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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 732 条记录，以下是461-470 订阅

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A Conceptual Framework for Bus Arrival Prediction Based on Spark Framework and machine learning Approaches 5

A Conceptual Framework for Bus Arrival Prediction Based on S...

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5th Joint international conference on Digital Arts, Media and Technology with 3rd ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020

作者： Ye, Li Thiengburanathum, Pree Thiengburanathum, Poon Chiang Mai University Software Engineering Department 50200 Thailand Chiang Mai University Faculty of Civil Engineering 50200 Thailand

ISBN: (纸本)9781728163987

Recently, the bus system has been deployed as a new option for public transportation in Chiang Mai. However, the reason why the people hesitate to take the bus because they are not confident about accuracy of the bus schedule. Predicting the bus arriving time at a certain bus station is a challenging problem, due to concerns with real-Time data pre-processing, numerous data inputs, and the level of predictive accuracy. Previous studies that related to bus time arrival prediction have applied statistical and machine-learning methods. However, the time series problem is rarely considered in the previous machine-learning prediction methods. Moreover, some models only analyze small amounts of data, which leads to poor accuracy and poor speed of prediction. This paper reviewed previous five years studies related to the prediction of bus arrival time. The paper highlights the current research gaps and applications of bus arrival time prediction. We proposed the research framework, as well as the possible research trends and challenges. © 2020 IEEE.

关键词： Forecasting

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Performance analysis of fingerprint recognition using machine learning algorithms 1

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3rd international conference on Computational Intelligence and Informatics, ICCII 2018

作者： Velapure, Akshay Talware, Rajendra Department of E&TC Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9789811514807

ISBN: (纸本)9789811514791

This work uses supervised machine learning algorithms to validate the performance of fingerprint recognition. We extracted fingerprint ridge contours, a level 3 feature, from low-resolution fingerprint images that are used for fingerprint recognition. The fingerprint classification is done using support vector machine and logistic regression classifiers. The database of color images taken from the Internet is split into training and test datasets. First, the images are enhanced using Gabor filter and wavelet. The ridge contours are extracted using canny edge detection filter. We create feature vectors from the extracted ridge contours. These features are used for the fingerprint recognition. The performance of the fingerprint recognition system is evaluated and analyzed using different machine learning algorithms. The Python 2.7 programming language using OpenCV, sklearn, Pickle, NumPy packages for image processing is used in this work. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Gabor filters

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A multimodal-signals-based gesture recognition method for human machine interaction 3

A multimodal-signals-based gesture recognition method for hu...

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3rd international conference on Unmanned Systems (ICUS)

作者： Zhao, Xiaochuan Huang, Jie Zheng, Junzhe Ma, Yanlin Tang, Hongyi Inst Comp Appl Technol China Weapon Ind Beijing Peoples R China Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781728180250

As the capacity for machines to extend human capabilities continues to grow, the communication channels used also need to expand. Allowing machines to translate gestural command into robot movements can make the communication between humans and machines more similar to interpersonal communication. Yet for operating requirements, instantaneity and precision of the interaction must reach the application level in realistic scenarios, and the accuracy of results cannot be guaranteed by the judgement of a single motion sensor. Therefore, exploring a gesture detection technology that integrates multisensor information is very necessary. The presented work takes a step towards real-time gesture detection by fusing multiple physiological signals with wearable motion sensors. An algorithm is presented for processing and extracting motion signal acquired via inertial measurement unit (IMU) and electromyography (EMG) with a high error-tolerant way of wearing, and it is applied to the gesture recognition model established in reasonable threshold and logical judgment. This enables real-time gesture detection of 24 various assembled movements such as rotating palms while bending arms, clenching fist when unwinding upper limb. The result of involving intentional behavior by analyzing electroencephalogram (EEG) is also added to the gesture recognition process for eliminating unconscious actions. Together, these pipelines offer efficient gesture vocabulary suitable for remotely controlling robots. Experiments evaluate classifier performance and interface efficacy. The system successfully detected 95.6% of 360 commands, and the average processing time was 25.4ms among all the 15 trials of the experiment.

