Journal of Artificial Intelligence and Data Mining最新文献

Nasal Breath Input: Exploring Nasal Breath Input Method for Hands-Free Input by Using a Glasses Type Device with Piezoelectric Elements 鼻腔呼吸输入:利用带有压电元件的眼镜式装置探索鼻腔呼吸输入的免提输入方法

Journal of Artificial Intelligence and Data Mining Pub Date : 2022-01-01 DOI: 10.26421/jdi3.4-2

Ryoma Ogawa, Kyosuke Futami, Kazuya Murao

{"title":"Nasal Breath Input: Exploring Nasal Breath Input Method for Hands-Free Input by Using a Glasses Type Device with Piezoelectric Elements","authors":"Ryoma Ogawa, Kyosuke Futami, Kazuya Murao","doi":"10.26421/jdi3.4-2","DOIUrl":"https://doi.org/10.26421/jdi3.4-2","url":null,"abstract":"Research on hands-free input methods has been actively conducted. However, most of the previous methods are difficult to use at any time in daily life due to using speech sounds or body movements. In this study, to realize a hands-free input method based on nasal breath using wearable devices, we propose a method for recognizing nasal breath gestures, using piezoelectric elements placed on the nosepiece of a glasses-type device. In the proposed method, nasal vibrations generated by nasal breath are acquired as sound data from the devices. Next, the breath pattern is recognized based on the factors of breath count, time interval, and intensity. We implemented a prototype system. The evaluation results for 10 subjects showed that the proposed method can recognize eight types of nasal breath gestures at 0.82% of F-value. The evaluation results also showed that the recognition accuracy is increased to more than 90% by limiting gestures to those with a different breath count or different breath interval. Our study provides the first glasses type wearable sensing technology that uses nasal breathing for hands-free input.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74052714","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

Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures 基于复杂深度学习自然架构的文本语料库情感挖掘与分析

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-11-01 DOI: 10.26421/jdi2.4-4

Teresa Alcamo, A. Cuzzocrea, G. Pilato, Daniele Schicchi

引用次数: 2

An Intelligent Model for Prediction of In-Vitro Fertilization Success using MLP Neural Network and GA Optimization 基于MLP神经网络和遗传算法优化的体外受精成功率智能预测模型

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-10-16 DOI: 10.22044/JADM.2021.10718.2208

E. Feli, R. Hosseini, S. Yazdani

引用次数: 0

Robust Vein Recognition against Rotation Using Kernel Sparse Representation 基于核稀疏表示的抗旋转鲁棒静脉识别

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-09-22 DOI: 10.22044/JADM.2021.10253.2164

Ali Nozaripour, Hadi Soltanizadeh

{"title":"Robust Vein Recognition against Rotation Using Kernel Sparse Representation","authors":"Ali Nozaripour, Hadi Soltanizadeh","doi":"10.22044/JADM.2021.10253.2164","DOIUrl":"https://doi.org/10.22044/JADM.2021.10253.2164","url":null,"abstract":"Sparse representation due to advantages such as noise-resistant and, having a strong mathematical theory, has been noticed as a powerful tool in recent decades. In this paper, using the sparse representation, kernel trick, and a different technique of the Region of Interest (ROI) extraction which we had presented in our previous work, a new and robust method against rotation is introduced for dorsal hand vein recognition. In this method, to select the ROI, by changing the length and angle of the sides, undesirable effects of hand rotation during taking images have largely been neutralized. So, depending on the amount of hand rotation, ROI in each image will be different in size and shape. On the other hand, because of the same direction distribution on the dorsal hand vein patterns, we have used the kernel trick on sparse representation to classification. As a result, most samples with different classes but the same direction distribution will be classified properly. Using these two techniques, lead to introduce an effective method against hand rotation, for dorsal hand vein recognition. Increases of 2.26% in the recognition rate is observed for the proposed method when compared to the three conventional SRC-based algorithms and three classification methods based sparse coding that used dictionary learning.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44463791","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

