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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

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machine learning and Its Applications in Wireless Communications 6th

Machine Learning and Its Applications in Wireless Communicat...

引用

6th international conference on Communications, signal processing, and Systems (ICCSPS)

作者： Lv, Jiaqi Na, Zhenyu Liu, Xin Deng, Zhian Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China

ISBN: (纸本)9789811065712;9789811065705

machine learning (ML) can improve system performance in many fields such as data dining, object tracking, spectrum sensing and indoor positioning. A review on ML was presented in this paper. Firstly, we looked back to the development, definition and classification of ML;secondly, we summarized the basic principle, mathematical formulation and application methods of two classic algorithms named error back-propagation (BP) and clustering;then, we focused on advanced and typical applications of ML in communication systems like cognitive radio networks (CRNs) and positioning system;finally, we concluded that the system performance could be improved by ML technique.

关键词： machine learning Artificial neural network Clustering Cognitive radio network Indoor positioning

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A New Model for Emotion Prediction in Music

A New Model for Emotion Prediction in Music

引用

international conference on signal processing and Communication (ICSC)

作者： Hardik Sharma Shelly Gupta Yukti Sharma Archana Purwar Department of CSE and IT JIIT Noida Campus India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

Music based sentiment analysis has various applications in the form of music recommendation system, sales and advertisement etc. Various studies have dealt with lyrics and used Natural Language processing to perform sentiment analysis. Others have directed their focus on the audio features to find relevant answers. But the biggest challenge faced while predicting music emotions is that no music depicts only a single emotion. therefore, in this study, Russell's scale is used to predict arousal and valence, rather than emotion. Audio feature selection via Multi-linear Regression is performed and comparative study is done between Linear Support Vector machine, Decision Tree, Kernel SVM, K nearest neighbours (K-NN), Naive Bayes, Logistic Regression and Random Forest on the audio features. Moreover, a hybrid model based on Multi-Layer Perceptron is proposed to enhance the precision of the predictions. the data set of this research has been taken from PMEmo 2019 data.

关键词： Support vector machines machine learning algorithms Feature extraction Hidden Markov models Regression tree analysis Logistics

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Novel AUD Likelihood detection based on EEG Classification 6

Novel AUD Likelihood detection based on EEG Classification

引用

6th IEEE international conference on Advances in Computing, Communication and Control, ICAC3 2019

作者： Patnaik, Suprava Ahire, Nitin Yadav, Sushilkumar Patel, Vaibhav Malliss, Jason Awale, R.N. School of Electronics Kalinga Inst. of Industrial Technology Bhubaneswar India Xavier Institute of Engineering Electronics and Telecommunication Dept. Mumbai India VJTIMumbai Dept. of Electrical Engineering India

ISBN: (纸本)9781728123868

this paper addresses a novel machine learning approach to classify EEG signals that can be useful for discovering likelihood of suffering from genetic alcoholism. Excessive consumption of alcohol can create genetic cognitive impairment and contribute to alcohol disorder in descendants. Children can inherit it either directly from parents or any ancestors. Revealing chances of AUD would help in deciding preventive intervention efficiently. Major contribution of this work is classification based on input augmentation and automatic feature extraction. Instead of using signal from many spatial nodes proposed classification task relies on EEG signal from only two electrodes, therefore it is computationally efficient. Results are shown for performance comparison between customary and augmented EEG as inputs to the classifier. Input augmentation is confirmed for 15% improvement in accuracy. © 2019 IEEE.

关键词： Biomedical signal processing

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A Relevance Vector machine Based Probability Prediction Method of Channel Available Time 6th

A Relevance Vector Machine Based Probability Prediction Meth...

