Computer Science and Information Systems最新文献

{"title":"Studying the Effects of Being Einstein: An Experiment in Social Virtual Reality","authors":"I. Cummins, D. Schofield","doi":"10.5539/cis.v16n2p1","DOIUrl":"https://doi.org/10.5539/cis.v16n2p1","url":null,"abstract":"Previous research has found that when using virtual reality, a person can have a sense of ownership over the body that is being substituted for their own in the virtual world, and that the body’s appearance may lead to different behavioral, attitudinal, and perceptual changes. As Virtual Reality (VR) becomes more widespread, it is increasingly important to understand the effects on its users. This study utilized a novel methodology to conduct remote testing in virtual reality, leveraging a popular social virtual reality platform to test whether participants using Einstein as their virtual body performed better at a cognitive task than participants using other virtual bodies, and did not find any effect or correlation of the virtual body with any of the factors measured. The results suggest that the effects of virtual embodiment on the user stemming from the virtual body's appearance is more complex than previously assumed, warranting further study.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"9 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77672577","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}

{"title":"The Role of Machine Learning in the Detection and Classification of Brain Tumors: A Literature Review of the Past Two Years","authors":"Jianyi Wang","doi":"10.5539/cis.v16n2p20","DOIUrl":"https://doi.org/10.5539/cis.v16n2p20","url":null,"abstract":"A brain tumor is an abnormal growth of cells in the brain. There are four common types of brain tumors.  Doctors can segment and identify the tumors manually, but it is very time-consuming. There exist automatic segmentation algorithms that can facilitate the process. Deep learning is a new method of creating powerful AI models. As a result, there is a need for automatic segmentation algorithms that can facilitate the process and improve the accuracy of brain tumor detection. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for developing such algorithms. In particular, deep learning (DL) methods, such as convolutional neural networks (CNNs), have shown great potential for accurately identifying brain tumors in medical images. This paper presents a literature review of recently published papers (2020-2022) on brain tumor classification and detection using artificial intelligence. The review covers various AI and DL methods, including supervised learning, reinforcement learning, and unsupervised learning. It evaluates their effectiveness in detecting and classifying brain tumors in medical images. The review also discusses the challenges and limitations of these methods, as well as future directions for research in this field.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"6 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91341475","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}

{"title":"From Zero to Infinity: Research on the Development Process of Intelligent Manufacturing","authors":"Chen Peng","doi":"10.5539/cis.v16n2p29","DOIUrl":"https://doi.org/10.5539/cis.v16n2p29","url":null,"abstract":"Intelligent manufacturing is flourishing in the 21st century. This paper takes the brief history of exploring the development of intelligent manufacturing as a staring point, explains the current intelligent and informative technologies and achievements of intelligent manufacturing by describing the impact of intelligent manufacturing on traditional manufacturing, proposes reflections on the shortcomings and limitations of intelligent manufacturing in China. And our country advocates human-centered intelligent manufacturing, continuously improves the system and innovates technology, while reasonably plans the three directions. Finally, we extend to digital twin technology and explore it in the intelligent manufacturing to promote the further upgrade. Besides, the study shows that the country will continue to use intelligent manufacturing as a traction and the industrial internet as a means to seize new opportunities and create a new ecology of production.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"107 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80787614","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}

{"title":"Probabilistic reasoning for diagnosis prediction of Coronavirus disease based on probabilistic ontology","authors":"Messaouda Fareh, Ishak Riali, Hafsa Kherbache, Marwa Guemmouz","doi":"10.2298/csis220829035f","DOIUrl":"https://doi.org/10.2298/csis220829035f","url":null,"abstract":"The novel Coronavirus has been declared a pandemic by the World Health Organization (WHO). Predicting the diagnosis of COVID-19 is essential for disease cure and control. The paper?s main aim is to predict the COVID-19 diagnosis using probabilistic ontologies to address the randomness and incomplete ness of knowledge. Our approach begins with constructing the entities, attributes, and relationships of COVID-19 ontology, by extracting symptoms and risk factors. The probabilistic components of COVID-19 ontology are developed by creating a Multi-Entity Bayesian Network, then determining its components, with the different nodes, as probability distribution linked to various nodes. We use probabilistic in ference for predicting COVID-19 diagnosis, using the Situation-Specific Bayesian Network (SSBN). To validate the solution, an experimental study is conducted on real cases, comparing the results of existing machine learning methods, our solution presents an encouraging result and, therefore enables fast medical assistance.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"54 84 1","pages":"1109-1132"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91076399","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}

