granting competitive advantage to banks. A relative importance index (RII) was conducted to rank the importance of variables’ statements and test the hypotheses. The results found the most important method through which banks can effectively mitigate the risk of electronic crimes and ensure the security of customers’ financial data is that banks utilize robust encryption technologies to ensure the protection of customer financial data while it is being transmitted and when it is stored (RII=0.740). About 81.5 % of the sample agree, also, banks that have a strong cyber security system provide a secure platform for digital financial services which increases the competitive advantage as they were ranked first for their relative importance at both the category level and overall ranking with (RII=0.754). The study recommended that the banking industry, must consistently educate its customers on information security techniques and how to avoid hacking into their accounts, and develop an alert system that can raise awareness for both banks and bank customers if there is any possible entry or access to the customer's account or organization confidential information.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89935216","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.2023.0140380
Soumaya Fatnassi, M. Yahia, Tarig Ali, M. Mortula
—This Mangrove forests in the United Arab Emirates (UAE) provide valuable ecosystem services such as coastal erosion protection, water purification and refuge for a wide variety of plants and animals. Therefore, the first step toward understanding the mangrove forests is the monitoring of this important ecological system. This paper proposes an original study to characterize the mangrove forest environment in the UAE by using polarimetric synthetic aperture radar (PolSAR) remote sensing. Free access C-band dual-PolSAR Sentinel 1 data have been exploited. The elements as of the covariance matrix as well as the entropy/alpha decomposition parameters have been studied. Results show that the VH intensity, the coherence between VV and VH polarimetric channels, the entropy and alpha angle provide the most pronounced signatures that discern mangrove forests. Thus, these parameters could be exploited to improve the accuracy of the remote sensing monitoring and mapping techniques of mangrove forests in the UAE.
{"title":"Polarimetric SAR Characterization of Mangrove Forest Environment in the United Arab Emirates (UAE)","authors":"Soumaya Fatnassi, M. Yahia, Tarig Ali, M. Mortula","doi":"10.14569/ijacsa.2023.0140380","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140380","url":null,"abstract":"—This Mangrove forests in the United Arab Emirates (UAE) provide valuable ecosystem services such as coastal erosion protection, water purification and refuge for a wide variety of plants and animals. Therefore, the first step toward understanding the mangrove forests is the monitoring of this important ecological system. This paper proposes an original study to characterize the mangrove forest environment in the UAE by using polarimetric synthetic aperture radar (PolSAR) remote sensing. Free access C-band dual-PolSAR Sentinel 1 data have been exploited. The elements as of the covariance matrix as well as the entropy/alpha decomposition parameters have been studied. Results show that the VH intensity, the coherence between VV and VH polarimetric channels, the entropy and alpha angle provide the most pronounced signatures that discern mangrove forests. Thus, these parameters could be exploited to improve the accuracy of the remote sensing monitoring and mapping techniques of mangrove forests in the UAE.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90912016","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.2023.0140512
T. Nguyen, Niansong Tu, T. Ha
www.ijacsa.thesai.org
www.ijacsa.thesai.org
{"title":"Input Value Chain Affect Vietnamese Rice Yield: An Analytical Model Based on a Machine Learning Algorithm","authors":"T. Nguyen, Niansong Tu, T. Ha","doi":"10.14569/ijacsa.2023.0140512","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140512","url":null,"abstract":"www.ijacsa.thesai.org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91274185","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.2023.01404104
Augustine Nwabuwe, Baljinder Sanghera, T. Alade, F. Olajide
{"title":"Fraud Mitigation in Attendance Monitoring Systems using Dynamic QR Code, Geofencing and IMEI Technologies","authors":"Augustine Nwabuwe, Baljinder Sanghera, T. Alade, F. Olajide","doi":"10.14569/ijacsa.2023.01404104","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01404104","url":null,"abstract":"","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89532843","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.2023.0140383
Wenzhi Wang, Zhanqiao Liu
—Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and loosely-coupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.
{"title":"Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic","authors":"Wenzhi Wang, Zhanqiao Liu","doi":"10.14569/ijacsa.2023.0140383","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140383","url":null,"abstract":"—Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and loosely-coupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89688781","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.2023.0141057
Shamsuddeen Adamu, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Ayed Alwadin, Abdullahi Abubakar Imam, Aliyu Garba, Yahaya Saidu
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0141027
Hafiz Burhan Ul Haq, Watcharapan Suwansantisuk, Kosin Chamnongthai
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.
由于数字视频技术的进步，监控视频现在能够在维护安全和保护方面发挥至关重要的作用。私营和公共企业都采用监视系统来监视和跟踪其日常运营。因此，视频会产生大量的数据，这些数据需要进一步处理才能满足安全协议的要求。分析视频需要大量的精力和时间，以及快速的设备。视频摘要的概念是为了克服这些限制而发展起来的。为了克服这些限制，视频摘要的概念出现了。在本研究中，提出了一种基于深度学习的自定义视频摘要方法。本研究使用户能够根据用户感兴趣的对象(User Object of Interest, UOoI)，如汽车、飞机、人、自行车、汽车等，制作视频摘要。在SumMe和self-created两个数据集上进行了多次实验，以评估所提出方法的效率。在SumMe和自建数据集上，总体准确率分别为98.7%和97.5%，总结率为93.5%和67.3%。对比研究表明，本文提出的方法在视频摘要的准确性和鲁棒性方面都优于现有的方法。此外，还创建了图形用户界面，以帮助用户使用UOoI总结视频。
{"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":null,"pages":null},"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}
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