Computer Science and Information Systems最新文献_第7页

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things 将深度学习应用于物联网条件下制造业印刷电路板自动检测系统

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220718020z

Kaiwen Zhang

{"title":"Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things","authors":"Kaiwen Zhang","doi":"10.2298/csis220718020z","DOIUrl":"https://doi.org/10.2298/csis220718020z","url":null,"abstract":"Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"18 1","pages":"723-741"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88056569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sustainability-oriented route generation for ridesharing services 面向可持续性的路线生成，用于拼车服务

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis221209053l

Mengya Liu, Vahid Yazdanpanah, Sebastian Stein, Enrico Gerding

{"title":"Sustainability-oriented route generation for ridesharing services","authors":"Mengya Liu, Vahid Yazdanpanah, Sebastian Stein, Enrico Gerding","doi":"10.2298/csis221209053l","DOIUrl":"https://doi.org/10.2298/csis221209053l","url":null,"abstract":"Sustainability is the ability to maintain and preserve natural and man made systems for the benefit of current and future generations. The three pillars of sustainability are social, economic, and environmental. These pillars are interdependent and interconnected, meaning that progress in one area can have positive or negative impacts on the others. This calls for smart methods to balance such benefits and find solutions that are optimal with respect to all the three pillars of sustainability. By using AI methods, in particular, genetic algorithms for multiobjective optimisation, we can better understand and manage complex systems in order to achieve sustainability. In the context of sustainability-oriented ridesharing, genetic algorithms can be used to optimise route finding in order to lower the cost of trans portation and reduce emissions. This work contributes to this domain by using AI, specifically genetic algorithms for multiobjective optimisation, to improve the efficiency and sustainability of transportation systems. By using this approach, we can make progress towards achieving the goals of the three pillars of sustainability.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

化工控制系统中的现场总线通信技术研究与应用化工控制系统中的现场总线通信技术研究与应用

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.37155/2717-5170-0507-29

李明

引用次数: 1

Analyzing feature importance for a predictive undergraduate student dropout model 预测大学生辍学模型的特征重要性分析

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis211110050j

Alberto Jiménez-Macías, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, Margarita Ortiz-Rojas, C. D. Kloos

{"title":"Analyzing feature importance for a predictive undergraduate student dropout model","authors":"Alberto Jiménez-Macías, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, Margarita Ortiz-Rojas, C. D. Kloos","doi":"10.2298/csis211110050j","DOIUrl":"https://doi.org/10.2298/csis211110050j","url":null,"abstract":"Worldwide, one of the main concerns of universities is to reduce the dropout rate. Several initiatives have been taken to avoid this problem; however, it is essential to recognize at-risk students as early as possible. This article is an extension of a previous study that proposed a predictive model to identify students at risk of dropout from the beginning of their university degree. The new contribution is the analysis of the feature importance for dropout segmented by faculty, degree program, and semester in the different predictive models. In addition, we propose a dropout model based on faculty characteristics to try to infer the dropout based on faculty features. We used data of 30,576 students enrolled in a Higher Education Institution ranging from years 2000 to 2020. The findings indicate that the variables related to Grade Point Average(GPA), socioeconomic factor, and a pass rate of courses taken have a more significant impact on the model, regardless of the semester, faculty, or program. Additionally, we found a significant difference in the predictive power between Science, Technology, Engineering, and Mathematics (STEM) and humanistic programs.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"6 1","pages":"175-194"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80524241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-perspective approach for curating and exploring the history of climate change in Latin America within digital newspapers 在数字报纸中策划和探索拉丁美洲气候变化历史的多视角方法

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220110008v

Genoveva Vargas-Solar, J. Zechinelli-Martini, Javier-Alfonso Espinosa-Oviedo, Luis Manuel Vilches Blázquez

