Sparklinglight Transactions on Artificial Intelligence and Quantum Computing最新文献_第2页

Hyperparameter Tuning of Deep learning Models in Keras Keras中深度学习模型的超参数调优

Sparklinglight Transactions on Artificial Intelligence and Quantum Computing Pub Date : 1900-01-01 DOI: 10.55011/staiqc.2021.1104

M. Z. Awang Pon, Krishna Prakash K K

{"title":"Hyperparameter Tuning of Deep learning Models in Keras","authors":"M. Z. Awang Pon, Krishna Prakash K K","doi":"10.55011/staiqc.2021.1104","DOIUrl":"https://doi.org/10.55011/staiqc.2021.1104","url":null,"abstract":"Hyperparameter tuning or optimization is one of the fundamental way to improve the performance of the machine learning models. Hyper parameter is a parameter passed during the learning process of the model to make corrections or adjustments to the learning process. To generalise diverse data patterns, the same machine learning model may require different constraints, weights, or learning rates. Hyperparameters are the term for these kind of measurements. These parameters have been trial-anderror tested to ensure that the model can solve the machine learning task optimally. This paper focus on the science of hyperparameter tuning using some tools with experimental values and results of each experiments. We have also documented 4 metrics to analyze the hyperparameter tuning results and benchmark the outcome. The experimental results of two tools used commonly for deep learning models namely Keras tuner and AiSara tuner are captured in the article. All relevant experimental code is also available for readers in authors github repository. The metrics used to benchmark the results are accuracy, search time, cost and complexity and expalinability. The results indicate the overall performance of AiSara tuner in search time, cost and complexity and expalinability matrices are superior to keras tuners. © 2021 STAIQC. All rights reserved.","PeriodicalId":231409,"journal":{"name":"Sparklinglight Transactions on Artificial Intelligence and Quantum Computing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781742","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: 5

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask 一种基于机器智能的带面具和不带面具人脸分类方法

Sparklinglight Transactions on Artificial Intelligence and Quantum Computing Pub Date : 1900-01-01 DOI: 10.55011/staiqc.2022.2104

K. K. Jena, K. Prasad. K, Rajermani Thinakaran

{"title":"A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask","authors":"K. K. Jena, K. Prasad. K, Rajermani Thinakaran","doi":"10.55011/staiqc.2022.2104","DOIUrl":"https://doi.org/10.55011/staiqc.2022.2104","url":null,"abstract":"The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.","PeriodicalId":231409,"journal":{"name":"Sparklinglight Transactions on Artificial Intelligence and Quantum Computing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125193901","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

A Review of the Edge Detection Technology 边缘检测技术综述

Sparklinglight Transactions on Artificial Intelligence and Quantum Computing Pub Date : 1900-01-01 DOI: 10.55011/staiqc.2021.1203

Shouming Hou, Chao Jia, Ya-Bing Wang, Mackenzie Brown

引用次数: 6

Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams 基于CNN优化骨架图的视频流人体异常检测

Sparklinglight Transactions on Artificial Intelligence and Quantum Computing Pub Date : 1900-01-01 DOI: 10.55011/staiqc.2022.2102

Bhagya Jyothi K, Vasudeva

{"title":"Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams","authors":"Bhagya Jyothi K, Vasudeva","doi":"10.55011/staiqc.2022.2102","DOIUrl":"https://doi.org/10.55011/staiqc.2022.2102","url":null,"abstract":"Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.","PeriodicalId":231409,"journal":{"name":"Sparklinglight Transactions on Artificial Intelligence and Quantum Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340424","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