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共 179 条记录，以下是151-160 订阅

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Design of Artificial Intelligence Control System for Automobile Electronic Information data

Design of Artificial Intelligence Control System for Automob...

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international conference on applied machine learning (ICAML)

作者： Hanhong Tan Guangdong University of Science & Technology Dongguan China

ISBN: (纸本)9781728192833

The calculation effect, rate of traditional automobile electronic information data was not good, artificial intelligence control system was added to this design. Through the core frame function module design, the design of the vehicle internal control components and the perfect design of the electronic artificial intelligence, the hardware design of the artificial intelligence of the vehicle electronic information data was completed. Through the control of interference rate design, automotive information electronic design and simplified manual control design, the artificial intelligence control software design of automotive electronic information data was completed. Intelligent data processing was performed to automotive electronic information through intelligent control. So as to extend the service life of automotive electronic control components, it could improve the maintenance and repair functions of automotive electrical appliances and circuits. According to the test of installing the artificial intelligence control system to monitor the electronic information usage data of the car, the final result was obtained. Under the control of artificial intelligence control system, the data processing speed of automotive electronic information was significantly improved to more than twice that of systemless control, providing more convenient automotive electronic information data intelligence processing function for the fast-growing information society.

关键词： Technological innovation Software design Process control Automotive electronics Maintenance engineering Control systems Automobiles

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Design and research of novel coronavirus epidemic screening system

Design and research of novel coronavirus epidemic screening ...

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international conference on applied machine learning (ICAML)

作者： Yue Chen Xiaomin Wang Airong Yu Jun Wang Hospital of Eastern Theater Command PLA Nanjing China The Army Engineering University of PLA Nanjing China School of Management Science and Engineering Nanjing University of information science &Technology Nanjing China

ISBN: (纸本)9781728192833

In view of the fact that there are many diagnosis and treatment areas under the jurisdiction of the eastern theater general hospital, which are widely distributed and difficult to centralize and manage, a new intelligent mode of “cloud + Network + terminal” is adopted to design and complete the network screening system for the new epidemic situation. The system uses distributed agent sharing mode to share data to the cloud database deployed in the hospital headquarters information private network in real time, realizing seamless connection with the epidemic situation screening Internet of things collection platform, telemedicine platform, isolated population health monitoring platform and epidemic situation screening real-time monitoring platform. It effectively improved the work of epidemic prevention and control, screening and collaborative management in the eastern theater general hospital.

关键词： COVID-19 Epidemics Time-frequency analysis Hospitals Telemedicine Distributed databases Collaboration

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Success of H1-B VISA Using ANN 2nd

Success of H1-B VISA Using ANN

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2nd international conference on machine learning and Information Processing, ICMLIP 2020

作者： Chatterjee, Priyadarshini Velpuru, Muni Sekhar Jagadeeswari, T. Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789813348585

The proposed work predicts the outcome of H1-B VISAS that are applied by professionals belonging to different fields. This VISA is applied on temporary basis and to specialized workers only. People with B-Tech degree or equivalent can apply for the VISA if the requisite skills are required by the employers of USA H1-B VISA has a time limit of 3 years but can be extended up to 6 years. Being one of the most sought-after VISAs, its approval rate is pretty low. In the year 2019, out of 200,000 applicants only 85,000 applications for the VISA got approved. So, the rate of approval is only 42%. On yearly basis, due to increase in competition, the approval rate of the VISA is getting stringent. There are several factors on which the selection rate of the VISA depends. For predicting the success rate of VISA approval, we have considered a proposed system that works on the data set downloaded from ***. The data set is then converted to numerical form using some encoding schemes. We also build an ANN model and use this data set to train the model. If the output is 0, then the VISA application is rejected, and if the output is 1, then the VISA application is accepted. This paper predicts the success of an individual in obtaining H1-B VISA. The proposed system in this paper obtains an accuracy of 94% in predicting the success of H1-B VISA. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Teaming Strategies Analysis with Network Model and Evaluation Model

Teaming Strategies Analysis with Network Model and Evaluatio...

