Computer Science and Information Systems最新文献

{"title":"A novel feature fusion model based on non-subsampled shear-wave transform for retinal blood vessel segmentation","authors":"Lijuan Feng, Zhang Fan","doi":"10.2298/csis221130028z","DOIUrl":"https://doi.org/10.2298/csis221130028z","url":null,"abstract":"Background: Fundus image is a projection of the inner surface of the eye, which can be used to analyze and judge the distribution of blood vessels on the retina due to its different shape, bifurcation and elongation. Vascular trees are the most stable features in medical images and can be used for biometrics. Ophthalmologists can effectively screen and determine the ophthalmic conditions of diabetic retinopathy, glaucoma and microaneurysms by the morphology of blood vessels presented in the fundus images. Traditional unsupervised learning methods include matched filtering method, morphological processing method, deformation model method, etc. However, due to the great difference in the feature complexity of different fundus image morphology, the traditional methods are relatively simple in coding, poor in the extraction degree of vascular features, poor in segmentation effect, and unable to meet the needs of practical clinical assistance. Methods: In this paper, we propose a new feature fusion model based on non-subsampled shearwave transform for retinal blood vessel segmentation. The contrast between blood vessels and background is enhanced by pre-processing. The vascular contour features and detailed features are extracted under the multi-scale framework, and then the image is postprocessed. The fundus images are decomposed into low frequency sub-band and high frequency sub-band by non-subsampled shear-wave transform. The two feature images are fused by regional definition weighting and guided filtering respectively, and the vascular detection image is obtained by calculating the maximum value of the corresponding pixels at each scale. Finally, the Otsu method is used for segmentation. Results: The experimental results on DRIVE data set show that the proposed method can accurately segment the vascular contour while retaining a large number of small vascular branches with high accuracy. Conclusion: The proposed method has a high accuracy and can perform vascular segmentation well on the premise of ensuring sensitivity.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464211","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":"RESNETCNN: An abnormal network traffic flows detection model","authors":"Yimin Li, Dezhi Han, Mingming Cui, Yuan Fan, Yachao Zhou","doi":"10.2298/csis221124004l","DOIUrl":"https://doi.org/10.2298/csis221124004l","url":null,"abstract":"Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network(RESNET) and parallel cross-convolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the fusion of deep learning and convolutional neural network (CNN), which improves the detection accuracy of abnormal data streams in unbalanced data streams, moreover, the oversampling method into the data preprocessing, to extract multiple types of unbalanced data stream features at the same time, effectively solving the problems of incomplete data feature extraction and low classification accuracy of unbalanced data streams. Finally, three improved versions of RESNETCNN networks are designed to meet the requirements of different traffic data processing, and the highest detection accuracy reaches 99.98% on the CICIDS 2017 dataset and 99.90% on the ISCXIDS 2012 dataset.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73630116","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":"Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying","authors":"Joe Tekli, Gilbert Tekli, R. Chbeir","doi":"10.2298/csis220228063t","DOIUrl":"https://doi.org/10.2298/csis220228063t","url":null,"abstract":"Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83670662","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 application of machine learning techniques in prediction of quality of life features for cancer patients","authors":"Milos Savic, V. Kurbalija, Mihailo Ilic, M. Ivanović, D. Jakovetić, A. Valachis, Serge Autexier, Johannes Rust, T. Kosmidis","doi":"10.2298/csis220227061s","DOIUrl":"https://doi.org/10.2298/csis220227061s","url":null,"abstract":"Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL information it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy preserving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of cancer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differential privacy).","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80673622","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":"Using neural network to automatic manufacture product label in enterprise under IoT environments","authors":"Kaiwen Zhang, C. Dong","doi":"10.2298/csis220703019z","DOIUrl":"https://doi.org/10.2298/csis220703019z","url":null,"abstract":"When the manufacturing industry is dealing with information technology, it has to face a large number of parameters and frequent adjustments. This study proposed artificial intelligence models to find out the hidden rules behind a large number of customized labels, through data processing and model building. Model and parameter experiments are used to improve the effectiveness of artificial intelligence models, and the method of cyclic testing is adopted to increase the diversity of the test set. The results of this paper, we integrate each stage and an auxiliary decision-making is established. The contributions of this paper, can improve the problem with reducing production line shutdown and improve factory productivity. The accuracy rate of the artificial intelligence model can be increased to 95%. The number of stoppages is reduced from 4 times to 1 time per month. Under full capacity, this assist the decision-making system can reduce loss cost.