关键词： Gestsure Recognition EMG EEG Inertial Measurement Unit Muscle-Computer-Interfaces

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Combining machine and Automata learning for Network Traffic Classification 1

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3rd IFIP WG 1.8 international conference on Topics in Theoretical Computer Science, TTCS 2020

作者： Sabahi-Kaviani, Zeynab Ghassemi, Fatemeh Alimadadi, Zahra School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences PO.Box 19395-5746 Tehran Iran

ISBN: (数字)9783030578527

ISBN: (纸本)9783030578510

Viewing the generated packets of an application as the words of a language, automata learning can be used to derive the behavioral packet-based model of applications. The alphabets of the learned automata, manually defined in terms of packets, may cause overfitting. As some packets always appear together, we apply machine learning techniques to automatically define the alphabet set based on the timing and statistical features of packets. Using the learned automata models, the classifier should detect the accepted words of the models in the input. To improve this time-consuming process, we present a framework, called NeTLang, that identifies the application model in terms of k-testable languages. The classification problem is reduced to observing only symbols from the input with the help of machine learning techniques. Our framework utilizes the two diverse automata learning and machine learning techniques to build on their strengths (to be fast and accurate) and to eliminate their weaknesses (i.e., ignoring temporal relations among packets). According to our results, NeTLang outperforms the state-of-the-art methods using each technique alone. © 2020, IFIP international Federation for Information processing.

关键词： machine learning

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On-line data processing, simulation and forecasting of the coronavirus disease (COVID-19) propagation in Ukraine based on machine learning approach 3rd

On-line data processing, simulation and forecasting of the c...

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3rd international conference on Data Stream and Mining and processing, DSMP 2020

作者： Chumachenko, Dmytro Chumachenko, Tetyana Meniailov, Ievgen Pyrohov, Pavlo Kuzin, Ihor Rodyna, Roman National Aerospace University "Kharkiv Aviation Institute" Kharkiv Ukraine Kharkiv National Medical University Kharkiv Ukraine State Institution "Public Health Center of the Ministry of Health of Ukraine" Kyiv Ukraine

ISBN: (纸本)9783030616557

The article analyzes the epidemic process of a new coronavirus infection. The influence of COVID-19 on society, education, healthcare and other areas is analyzed. The analysis of restrictive measures that are implemented in different countries is carried out. Based on machine learning methods, a model for the spread of the incidence of COVID-19 has been developed. The forecast of incidence in Ukraine is calculated. The accuracy of the forecast is 97.6%. For automatic calculation of predicted morbidity, a web service has been developed for processing data in real time. The developed model enables to conduct prospective monitoring of the epidemic situation, redistribute limited resources between different regions of the country that need them more at the forecast time, introduce new control methods or, on the contrary, weaken restrictive measures, and adjust measures depending on the forecast situation on the simulated territory. © 2020, Springer Nature Switzerland AG.

关键词： Coronavirus

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3rd international conference on Data Management, Analytics and Innovation, ICDMAI 2019

3rd International Conference on Data Management, Analytics a...

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3rd international conference on Data Management, Analytics and Innovation, ICDMAI 2019

ISBN: (纸本)9789813299481

The proceedings contain 35 papers. The special focus in this conference is on Data Management, Analytics and Innovation. The topics include: Automated Scheduling of Hostel Room Allocation Using Genetic Algorithm;Evaluation of ASTER TIR Data-Based Lithological Indices in Parts of Madhya Pradesh and Chhattisgarh State, India;Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data;automatic Robot processing Using Speech Recognition System;banking and FinTech (Financial Technology) Embraced with IoT Device;GRNN++: A Parallel and Distributed Version of GRNN Under Apache Spark for Big Data Regression;an Entropy-Based Technique for conferences Ranking;MapReduce mRMR: Random Forests-Based Email Spam Classification in Distributed Environment;the Impact of Sustainable Development Report Disclosure on Tax Planning in Thailand;ASK Approach: A Pre-migration Approach for Legacy Application Migration to Cloud;clustering and Labeling Auction Fraud Data;big Data Security Challenges and Preventive Solutions;role and Challenges of Unstructured Big Data in Healthcare;zip Zap Data—A Framework for ‘Personal Data Preservation’;a Systematic Mapping Study of Cloud Large-Scale Foundation—Big Data, IoT, and Real-Time Analytics;studies on Radar Imageries of Thundercloud by Image processing Technique;PURAN: Word Prediction System for Punjabi Language News;Implementation of hDE-HTS Optimized T2FPID Controller in Solar-Thermal System;Design of Sigma-Delta Converter Using 65 nm CMOS Technology for Nerves Organization in Brain machine Interface;Performance Comparison of machine learning Techniques for Epilepsy Classification and Detection in EEG signal;novel Approach for Plant Disease Detection Based on Textural Feature Analysis.

