2010 Second International Conference on Machine Learning and Computing最新文献_第5页

Microcontroller Based Neural Network Controlled Low Cost Autonomous Vehicle 基于微控制器的神经网络控制低成本自动驾驶汽车

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.71

U. Farooq, Muhammad Amar, Eitzaz ul Haq, M. Asad, Hafiz Muhammad Atiq

{"title":"Microcontroller Based Neural Network Controlled Low Cost Autonomous Vehicle","authors":"U. Farooq, Muhammad Amar, Eitzaz ul Haq, M. Asad, Hafiz Muhammad Atiq","doi":"10.1109/ICMLC.2010.71","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.71","url":null,"abstract":"In this paper, design of a low cost autonomous vehicle based on neural network for navigation in unknown environments is presented. The vehicle is equipped with four ultrasonic sensors for hurdle distance measurement, a wheel encoder for measuring distance traveled, a compass for heading information, a GPS receiver for goal position information, a GSM modem for changing destination place on run time and a nonvolatile RAM for storing waypoint data; all interfaced to a low cost AT89C52 microcontroller. The microcontroller processes the information acquired from the sensors and generates robot motion commands accordingly through neural network. The neural network running inside the microcontroller is a multilayer feed-forward network with back-propagation training algorithm. The network is trained offline with tangent-sigmoid as activation function for neurons and is implemented in real time with piecewise linear approximation of tangent-sigmoid function. Results have shown that upto twenty neurons can be implemented in hidden layer with this technique. The vehicle is tested with varying destination places in outdoor environments containing stationary as well as moving obstacles and is found to reach the set targets successfully.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130710913","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: 27

Classification of Alzheimer's Disease and Parkinson's Disease by Using Machine Learning and Neural Network Methods 用机器学习和神经网络方法分类阿尔茨海默病和帕金森病

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.45

S. Joshi, Deepa V Shenoy, Vibhudendra Simha Gg, P.L. Rrashmi, K. Venugopal, L. Patnaik

{"title":"Classification of Alzheimer's Disease and Parkinson's Disease by Using Machine Learning and Neural Network Methods","authors":"S. Joshi, Deepa V Shenoy, Vibhudendra Simha Gg, P.L. Rrashmi, K. Venugopal, L. Patnaik","doi":"10.1109/ICMLC.2010.45","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.45","url":null,"abstract":"Data mining is a fast evolving technology, is being adopted in biomedical sciences and research. Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of neurodegenerative diseases. Given a data set of consists of 487 patients records collected from ADRC, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for AD (65%) and PD (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimer’s disease and Parkinson’s disease. This paper proposes a new model for the classification of Alzheimer’s disease and Parkinson’s disease by considering the most influencing risk factors. The main focus was on the selection of most influencing risk factors for both AD and PD using various attribute evaluation scheme with ranker search method. Different models for the classification of AD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. It was found that some specific genetic factors, diabetes, age and smoking were the strongest risk factors for Alzheimer’s disease. Similarly, for the classification of Parkinson’s disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131928666","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: 61

Autonomous Navigation in Rubber Plantations 橡胶种植园的自主导航

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.53

Santhosh Simon

{"title":"Autonomous Navigation in Rubber Plantations","authors":"Santhosh Simon","doi":"10.1109/ICMLC.2010.53","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.53","url":null,"abstract":"Agriculture is the main revenue resource of many states of India. But due to new opportunities and higher pays the strength of the labour community is getting greatly reduced. Since the farming community is unable to sustain continuation of farming, the corporate world in now becoming the caretaker of the agricultural industry. This has lead to a new era of agriculture. Today’s agriculture needs to find new ways to improve efficiency. The key point here is to replace the labourers with intelligent machines. One way to achieve this goal is to utilize available robotics and artificial intelligence technologies in the form of smarter intelligent machines to reduce energy inputs in more effective ways. These machines should be able to work like humans in farms and produce results equal to or higher than the existing ones. The advent of autonomous system architecture gives us the opportunity to develop a complete new range of agricultural equipment based on smart machines. By using these machines we can increase the productivity, improve safety, and reduce costs for many agricultural purposes. The author proposes the development of a machine which can autonomously navigate through rubber plantations with obstacle detection capabilities. Sub tasks like tapping and latex collection can be attached to this and can be utilized to improve the efficiency and reduce the cost of production of natural rubber.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115045241","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

