Determine whether the vehicle is parked in the specified area, which has high application value in industry, transportation, parking lot, etc. Aiming at the problems of rough results and high maintenance costs, a vehicle parking position detection method based on Swing Transformer semantic segmentation is proposed. The vehicle semantic results obtained by Swin Transformer semantic segmentation algorithm are taken as the main features of vehicles in the picture. Canny algorithm is used to obtain vehicle contour to improve detection accuracy. Calculate the relationship between the vehicle contour and the hand drawn warning line, and compare with the threshold value to determine whether the vehicle is parked in the specified area. Through simulation, industrial application and road application, the method can realize the normative detection of vehicle parking position.
{"title":"Research on Vehicle Parking Position Detection Based on Swin Transformer Semantic Segmentation","authors":"Hong-Tu Shi, Jian-Zhang Liu, Ruochao Wang, Yanpeng Huo, Chao-Wei Cui, Yong-qiang Zhang","doi":"10.1109/ICSAI57119.2022.10005500","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005500","url":null,"abstract":"Determine whether the vehicle is parked in the specified area, which has high application value in industry, transportation, parking lot, etc. Aiming at the problems of rough results and high maintenance costs, a vehicle parking position detection method based on Swing Transformer semantic segmentation is proposed. The vehicle semantic results obtained by Swin Transformer semantic segmentation algorithm are taken as the main features of vehicles in the picture. Canny algorithm is used to obtain vehicle contour to improve detection accuracy. Calculate the relationship between the vehicle contour and the hand drawn warning line, and compare with the threshold value to determine whether the vehicle is parked in the specified area. Through simulation, industrial application and road application, the method can realize the normative detection of vehicle parking position.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126312478","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005508
Almustafa Abed, Belhassen Akrout, Ikram Amous
This study aims to present a deep learning approach utilizing transfer learning and an RGB-D dataset termed HADA (Hands dataset) acquired by a depth sensor from a top-view configuration capable of monitoring customers and classifying their interaction in intelligent retail settings. With the intention of developing an automated RGB-D approach for video analysis, we provide an innovative, intelligent technology that can comprehend customer behavior, in particular their interactions with items on the shelves. The camera system identifies the presence of humans and classifies their interactions with products accurately. Through the RGB and depth frames, the system determines consumer interactions with shelf objects and identifies if a product is picked up, taken and subsequently returned, or if there is no touch at all. Our approach obtained good accuracy, precision, and recall, demonstrating the efficiency of the proposed model, and testing findings have proved that its performance in real-world conditions is adequate.
{"title":"Shoppers Interaction Classification Based on An Improved DenseNet Model Using RGB-D Data","authors":"Almustafa Abed, Belhassen Akrout, Ikram Amous","doi":"10.1109/ICSAI57119.2022.10005508","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005508","url":null,"abstract":"This study aims to present a deep learning approach utilizing transfer learning and an RGB-D dataset termed HADA (Hands dataset) acquired by a depth sensor from a top-view configuration capable of monitoring customers and classifying their interaction in intelligent retail settings. With the intention of developing an automated RGB-D approach for video analysis, we provide an innovative, intelligent technology that can comprehend customer behavior, in particular their interactions with items on the shelves. The camera system identifies the presence of humans and classifies their interactions with products accurately. Through the RGB and depth frames, the system determines consumer interactions with shelf objects and identifies if a product is picked up, taken and subsequently returned, or if there is no touch at all. Our approach obtained good accuracy, precision, and recall, demonstrating the efficiency of the proposed model, and testing findings have proved that its performance in real-world conditions is adequate.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123020132","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005488
Sucianna Ghadati Rabiha, I. H. Kartowisastro, Reina Setiawan, W. Budiharto
Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture.
{"title":"Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition","authors":"Sucianna Ghadati Rabiha, I. H. Kartowisastro, Reina Setiawan, W. Budiharto","doi":"10.1109/ICSAI57119.2022.10005488","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005488","url":null,"abstract":"Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053313","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005492
Wenbin Zhang, Jiaju She, Yingqiu Wang, Meng Zhao, Yi Wang, Chao Liu
Knowledge base with multiple hops quizzing aims to discover the subject entity in a question at a distance from the knowledge base’s answer entity for multiple hops. The lack of supervised signals for the intermediate phases of multi-hop inference, which leaves a model only able to get input on the final output, is a significant difficulty for the study, where the inference instructions for the intermediate steps cannot be effectively optimized and the forward propagation of inference states is weakened. Most of the existing research approaches use global attention to motivate the model to learn the inference instructions of each hop, which has been shown to fail to achieve effective performance in weakly supervised tasks. To address this challenge, this paper proposes an intermediate inference attention mechanism to handle multi-hop knowledge base quizzing tasks. Inspired by the human execution of multi-hop quizzing where each hop question is influenced by the previous hop answer, in this approach, the model pays more attention to the inference state generated by the previous hop inference instruction when generating each hop inference instruction, prompting a close interaction between the inference state of the intermediate step and the inference instruction, and providing effective attentional feedback for the optimization of the intermediate step inference instruction. On the KBQA dataset in the integrated energy service domain, which is self-constructed in this research, we conduct comprehensive comparison experiments. The findings suggest that the technique we provided achieves optimum performance in this study.