{"title":"A study of identity authentication using blockchain technology in a 5G multi-type network environment","authors":"Jui-Hung Kao, Yu-Yu Yen, Wei-Chen Wu, Horng-Twu Liaw, Shiou-Wei Fan, Yi-Chen Kao","doi":"10.2298/csis221115070k","DOIUrl":"https://doi.org/10.2298/csis221115070k","url":null,"abstract":"The 5G technology, known for its large bandwidth, high speed, low latency, and multi-connection capabilities, significantly accelerates digital transformation in enterprises, especially in addressing factory automation challenges. It facilitates efficient machine-to-machine (M2M) and device-to-device (D2D) connectivity, ensuring rapid data transfer and seamless process convergence under 5G standards. Although 5G offers substantial communication and low latency benefits, its limited indoor coverage requires the deployment of decentralized antennas or small base stations. In contrast, Wi-Fi 6 seamlessly complements 5G, providing superior indoor mobile connectivity. This integration is crucial for businesses looking to accelerate digital transformation. To optimize 5G, the deployment of devices such as bypass switches, SDN switches, and MEC in the 5G Local Breakout network enables user access control and fast authentication. Real-world validation confirms the effectiveness of these measures, which are expected to lead to the future of 5G mobile networks.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"365 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":"135446193","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}

{"title":"Applying spin checker on 5G EAP-TLS authentication protocol analysis","authors":"Qianli Wang","doi":"10.2298/csis230611068w","DOIUrl":"https://doi.org/10.2298/csis230611068w","url":null,"abstract":"Currently, there is relatively little formal analysis and verification work on the 5G EAP-TLS authentication protocol. In this paper, we use the model checker SPIN to perform a formal analysis of the 5G EAP-TLS authentication protocol. Firstly, we analyze the process of the 5G EAP-TLS authentication protocol and abstract it to obtain a formal model of the protocol. Then, we describe the construction of the protocol model based on the Promela language. The unique feature of this paper is the replacement of the hash value of the 5G EAP-TLS authentication protocol with the message content field encrypted by an unknown subject public key. This is because the Promela language in SPIN has an eval function that can check the value of each field. This can replace the function of the hash function and make the Promela model construction more portable. The paper analyzes the attack paths of the protocol and reveals design flaws that undermine the expected identity authentication attributes and secret consistency of the protocol. The results not only provide a comprehensive understanding of the security properties of the 5G EAP-TLS authentication protocol but also offer valuable insights and guidance for the verification of the protocol's security properties, security design, and optimization of protocol implementation and interoperability.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"72 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":"135446441","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}

{"title":"3D convolutional long short-term encoder-decoder network for moving object segmentation","authors":"Anil Turker, Ender Eksioglu","doi":"10.2298/csis230129044t","DOIUrl":"https://doi.org/10.2298/csis230129044t","url":null,"abstract":"Moving object segmentation (MOS) is one of the important and well studied computer vision tasks that is used in a variety of applications, such as video surveillance systems, human tracking, self-driving cars, and video compression. While traditional approaches to MOS rely on hand-crafted features or background modeling, deep learning methods using Convolution Neural Networks (CNNs) have been shown to be more effective in extracting features and achieving better accuracy. However, most deep learning-based methods for MOS offer scene-dependent solutions, leading to reduced performance when tested on previously unseen video content. Because spatial features are insufficient to represent the motion information, the spatial and temporal features should be used together to succeed in un seen videos. To address this issue, we propose the MOS-Net deep framework, an encoder-decoder network that combines spatial and temporal features using the flux tensor algorithm, 3D CNNs, and ConvLSTM in its different variants. MOS-Net 2.0 is an enhanced version of the base MOS-Net structure, where additional ConvL STM modules are added to 3D CNNs for extracting long-term spatiotemporal features. In the final stage of the framework the output of the encoder-decoder network, the foreground probability map, is thresholded for producing a binary mask where moving objects are in the foreground and the rest forms the background. In addition, an ablation study has been conducted to evaluate different combinations as inputs to the proposed network, using the ChangeDetection2014 (CDnet2014) which in cludes challenging videos such as those with dynamic backgrounds, bad weather, and illumination changes. In most approaches, the training and test strategy are not announced, making it difficult to compare the algorithm results. In addition, the pro posed method can be evaluated differently as video-optimized or video-agnostic. In video-optimized approaches, the training and test set is obtained randomly and sep arated from the overall dataset. The results of the proposed method are compared with competitive methods from the literature using the same evaluation strategy. It has been observed that the introduced MOS networks give highly competitive re sults on the CDnet2014 dataset. The source code for the simulations provided in this work is available online.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"27 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":"136206823","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}