{"title":"Multi-perspective approach for curating and exploring the history of climate change in Latin America within digital newspapers","authors":"Genoveva Vargas-Solar, J. Zechinelli-Martini, Javier-Alfonso Espinosa-Oviedo, Luis Manuel Vilches Blázquez","doi":"10.2298/csis220110008v","DOIUrl":"https://doi.org/10.2298/csis220110008v","url":null,"abstract":"This paper introduces a multi-perspective approach to deal with curation and exploration issues in historical newspapers. It has been implemented in the platform LACLICHEV (Latin American Climate Change Evolution platform). Exploring the history of climate change through digitalized newspapers published around two centuries ago introduces four challenges: (1) curating content for tracking entries describing meteorological events; (2) processing (digging into) colloquial language (and its geographic variations5) for extracting meteorological events; (3) analyzing newspapers to discover meteorological patterns possibly associated with climate change; (4) designing tools for exploring the extracted content. LACLICHEV provides tools for curating, exploring, and analyzing historical news paper articles, their description and location, and the vocabularies used for referring to meteorological events. This platform makes it possible to understand and identify possible patterns and models that can build an empirical and social view of the history of climate change in the Latin American region.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"11 1","pages":"1179-1205"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81007825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intrusion detection model of internet of things based on deep learning 基于深度学习的物联网入侵检测模型

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230418058w

Yan Wang, Dezhi Han, Mingming Cui

{"title":"Intrusion detection model of internet of things based on deep learning","authors":"Yan Wang, Dezhi Han, Mingming Cui","doi":"10.2298/csis230418058w","DOIUrl":"https://doi.org/10.2298/csis230418058w","url":null,"abstract":"The proliferation of Internet of Things (IoTs) technology is being seriously impeded by insecure networks and data. An effective intrusion detection model is essential for safeguarding the network and data security of IoTs. In this pa per, a hybrid parallel intrusion detection model based on deep learning (DL) called HPIDM features a three-layer parallel neural network structure. Combining stacked Long short-term memory (LSTM) neural networks with convolutional neural net work (CNN) and SK Net self-attentive mechanism in the model allows HPIDM to learn temporal and spatial features of traffic data effectively. HPIDM fuses the acquired temporal and spatial feature data and then feeds it into the CosMargin classifier for classification detection to reduce the impact of data imbalance on the 23 performance of the Intrusion Detection System (IDS). Finally, HPIDM was experimentally compared with classical intrusion detection models and the two comparative models designed in this paper, and the experimental results show that HPIDM achieves 99.87% accuracy on the ISCX-IDS 2012 dataset and 99.94% accuracy on the CICIDS 2017 dataset. In addition, it outperforms other comparable models in terms of recall, precision, false alarm rate (FAR), and F1 score, showing its feasibility and superiority.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1519-1540"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble of top3 prediction with image pixel interval method using deep learning 基于深度学习的图像像素间隔方法集成top3预测

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230223056a

Abdulaziz Anorboev, Javokhir Musaev, Sarvinoz Anorboeva, Jeongkyu Hong, Yeong-Seok Seo, N. Nguyen, D. Hwang

{"title":"Ensemble of top3 prediction with image pixel interval method using deep learning","authors":"Abdulaziz Anorboev, Javokhir Musaev, Sarvinoz Anorboeva, Jeongkyu Hong, Yeong-Seok Seo, N. Nguyen, D. Hwang","doi":"10.2298/csis230223056a","DOIUrl":"https://doi.org/10.2298/csis230223056a","url":null,"abstract":"Computer vision (CV) has been successfully used in picture categorization applications in various fields, including medicine, production quality control, and transportation systems. CV models use an excessive number of photos to train potential models. Considering that image acquisition is typically expensive and time-consuming, in this study, we provide a multistep strategy to improve image categorization accuracy with less data. In the first stage, we constructed numerous datasets from a single dataset. Given that an image has pixels with values ranging from 0 to 255, the images were separated into pixel intervals based on the type of dataset. The pixel interval was split into two portions when the dataset was grayscale and five portions when it was composed of RGB images. Next, we trained the model using both the original and newly constructed datasets. Each image in the training process showed a non-identical prediction space, and we suggested using the top three prediction probability ensemble technique. The top three predictions for the newly created images were combined with the corresponding probability for the original image. The results showed that learning patterns from each interval of pixels and ensembling the top three predictions significantly improve the performance and accuracy, and this strategy can be used with any model.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1503-1517"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human action recognition based on skeleton features 基于骨骼特征的人体动作识别

