Transactions on Machine Learning and Artificial Intelligence最新文献_第9页

Super-Quantum Correlations: How to Interpret the No-Signaling Condition? 超量子关联:如何解释无信号条件?

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-07-07 DOI: 10.14738/tmlai.103.12580

Pierre Uzan

引用次数: 0

Ensemble Graph Attention Networks 集成图注意网络

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-06-12 DOI: 10.14738/tmlai.103.12399

Nan Wu, Chaofan Wang

{"title":"Ensemble Graph Attention Networks","authors":"Nan Wu, Chaofan Wang","doi":"10.14738/tmlai.103.12399","DOIUrl":"https://doi.org/10.14738/tmlai.103.12399","url":null,"abstract":"Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked. In this work, we propose a simple generic bagging-based ensemble learning strategy which is applicable to any backbone graph models. We then propose two ensemble graph neural network models – Ensemble-GAT and Ensemble-HetGAT by applying the ensemble strategy to the graph attention network (GAT), and a heterogeneous graph attention network (HetGAT). We demonstrate the effectiveness of the proposed ensemble strategy on GAT and HetGAT through comprehensive experiments with four real-world homogeneous graph datasets and three real-world heterogeneous graph datasets on node classification tasks. The proposed Ensemble-GAT and Ensemble-HetGAT outperform the state-of-the-art graph neural network and heterogeneous graph neural network models on most of the benchmark datasets. The proposed ensemble strategy also alleviates the over-smoothing problem in GAT and HetGAT.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131048844","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: 1

Recognition of Geometric Images by Linguistic Method 基于语言方法的几何图像识别

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-05-12 DOI: 10.14738/tmlai.103.12228

S. Sargsyan, A. Hovakimyan

{"title":"Recognition of Geometric Images by Linguistic Method","authors":"S. Sargsyan, A. Hovakimyan","doi":"10.14738/tmlai.103.12228","DOIUrl":"https://doi.org/10.14738/tmlai.103.12228","url":null,"abstract":"Image recognition is currently one of the fastest-growing areas in applied mathematics. Of the many methods for solving problems in this area, the grammatical (linguistic) method of pattern recognition is the least studied. The essence of the grammar method is to construct appropriate grammar for object classes. In this case, the object recognition problem is related to the language generated by the given grammar. Using the linguistic method, an algorithm and software for recognizing geometric images have been developed. While the development the following tasks were solved. Methods have been developed for describing geometric images (triangles, squares, polygons) and corresponding grammars have been constructed for them so that the chains generated by this grammar represent objects of this class. The problems of constructing given classes of geometric images, as well as constructing a grammar for each class, are solved. \u0000At the training stage, classes are considered, each of which is described by a finite set of chains. To classify a new image, that is, to determine which class it belongs to, a parsing of the corresponding chain of this image was performed using grammars. Thus, the belonging of the chain to the language born by this grammar was clarified.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129908290","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

Masking the Backgrounds to Produce Object-Focused Images and Comparing that Augmentation Method to Other Methods 掩蔽背景以产生聚焦对象的图像，并将该增强方法与其他方法进行比较

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-05-12 DOI: 10.14738/tmlai.103.12245

A. Hammoud, A. Ghandour

{"title":"Masking the Backgrounds to Produce Object-Focused Images and Comparing that Augmentation Method to Other Methods","authors":"A. Hammoud, A. Ghandour","doi":"10.14738/tmlai.103.12245","DOIUrl":"https://doi.org/10.14738/tmlai.103.12245","url":null,"abstract":"Image augmentation is a very powerful method to expand existing image datasets. This paper presents a novel method for creating a variation of existing images, called Object-Focused Image (OFI). This is when an image includes only the labeled object and everything else is made white. This paper elaborates on the OFI approach, explores its efficiency, and compares the validation accuracy of 780 notebooks. The presented testbed makes use of a subset of ImageNet Dataset (8,000 images of 14 classes) and incorporates all available models in Keras. These 26 models are tested before augmentation and after applying 9 different categories of augmentation methods. Each of these 260 notebooks is tested in 3 different scenarios: scenario A (ImageNet weights are not used and network layers are trainable), scenario B (ImageNet weights are used and network layers are trainable) and scenario C (ImageNet weights are used and network layers are not trainable). The experiments presented in this paper show that using OFI images along with the original images can be better than other augmentation methods in 16.4% of the cases. It was also shown that OFI method could help some models learn although they could not learn when other augmentation methods were applied. The conducted experiments also proved that the Kernel filters and the color space transformations are among the best data augmentation methods.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113977381","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

Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques 使用机器学习技术评估神经系统疾病的手写签名生物特征数据分析研究

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-04-30 DOI: 10.14738/tmlai.102.12210

S. Gornale, Sathish Kumar, Rashmi Siddalingappa, P. Hiremath

引用次数: 5

An Introduction to Data Encryption and Future Trends in Lightweight Cryptography and Securing IoT Environments 介绍数据加密和轻量级加密和保护物联网环境的未来趋势

