Transactions on Machine Learning and Artificial Intelligence最新文献_第10页

A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques 基于智能手机的植物病害检测和治疗推荐系统，使用机器学习技术

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-01-13 DOI: 10.14738/tmlai.101.11313

F. Isinkaye

引用次数: 1

Algorithm for the set of similarity criteria formation for a physical process in the class of homogeneous functions 齐次函数中物理过程相似准则集形成的算法

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-12-06 DOI: 10.14738/tmlai.96.11295

A. Alpatov, V. Kravets, D. Kolosov, V. Kravets, E. Lapkhanov

引用次数: 0

AI-based Analysis on Relationship between Genes and Personality 基于人工智能的基因与人格关系分析

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-12-06 DOI: 10.14738/tmlai.96.11290

Masayuki Kanazawa

引用次数: 0

Nearly Reversible ECG Steganography based on Polar Coordinate System with Digital Replacement Technique 基于极坐标系统的心电隐写数字替代技术

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-11-20 DOI: 10.14738/tmlai.96.11177

Ching-Yu Yang, Chi-Kai Huang

引用次数: 0

Self-Driving Vehicles Using End to End Deep Imitation Learning 使用端到端深度模仿学习的自动驾驶汽车

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-11-07 DOI: 10.14738/tmlai.95.10795

Ashraf Nabil, Ayman Kassem

{"title":"Self-Driving Vehicles Using End to End Deep Imitation Learning","authors":"Ashraf Nabil, Ayman Kassem","doi":"10.14738/tmlai.95.10795","DOIUrl":"https://doi.org/10.14738/tmlai.95.10795","url":null,"abstract":"Autonomous Driving is one of the difficult problems faced the automotive applications. Nowadays, it is restricted due to the presence of some laws that prevent cars from being fully autonomous for the fear of accidents occurrence. Researchers try to improve the accuracy and safety of their models with the aim of having a strong push against these restricted Laws. \u0000Autonomous driving is a sought-after solution which isn’t easily solved by classical approaches. Deep Learning is considered as a strong Artificial Intelligence paradigm which can teach machines how to behave in difficult situations. It proved its success in many differ domains, but it still has sometime in the automotive applications. \u0000The presented work will use the end-to-end deep machine learning field in order to reach to our goal of having Full Autonomous Driving Vehicle that can behave correctly in different scenarios. CARLA simulator will be used to learn and test the deep neural networks. Results will show not only performance on CARLA’s simulator as an end-to-end solution for autonomous driving, but also how the same approach can be used on one of the most popular real datasets of automotive that includes camera images with the corresponding driver’s control action.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytical modeling of the binary dynamic circuit motion 二元动态电路运动的解析建模

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-10-21 DOI: 10.14738/tmlai.95.10922

A. Alpatov, V. Kravets, V. Kravets, E. Lapkhanov

{"title":"Analytical modeling of the binary dynamic circuit motion","authors":"A. Alpatov, V. Kravets, V. Kravets, E. Lapkhanov","doi":"10.14738/tmlai.95.10922","DOIUrl":"https://doi.org/10.14738/tmlai.95.10922","url":null,"abstract":"The binary dynamic circuit, which can be a design scheme for a number of technical systems is considered in the paper. The dynamic circuit is characterized by the kinetic energy of the translational motion of two masses, the potential energy of these masses’ elastic interaction and the dissipative function of energy dissipation during their motion. The free motion of a binary dynamic circuit is found according to a given initial phase state. A mathematical model of the binary dynamic circuit motion in the canonical form and the corresponding characteristic equation of the fourth degree are compiled. Analytical modeling of the binary dynamic circuit is carried out on the basis of the proposed particular solution of the complete algebraic equation of the fourth degree. A homogeneous dynamic circuit is considered and the reduced coefficients of elasticity and damping are introduced. The dependence of the reduced coefficients of elasticity and damping is established, which provides the required class of solutions to the characteristic equation. An ordered form of the analytical representation of a dynamic process is proposed in symmetric determinants, which is distinguished by the conservatism of notation with respect to the roots of the characteristic equation and phase coordinates.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122787333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques 使用机器学习技术进行个性预测系统的手写签名生物特征数据分析