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220131067g

Yi Gao, Haitao Wu, Xinmeng Wu, Zilin Li, Xiaofan Zhao

引用次数: 3

Using machine learning approach to construct the people flow tracking system for smart cities 利用机器学习方法构建智慧城市人流跟踪系统

IF 1.4 4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis220813014y

Baofeng Yao, Shijun Liu, Lei Wang

{"title":"Using machine learning approach to construct the people flow tracking system for smart cities","authors":"Baofeng Yao, Shijun Liu, Lei Wang","doi":"10.2298/csis220813014y","DOIUrl":"https://doi.org/10.2298/csis220813014y","url":null,"abstract":"In the crowd congestion in smart cities, the people flow statistics is necessary in public areas to reasonably control people flow. The You Only Look Once-v3 (YOLOv3) algorithm is employed for pedestrian detection, and the Smooth_L1 loss function is introduced to update the back propagation parameters to ensure the stability of the object detection model. After the pedestrian is detected, tracking the pedestrian for a certain time is necessary to count out the specific number of pedestrians entering and leaving. Specifically, the Mean Shift algorithm is combined with the Kalman filter to track the target. When the target is lost, the Mean Shift algorithm is used for iterative tracking, and then the Kalman prediction is updated. In the experiment, 7,000 original images are collected from the library, mentioning 88 people of which 82 are recognized, and the detection accuracy reaches 93.18%. The 12,200 original images collected in the teaching building include149 people, of which 139 are recognized, with the detection accuracy reaching 93.29%. Therefore, the people flow statistics system based on machine vision and deep learning can detect and track pedestrians effectively, which is of great significance for the people flow statistics in public areas in smart cities and for the smooth development of various activities.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"47 1","pages":"679-700"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77357286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs dg_sum:用于个性化汇总异构数据图的模式驱动方法

4区计算机科学

Computer Science and Information Systems Pub Date : 2023-01-01 DOI: 10.2298/csis230331062b

Amal Beldi, Salma Sassi, Richard Chbeir, Abderrazek Jemai

{"title":"DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs","authors":"Amal Beldi, Salma Sassi, Richard Chbeir, Abderrazek Jemai","doi":"10.2298/csis230331062b","DOIUrl":"https://doi.org/10.2298/csis230331062b","url":null,"abstract":"Advances in computing resources have enabled the processing of vast amounts\u0000 of data. However, identifying trends in such data remains challenging for\u0000 humans, especially in fields like medicine and social networks. These\u0000 challenges make it difficult to process, analyze, and visualize the data. In\u0000 this context, graph summarization has emerged as an effective framework\u0000 aiming to facilitate the identification of structure and meaning in data.\u0000 The problem of graph summarization has been studied in the literature and\u0000 many approaches for static contexts are proposed to summarize the graph.\u0000 These approaches provide a compressed version of the graph that removes many\u0000 details while retaining its essential structure. However, they are\u0000 computationally prohibitive and do not scale to large graphs in terms of\u0000 both structure and content. Additionally, there is no framework providing\u0000 summarization of mixed sources with the goal of creating a dynamic,\u0000 syntactic, and semantic data summary. In this paper, our key contribution is\u0000 focused on modeling data graphs, summarizing data from multiple sources\u0000 using a schema-driven approach, and visualizing the graph summary version\u0000 according to the needs of each user. We demonstrate this approach through a\u0000 case study on the use of the E-health domain.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0