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-04-16 DOI: 10.14738/tmlai.102.11939

S. Bagui, Raffaele Galliera

引用次数: 0

A Framework for Testing the Reliability and Validity of a Novel Non-Invasive Digital Biomarker Instrument Using Statistical Techniques: A Case Study with Lyfas 使用统计技术测试一种新型无创数字生物标志物仪器的可靠性和有效性的框架:以Lyfas为例

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-03-24 DOI: 10.14738/tmlai.102.11845

S. Chattopadhyay, Rupam Das

{"title":"A Framework for Testing the Reliability and Validity of a Novel Non-Invasive Digital Biomarker Instrument Using Statistical Techniques: A Case Study with Lyfas","authors":"S. Chattopadhyay, Rupam Das","doi":"10.14738/tmlai.102.11845","DOIUrl":"https://doi.org/10.14738/tmlai.102.11845","url":null,"abstract":"Background: This paper demonstrates a framework for testing of efficacy (reliability and validity) of a novel instrument against a gold-standard instrument. Lyfas is a novel, non-wearable, non-invasive, and economic optical biomarker instrument that runs on android smartphones. By capturing the Pulse Rate (PR) and Pulse Rate Variability (PRV) from the index finger capillary using photoplethysmography, it measures the Cardiovascular Autonomic Modulation (CvAM). The Polar H10 sensor is a gold-standard electrical biofeedback instrument that comes with a wearable chest belt and is a relatively costly device. It captures the Heart Rate (HR) and Heart Rate Variability (HRV) that surrogates Cardiac Autonomic Modulation (CAM). Objective: To showcase the statistical framework in mining the efficacy of Lyfas as a biofeedback instrument by comparing it with that of the Polar H10 instrument following a ‘6Minute Walk Test’. Method: Using Lyfas and Polar H10 HR sensor, PR and HR were captured from 567 subjects(n=567, 312 healthy adult males, and 255 females, respectively). The data was checked for the (a) internal consistency (Cronbach’s alpha), (b) its distribution (Q-Q plots), (c) descriptive statistics (box plots), (d) Root Mean Square difference between the HR and PR, (e) reliability (Bland-Altman Reliability Test), and (f) correlations using (i) Pearson’s inter-class correlations (r), and (ii) Linear regressions (R2). Results: The efficacy of Lyfas as a biofeedback instrument has finally been computed by averaging the mean scores of BART (93.53%), ‘r’ (86.96%), and R2 (87.58%) for the sample and found to be 87.27%. Conclusion: Lyfas can also be used as a biofeedback instrument.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122945141","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: 3

Using Artificial Intelligence for Analyzing Retinal Images (OCT) in People with Diabetes: Detecting Diabetic Macular Edema Using Deep Learning Approach 使用人工智能分析糖尿病患者视网膜图像(OCT):使用深度学习方法检测糖尿病黄斑水肿

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-02-23 DOI: 10.14738/tmlai.101.11805

Tahani Daghistani

{"title":"Using Artificial Intelligence for Analyzing Retinal Images (OCT) in People with Diabetes: Detecting Diabetic Macular Edema Using Deep Learning Approach","authors":"Tahani Daghistani","doi":"10.14738/tmlai.101.11805","DOIUrl":"https://doi.org/10.14738/tmlai.101.11805","url":null,"abstract":"Medical imaging evolved rapidly to play a vital role in diagnosis and treatment of a disease. Automate analysis of medical image analysis has increased effectively through the use of deep learning techniques to obtain much quicker classifications once trained and learn relevant features for specific tasks, shown to be assessable in clinical practice and valuable tool to support decision making in medical field. Within Opthalmology, Optical Coherence Tomography (OCT) is a volumetric imaging procedure that uses in the diagnosis, monitoring and measuring response to treatment in eyes. Early detection of eyes diseases including Diabetic Macular Edema (DME) is vital process to avoid complications such as blindness. This work employed a deep convolutional neural network (CNN) based method for DME classification task. To demonstrate the impact of convolutional, five models with different Convolutional layers built then the best one selected based on evaluation metrics. The accuracy of model improved while increasing the number of Convolutional Layers and achieved 82% by 5-Convolutional Layer, Precision and Recall of CNN model per DME class was 87%% and 74%, respectively. These results highlighted the potential of deep learning in assisting decision-making in patients with DME.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133381511","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: 1

Real-Robot Friendly Passing Motion Planner for Autonomous Navigation in Crowds 人群自主导航的实时机器人友好通过运动规划

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-01-28 DOI: 10.14738/tmlai.101.11616

Shun Niijima, Y. Sasaki, H. Mizoguchi

引用次数: 0

Kullback-Leibler Divergence of Mixture Autoregressive Random Processes via Extreme-Value-Distributions (EVDs) Noise with Application of the Processes to Climate Change 极值分布(EVDs)噪声下混合自回归随机过程的Kullback-Leibler散度及其在气候变化中的应用

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-01-21 DOI: 10.14738/tmlai.101.11544

R. O. Olanrewaju, A. Waititu

引用次数: 0