2010 13th International Conference on Computer and Information Technology (ICCIT)最新文献_第3页

Comparison of artificially intelligent methods in short term rainfall forecast 人工智能方法在短期降雨预报中的比较

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723826

S. Monira, Zaman M. Faisal, Hideo Hirose

{"title":"Comparison of artificially intelligent methods in short term rainfall forecast","authors":"S. Monira, Zaman M. Faisal, Hideo Hirose","doi":"10.1109/ICCITECHN.2010.5723826","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723826","url":null,"abstract":"Rainfall forecasting has been one of the most scientifically and technologically challenging task in the climate dynamics and climate prediction theory around the world in the last century. This is due to the great effect of forecasting on human activities and also for the significant computational advances that are utilized in this research field. In this paper our main objective is to forecast over a very short-term and specified local area weather using local data which is not always available by forecast center but will be available in the future by social network or some other methods. For this purpose in this paper we have applied three different algorithms belonging to the paradigm of artificial intelligence in short-term forecast of rainfalls (24 hours) using a regional rainfall data of Bihar (India) as a case study. This forecast is about predicting the categorical rainfall (some pre-defined category based on the amount of total daily rainfall) amount for the next day. We have used two classifier ensemble methods and a single classifier model for this purpose. The ensemble methods used in this paper are LogitBoosting (LB), and Random Forest (RF). The single classifier model is a Least Square Support Vector Machine (LS-SVM). We have optimized each of the models on validation sets and then forecast with the optimum model on the out of sample (or test) dataset. We have also verified our forecast results with some of the latest verification tools available. The experimental and verification results suggest that these methods are capable of efficiently forecasting the categorical rainfall amount in short term.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133333512","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: 17

Multi-layer neural network classification of tongue movement ear pressure signal for human machine interface 面向人机界面的舌动耳压信号多层神经网络分类

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723896

K. Mamun, Manoj Banik, M. Mace, Mark E. Lutmen, R. Vaidyanathan, Shouyan Wang

{"title":"Multi-layer neural network classification of tongue movement ear pressure signal for human machine interface","authors":"K. Mamun, Manoj Banik, M. Mace, Mark E. Lutmen, R. Vaidyanathan, Shouyan Wang","doi":"10.1109/ICCITECHN.2010.5723896","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723896","url":null,"abstract":"Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected, corresponding to six types of controlled movements and activity relating to the potentially interfering environment including when a subject spoke, coughed or drank. The signal processing algorithm involved TMEP signal detection, segmentation, feature extraction and selection, and classification. The features of the segmented TMEP signals were extracted using the wavelet packet transform (WPT). A multi-layer neural network was then designed and tested based on statistical properties of the WPT coefficients. The average classification performance for discriminating interference and controlled movement related TMEP signal achieved 97.05%. The classification of TMEP signals based on the WPT is robust and the interferences to the controlling commands of TMEP signals in assistive human machine interface can be significantly reduced using the multi-layer neural network when considered in this challenging environment.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199662","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

Dynamic TDMA slot reservation protocol for cognitive radio ad hoc networks 认知无线电自组织网络的动态TDMA时隙预留协议

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723844

S. M. Kamruzzaman, M. S. Alam

引用次数: 17

Maximization of the gradient function for efficient neural network training 最大化梯度函数的有效神经网络训练

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723895

S. U. Ahmed, M. Shahjahan, K. Murase

引用次数: 5

Soft computing models to predict daily temperature of Dhaka 预测达卡日气温的软计算模型

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723832

S. Banik, M. Anwer, A. Khan

引用次数: 3

Leaf shape identification based plant biometrics 基于植物生物特征的叶片形状识别

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723901

Javed Hossain, M. Ashraful Amin

{"title":"Leaf shape identification based plant biometrics","authors":"Javed Hossain, M. Ashraful Amin","doi":"10.1109/ICCITECHN.2010.5723901","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723901","url":null,"abstract":"This paper presents a simple and computationally efficient method for plant species recognition using leaf image. This method works only for the plants with broad flat leaves which are more or less two dimensional in nature. The method consists of five major parts. First, images of leaf are acquired with digital camera or scanners. Then the user selects the base point of the leaf and a few reference points on the leaf blades. Based on these points the leaf shape is extracted from the background and a binary image is produced. After that the leaf is aligned horizontally with its base point on the left of the image. Then several morphological features, such as eccentricity, area, perimeter, major axis, minor axis, equivalent diameter, convex area and extent, are extracted. A unique set of features are extracted from the leaves by slicing across the major axis and parallel to the minor axis. Then the feature pointes are normalized by taking the ratio of the slice lengths and leaf lengths (major axis). These features are used as inputs to the probabilistic neural network. The network was trained with 1200 simple leaves from 30 different plant species. The proposed method has been tested using ten-fold cross-validation technique and the system shows 91.41% average recognition accuracy.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284671","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: 110

Kinetisation of view of 3D point set 三维点集视图的运动化

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723878

M. A. Wahid, M. Kaykobad, M. Hasan

引用次数: 2

Facial expression recognition based on a weighted Local Binary Pattern 基于加权局部二值模式的面部表情识别

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723877

M. Shoyaib, M. Abdullah-Al-Wadud, Jo Moo Youl, Muhammad Mahbub Alam, O. Chae

引用次数: 4

Special feature extraction techniques for Bangla speech 孟加拉语语音特征提取技术

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723839

M. M. Rahaman, Anindya Das, M. Z. Nayen, Md. Saidur Rahman

引用次数: 4

Optimization technique for configuring IEEE 802.11b access point parameters to improve VoIP performance 配置IEEE 802.11b接入点参数的优化技术，提高VoIP性能

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI: 10.1109/ICCITECHN.2010.5723919

T. Chakraborty, A. Mukhopadhyay, I. Saha Misra, S. Sanyal

引用次数: 13