Journal of Experimental & Theoretical Artificial Intelligence最新文献_第5页

Study on the classification problem of the coping stances in the Satir model based on machine learning 基于机器学习的Satir模型应对姿态分类问题研究

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-13 DOI: 10.1080/0952813X.2021.1960628

Xi Wang, Yu Zhao, Guangping Zeng, Peng Xiao, Zhiliang Wang

引用次数: 0

Attention-based 3D convolutional networks 基于注意力的3D卷积网络

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-13 DOI: 10.1080/0952813X.2021.1960625

Enjie Ding, Dawei Xu, Yingfei Zhao, Zhongyu Liu, Yafeng Liu

{"title":"Attention-based 3D convolutional networks","authors":"Enjie Ding, Dawei Xu, Yingfei Zhao, Zhongyu Liu, Yafeng Liu","doi":"10.1080/0952813X.2021.1960625","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1960625","url":null,"abstract":"ABSTRACT Being simple and portable, the three-dimensional (3D) convolution network has achieved great success in action recognition. However, its applicability in spatiotemporal feature learning is not evident. This study aims to improve the 3D convolution model and propose a flexible and significant attention module for the extraction of spatiotemporal information. Our first contribution is a self-additive attention module and a feature-based attention module, which is a simple yet effective method for measuring the spatiotemporal importance of a video. In self-additive attention, the spatiotemporal fusion between the frames is defined intuitively, where we set equivalent weights between the video frames manually. Further, the feature-based attention that is trained adaptively by the 3D convolution process combines the spatiotemporal information from the feature map. This study also focuses on attention fusion in learning the spatiotemporal characteristics for 3D convolution. The proposed attention fusion method exhibits outstanding performance in comparison to the recently developed attention modules and the latest 3D networks when applied to the data from the UCF101 and HMDB51 datasets. The experiments show consistent improvements, affirming the robustness of the method in extracting spatiotemporal attention.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"13 1","pages":"93 - 108"},"PeriodicalIF":2.2,"publicationDate":"2022-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83486113","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

Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition 模糊鲸优化算法:一种新的混合方法用于自动声纳目标识别

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-13 DOI: 10.1080/0952813X.2021.1960639

A. Saffari, S. Zahiri, M. Khishe

引用次数: 6

The extent of the teacher academic development from the accreditation evaluation system perspective using machine learning 从认证评价体系的角度运用机器学习对教师学术发展程度进行研究

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-09 DOI: 10.1080/0952813X.2021.1960635

A. S. Rashid

{"title":"The extent of the teacher academic development from the accreditation evaluation system perspective using machine learning","authors":"A. S. Rashid","doi":"10.1080/0952813X.2021.1960635","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1960635","url":null,"abstract":"ABSTRACT The purpose of this study to utilising Machine learning to discover knowledge which is called supervised and unsupervised learning when it is taught the actual outcome for the training instances like progressed or non-progressed performance and investigate the impact of the quality assurance process on the teacher’s academic performance utilising teaching methods, student feedback, teacher portfolio, and academic benchmarks. Moreover, it aims to assess and improve the academic staff members performing the Accreditation Evaluation System (AES) that involves Student Feedback System (SFS), Teacher Portfolio Assessment (TPA), as well as Continuous Academic Development (CAD) for the academic year (2016–2017) which compiled of (1556) academic staff at the University of Sulaimani. Overall, the conclusions of this study confirmed that the quality assurance has progressed, and enhanced the quality of the teacher performance, also reinforces all dimensions of the teaching, academic, and research performance of teachers by applying the K-Means Clustering Algorithm methodology to analyse and assemble a big data according to the teacher academic titles. In addition, the binary logistic regression analysis was executed to reveal and prophesy the significant influences of academic titles on the teacher progression of the Accreditation Evaluation System performance. The K-Means Clustering Algorithm showed better results than Logistic regression by having 90% testing accuracy. In the future, Un-Supervised Learning can be used for better accuracy.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"16 1","pages":"535 - 555"},"PeriodicalIF":2.2,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73894843","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: 3

Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals 探索性数据分析、分类、对比分析、病例严重程度检测、物联网在智慧医院新型冠状病毒远程监控中的应用

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-09 DOI: 10.1080/0952813X.2021.1960634

Aysha Shabbir, Maryam Shabbir, A. R. Javed, M. Rizwan, C. Iwendi, Chinmay Chakraborty

{"title":"Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals","authors":"Aysha Shabbir, Maryam Shabbir, A. R. Javed, M. Rizwan, C. Iwendi, Chinmay Chakraborty","doi":"10.1080/0952813X.2021.1960634","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1960634","url":null,"abstract":"ABSTRACT The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"1 1","pages":"507 - 534"},"PeriodicalIF":2.2,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77218882","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: 28

Intelligent computational of Experimental Study of Intermittent Feed High-speed Grinding Method utilising PSO basis FEM Solver 基于粒子群算法的间歇进给高速磨削实验智能计算研究

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-09 DOI: 10.1080/0952813X.2021.1960636