引用

6th international conference on Communications, signal processing, and Systems (ICCSPS)

作者： Xu, Zhenyu Li, Dezhi Shi, Shuo Wang, Zhenbang Jiang, Jin Yao Harbin Inst Technol Commun Res Ctr Harbin Heilongjiang Peoples R China State Grid Harbin Elect Power Supply Co Harbin Heilongjiang Peoples R China Peoples Liberat Army Arch Troops 61741 Beijing Peoples R China

ISBN: (纸本)9789811065712;9789811065705

the uncertainty of spectrum resources will seriously affect the prediction results of cognitive radio, and then affect the communication channel allocation and spectrum access. therefore, it is very important to judge, analyze and estimate the state change of the spectrum resources. this paper introduces the RVM (Relevance Vector machine) theory and put forward the probability interval prediction method of channel state duration. Based on the traditional machine learning, RVM is integrated with the Bayesian inference framework, and it can give the estimate value of the prediction error and give the prediction interval, which can cover the real value well.

关键词： Cognitive radio

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Efficient Traffic Density Estimation Using Convolutional Neural Network

Efficient Traffic Density Estimation Using Convolutional Neu...

引用

international conference on signal processing and Communication (ICSC)

作者： Anant Agarwal Harshit Rana Vaibhav Vats Mukesh Saraswat Jaypee Institute of Information Technology Noida India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

Measuring traffic density is a problem which has been extensively worked upon by the Computer Vision community. Various solutions have been proposed over the years which have surely attributed to the deepening of the field of image analysis but the most significant improvements have been made in recent years after the huge advancements in the area of Deep neural networks. In this paper, an efficient convolutional neural network has been proposed to estimate the traffic density. For the same, a new dataset of labeled images are generated from openly available traffic video footage. the method is compared with state-of-the-art method in terms of accuracy and loss. the results show the significant improvement in the traffic density estimation.

关键词： Estimation Convolutional neural networks Convolution Training Cameras machine learning Optical imaging

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the Kernel Recursive Generalized Cauchy Kernel Loss Algorithm 6

The Kernel Recursive Generalized Cauchy Kernel Loss Algorith...

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6th international conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Shi, Wei Xiong, Kui Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781728158570

As a nonlinear measure similarity developed in a kernel space, the generalized correntropic loss (GC-Loss) has been successfully used for signal processing and machine learning in non-Gaussian situations thanks to its ability of extracting high-order statistical properties of data. However, a highly non-convex performance surface in GC-Loss leads to poor optimization performance. To address this issue, we propose a new similarity measure developed in the kernel space by combining a generalized Gaussian distributed kernel function into the Cauchy loss. By minimizing the proposed loss function and using the kernel method, a novel kernel recursive generalized Cauchy kernel loss (KRGCKL) algorithm in the reproducing kernel Hilbert space (RKHS) is proposed. Simulations on different examples under non-Gaussian noises show the superiorities of KRGCKL over other representative algorithms in terms of filtering accuracy and robustness to large outliers. © 2019 IEEE.

关键词： Gaussian noise (electronic)

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Real Time Weather Prediction System Using IOT and machine learning

Real Time Weather Prediction System Using IOT and Machine Le...

引用

international conference on signal processing and Communication (ICSC)

作者： Gaurav Verma Pranjul Mittal Shaista Farheen Department of Electronics & Communication Jaypee Institute of Information Technology Noida U.P. India Department of Electronics & Communication Dayananda Sagar College of Engineering Bangalore Karnataka India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

Today's the focus has been shifted towards intelligent technologies like IoT and machine learning. Many IoT hardware platforms are available for IoT implementations. ESP8266 chip is one of them. this paper implements the real time weather prediction system that can be used in number of applications like homes, industries, agriculture, stadiums etc. for predicting the weather information. the system utilizes a temperature and humidity sensor i.e. DHT11 and a light intensity sensor i.e. LDR. the sensed data from the sensors are uploaded to a thingSpeak cloud server using NodeMCU and ESP8266-01 module. the data is also displayed on a customized HTML webpage for monitoring the real time values. A logistic regression model is used for setting up the machine learning environment. this model is trained using the pre- recorded values of sensor data. Further, NodeMCU records the data from sensors i.e. temperature, humidity and light intensity and then the values are transferred to the Jupyter notebook that utilizes a python environment. this real time data is used to test the model and prediction is done for a particular value by blinking the led connected to NodeMCU.