{"title":"Multi-hop Knowledge Base Q&A in Integrated Energy Services Based on Intermediate Reasoning Attention","authors":"Wenbin Zhang, Jiaju She, Yingqiu Wang, Meng Zhao, Yi Wang, Chao Liu","doi":"10.1109/ICSAI57119.2022.10005492","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005492","url":null,"abstract":"Knowledge base with multiple hops quizzing aims to discover the subject entity in a question at a distance from the knowledge base’s answer entity for multiple hops. The lack of supervised signals for the intermediate phases of multi-hop inference, which leaves a model only able to get input on the final output, is a significant difficulty for the study, where the inference instructions for the intermediate steps cannot be effectively optimized and the forward propagation of inference states is weakened. Most of the existing research approaches use global attention to motivate the model to learn the inference instructions of each hop, which has been shown to fail to achieve effective performance in weakly supervised tasks. To address this challenge, this paper proposes an intermediate inference attention mechanism to handle multi-hop knowledge base quizzing tasks. Inspired by the human execution of multi-hop quizzing where each hop question is influenced by the previous hop answer, in this approach, the model pays more attention to the inference state generated by the previous hop inference instruction when generating each hop inference instruction, prompting a close interaction between the inference state of the intermediate step and the inference instruction, and providing effective attentional feedback for the optimization of the intermediate step inference instruction. On the KBQA dataset in the integrated energy service domain, which is self-constructed in this research, we conduct comprehensive comparison experiments. The findings suggest that the technique we provided achieves optimum performance in this study.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"97 1-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114022577","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 : 2022-12-10DOI: 10.1109/icsai57119.2022.10005550
Yina Yang, Chaoliang Wang, Jie Yin, Jing Ye, An Wen, Qiang Wang
Electricity meter wrong wiring is one of the common defects of electric energy metering device, which will cause safety and economic problems. Traditional wrong wiring detection of electricity meter mainly relies on manpower resources. In this paper, an automatic vision-based detection method is proposed for detecting the wrong wiring of electricity meter. Firstly, based on our own dataset, we modify and fine-tune the object detection model YOLOv3 to detect the meter type and the wiring area location. Then by using multiple image processing techniques including color segmentation, contour extraction and similarity analysis, we detect the wiring color sequence and compare it to the expected sequence to get the final result. The experiment result shows that the accuracy and real-time of this method is satisfactory.
{"title":"Wrong Wiring Detection of Electricity Meter Based on Image Processing","authors":"Yina Yang, Chaoliang Wang, Jie Yin, Jing Ye, An Wen, Qiang Wang","doi":"10.1109/icsai57119.2022.10005550","DOIUrl":"https://doi.org/10.1109/icsai57119.2022.10005550","url":null,"abstract":"Electricity meter wrong wiring is one of the common defects of electric energy metering device, which will cause safety and economic problems. Traditional wrong wiring detection of electricity meter mainly relies on manpower resources. In this paper, an automatic vision-based detection method is proposed for detecting the wrong wiring of electricity meter. Firstly, based on our own dataset, we modify and fine-tune the object detection model YOLOv3 to detect the meter type and the wiring area location. Then by using multiple image processing techniques including color segmentation, contour extraction and similarity analysis, we detect the wiring color sequence and compare it to the expected sequence to get the final result. The experiment result shows that the accuracy and real-time of this method is satisfactory.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114077958","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005410
Chen Wang, Y. Li, Jinghua Du, G. Corzo
China started Sponge City Programme (SCP) to manage urban floods and to improve the quality of water environment in 2015. So far, the first two batches of pilot cities of SCP have been finished construction and two more batches of demonstration cities are ongoing. How to transfer the knowledge which learnt by the pilot cities to other cities is a key problem to enhance both the efficiency and the performance of SCP. This paper introduces the first step to develop a fuzzy system to represent knowledge which is acquired from stakeholders and decision makers, then to transform into if-then rules. This fuzzy logic inference system aims to simulate scenarios of real-life decision making. How to rate the performance of SCP in one city is chosen as an example in this paper. A Mamdani fuzzy inference system is applied, the fuzzy variables and fuzzy rules are hypothetical based on surveys of Qian’an, one of the pilot cities of SCP. Data from Qian’an is used to test and results show that the performance of SCP in Qian’an is good (74.1). The Mamdani fuzzy system is proved to be a powerful tool to represent knowledge and in the future other aspects of knowledge from SCP will be applied.
