International journal of artificial intelligence & applications最新文献_第7页

Application of Big Data Analysis and Internet of Things to the Intelligent Active Robotic Prosthesis for Transfemoral Amputees 大数据分析和物联网技术在智能主动式机器人股骨头切除术中的应用

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10502

Zlata Jelačić, Haris Velijević

{"title":"Application of Big Data Analysis and Internet of Things to the Intelligent Active Robotic Prosthesis for Transfemoral Amputees","authors":"Zlata Jelačić, Haris Velijević","doi":"10.5121/ijaia.2019.10502","DOIUrl":"https://doi.org/10.5121/ijaia.2019.10502","url":null,"abstract":"With the advent and rising usage of Internet of Things (IoT) eco-systems, there is a consequent, parallel rise in opportunities where technology can find its place to improve a number of human conditions. However, this is nothing new - we have been perfecting the usage of tools to aid our daily living throughout history. The true evolution lies in the interaction between us and the tools we create. Tools are now smart devices, yielding an opportunity where human-device interaction is giving us the very knowledge on how to improve that particular synthesis. From improving our fitness to detecting bradycardia and response of traumatic brain injury patient, we have come to a point where we are able to gain actionable insight into a lot of aspects of our health and condition. This creates a certain autonomy in understanding the unique make-up of every single person, in addition to yielding information that can be used by health practitioners to help in diagnosis, determination of medical approach and right recovery and follow-up methods. All of this supported by two major factors: IoT platforms and Big Data Analysis (BDA). This paper takes a deep dive into exemplary set-up of IoT platform and BDA framework necessary to support the improvement of human condition. Our SmartLeg prosthetic device integrates advanced prosthetic and robotic technology with the state-of-the-art machine learning algorithms capable of adapting the working of the prosthesis to the optimal gait and power consumption patterns, which provide means to customize the device to a particular user.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2019.10502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41687172","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

Customer Opinions Evaluation: A Case Study on Arabic Tweets 客户意见评价——以阿拉伯语推文为例

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10503

Manal Mostafa Ali

引用次数: 1

An Application of Convolutional Neural Networks on Human Intention Prediction 卷积神经网络在人类意向预测中的应用

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10501

Lin Zhang, Shengchao Li, Hao Xiong, Xiumin Diao, Ou Ma

{"title":"An Application of Convolutional Neural Networks on Human Intention Prediction","authors":"Lin Zhang, Shengchao Li, Hao Xiong, Xiumin Diao, Ou Ma","doi":"10.5121/ijaia.2019.10501","DOIUrl":"https://doi.org/10.5121/ijaia.2019.10501","url":null,"abstract":"Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed. The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2019.10501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48747972","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: 4

A Smart Brain Controlled Wheelchair Based Microcontroller System 基于单片机的智能脑控轮椅系统

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10506

Sherif Kamel Hussein Hassan Ratib

引用次数: 5

Transfer Learning with Convolutional Neural Networks for IRIS Recognition 基于卷积神经网络的IRIS识别迁移学习

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10505

Maram.G Alaslni, Lamiaa A. Elrefaei

{"title":"Transfer Learning with Convolutional Neural Networks for IRIS Recognition","authors":"Maram.G Alaslni, Lamiaa A. Elrefaei","doi":"10.5121/ijaia.2019.10505","DOIUrl":"https://doi.org/10.5121/ijaia.2019.10505","url":null,"abstract":"Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2019.10505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42556763","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: 11

A Hybrid Learning Algorithm in Automated Text Categorization of Legacy Data 一种用于遗留数据自动文本分类的混合学习算法

International journal of artificial intelligence & applications Pub Date : 2019-09-30 DOI: 10.5121/ijaia.2019.10504

Dali Wang, Ying Bai, David Hamblin

引用次数: 0

Data Mining for Integration and Verification of Socio-Geographical Trend Statements in the Context of Conflict Risk 冲突风险背景下整合和验证社会地理趋势声明的数据挖掘

International journal of artificial intelligence & applications Pub Date : 2019-07-31 DOI: 10.5121/IJAIA.2019.10401

V. Kamp, Jean Pierre Knust, R. Moratz, Kevin Stehn, Soeren Stoehrmann

{"title":"Data Mining for Integration and Verification of Socio-Geographical Trend Statements in the Context of Conflict Risk","authors":"V. Kamp, Jean Pierre Knust, R. Moratz, Kevin Stehn, Soeren Stoehrmann","doi":"10.5121/IJAIA.2019.10401","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10401","url":null,"abstract":"Data mining enables an innovative, largely automatic meta-analysis of the relationship between political and economic geography analyses of crisis regions. As an example, the two approaches Global Conflict Risk Index (GCRI) and Fragile States Index (FSI) can be related to each other. The GCRI is a quantitative conflict risk assessment based on open source data and a statistical regression method developed by the Joint Research Centre of the European Commission. The FSI is based on a conflict assessment framework developed by The Fund for Peace in Washington, DC. In contrast to the quantitative GCRI, the FSI is essentially focused on qualitative data from systematic interviews with experts. Both approaches therefore have closely related objectives, but very different methodologies and data sources. It is therefore hoped that the two complementary approaches can be combined to form an even more meaningful meta-analysis, or that contradictions can be discovered, or that a validation of the approaches can be obtained if there are similarities. We propose an approach to automatic meta-analysis that makes use of machine learning (data mining). Such a procedure represents a novel approach in the meta-analysis of conflict risk analyses.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44960478","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

Transfer Learning Based Image Visualization Using CNN 基于迁移学习的CNN图像可视化

International journal of artificial intelligence & applications Pub Date : 2019-07-31 DOI: 10.5121/IJAIA.2019.10404

Santosh Giri, Basanta Joshi

引用次数: 6

A Comparative Study of LSTM and Phased LSTM for Gait Prediction LSTM与相位LSTM在步态预测中的比较研究

International journal of artificial intelligence & applications Pub Date : 2019-07-31 DOI: 10.5121/IJAIA.2019.10405

Qili Chen, Bofan Liang, Jiuhe Wang

引用次数: 8

Motion Prediction Using Depth Information of Human Arm Based on Alexnet 基于Alexnet的人体手臂深度信息运动预测

International journal of artificial intelligence & applications Pub Date : 2019-07-31 DOI: 10.5121/IJAIA.2019.10402

Jing Zhu, ShuoJin Li, Ruonan Ma, Jingwang Cheng

{"title":"Motion Prediction Using Depth Information of Human Arm Based on Alexnet","authors":"Jing Zhu, ShuoJin Li, Ruonan Ma, Jingwang Cheng","doi":"10.5121/IJAIA.2019.10402","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10402","url":null,"abstract":"The development of convolutional neural networks(CNN) has provided a new tool to make classification and prediction of human's body motion. This project tends to predict the drop point of a ball thrown out by experimenters by classifying the motion of their body in the process of throwing. Kinect sensor v2 is used to record depth maps and the drop points are recorded by a square infrared induction module. Firstly, convolutional neural networks are made use of to put the data obtained from depth maps in and get the prediction of drop point according to experimenters' motion. Secondly, huge amount of data is used to train the networks of different structure, and a network structure that could provide high enough accuracy for drop point prediction is established. The network model and parameters are modified to improve the accuracy of the prediction algorithm. Finally, the experimental data is divided into a training group and a test group. The prediction results of test group reflect that the prediction algorithm effectively improves the accuracy of human motion perception.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44241292","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