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80801784","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":"Sternum age estimation with dual channel fusion CNN model","authors":"Fuat Türk, M. Kaya, Burak Akhan, Sümeyra Çayiröz, E. Ilgit","doi":"10.2298/csis220825054t","DOIUrl":"https://doi.org/10.2298/csis220825054t","url":null,"abstract":"Although age determination by radiographs of the hand and wrist before the age of 18 is an area where there is a lot of radiological knowledge and many studies are carried out, studies on age determination for adults are limited. Studies on adult age determination through sternum multidetector computed tomography (MDCT) images using artificial intelligence algorithms are much fewer. The reason for the very few studies on adult age determination is that most of the changes observed in the human skeleton with age are outside the limits of what can be perceived by the human eye. In this context, with the dual-channel Convolutional Neural Network (CNN) we developed, we were able to predict the age groups defined as 20-35, 35-50, 51-65, and over 65 with 73% accuracy over sternum MDCT images. Our study shows that fusion modeling with dual-channel convolutional neural networks and using more than one image from the same patient is more successful. Fusion models will make adult age determination, which is often a problem in forensic medicine, more accurate.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79142762","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":"Solving the p-second center problem with variable neighborhood search","authors":"D. Ristić, D. Urošević, N. Mladenović, R. Todosijević","doi":"10.2298/csis210804049r","DOIUrl":"https://doi.org/10.2298/csis210804049r","url":null,"abstract":"The p-center problem is a well-known and highly studied problem pertaining to the identification of p of the potential n center locations in such a way as to minimize the maximum distance between the users and the closest center. As opposed to the p-center, the p-second center problem minimizes the maximum sum of the distances from the users to the closest and the second closest centers. In this paper, we propose a new Variable Neighborhood Search based algorithm for solving the p-second center problem. Its performance is assessed on the benchmark instances from the literature. Moreover, to further evaluate the algorithm?s performance, we generated larger instances with 1000, 1500, 2000, and 2500 nodes and instances defined over graphs up to 1000 nodes with different densities. The obtained results clearly demonstrate the effectiveness and efficiency of the proposed algorithm.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83670909","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":"Ownership protection system for partial areas on image data using ethereum blockchain","authors":"Natsuki Fujiwara, Shohei Yokoyama","doi":"10.2298/csis230320061f","DOIUrl":"https://doi.org/10.2298/csis230320061f","url":null,"abstract":"Our proposed method utilizes blockchain technology to safeguard the ownership of specific regions within image data. In our approach, diverse values could be assigned to each region based on its importance, and only users with ownership rights can access these designated regions. This ensures the protection of ownership rights for individuals in any given region of an image. Identified regions are individually encrypted using an XOR cipher, and a corresponding key image is generated for decryption, thereby preserving the privacy of the encrypted region. Non-fungible tokens (NFTs) are employed to protect the key image and manage the ownership of each object in the image data. The NFT for the key image is generated by the key holder (who possesses the entire image), and the ownership NFT is acquired by the user who needs access to the key NFT. Furthermore, the ownership NFT and the key NFT are verified for a match by the judgment function, and only upon successful validation, the NFT is displayed on the screen. This method enables different values to be assigned to various parts of an image, facilitating the transfer and sharing of ownership. Additionally, the original image?s owner can benefit financially based on the value of the image, thus enhancing the overall security of image data.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","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":"135401334","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 novel multipath QUIC protocol with minimized flow complete time for internet content distribution","authors":"Fang-Yi Lin, Wu-Min Sung, Lin Hui, Chih-Lin Hu, Nien-Tzu Hsieh, Yung-Hui Chen","doi":"10.2298/csis230818078l","DOIUrl":"https://doi.org/10.2298/csis230818078l","url":null,"abstract":"The rapid growth of network services and applications has led to an exponential increase in data flows on the internet. Given the dynamic nature of data traffic in the realm of internet content distribution, traditional TCP/IP network systems often struggle to guarantee reliable network resource utilization and management. The recent advancement of the Quick UDP Internet Connect (QUIC) protocol equips media transfer applications with essential features, including structured flow controlled streams, quick connection establishment, and seamless network path migration. These features are vital for ensuring the efficiency and reliability of network performance and resource utilization, especially when network hosts transmit data flows over end-to-end paths between two endpoints. QUIC greatly improves media transfer performance by reducing both connection setup time and transmission latency. However, it is still constrained by the limitations of single-path bandwidth capacity and its variability. To address this inherent limitation, recent research has delved into the concept of multipath QUIC, which utilizes multiple network paths to transmit data flows concurrently. The benefits of multipath QUIC are twofold: it boosts the overall bandwidth capacity and mitigates flow congestion issues that might plague individual paths. However, many previous studies have depended on basic scheduling policies, like round-robin or shortest-time-first, to distribute data transmission across multiple paths. These policies often overlook the subtle characteristics of network paths, leading to increased link congestion and transmission costs. In this paper, we introduce a novel multipath QUIC strategy aimed at minimizing flow completion time while taking into account both path delay and packet loss rate. Experimental results demonstrate the superiority of our proposed method compared to standard QUIC, Lowest-RTT-First (LRF) QUIC, and Pluginized QUIC schemes. The relative performance underscores the efficacy of our design in achieving efficient and reliable data transfer in real-world scenarios using the Mininet simulator.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","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":"135445069","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":"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":null,"pages":null},"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}