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international conference on applied machine learning (ICAML)

作者： Kaiwei Zhang Haolang Shen Yujia He College of Information Science and Technology Jinan University Guangzhou China Jinan University - University of Birmingham Joint Institute Jinan University Guangzhou China School of Management Jinan University Guangzhou China

ISBN: (纸本)9781728192833

With the rapid development of network science and big data technology, managers are no longer satisfied with simple empirical theories, and have begun to use more complex mathematical models to study the cooperation and communication between people. Our model is built on this background. In this paper, we present a team cooperation evaluation and improvement model using the Huskies as an example. Known as the world's first sport, football has always been one of the most important sports in various countries. In order to win a football match, the physical fitness of the individual and the cooperation between the players are particularly important. With the intensive training of players from various countries, physical fitness of players is no longer the main reason for widening the gap. The cooperation between players and the overall strategy of the team have become crucial factors for winning on the court. No matter considering various kinds arrangement of sports venues or the division of labor and cooperation in social life, people can get inspiration from our model.

关键词： Training Analytical models Visualization Games Organizations Rhythm Market research

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A Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images 2nd

A Privacy-Preserving Federated-MobileNet for Facial Express...

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2nd international conference on applied Intelligence and Informatics, AII 2022

作者： Ghosh, Tapotosh Banna, Md. Hasan Al Nahian, Md. Jaber Al Kaiser, M. Shamim Mahmud, Mufti Li, Shaobao Pillay, Nelishia United International University Dhaka Bangladesh Bangladesh University of Professionals Dhaka Bangladesh Jahangirnagar University Dhaka Savar Bangladesh Department of Computer Science Nottingham Trent University Clifton Campus NottinghamNG11 8NS United Kingdom MTIF and CIRC Nottingham Trent University Clifton Campus NottinghamNG11 8NS United Kingdom School of Electrical Engineering Yanshan University Qinhuangdao066004 China Department of Computer Science University of Pretoria Hillcrest Pretoria South Africa

ISBN: (纸本)9783031248009

Facial expression recognition is an intriguing research area that has been explored and utilized in a wide range of applications such as health, security, and human-computer interactions. The ability to recognize facial expressions accurately is crucial for human-computer interactions. However, most of the facial expression analysis techniques have so far paid little or no concern to users’ data privacy. To overcome this concern, in this paper, we incorporated Federated learning (FL) as a privacy-preserving machine learning approach in the field of facial expression recognition to develop a shared model without exposing personal information. The individual models are trained on the different client devices where the data is stored. In this work, a lightweight Convolutional Neural Network (CNN) model called the MobileNet architecture is utilised to detect expressions from facial images. To evaluate the model, two publicly available datasets are used and several experiments are conducted. The result shows that the proposed privacy-preserving Federated-MobileNet approach could recognize facial expressions with considerable accuracy compared to the general approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

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Time Series Anomaly Detection Based on Graph Convolutional Networks

Time Series Anomaly Detection Based on Graph Convolutional N...

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international conference on applied machine learning (ICAML)

作者： Zhiwei Hu Tao Wu Yunan Zhang Jintao Li Longsheng Jiang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781728192833

With the popularization of my country's electric vehicles and the continuous deepening of smart grid upgrades, the electricity consumption data of domestic power grid companies has increased significantly. Due to inadequate detection and clever power theft methods of users, a large number of abnormal power data appear in the power consumption data of power companies. Aiming at the power theft problem of power grid companies and abnormal power data, this article suggests a power data outlier detection model in view of the combination of graph convolution and characteristic pool. First, use the time a train of directed horizontal view algorithm to delegate the power data as graph data; then, use the graph convolutional neural network and feature pooling layer to detect the abnormality of the transformed graph data. The validity of the advanced model is proving by standard graph data set and power used record data set of the power grid company. The method proposed in this paper provides an effective solution for the power theft problem and abnormal power data in the power grid.

关键词： Analytical models Power demand Convolution Time series analysis Companies Feature extraction data models

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BiLSTM-A: A missing value imputation method for PM2.5 prediction

BiLSTM-A: A missing value imputation method for PM2.5 predic...