{"title":"Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system","authors":"Eyad Kannout, Michał Grodzki, Marek Grzegorowski","doi":"10.2298/csis221116052k","DOIUrl":"https://doi.org/10.2298/csis221116052k","url":null,"abstract":"This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items? cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"98 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":"136208670","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}

{"title":"Evaluation of smart city construction and optimization of city brand model under neural networks","authors":"Yingji Li, Yufeng Qian, Qiang Li, Linna Li","doi":"10.2298/csis220715010l","DOIUrl":"https://doi.org/10.2298/csis220715010l","url":null,"abstract":"The study aims to avoid the phenomenon that thousand cities seem the same in the construction of smart cities and the efforts of all walks of life are jointed to implement the construction of smart cities and the creation of city brands. First, the basic theory of smart city construction is introduced. Second, the restricting and promoting factors influencing smart city construction and development are analyzes, and the evaluation system of smart city development is established. Then, the model for smart city construction and development based on the neural network is implemented. Finally, some domestic cities are selected as the dataset to build a training model, and the city brand optimization strategy is proposed. On this basis, an evaluation system for smart city development based on the intelligent neural network and Grey relational analysis Back Propagation Neural Network (GRA-BPNN) is obtained. The entropy weight method (EWM), grey relational analysis (GRA) method and the evaluation method based on Technique for Order Preference by Similarity to an Ideal Solutionv (TOPSIS) are regarded as the members of the control group, and the results of different methods for evaluating the development of smart cities are compared. The results show that the modeling of smart city construction based on neural networks can help to implement an evaluation model for smart city construction and development, and it can help evaluate smart city construction and development accurately. And then the corresponding strategies are proposed to speed up the construction of smart cities with its local characteristics, and then city brands are built. Compared with other evaluation algorithms, the performance of the algorithm proposed is better and more stable and the evaluation results are more reasonable than those of the others, which can prove that the evaluation algorithm is feasible and scientific. This study provides a new idea for the application of deep learning to smart city construction and city brand building","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"16 1","pages":"573-593"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74958698","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}

{"title":"Heart sounds classification using adaptive wavelet threshold and 1D LDCNN","authors":"Jianqiang Hu, Qingli Hu, Mingfeng Liang","doi":"10.2298/csis230418059h","DOIUrl":"https://doi.org/10.2298/csis230418059h","url":null,"abstract":"Heart sounds classification plays an important role in cardiovascular disease detection. Currently, deep learning methods for heart sound classification with heavy parameters consumption cannot be deployed in environments with limited memory and computational budgets. Besides, de-noising of heart sound signals (HSSs) can affect accuracy of heart sound classification, because erroneous removal of meaningful components may lead to heart sound distortion. In this paper, an automated heart sound classification method using adaptive wavelet threshold and 1D LDCNN (One-dimensional Lightweight Deep Convolutional Neural Net work) is proposed. In this method, we exploit WT (Wavelet Transform) with an adaptive threshold to de-noise heart sound signals (HSSs). Furthermore, we utilize 1D LDCNN to realize automatic feature extraction and classification for de-noised heart sounds. Experiments on PhysioNet/CinC 2016 show that our proposed method achieves the superior classification results and excels in consumption of parameter comparing to state-of-the-art methods.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1483-1501"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464415","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}