Computing and Informatics最新文献

Integration a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles 基于多进程架构的自动驾驶车辆情境观测系统集成

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_716

Ahmed-Chawki Chaouche, J. Ilié, Assem Hebik, François Pêcheux

引用次数: 0

mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data mTreeIllustrator:用于多维层次数据可视化探索性分析的混合主动框架

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_690

Guijuan Wang, Yu Zhao, Boyou Tan, Zhong Wang, Jiansong Wang, Hao Guo, Yadong Wu

引用次数: 0

Large Scale Fine-Tuned Transformers Models Application for Business Names Generation 大规模微调变压器模型在企业名称生成中的应用

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_525

Mantas Lukauskas, Tomas Rasymas, Matas Minelga, Domas Vaitmonas

引用次数: 0

EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training 手康复训练混合脑机接口的脑肌电分析方法

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_741

Lubo Fu, Hao Li, Hongfei Ji, Jie Li

引用次数: 0

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network 基于脉冲耦合神经网络的图像认证隐写方法

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_591

R. Forgác, M. Očkay, Martin Javurek, Bianca Badidová

引用次数: 0

Classification of Sentiment Using Optimized Hybrid Deep Learning Model 基于优化混合深度学习模型的情感分类

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_651

Chaima Ahle Touate, R. Ayachi, M. Biniz

引用次数: 0

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game 新型博弈论卷积神经网络在多追踪者多逃避者博弈中的应用

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_546

Nabila Sid, M. Djezzar, Mohammed El Habib Souidi, M. Hemam

{"title":"New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game","authors":"Nabila Sid, M. Djezzar, Mohammed El Habib Souidi, M. Hemam","doi":"10.31577/cai_2023_3_546","DOIUrl":"https://doi.org/10.31577/cai_2023_3_546","url":null,"abstract":". Pursuit-Evasion Game (PEG) can be defined as a set of agents known as pursuers, which cooperate with the aim forming dynamic coalitions to capture dynamic evader agents, while the evaders try to avoid this capture by moving in the environment according to specific velocities. The factor of capturing time was treated by various studies before, but remain the powerful tools used to satisfy this factor object of research. To improve the capturing time factor we proposed in this work a novel online decentralized coalition formation algorithm equipped with Convolutional Neural Network (CNN) and based on the Iterated Elimination of Dominated Strategies (IEDS). The coalition is formed such that the pursuer should learn at each iteration the approximator formation achieving the capture in the shortest time. The pursuer’s learning process depends on the features extracted by CNN at each iteration. The proposed supervised technique is compared through simulation, with the IEDS algorithm, AGR algorithm. Simulation results show that the proposed learning technique outperform the IEDS algorithm and the AGR algorithm with respect to the learning time which represents an important factor in a chasing game.","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"546-567"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70010097","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

BERTDom: Protein Domain Boundary Prediction Using BERT BERT:基于BERT的蛋白质结构域边界预测

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_667

Ahmad Haseeb, Maryam Bashir, Aamir Wali

{"title":"BERTDom: Protein Domain Boundary Prediction Using BERT","authors":"Ahmad Haseeb, Maryam Bashir, Aamir Wali","doi":"10.31577/cai_2023_3_667","DOIUrl":"https://doi.org/10.31577/cai_2023_3_667","url":null,"abstract":". The domains of a protein provide an insight on the functions that the protein can perform. Delineation of proteins using high-throughput experimental methods is difficult and a time-consuming task. Template-free and sequence-based computational methods that mainly rely on machine learning techniques can be used. However, some of the drawbacks of computational methods are low accuracy and their limitation in predicting different types of multi-domain proteins. Biological language modeling and deep learning techniques can be useful in such situations. In this study, we propose BERTDom for segmenting protein sequences. BERTDOM uses BERT for feature representation and stacked bi-directional long short term memory for classification. We pre-train BERT from scratch on a corpus of protein sequences obtained from UniProt knowledge base with reference clusters. For comparison, we also used two other deep learning architectures: LSTM and feed-forward neural networks. We also experimented with protein-to-vector (Pro2Vec) feature representation that uses word2vec to encode protein bio-words. For testing, three other bench-marked datasets were used. The experimental re-sults on benchmarks datasets show that BERTDom produces the best F-score as compared to other template-based and template-free protein domain boundary prediction methods. Employing deep learning architectures can significantly improve domain boundary prediction. Furthermore, BERT used extensively in NLP for feature representation, has shown promising results when used for encoding bio-words. The code is available at https://github.com/maryam988/BERTDom-Code .","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"667-689"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70009806","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

Attribute-Based Access Control Policy Generation Approach from Access Logs Based on the CatBoost 基于CatBoost的基于属性的访问控制策略生成方法

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_615

Shan Quan, Yongdan Zhao, N. Helil

引用次数: 0

Model for Spatiotemporal Crime Prediction with Improved Deep Learning 基于改进深度学习的时空犯罪预测模型

IF 0.7 4区计算机科学

Computing and Informatics Pub Date : 2023-01-01 DOI: 10.31577/cai_2023_3_568

Ature Angbera, H. Chan

引用次数: 0