Pub Date : 2022-07-07DOI: 10.14738/tmlai.103.12580
Pierre Uzan
This article deals with the question of the maximal correlation degree of two intelligent machines that cannot exchange any signals. After reminding the reader of the incorrectness of the mainstream statistical interpretation of the “no-signaling” condition, its informational meaning is explored. It is emphasized that if Pawlowski et al.’s Information Causality Principle correctly expresses (and generalizes) the no-signaling condition, its application is, for now, based on a specific scenario (suggested by van Dam) and a no less specific (and simplified) relationship between mutual information and correlators. A more general informational interpretation of the no-signaling condition from which the Tsirelson bound can be derived is then formulated in terms of correlational independence.
{"title":"Super-Quantum Correlations: How to Interpret the No-Signaling Condition?","authors":"Pierre Uzan","doi":"10.14738/tmlai.103.12580","DOIUrl":"https://doi.org/10.14738/tmlai.103.12580","url":null,"abstract":"This article deals with the question of the maximal correlation degree of two intelligent machines that cannot exchange any signals. After reminding the reader of the incorrectness of the mainstream statistical interpretation of the “no-signaling” condition, its informational meaning is explored. It is emphasized that if Pawlowski et al.’s Information Causality Principle correctly expresses (and generalizes) the no-signaling condition, its application is, for now, based on a specific scenario (suggested by van Dam) and a no less specific (and simplified) relationship between mutual information and correlators. A more general informational interpretation of the no-signaling condition from which the Tsirelson bound can be derived is then formulated in terms of correlational independence.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127604051","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}
Pub Date : 2022-06-12DOI: 10.14738/tmlai.103.12399
Nan Wu, Chaofan Wang
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.
{"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":"51 1","pages":"0"},"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}
Pub Date : 2022-05-12DOI: 10.14738/tmlai.103.12228
S. Sargsyan, A. Hovakimyan
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. At 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.
{"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":"1 1","pages":"0"},"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}
Pub Date : 2022-05-12DOI: 10.14738/tmlai.103.12245
A. Hammoud, A. Ghandour
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.
{"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":"16 4","pages":"0"},"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}
Pub Date : 2022-04-30DOI: 10.14738/tmlai.102.12210
S. Gornale, Sathish Kumar, Rashmi Siddalingappa, P. Hiremath
The handwritten signature is considered one of the most widely accepted personal behavioral traits in Biometric system. Handwriting analysis has wide applications in multiple domains such as psychological disorders, medical diagnosis, and recruitment of staff, career counseling, writer credentials, forensic studies, matrimonial sites, e-security, e-health and many more. In this paper, we recapitulate the state-of-the-art techniques and applications based on the handwriting signature analysis for the Assessment of Neurological Disorder using Machine Learning Techniques, In addition to this, achievements and challenges the scientific community should address. Thus, an integrated discussion of various datasets used, feature extraction techniques and classification schemes regarding Parkinson’s disease (PD) and Alzheimer’s disease (AD) is done and surveyed scientifically. The present research paper aims to provide an extensive review of scientific literature, ascertain vulnerable challenges and offer new research directions in the field.
{"title":"Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques","authors":"S. Gornale, Sathish Kumar, Rashmi Siddalingappa, P. Hiremath","doi":"10.14738/tmlai.102.12210","DOIUrl":"https://doi.org/10.14738/tmlai.102.12210","url":null,"abstract":"The handwritten signature is considered one of the most widely accepted personal behavioral traits in Biometric system. Handwriting analysis has wide applications in multiple domains such as psychological disorders, medical diagnosis, and recruitment of staff, career counseling, writer credentials, forensic studies, matrimonial sites, e-security, e-health and many more. In this paper, we recapitulate the state-of-the-art techniques and applications based on the handwriting signature analysis for the Assessment of Neurological Disorder using Machine Learning Techniques, In addition to this, achievements and challenges the scientific community should address. Thus, an integrated discussion of various datasets used, feature extraction techniques and classification schemes regarding Parkinson’s disease (PD) and Alzheimer’s disease (AD) is done and surveyed scientifically. The present research paper aims to provide an extensive review of scientific literature, ascertain vulnerable challenges and offer new research directions in the field.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117211031","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}
Pub Date : 2022-04-16DOI: 10.14738/tmlai.102.11939
S. Bagui, Raffaele Galliera
This paper presents an overview of the basic concepts of cryptography and encryption. The work aims at presenting the main concepts and concerns of encryption on a high-level of abstraction, allowing non-domain expert readers to navigate through these topics. Less traditional arguments are also shown, from the relevance of Key Management Services with its usage in Envelope Encryption, to Zero Knowledge proofs and their innovative applications. The crucial importance of securing communications between IoT devices and widely used algorithms to do so, are also discussed.