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-09-25 DOI: 10.14738/tmlai.95.10808

S. Gornale, Sathish Kumar, P. Hiremath

引用次数: 2

Connecting Smart Homes to Healthcare Services for People with Neurodegenerative disorders. 将智能家居与神经退行性疾病患者的医疗保健服务相连接。

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-08-17 DOI: 10.14738/tmlai.94.10386

M. Nour

{"title":"Connecting Smart Homes to Healthcare Services for People with Neurodegenerative disorders.","authors":"M. Nour","doi":"10.14738/tmlai.94.10386","DOIUrl":"https://doi.org/10.14738/tmlai.94.10386","url":null,"abstract":"The objective of this research is to provide an approach for the design of what we have called ‘connected homes’ with a study case for elderly people with dementia living alone. These homes would be connected to a center of surveillance for direct and automatic view of multiple status of the day such as patient security and general health indicators (body temperature, heart rate, blood pressure etc.), detect the intake of meals, eating motivation, humor detection prevention of falls, Alcohol consumption detection, safe use of medicines and emergency situations and other Human Activity Recognition (HAR). The model may also predict situations by using past data accumulation. The model could even send alerts in case of emergency. \u0000This service would mean that there would minimum intervention from caregivers thanks to the Artificial Intelligence. \u0000As a case study, we proposed a new approach for the conception of connected homes for people with dementia to a central office for automatic human activity detection and help and support accordingly. Such conception includes home design concepts according to standard recommendations and the implementation of new added assistive technology tools to permit the automatic surveillance without violating the ethic requirements. \u0000Two installation models will be proposed to consider the financial situation of the patient: a unit or appliance at the patient’s home or a home that is connected to a central office.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114181700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Efficiency of Aggregation Methods in Ensemble Filter Feature Selection Models 集成过滤特征选择模型中聚合方法的效率

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-08-17 DOI: 10.14738/tmlai.94.10101

N. Noureldien, Saffa Mohmoud

{"title":"The Efficiency of Aggregation Methods in Ensemble Filter Feature Selection Models","authors":"N. Noureldien, Saffa Mohmoud","doi":"10.14738/tmlai.94.10101","DOIUrl":"https://doi.org/10.14738/tmlai.94.10101","url":null,"abstract":"Ensemble feature selection is recommended as it proves to produce a more stable subset of features and a better classiﬁcation accuracy when compared to the individual feature selection methods. In this approach, the output of feature selection methods, called base selectors, are combined using some aggregation methods. For filter feature selection methods, a list aggregation method is needed to aggregate output ranked lists into a single list, and since many list aggregation methods have been proposed the decision on which method to use to build the optimum ensemble model is a de facto question. \u0000 In this paper, we investigate the efficiency of four aggregation methods, namely; Min, Median, Arithmetic Mean, and Geometric Mean. The performance of aggregation methods is evaluated using five datasets from different scientific fields with a variant number of instances and features. Besides, the classifies used in the evaluation are selected from three different classes, Trees, Rules, and Bayes. \u0000 The experimental results show that 11 out of the 15 best performance results are corresponding to ensemble models. And out of the 11 best performance ensemble models, the most efficient aggregation methods are Median (5/11), followed by Arithmetic Mean (3/11) and Min (3/11). Also, results show that as the number of features increased, the efficient aggregation method changes from Min to Median to Arithmetic Mean. This may suggest that for a very high number of features the efficient aggregation method is the Arithmetic Mean. And generally, there is no aggregation method that is the best for all cases.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Representation of the kinematics of the natural trihedral of a spiral-helix trajectory by quaternion matrices 用四元数矩阵表示螺旋-螺旋轨迹的自然三面体运动学

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2021-07-29 DOI: 10.14738/tmlai.94.10523

A. Alpatov, V. Kravets, V. Kravets, E. Lapkhanov

引用次数: 1