Acadlore Transactions on AI and Machine Learning最新文献_第4页

Gait Based Person Identification Using Deep Learning Model of Generative Adversarial Network (GAN) 基于步态的生成对抗网络(GAN)深度学习模型的人识别

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-12-31 DOI: 10.56578/ataiml010203

Ramesh Vatambeti, Vijay Kumar Damera

{"title":"Gait Based Person Identification Using Deep Learning Model of Generative Adversarial Network (GAN)","authors":"Ramesh Vatambeti, Vijay Kumar Damera","doi":"10.56578/ataiml010203","DOIUrl":"https://doi.org/10.56578/ataiml010203","url":null,"abstract":"The proliferation of digital age security tools is often attributed to the rise of visual surveillance. Since an individual's gait is highly indicative of their identity, it is becoming an increasingly popular biometric modality for use in autonomous visual surveillance and monitoring. There are various steps used in gait recognition frameworks such as segmentation, feature extraction, feature learning and similarity measurement. These steps are mutually independent with each part fixed, which results in a suboptimal performance in a challenging condition. It can be done independently of the users' involvement. Low-resolution video and straightforward instrumentation can verify an individual's identity, making impersonation a rarity. Using the benefits of the Generative Adversarial Network (GAN), this investigation tackles the problem of unevenly distributed unlabeled data with infrequently performed tasks. To estimate the data circulation in various circumstances using constrained observed gait data, a multimodal generator is applied here. When it comes to sharing knowledge, the variety provided by the data generated by a multimodal generator is hard to beat. The capability to distinguish gait activities with varying patterns due to environmental dynamics is enhanced by this multimodal generator. This system is more stable than other gait-based recognition methods because it can process data that is not equally dispersed throughout a different environment. The system's reliability is enhanced by the multimodal generator's capacity to produce a wide variety of outputs. The testing results show that this algorithm is superior to other gait-based recognition methods because it can adapt to changing environments.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115066722","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

Human Behavior Identification Based on Graphology Using Artificial Neural Network 基于笔迹学的人工神经网络人类行为识别

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-12-31 DOI: 10.56578/ataiml010204

S. Deore

{"title":"Human Behavior Identification Based on Graphology Using Artificial Neural Network","authors":"S. Deore","doi":"10.56578/ataiml010204","DOIUrl":"https://doi.org/10.56578/ataiml010204","url":null,"abstract":"Handwriting reflects a person's true nature, phobias, emotional outbursts, honesty, defenses and many more characteristics. Analysis of handwriting, also known as graphology, is a science that uses the strokes and patterns disclosed by handwriting to identify, evaluate, and analyze personality. It is the study of the patterns and physical characteristics of handwriting to identify the author, indicate the author's psychological state while writing, or analyze personality traits. Traditionally, professionals also called graphologists predict the behavior of the writer by analyzing their handwriting, but this procedure is tedious and expensive. Therefore, this paper focuses on developing an application for personality identification that can predict behavioral characteristics directly using a computer without any human involvement. Most of the existing applications use English as the primary language to identify the personality trait of the writer however, our approach uses Devanagari scripts for prediction, thereby eliminating the language barrier. Our proposed method uses a machine learning approach to predict personality by analyzing Devanagari samples using Artificial Neural Network. We have created our own Devanagari word dataset. There are almost 4000 images which belong to 5 classes namely Introvert, Extrovert, Optimistic, Pessimistic and Stable mind-set. The testing accuracy achieved by the proposed method is 94.75%.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133064749","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

