2017 5th International Symposium on Computational and Business Intelligence (ISCBI)最新文献

Siamese-twin random projection neural network with Bagging Trees tuning for unsupervised binary image hashing 具有Bagging树调优的无监督二值图像哈希的连体-孪生随机投影神经网络

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053536

Mohamed Waleed Fahkr, Mohamed Moheeb Emara, M. B. Abdelhalim

{"title":"Siamese-twin random projection neural network with Bagging Trees tuning for unsupervised binary image hashing","authors":"Mohamed Waleed Fahkr, Mohamed Moheeb Emara, M. B. Abdelhalim","doi":"10.1109/ISCBI.2017.8053536","DOIUrl":"https://doi.org/10.1109/ISCBI.2017.8053536","url":null,"abstract":"In this paper a Siamese-Twin Random Projection Neural Network (ST-RPNN) is proposed for unsupervised binary hashing of images. ST-RPNN is made of two identical random projection neural networks with hard threshold neurons where the binary code is taken as the neuron outputs. The learning objective is to produce similar binary codes for similar input image pairs and different binary codes otherwise. The learning process is divided into two steps. Firstly, overcomplete random projection is used to produce a sufficiently long code, followed by a fast sparse technique for neurons selection (FSNS). Bootstrap Aggregation Trees or Bagging Trees (BT) is then used to make a refined compact code section. BT is also used as a fast retrieval tool that ranks the database with respect to a query without distance calculations and with a significantly lower complexity than Hamming distance approach. The proposed technique is compared with 10 unsupervised image binary hashing techniques on the COREL1K dataset and the CIFAR10 dataset. The proposed technique obtained better precision-recall results than all compared techniques on the COREL1K dataset, and better than 8 of them on the CIFAR10 dataset.","PeriodicalId":128441,"journal":{"name":"2017 5th International Symposium on Computational and Business Intelligence (ISCBI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122547505","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

Triangle similarity approach for detecting eyeball movement 检测眼球运动的三角形相似度方法

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053540

R. Prasetya, Fitri Utaminingrum

{"title":"Triangle similarity approach for detecting eyeball movement","authors":"R. Prasetya, Fitri Utaminingrum","doi":"10.1109/ISCBI.2017.8053540","DOIUrl":"https://doi.org/10.1109/ISCBI.2017.8053540","url":null,"abstract":"Eye movement detection is one of the most developed technologies in the field of human and computer interaction especially as a control instrument of an automatic device. Eye movement of user can provide an easy input source, natural and high-bandwidth. By tracking the user's point of view, the ease of communication from the user to the automatic device can be improved. In general, the process of tracking the eye movement involves the process of detecting the eye area first. However, unlike the methods development in the eye detection process, methods development to detect eye movements still need to be improved. In this paper, we used a triangle similarity formulation to derive angular values by calculating the angle of a line between two tracked points obtained from face and eyeball midpoint, relative to the horizontal. The angle value and the length of the slash line are used as an indicator of the eyeball movement. This approach can accommodate various directions of movement of the eye even produce accuracy and precision well enough evidenced by the percentage of detection success reached 79%. This approach can also be used as a possible alternative way to detect the eyeball movements.","PeriodicalId":128441,"journal":{"name":"2017 5th International Symposium on Computational and Business Intelligence (ISCBI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658567","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: 8

Classification of dental diseases using CNN and transfer learning 基于CNN和迁移学习的牙病分类

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053547

S. A. Prajapati, R. Nagaraj, S. Mitra

引用次数: 101

A framework for identifying and evaluating technologies of interest for effective business strategy: Using text analytics to augment technology forecasting 用于识别和评估对有效业务策略感兴趣的技术的框架:使用文本分析来增强技术预测