Improving Speed and Efficiency of Dynamic Programming Methods through Chaos 利用混沌提高动态规划方法的速度和效率

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-09-01 DOI: 10.22044/JADM.2021.10520.2191

H. Khodadadi, V. Derhami

{"title":"Improving Speed and Efficiency of Dynamic Programming Methods through Chaos","authors":"H. Khodadadi, V. Derhami","doi":"10.22044/JADM.2021.10520.2191","DOIUrl":"https://doi.org/10.22044/JADM.2021.10520.2191","url":null,"abstract":"A prominent weakness of dynamic programming methods is that they perform operations throughout the entire set of states in a Markov decision process in every updating phase. This paper proposes a novel chaos-based method to solve the problem. For this purpose, a chaotic system is first initialized, and the resultant numbers are mapped onto the environment states through initial processing. In each traverse of the policy iteration method, policy evaluation is performed only once, and only a few states are updated. These states are proposed by the chaos system. In this method, the policy evaluation and improvement cycle lasts until an optimal policy is formulated in the environment. The same procedure is performed in the value iteration method, and only the values of a few states proposed by the chaos are updated in each traverse, whereas the values of other states are left unchanged. Unlike the conventional methods, an optimal solution can be obtained in the proposed method by only updating a limited number of states which are properly distributed all over the environment by chaos. The test results indicate the improved speed and efficiency of chaotic dynamic programming methods in obtaining the optimal solution in different grid environments.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49057333","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

Software Testing using an Adaptive Genetic Algorithm 使用自适应遗传算法的软件测试

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-08-31 DOI: 10.22044/JADM.2021.10018.2138

A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed

引用次数: 5

Multi-Sentence Hierarchical Generative Adversarial Network GAN (MSH-GAN) for Automatic Text-to-Image Generation 用于文本到图像自动生成的多句子分层生成对抗网络GAN (MSH-GAN)

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-08-31 DOI: 10.22044/JADM.2021.10837.2224

Elham Pejhan, M. Ghasemzadeh

{"title":"Multi-Sentence Hierarchical Generative Adversarial Network GAN (MSH-GAN) for Automatic Text-to-Image Generation","authors":"Elham Pejhan, M. Ghasemzadeh","doi":"10.22044/JADM.2021.10837.2224","DOIUrl":"https://doi.org/10.22044/JADM.2021.10837.2224","url":null,"abstract":"This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our proposed method is a Multi Sentences Hierarchical GAN (MSH-GAN) for text to image generation. In this research project, we have considered two main strategies: 1) produce a higher quality image in the first step, and 2) use two additional descriptions to improve the original image in the next steps. Our goal is to focus on using more information to generate images with higher resolution by using more than one sentence input text. We have proposed different models based on GANs and Memory Networks. We have also used more challenging dataset called ids-ade. This is the first time; this dataset has been used in this area. We have evaluated our models based on IS, FID and, R-precision evaluation metrics. Experimental results demonstrate that our best model performs favorably against the basic state-of-the-art approaches like StackGAN and AttGAN.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48550603","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

DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA DENOVA：基于ANOVA的深度学习预测五因素模型

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-08-31 DOI: 10.22044/JADM.2021.10471.2186

M. Nasiri, H. Rahmani

引用次数: 0

Object Segmentation using Local Histograms, Invasive Weed Optimization Algorithm and Texture Analysis 基于局部直方图、入侵杂草优化算法和纹理分析的目标分割

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-08-23 DOI: 10.22044/JADM.2021.10200.2158

Somayye Bayatpour, S. Hasheminejad

引用次数: 1

DTEC-MAC: Diverse Traffic with Guarantee Energy Consumption for MAC in Wireless Body Area Networks DTEC-MAC:无线体域网络中具有保证能耗的多流量MAC

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-07-24 DOI: 10.22044/JADM.2021.10117.2149

F. Yazdi, M. Hosseinzadeh, S. Jabbehdari

引用次数: 3