关键词： Humidity Temperature measurement Temperature sensors Weather forecasting Data mining Real-time systems

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Diagnosis Based on machine learning for LTE Self-Healing 6th

Diagnosis Based on Machine Learning for LTE Self-Healing

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6th international conference on Communications, signal processing, and Systems (ICCSPS)

作者： Liu, Xuewen Chuai, Gang Gao, Weidong Ren, Yifang Zhang, Kaisa Beijing Univ Posts & Telecommun Minist Educ Key Lab Univ Wireless Commun Beijing 100876 Peoples R China

ISBN: (纸本)9789811065712;9789811065705

Self-Healing is one of the most important parts of self-organizing communication networks that offer detecting cells with a service of degradation, finding out the fault cause, and executing compensation and repairing actions. Diagnosis identifying the fault caused by problem cells is one of the most complex tasks. To perform the diagnosis, this paper presents two multi-classification diagnosis system based on machine learning methods, namely SVM (Support Vector machine) and AdaBoost (Adaptive Boosting). Results show that the performance of the AdaBoost method compared to SVM with linear kernel is significantly better in terms of diagnosis error rate and undetected rate, but the false positive rate of AdaBoost is a little higher than SVM. SVM is more focusing on filtering normal cases, but AdaBoost is more inclined to find out fault cases. It indicates that the diagnosis system based on AdaBoost has high accuracy and reliability than SVM in this data set.

关键词： Adaptive boosting

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A Complex Systems Approach To Feature Extraction For Chaotic Behavior Recognition

A Complex Systems Approach To Feature Extraction For Chaotic...

引用

international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Alireza Pourafzal Alireza Fereidunian Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Chaotic behavior may be observed in many natural and human-made time series, thus one of the first things to acquire is the knowledge on their chaotic behavior. Several approaches including model-based and data-driven classifications are utilized to address this issue; however, the computational burden arisen with the higher performance seems to be a challenge in real world problems. Chaos theory and the complexity theory share considerable similarities, which inspired us to exploit complex system characteristics, such as complexity, emergence, self-organization and predictability as the features of chaotic time series. the degree of complexity in Lorenz model is a well-known instance of chaotic behavior, demonstrated from periodic nonlinear to a chaotic state by analysis of Hopf and Pitchfork bifurcations and then the corresponding time series are gathered in a dataset. Subsequently, the complex systems characteristics are introduced as proper features for the generated dataset, and finally these features are extracted and validated via different machine learning methods with different evaluation metrics. the simulation results reveal the excellent performance of 92 percent precision on the test data, based on the extracted features. It is shown that the most important features to recognize the chaotic behavior are complexity, emergence, predictability, and self-organization.

关键词： Simulation Time series analysis Weather forecasting machine learning Feature extraction Complexity theory Complex systems

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Pose Estimation on 3-D Models Using ConvNets

Pose Estimation on 3-D Models Using ConvNets

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international conference on signal processing and Communication (ICSC)

作者： Keshav Bansal Abhishek Kumar Gupta Sushant Rai Bajrang Bansal Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida Uttar Pradesh India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

Pose estimation of various human activities has seen tremendous progress over the past few decades due to the advancement of computer vision technology. However, the current status of pose estimation is quite stagnated when seen in context to human behaviour analysis by using the pose. In this paper, pose estimation has made significant advancement in terms of dexterity and ingenuity, which is sufficient for human pose estimation. this work includes a large dataset of various human activities. the collected images cover a wide range of human activities that include various activities in various postures in different angles of viewpoints. We have annotated all images for various joint locations such as head, elbow, hands, and eyes via CSV data file. the pose analysis of this work will provide various ongoing research insights about the use of computer vision and Convolution neural networks (ConvNets) for practical purposes and would further instigate this technique as an alternative for VFX in the film industry.

关键词： Computational modeling Training Pose estimation Computer vision Computer architecture machine learning Predictive models

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