Liver Lesion Segmentation Using Deep Learning Models 基于深度学习模型的肝脏病变分割

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010108

Aasia Rehman, M. A. Butt, Majid Zaman

{"title":"Liver Lesion Segmentation Using Deep Learning Models","authors":"Aasia Rehman, M. A. Butt, Majid Zaman","doi":"10.56578/ataiml010108","DOIUrl":"https://doi.org/10.56578/ataiml010108","url":null,"abstract":"An estimated 9.6 million deaths, or one in every six deaths, were attributed to cancer in 2018, making it the second highest cause of death worldwide. Men are more likely to develop lung, prostate, colorectal, stomach, and liver cancer than women, who are more likely to develop breast, colorectal, lung, cervical, and thyroid cancer. The primary goals of medical image segmentation include studying anatomical structure, identifying regions of interest (RoI), and measuring tissue volume to track tumor growth. It is crucial to diagnose and treat liver lesions quickly in order to stop the tumor from spreading further. Deep learning model-based liver segmentation has become very popular in the field of medical image analysis. This study explores various deep learning-based liver lesion segmentation algorithms and methodologies. Based on the developed models, the performance, and their limitations of these methodologies are contrasted. In the end, it was concluded that small size lesion segmentation, in particular, is still an open research subject for computer-aided systems of liver lesion segmentation, for there are still a number of technical issues that need to be resolved.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129141266","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

Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19 COVID-19下基于YOLOv5目标检测算法的口罩佩戴检测

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010106

Jiuchao Xie, Rui Xi, Daofang Chang

引用次数: 1

House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection 基于探索性数据分析和机器学习特征选择的房价预测

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010103

F. M. Basysyar, Gifthera Dwilestari

{"title":"House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection","authors":"F. M. Basysyar, Gifthera Dwilestari","doi":"10.56578/ataiml010103","DOIUrl":"https://doi.org/10.56578/ataiml010103","url":null,"abstract":"In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127201376","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

Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data 三种分类器在基于心电数据的胎儿健康分类中的性能比较

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010107

V. Khare, Sakshi Kumari

引用次数: 2

A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine 腰椎计算机断层图像骨质疏松预测的双选择通道注意网络

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010105

Linyan Xue, Ya Hou, Shiwei Wang, Cheng Luo, Zhiyin Xia, Geng Qin, Shuang Liu, Zhong-Quan Wang, Wenshan Gao, Kun Yang

{"title":"A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine","authors":"Linyan Xue, Ya Hou, Shiwei Wang, Cheng Luo, Zhiyin Xia, Geng Qin, Shuang Liu, Zhong-Quan Wang, Wenshan Gao, Kun Yang","doi":"10.56578/ataiml010105","DOIUrl":"https://doi.org/10.56578/ataiml010105","url":null,"abstract":"Osteoporosis is a common systemic bone disease with insidious onset and low treatment efficiency. Once it occurs, it will increase bone fragility and lead to fractures. Computed tomography (CT) is a non-invasive medical examination method that can identify the bone condition of patients. In this paper, we propose a novel channel attention module, which is subsequently integrated into the supervised deep convolutional neural network (DCNN) termed DSNet, which can perform feature fusion from two different scales, and use the method of quadratic weight calculation to enhance the interconnection among feature map channels and improve the detection and classification performance for the bone condition in lumbar spine CT images. To train and test the proposed framework, we retrospectively collect 4805 CT images of 133 patients, using DXA as the gold standard. According to the T-value diagnostic criteria defined by WHO, the vertebral bodies of L1 - L4 in CT images are labeled and classified into osteoporosis, osteopenia and normal bone mineral density. Meanwhile, the training set and test set are constructed in the ratio of 4:1. As a result, the DSNet achieves a prediction accuracy of 83.4% and a recall rate of 90.0% on the test set, indicating that the proposed model has the potential to assist clinicians in diagnosing individuals with abnormal BMD and may alert patients at high risk of osteoporosis for timely treatment.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126761003","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

Editorial to the Inaugural Issue 创刊号的社论

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010101

A. Pester

引用次数: 0

A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data 面向数据网络的电影资源语义标注的多媒体本体研究

Acadlore Transactions on AI and Machine Learning Pub Date : 2022-11-20 DOI: 10.56578/ataiml010102

Samdalle Amaria, Kaladzavi Guidedi, Warda Lazarre, Kolyang

引用次数: 1