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CNN based disease detection approach on potato leaves 3

CNN based disease detection approach on potato leaves

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Asif, Md. Khalid Rayhan Rahman, Md. Asfaqur Hena, Most. Hasna Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728170893

Potatoes are a well-known vegetable to all of us. If the other countries are taken into consideration, it can be easily concluded that potatoes are the number one vegetable all over the world, which has been increasingly claimed by many Agricultural departments. Despite the hype, potato leaf disease causes significant damage to the potatoes.. Various types of diseases such as early blight, late blight, septoria blight etc. will attack potato plants and exhibit their syndrome in the leaf of these disorders. The farmer would not face incurring major economic losses if these outbreaks are detected at the primary stage and sufficient action is taken. The proposed model will strongly identify and detect diseases of potato leaf stand on image processing methods in this research paper. machine learning includes several algorithms, but the CNN model is used for this research to detect the disease from images of the potato leaf because in CNN is used for image classification it gives the best result than others. There are 5 algorithms is used for this research they are AlexNet, VggNet, ResNet, LeNet Sequential model which is our offered one. Normal disorder-impacted leaf were used for the model provided in order to segregate normal and abnormal aspects of potato leaf. Those kind of photographs are then analyzed through the algorithm provide the potato plant leaf is labeled as either diseased or normal. This provided model established 97% of great precision. © 2020 IEEE.

关键词： Vegetables

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Telecommunication Service Subscriber Churn Likelihood Prediction Analysis Using Diverse machine learning Model 3

Telecommunication Service Subscriber Churn Likelihood Predic...

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3rd international conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2020

作者： Oka, Ngurah Putu H. Arifin, Ajib Setyo Universitas Indonesia Department of Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9781728174037

The biggest problem that occurs in the telecommunication industry is increased level of customer churn. This is a very important problem that must be resolved by the company because customers who stop will have an impact on company retention. The usage of the machine learning model will certainly be able to help to predict customer trends and making precise decisions in the future. To get good results, this study is analyzed with one algorithm that had never been analyzed in previous studies to make predictions, namely Deep Neural Network (DNN). DNN compared to models that have been tested before, Random Forest and Extreme Gradient Boosting (XGBoost). This research analyzed the importance of the features, the handling toward the selection of appropriate features, and simplified the process of gathering data. The proposed model was trained and tested over Google Colaboratory using TensorFlow backend. The testing that has been done produces very good results for the Deep Neural Network (DNN) model, with a process of 68 seconds and an accuracy of 80.62%. Extreme Gradient Boosting (XGBoost) produces 76.45% accuracy with a processing time of 175 seconds, and random forest produces 77.87% with a sufficiently long processing time of up to 529 seconds. © 2020 IEEE.

关键词： Deep neural networks

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Digital signal Modulation Classification using Data Conversion Method based on Convolutional Neural Network 3

Digital Signal Modulation Classification using Data Conversi...

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2020 3rd international conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Yang, Yong Chen, Menghan Wang, Yizhu Ma, Piming 54th Research Institute of CETC Shijiazhuang050081 China Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control Shijiazhuang050081 China School of Information Science and Engineering Shandong University Qingdao266237 China

Automated modulation classification (AMC) plays a very important part in cognitive electronic warfare. Deep learning (DL) is a new machine learning (ML) method, which has been successfully implemented in many application fields. This paper proposes a new data conversion algorithm in order to gain a better classification accuracy of target signal modulation. Then a Convolutional Neural Network (CNN) architecture is developed, and its performance is proved to be better than the traditional data conversion method. In addition, the impacts of representation on classification performance are also analysed. Experimental results demonstrate the significant performance advantage of the proposed data conversion method using DL-based approach for modulation classification. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Lossy Data Compression using K-Means Clustering on Retinal Images using RStudio

Lossy Data Compression using K-Means Clustering on Retinal I...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： S. Sivaarunagirinathan B. Ajith Bala Shaik Fairooz G. Sasi Har Narayan Upadhyay V. Elamaran School of EEE SASTRA Deemed University Thanjavur India Malla Reddy Engineering College Secunderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (纸本)9781665438124

The massive volume of data limits the use of data on portable devices like mobile phones, as well as memory-limited devices like video game consoles. As a result, the demand for data compression methods in the field of signal and image processing is skyrocketing. Data compression required advanced ways to serve better, with a greater compression ratio, due to the rising demands, which expected more sophisticated algorithms. This article primarily focuses on image compression (retinal images) utilizing the k-means clustering technique, which is a popular unsupervised classification strategy in machine learning. The compression challenge in this study uses two retinal pictures. The most useful open-source statistical calculation tool is R programming, which is employed here with RStudio, an Integrated Development Environment (IDE). The use of R (RStudio) has had an impact across all domains of research and engineering. The test retinal images had a compression ratio of 33.3 percent, according to simulation data.

关键词： Image coding machine learning algorithms Data compression Clustering algorithms Process control Programming Retina

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