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053555

Rakesh Rana, Alexander Karlsson, G. Falkman

引用次数: 1

Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach 基于贪婪方法的深度信念网络诊断注意缺陷多动障碍

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053552

Saeed Farzi, S. Kianian, Ilnaz Rastkhadive

{"title":"Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach","authors":"Saeed Farzi, S. Kianian, Ilnaz Rastkhadive","doi":"10.1109/ISCBI.2017.8053552","DOIUrl":"https://doi.org/10.1109/ISCBI.2017.8053552","url":null,"abstract":"Attention deficit hyperactivity disorder creates conditions for the child as s/he cannot sit calm and still, control his/her behavior and focus his/her attention on a particular issue. Five out of every hundred children are affected by the disease. Boys are three times more than girls at risk for this complication. The disorder often begins before age seven, and parents may not realize their children problem until they get older. Children with hyperactivity and attention deficit are at high risk of conduct disorder, antisocial personality, and drug abuse. Most children suffering from the disease will develop a feeling of depression, anxiety and lack of self-confidence. Given the importance of diagnosis the disease, Deep Belief Networks (DBNs) were used as a deep learning model to predict the disease. In this system, in addition to FMRI images features, sophisticated features such as age and IQ as well as functional characteristics, etc. were used. The proposed method was evaluated by two standard data sets of ADHD-200 Global Competitions, including NeuroImage and NYU data sets, and compared with state-of-the-art algorithms. The results showed the superiority of the proposed method rather than other systems. The prediction accuracy has improved respectively as +12.04 and +27.81 over NeuroImage and NYU datasets compared to the best proposed method in the ADHD-200 Global competition.","PeriodicalId":128441,"journal":{"name":"2017 5th International Symposium on Computational and Business Intelligence (ISCBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129469838","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: 10

Determining direction of moving object using object tracking for smart weelchair controller 基于目标跟踪的智能轮椅控制器运动目标方向确定

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053534

Fitri Utaminingrum, M. A. Fauzi, Dahnial Syauqy, R. Wihandika, A. Hapsani

引用次数: 3

Fuzzy random auto-regression time series model in enrollment university forecasting 模糊随机自回归时间序列模型在高校招生预测中的应用

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053545

R. Efendi, N. Samsudin, N. Arbaiy, M. M. Deris

引用次数: 2

Tomato ripeness clustering using 6-means algorithm based on v-channel otsu segmentation 基于v通道otsu分割的6均值番茄成熟度聚类

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053539

Y. A. Sari, Sigit Adinugroho

{"title":"Tomato ripeness clustering using 6-means algorithm based on v-channel otsu segmentation","authors":"Y. A. Sari, Sigit Adinugroho","doi":"10.1109/ISCBI.2017.8053539","DOIUrl":"https://doi.org/10.1109/ISCBI.2017.8053539","url":null,"abstract":"Segmentation process in an essential part in image processing to obtain good preparation either for further process of data mining or object recognition. This paper proposes a new method of segmenting tomato image for clustering its ripeness. The tomato images are taken from three types of smartphone camera in various lighting condition with white background. When taking picture by using smartphone camera, the image is a bit darker or lighter in certain side, so the segmentation is involved to the following stage. Color transformation is needed at the first stage of preprocessing which converts RGB channel to YUV channel in order to apply histogram equalization. YUV is better to perceptual similarities in machine vision than RGB. Histogram equalization is applied in single Y channel of an image. Afterwards merge a V channel to YUV channel then transform it to RGB color model to observe the difference and convert it back to YUV for segmentation. Otsu combined with V channel thresholding is utilized to segment image better. To evaluate the segmentation performance, clustering method is computed based on retrieved color of segmented image using K-Means, in which k=6 because of there are 6 stages of tomato ripeness. Color feature extraction by means of R, G, a∗, and b∗ color channel are treated subsequently. Experimental results show the system yield 1% Mean Square Error in clustering the ripeness of tomatoes.","PeriodicalId":128441,"journal":{"name":"2017 5th International Symposium on Computational and Business Intelligence (ISCBI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126903188","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: 12

Onward movement detection and distance estimation of object using disparity map on stereo vision 基于立体视觉视差图的物体向前运动检测与距离估计

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053541

A. Hapsani, Dahnial Syauqy, Fitri Utaminingrum, P. P. Adikara, Sigit Adinugroho

引用次数: 3

Optimizing K-means text document clustering using latent semantic indexing and pillar algorithm 使用潜在语义索引和支柱算法优化K-means文本文档聚类

2017 5th International Symposium on Computational and Business Intelligence (ISCBI) Pub Date : 2017-08-01 DOI: 10.1109/ISCBI.2017.8053549

Sigit Adinugroho, Y. A. Sari, M. A. Fauzi, P. P. Adikara

引用次数: 9