Wall Static Pressure Variation in Sudden Expansion in Flow Through De Laval Nozzles at Mach 1.74 And 2.23: A Fuzzy Logic Approach 1.74和2.23马赫数条件下德拉瓦尔喷管突然膨胀时壁面静压变化的模糊逻辑分析

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.75

K. Pandey

引用次数: 4

Real-Time Robotic Hand Control Using Hand Gestures 使用手势的实时机器人手控制

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.12

J. Raheja, Radhey Shyam, Umesh Kumar, B. Prasad

{"title":"Real-Time Robotic Hand Control Using Hand Gestures","authors":"J. Raheja, Radhey Shyam, Umesh Kumar, B. Prasad","doi":"10.1109/ICMLC.2010.12","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.12","url":null,"abstract":"this paper presents a new approach for controlling robotic hand or an individual robot by merely showing hand gestures in front of a camera. With the help of this technique one can pose a hand gesture in the vision range of a robot and corresponding to this notation, desired action is performed by the robotic system. Simple video camera is used for computer vision, which helps in monitoring gesture presentation. This approach consists of four modules: (a) A real time hand gesture formation monitor and gesture capture, (b) feature extraction, (c) Pattern matching for gesture recognition, (d) Command determination corresponding to shown gesture and performing action by robotic system. Real-time hand tracking technique is used for object detection in the range of vision. If a hand gesture is shown for one second, the camera captures the gesture. Object of interest is extracted from the background and the portion of hand, representing the gesture, is cropped out using the statistical property of hand. Extracted hand gesture is matched with the stored database of hand gestures using pattern matching. Corresponding to the matched gesture, action is performed by the robot","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122654299","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: 125

Forecasting Employee Retention Probability Using Back Propagation Neural Network Algorithm 利用反向传播神经网络算法预测员工留任概率

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.35

Gaurang Panchal, A. Ganatra, Y. Kosta, D. Panchal

引用次数: 22

Experimental Comparison of Advance Control Strategies which Use Pattern Recognition Technique for Nonlinear System 非线性系统模式识别超前控制策略的实验比较

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.15

Polaiah Bojja, K. Abraham, S. Varadarajan, M. N. G. Prasad

{"title":"Experimental Comparison of Advance Control Strategies which Use Pattern Recognition Technique for Nonlinear System","authors":"Polaiah Bojja, K. Abraham, S. Varadarajan, M. N. G. Prasad","doi":"10.1109/ICMLC.2010.15","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.15","url":null,"abstract":"On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this work, cutting force measurement is used for a reliable on-line flank wear estimation and tool life monitoring. Models for flank wear will be obtained as a function of machining parameters and dynamic cutting forces. The coefficients for flank wear models are obtained by using the experimental results. Then the non-linear dynamic models obtained are calibrated with the actual conditions. These developed models will be used for the simulation of flank wear and using control variable such as cutting speed; the flank wear will be controlled. For model validation, the flank wear is estimated using a non-linear model. In the present work, an attempt has been made to control the flank wear during turning of on-line cutting process using the Fuzzy Logic Controller and Neural network based on self-tuning of PID controller approaches. Those approaches are treat the material as dynamic system and involve developing state space models from available material behavior model. The evaluation of performance criteria can be compared for those approaches of PI controller with Fuzzy Logic Controller and Neural network based on self-tuning of PID controller. Simulation studies are carried-out for the non-linear system using MATLAB software.","PeriodicalId":423912,"journal":{"name":"2010 Second International Conference on Machine Learning and Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121781928","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: 5

Recognition of Faces Using Improved Principal Component Analysis 基于改进主成分分析的人脸识别

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.48

E. Gomathi, Senior Lecturer, K Baskaran

引用次数: 9

SVM Model for Amino Acid Composition Based Prediction of MMPs and ADAMs 基于MMPs和ADAMs的氨基酸组成预测SVM模型

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.21

Kumud Pant, B. Pant, K. Pardasani

引用次数: 1

Using Chemoinformatics and Rough Set Rule Induction for HIV Drug Discovery 基于化学信息学和粗糙集规则归纳法的HIV药物发现

2010 Second International Conference on Machine Learning and Computing Pub Date : 2010-02-09 DOI: 10.1109/ICMLC.2010.49

G. Mohaar, Ramanpreet Singh, Vaneet Singh

引用次数: 1