{"title":"Mamdani Fuzzy Inference System for Rating the Performance of Sponge City Programme","authors":"Chen Wang, Y. Li, Jinghua Du, G. Corzo","doi":"10.1109/ICSAI57119.2022.10005410","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005410","url":null,"abstract":"China started Sponge City Programme (SCP) to manage urban floods and to improve the quality of water environment in 2015. So far, the first two batches of pilot cities of SCP have been finished construction and two more batches of demonstration cities are ongoing. How to transfer the knowledge which learnt by the pilot cities to other cities is a key problem to enhance both the efficiency and the performance of SCP. This paper introduces the first step to develop a fuzzy system to represent knowledge which is acquired from stakeholders and decision makers, then to transform into if-then rules. This fuzzy logic inference system aims to simulate scenarios of real-life decision making. How to rate the performance of SCP in one city is chosen as an example in this paper. A Mamdani fuzzy inference system is applied, the fuzzy variables and fuzzy rules are hypothetical based on surveys of Qian’an, one of the pilot cities of SCP. Data from Qian’an is used to test and results show that the performance of SCP in Qian’an is good (74.1). The Mamdani fuzzy system is proved to be a powerful tool to represent knowledge and in the future other aspects of knowledge from SCP will be applied.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115535619","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005532
Gernot Fiala, Zhenyu Ye, C. Steger
Machine vision systems (MVS) use image sensors to process and analyze image data. Depending on the application, the image sensor parameters are configured differently. However, some parameters are fixed for a specific product generation or product line. One of these parameters is the pixel pitch, the distance from one physical pixel to another. In this work, we introduce a framework, which allows to optimize design parameters of image sensors for pupil detection. We compare 2 different image sensor models with different pixel designs and generate images with different bit depths and resolutions. An evaluation of the design parameters is done with the generated images and a pupil detection algorithm. Furthermore, an existing pupil detection dataset is extended.
{"title":"Framework for Image Sensor Design Parameter Optimization for Pupil Detection","authors":"Gernot Fiala, Zhenyu Ye, C. Steger","doi":"10.1109/ICSAI57119.2022.10005532","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005532","url":null,"abstract":"Machine vision systems (MVS) use image sensors to process and analyze image data. Depending on the application, the image sensor parameters are configured differently. However, some parameters are fixed for a specific product generation or product line. One of these parameters is the pixel pitch, the distance from one physical pixel to another. In this work, we introduce a framework, which allows to optimize design parameters of image sensors for pupil detection. We compare 2 different image sensor models with different pixel designs and generate images with different bit depths and resolutions. An evaluation of the design parameters is done with the generated images and a pupil detection algorithm. Furthermore, an existing pupil detection dataset is extended.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"460 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123457293","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005412
Jin Liu, Yanqin Kang, Tao Liu, Tingyu Zhang, Yikun Zhang
clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian.
{"title":"Deep Iterative Reconstruction Network Based on Residual Constraint for Low-Dose CT Imaging","authors":"Jin Liu, Yanqin Kang, Tao Liu, Tingyu Zhang, Yikun Zhang","doi":"10.1109/ICSAI57119.2022.10005412","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005412","url":null,"abstract":"clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831597","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 : 2022-12-10DOI: 10.1109/ICSAI57119.2022.10005386
Théophile K. Dagba, Mahussi Franck Dominique Lokossou
This paper presents a system to predict the risk of non-payment of premium in health insurance. The data corpus includes a total of 186 instances divided into 127 samples (70%) for the learning phase and 59 samples (30%) for the validation and test phase. Each example is characterized by age, marital status, the presence of a recent illness or not, the wearing of medical glasses or prostheses, the gender, the recovery rate and the ceiling exceeded. After normalizing the data, an analysis has been performed to ensure non-redundancy by calculating the covariance. The error back propagation algorithm is used for the learning phase. The minimization of the quadratic error has allowed to retain the number of neurons on the hidden layer. Neuroph library is applied for the implementation. The performance of the system is rated at 88.71%.
{"title":"Neural Network For Risk Assessment In Life Insurance Industry: A Case Study","authors":"Théophile K. Dagba, Mahussi Franck Dominique Lokossou","doi":"10.1109/ICSAI57119.2022.10005386","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005386","url":null,"abstract":"This paper presents a system to predict the risk of non-payment of premium in health insurance. The data corpus includes a total of 186 instances divided into 127 samples (70%) for the learning phase and 59 samples (30%) for the validation and test phase. Each example is characterized by age, marital status, the presence of a recent illness or not, the wearing of medical glasses or prostheses, the gender, the recovery rate and the ceiling exceeded. After normalizing the data, an analysis has been performed to ensure non-redundancy by calculating the covariance. The error back propagation algorithm is used for the learning phase. The minimization of the quadratic error has allowed to retain the number of neurons on the hidden layer. Neuroph library is applied for the implementation. The performance of the system is rated at 88.71%.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315658","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 : 2022-12-10DOI: 10.1109/icsai57119.2022.10005448
{"title":"ICSAI 2022 Cover Page","authors":"","doi":"10.1109/icsai57119.2022.10005448","DOIUrl":"https://doi.org/10.1109/icsai57119.2022.10005448","url":null,"abstract":"","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331469","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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