Mohammed Al-Nehari, Guoxing Liang, Yonggui Huang, M. Lv, Waled Yahya

{"title":"Intelligent computational of Experimental Study of Intermittent Feed High-speed Grinding Method utilising PSO basis FEM Solver","authors":"Mohammed Al-Nehari, Guoxing Liang, Yonggui Huang, M. Lv, Waled Yahya","doi":"10.1080/0952813X.2021.1960636","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1960636","url":null,"abstract":"ABSTRACT The high-speed grinding and polishing machine can not only significantly increase the performance of grinding but also improve the consistency of processing effectively. It is one of the most significant guidelines for the advancement of grinding technology today. Since the high-speed grinding technology has been booming these days, this study develops intermittent high-speed grinding technique for feed which has been under the basic high-speed grinding process. Using a simple list of a straight line, a piece of work can be easily retreated and fed. By affordable arrangement distance of feed-in single grinding, time of action on grinding work piece and grinding wheel has been reduced, it will affect the process of grinding heat sending that does not get fixed sate, it was decreasing grinding temperature at the time of single grinding, the surface temperature was freezer to the closed room temperature throughout the room. The lower grinding temperature was achieved according to this path. The simulation study and experiment were performed on TC4 titanium alloy materials in intermittent high-speed feed grinding, and critics of grinding elements considered grinding temperature along with grinding force have been implemented as end production using finite element analysis of the particle swarm optimization (PSO) basis.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"10 1","pages":"557 - 571"},"PeriodicalIF":2.2,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78483252","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

Blockchain based holistic trust management protocol for ubiquitous and pervasive IoT network 泛在物联网网络中基于区块链的整体信任管理协议

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-09 DOI: 10.1080/0952813X.2021.1960641

Anup Patnaik, Banitamani Mallik, M. Krishna

{"title":"Blockchain based holistic trust management protocol for ubiquitous and pervasive IoT network","authors":"Anup Patnaik, Banitamani Mallik, M. Krishna","doi":"10.1080/0952813X.2021.1960641","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1960641","url":null,"abstract":"ABSTRACT The new emerging blockchain (BC) technology integrated with the IoT ecosystem revolutionised the IoT world. Classic BC with bitcoin method was realised as very expensive operations difficult to adopt for smart IoT applications; therefore, we integrated IoT network with overlay BC with distributed ledger capability to provide a secure trust management system, which can address access control issues of devices on resources. Further, the R-LEACH protocol followed by the same group urges additional cluster head requirement to establish trust between nodes is not considered in our proposed approach. The main advantage of this method is utilising ledgers for holding the trust and IoT information ensuring tamper-proof data. The miners of blockchain layer perform the trust value calculations based on trust evidence and achieved fast trust convergence, accuracy, and resilience against adversary attacks. Our proposed approach enhances privacy, reliability, availability, and more importantly, sharing and storage of trust information and also followed the consensus mechanism Proof-of-Authority (PoA) to approve the synthesis trust value of related transactions by the pre-authenticated miners/validators, from which we can take more accurate trust-based decisions. Performance results of our blockchain-based trust management approach outperformed literature review trust mechanisms for protecting trust data manipulation against the malicious nodes.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"124 1","pages":"629 - 648"},"PeriodicalIF":2.2,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87889817","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: 2

Robot arm navigation using deep deterministic policy gradient algorithms 基于深度确定性策略梯度算法的机械臂导航

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-08 DOI: 10.1080/0952813X.2021.1960640

W. Farag

引用次数: 0

Early diagnosis of COVID-19 patients using deep learning-based deep forest model 基于深度学习的深度森林模型的COVID-19患者早期诊断

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-01-06 DOI: 10.1080/0952813X.2021.2021300

Dilbag Singh, Vijay Kumar, Manjit Kaur, R. Kumari

{"title":"Early diagnosis of COVID-19 patients using deep learning-based deep forest model","authors":"Dilbag Singh, Vijay Kumar, Manjit Kaur, R. Kumari","doi":"10.1080/0952813X.2021.2021300","DOIUrl":"https://doi.org/10.1080/0952813X.2021.2021300","url":null,"abstract":"ABSTRACT Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"14 1","pages":"365 - 375"},"PeriodicalIF":2.2,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89236639","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: 4

Improving Domain-Independent Heuristic State-Space Planning via plan cost predictions 基于计划成本预测的域独立启发式状态空间规划改进

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-10-31 DOI: 10.1080/0952813X.2021.1970239

Francesco Percassi, A. Gerevini, Enrico Scala, I. Serina, M. Vallati

{"title":"Improving Domain-Independent Heuristic State-Space Planning via plan cost predictions","authors":"Francesco Percassi, A. Gerevini, Enrico Scala, I. Serina, M. Vallati","doi":"10.1080/0952813X.2021.1970239","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1970239","url":null,"abstract":"ABSTRACT Automated planning is a prominent Artificial Intelligence (AI) challenge that has been extensively studied for decades, which has led to the development of powerful domain-independent planning systems. The performance of domain-independent planning systems are strongly affected by the structure of the search space, that is dependent on the application domain and on its encoding. This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"91 1","pages":"849 - 875"},"PeriodicalIF":2.2,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73558074","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