{"title":"An Introduction to Data Encryption and Future Trends in Lightweight Cryptography and Securing IoT Environments","authors":"S. Bagui, Raffaele Galliera","doi":"10.14738/tmlai.102.11939","DOIUrl":"https://doi.org/10.14738/tmlai.102.11939","url":null,"abstract":"This paper presents an overview of the basic concepts of cryptography and encryption. The work aims at presenting the main concepts and concerns of encryption on a high-level of abstraction, allowing non-domain expert readers to navigate through these topics. Less traditional arguments are also shown, from the relevance of Key Management Services with its usage in Envelope Encryption, to Zero Knowledge proofs and their innovative applications. The crucial importance of securing communications between IoT devices and widely used algorithms to do so, are also discussed.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117018742","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}
Pub Date : 2022-03-24DOI: 10.14738/tmlai.102.11845
S. Chattopadhyay, Rupam Das
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.
{"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":"1 1","pages":"0"},"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}
Pub Date : 2022-02-23DOI: 10.14738/tmlai.101.11805
Tahani Daghistani
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.
{"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":"10 1","pages":"0"},"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}
Pub Date : 2022-01-28DOI: 10.14738/tmlai.101.11616
Shun Niijima, Y. Sasaki, H. Mizoguchi
This study proposes a real‐robot friendly passing motion planner to be used in crowds. The proposed method learns to pass pedestrians with smooth acceleration and deceleration by using passing motion learning. A key feature of the proposed method is that it is trained on a simple crowd simulation with both dynamic and stationary pedestrians. The learned passing behaviour can be used directly in autonomous navigation. Evaluations using the crowd simulations indicate that the proposed method outperforms the existing ones in terms of success rate, arrival time, and keeping a certain distance from the pedestrians. The proposed navigation framework is implemented on a mobile robot and demonstrated its successful navigation between pedestrians in a science museum.
{"title":"Real-Robot Friendly Passing Motion Planner for Autonomous Navigation in Crowds","authors":"Shun Niijima, Y. Sasaki, H. Mizoguchi","doi":"10.14738/tmlai.101.11616","DOIUrl":"https://doi.org/10.14738/tmlai.101.11616","url":null,"abstract":"This study proposes a real‐robot friendly passing motion planner to be used in crowds. The proposed method learns to pass pedestrians with smooth acceleration and deceleration by using passing motion learning. A key feature of the proposed method is that it is trained on a simple crowd simulation with both dynamic and stationary pedestrians. The learned passing behaviour can be used directly in autonomous navigation. Evaluations using the crowd simulations indicate that the proposed method outperforms the existing ones in terms of success rate, arrival time, and keeping a certain distance from the pedestrians. The proposed navigation framework is implemented on a mobile robot and demonstrated its successful navigation between pedestrians in a science museum.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975613","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}
Pub Date : 2022-01-21DOI: 10.14738/tmlai.101.11544
R. O. Olanrewaju, A. Waititu
This paper designs inter-switch autoregressive random processes in a mixture manner with Extreme-Value-Distributions (EVDs) random noises to give EVDs-MAR model. The EVDs-MAR model comprises of Fréchet, Gumbel, and Weibull distributional error terms to form FMA, GMA, and WMA models with their embedded inter-switching transitional weights (wk) , distributional parameters, and autoregressive coefficients . The Kullback-Leibler divergence was used to measure the proximity (D) between finite/ delimited mixture density and infinite mixture density of the EVDs-MAR model with Expectation-Maximization (EM) algorithm adopted as the parameter estimation technique for the extreme mixture model. The FMA, GMA, and WMA models were subjected to monthly temperature in Celsius (oC) from 1900 to 2020 and annual rainfall in Millimeter (mm) from 1960 to 2020 datasets in Nigeria context.
{"title":"Kullback-Leibler Divergence of Mixture Autoregressive Random Processes via Extreme-Value-Distributions (EVDs) Noise with Application of the Processes to Climate Change","authors":"R. O. Olanrewaju, A. Waititu","doi":"10.14738/tmlai.101.11544","DOIUrl":"https://doi.org/10.14738/tmlai.101.11544","url":null,"abstract":"This paper designs inter-switch autoregressive random processes in a mixture manner with Extreme-Value-Distributions (EVDs) random noises to give EVDs-MAR model. The EVDs-MAR model comprises of Fréchet, Gumbel, and Weibull distributional error terms to form FMA, GMA, and WMA models with their embedded inter-switching transitional weights (wk) , distributional parameters, and autoregressive coefficients . The Kullback-Leibler divergence was used to measure the proximity (D) between finite/ delimited mixture density and infinite mixture density of the EVDs-MAR model with Expectation-Maximization (EM) algorithm adopted as the parameter estimation technique for the extreme mixture model. The FMA, GMA, and WMA models were subjected to monthly temperature in Celsius (oC) from 1900 to 2020 and annual rainfall in Millimeter (mm) from 1960 to 2020 datasets in Nigeria context.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126194522","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：