2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第5页

Freight Vehicle Travel Time Prediction Using Gradient Boosting Regression Tree 基于梯度增强回归树的货车行驶时间预测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0182

Xia Li, Ruibin Bai

{"title":"Freight Vehicle Travel Time Prediction Using Gradient Boosting Regression Tree","authors":"Xia Li, Ruibin Bai","doi":"10.1109/ICMLA.2016.0182","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0182","url":null,"abstract":"Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time predictions for freight vehicles at individual level using Gradient Boosting Regression Tree (GBRT) models. All the features were extracted or composed from vehicles' temporally sparse trajectory data. Three routes were selected for the prediction experiments. Bayesian optimisation was adopted for model fitting while the results show that both pre-start (before trip starts) and post-start (after trip starts) predictions accuracies reach above 80%. The results also show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 minutes as the real-time information. And the prediction performance can be further improved by about 2% by adding more mean speed estimates even if an unusual and non-recurring events occurred at a location of a route segment. This study shows the feasibility of both pre-start and continuous post-start prediction with limited amount of temporally sparse trajectory data for real-world practice.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133824363","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: 40

Using Temporal Discovery and Data-Driven Journey-Maps to Predict Customer Satisfaction 使用时间发现和数据驱动的旅程图来预测客户满意度

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0152

Joseph Bockhorst, Yingjian Wang, Sukrat Gupta, M. Qazi, Mingju Sun, G. Fung

引用次数: 0

Improving HSDPA Traffic Forecasting Using Ensemble of Neural Networks 基于神经网络集成的HSDPA流量预测改进

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0057

I. A. Lawal, S. A. Abdulkarim, M. K. Hassan, Jibrin M. Sadiq

{"title":"Improving HSDPA Traffic Forecasting Using Ensemble of Neural Networks","authors":"I. A. Lawal, S. A. Abdulkarim, M. K. Hassan, Jibrin M. Sadiq","doi":"10.1109/ICMLA.2016.0057","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0057","url":null,"abstract":"Accurate forecasting of data traffic demand is very crucial for the profitable operation of cellular data networks because it helps in facilitating the optimization and planning of the network resources. Many machine learning regression models including Support Vector Regression and Abductive Networks have been applied to this problem, but this paper studies the concept of ensemble method for improving the forecasting accuracy. Specifically, a cooperative ensemble training strategy using two optimization algorithms is proposed to train a Neural Network model. The trained model is characterized with good forecasting performance due to the exchange of experience and knowledge of the two optimization algorithm during the training process. A dataset consisting of 44160 recordings of hourly High-Speed Data Packet Access (HSDPA) data traffic, which was collected over a period of 30 days from sixty different sites of a UMTS-based cellular operator was used to evaluate the performance of the proposed method. Experimental results show the superiority of the Neural Network model trained with the proposed ensemble training strategy over other state-of-the-art methods.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130344455","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: 9

Basic Investigation on a Robust and Practical Plant Diagnostic System 鲁棒实用植物诊断系统的基础研究

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0178

Erika Fujita, Yusuke Kawasaki, H. Uga, S. Kagiwada, H. Iyatomi

引用次数: 113

A New Feature for Cross-Day Psychophysiological Workload Estimation 跨日心理生理工作量估算的新特征

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0140

R. Hefron, B. Borghetti

{"title":"A New Feature for Cross-Day Psychophysiological Workload Estimation","authors":"R. Hefron, B. Borghetti","doi":"10.1109/ICMLA.2016.0140","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0140","url":null,"abstract":"Classification of operator functional state for workload estimation using electroencephalograph (EEG) has proven difficult in cross-day scenarios due to non-stationarity of the feature and target distributions. This study analyzes multi-day data collected from a Multi-Attribute Task Battery (MATB) workload study using a new feature generation methodology which examines not just the average power, but also the variability of the power distribution in the clinical frequency bands over a 10 second sliding temporal window. High versus low workload levels were predicted for day five of the study based on training three traditional classifiers–Linear Discriminant Analysis (LDA), random forest, and K-Nearest Neighbors (KNN)–on the first four days' results. Frequency-domain power distribution variance was statistically significant between conditions, suggesting it as a salient feature. Including variance as a feature enabled a crossday workload classification accuracy improvement of 5.8% above models only using mean power. Furthermore, the individual classifiers were combined into a time-smoothed composite classifier which capitalized on the differences in features selected in the models to improve overall classification accuracy to greater than 80%.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"51 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590339","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: 6

Recognition and Analysis of the Contours Drawn during the Poppelreuter's Test Poppelreuter试验中轮廓的识别与分析

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0036

S. Nõmm, K. Bardos, I. Masarov, Julia Kozhenkina, A. Toomela, T. Toomsoo

{"title":"Recognition and Analysis of the Contours Drawn during the Poppelreuter's Test","authors":"S. Nõmm, K. Bardos, I. Masarov, Julia Kozhenkina, A. Toomela, T. Toomsoo","doi":"10.1109/ICMLA.2016.0036","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0036","url":null,"abstract":"This study aims to digitalize the Poppelreuter's overlapping figures test. The Poppelreuter's test used in psychology and neurology to assess visual perceptual function. Its recent modification performed with pencil and paper. Replacing the pencil and paper by the tablet computer equipped with the stylus, allows recording and analyzing fine motor motions observed during the test. On the one hand, this provides an opportunity to compute the measures describing condition of the participant. On the other hand, this possess two major problems to be tackled. The first one is to recognize the contours of the overlapping objects drawn by the participant. In the case of severe neurologic disorder, dissimilarity between the etalon shape and drawn contour may be very high. The second problem is to identify errors made during the drawing. The both problems are addressed within this study. Traditional machine learning techniques K-means, k-nearest neighbors and random forest used in this study to identify drawn contours and drawing mistakes. Finally, to demonstrate applicability of the proposed approach, kinematic parameters analyzed for the pilot groups of Parkinson Disease patients and healthy individuals.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116201227","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: 7

Spontaneous Facial Expression Recognition: A Part Based Approach 自发面部表情识别:基于局部的方法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0147

N. Perveen, Dinesh Singh, C. Mohan

引用次数: 44

Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals 基于空间变压器网络和连接部件区域的集装箱代码自动识别方案

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0130

Ankit Verma, Monika Sharma, R. Hebbalaguppe, Ehtesham Hassan, L. Vig

{"title":"Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals","authors":"Ankit Verma, Monika Sharma, R. Hebbalaguppe, Ehtesham Hassan, L. Vig","doi":"10.1109/ICMLA.2016.0130","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0130","url":null,"abstract":"Container identification and recognition is still performed manually or in a semi-automatic fashion in multiple ports globally. This results in errors and inefficiencies in port operations. The problem of automatic container identification and recognition is challenging as the ISO standard only prescribes the pattern of the code and does not specify other parameters such as the foreground and background colors, font type and size, orientation of characters (horizontal or vertical) so on. Additionally, the corrugated surface of container body makes the two dimensional projection of the text on three dimensional containers slanted and jagged. We propose a solution in the form of an end-to-end pipeline that uses Region Proposals generated based on Connected Components for text detection in conjunction with Spatial Transformer Networks for text recognition. We demonstrate via our experimental results that the pipeline is reliable and robust even in situations when the code characters are highly distorted and outperforms the state-of-the-art results for text detection and recognition over the containers. We achieve text coverage rate of 100% and text recognition rate of 99.64%.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128590068","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: 14

Parallel Text Identification Using Lexical and Corpus Features for the English-Maori Language Pair 基于词汇和语料库特征的英语-毛利语对平行文本识别

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0163

Mahsa Mohaghegh, A. Sarrafzadeh

引用次数: 2

Exposing Inpainting Forgery in JPEG Images under Recompression Attacks 在重新压缩攻击下暴露JPEG图像中的绘画伪造

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0035

Qingzhong Liu, A. Sung, Bing Zhou, Mengyu Qiao

{"title":"Exposing Inpainting Forgery in JPEG Images under Recompression Attacks","authors":"Qingzhong Liu, A. Sung, Bing Zhou, Mengyu Qiao","doi":"10.1109/ICMLA.2016.0035","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0035","url":null,"abstract":"Inpainting, originally designed in computer vision to reconstruct lost or deteriorated parts of images and videos, has been used for image tampering, including region filling and object removal to alter the truth. While several types of tampering including copy-move and seam carving forgery can now be successfully exposed in image forensics, there has been very little study to tackle inpainting forgery in JPEG images, the detection of which is extremely challenging due to the post-recompression attacks performed to cover or compromise original inpainting traces. To date, there is no effective way to detect inpainting image forgery under combined recompression attacks. To fill such a gap in image forensics and reveal inpainting forgery from the post-recompression attacks in JPEG images, we propose in this paper an approach that begins with large feature mining in discrete transform domain, ensemble learning is then applied to deal with the high feature dimensionality and to prevent the overfitting that generally happens to some regular classifiers under high feature dimensions. Our study shows the proposed approach effectively exposes inpainting forgery under post recompression attacks, especially, it noticeably improves the detection accuracy while the recompression quality is lower than the original JPEG image quality, and thus bridges a gap in image forgery detection.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957769","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: 9