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international conference on applied machine learning (ICAML)

作者： Nan Jiang Yanan Li Hua Zuo Hui Zheng Qinghe Zheng Qingdao Research Academy of Environmental Sciences Qingdao environmental protection scientific research and Design Co. Ltd Qingdao China Qingdao Research Academy of Environmental Sciences Qingdao China Shandong Provincial Big Data Bureau Jinan China School of Information Science and Engineering Shandong University Qingdao China

ISBN: (纸本)9781728192833

With the frequent occurrence of haze in recent years, air quality has attracted more and more public attention. Realtime air quality acquisition can help people to obtain environmental status in time and avoid environmental pollution exposure. However, confusion in sensor system behavior or errors in the transmission process can result in missing data. Missing data interpolation (MDI) is the task of predicting missing parts in the dataset. The problem of missing data draws difficulties in decision-making process, and there is an urgent need for accurate and effective estimation methods to impute the data. The existing imputation algorithms are usually assume that the data have obvious statistical regularity, which limit the interpolation model to the mathematical model with a clear and simple description, ignoring a large number of hidden key factors. Our goal is to address fewer data missing situations. In this study, a novel method based on bidirectional Long Short-Term Memory (LSTM) named BiLSTM-A is proposed for missing value imputation. We evaluated our method using a dataset containing daily PM2.5 measurements from the beijing region in 2015. The results show that our model has excellent performance. In all time intervals, our model achieves the minimum error.

关键词： Interpolation Pollution Estimation machine learning Air quality Prediction algorithms Mathematical models

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MM Big data Applications: Statistical Resultant Analysis of Psychosomatic Survey on Various Human Personality Indicators 2nd

MM Big Data Applications: Statistical Resultant Analysis of ...

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2nd international conference on Computational Intelligence, ICCI 2018

作者： Rastogi, Rohit Chaturvedi, Devendra Kumar Satya, Santosh Arora, Navneet Trivedi, Piyush Gupta, Mayank Singhal, Parv Gulati, Muskan Department of Computer Science and Engineering ABESEC Ghaziabad India Department of Electrical Engineering DEI Agra India Department of Rural Development IIT-Delhi New Delhi India Department of Mechanical Engineering IIT-Roorkee Roorkee India Centre of Scientific Spirituality DSVV Haridwar India IT Analyst Tata Consultancy Services Noida India

ISBN: (纸本)9789811382215

machines are getting intelligent day by day. Nowadays, the human being is so much complex that nobody can do any concrete or accurate behavioural or action-related predictions. The mood swing has been also an epidemic in modern India and all metro cities of the developed countries. We have grown rich in terms of money and physical facilities;modern science has gifted us many boons, but simultaneously the mental, physical and spiritual disorders have surprisingly disturbed the smile, peace and definite attitude and life style of individual and all human beings. The 3 M—Mobile, Money and Marriage are essential in one’s life, but the over addiction and misuse of resources, facilities and lack of right understanding with selfishness and ungrateful conduct have changed all the social parameters. So, presently the stress has been the biggest challenge against mankind like nuclear weapons, global warming and epidemics. It leads towards tension, frustration and depression and ultimately in extreme cases towards the self-suicide or the murder of innocents. The happiness index, safety of individual, living parameters have been drastically challenged us, and India specially has pathetic situation among global quality of life (QoL) index. The present paper is an effort to define a simulated model and framework for the subjective quality of stress into quantitative parameters and mathematically analysing it with the help of popular machine learning tools and applied methods. With the help of machine intelligence, authors are trying to establish a framework which may work as an expert system and may help the individual to grow self as a better human being. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Python

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A Framework Using Markov-Bayes’ Model for Intrusion Detection in Wireless Sensor Network 2nd

A Framework Using Markov-Bayes’ Model for Intrusion Detecti...

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2nd international conference on data science, machine learning and Applications, ICDSMLA 2020

作者： Kalnoor, Gauri Gowrishankar, S. Department of Computer Science and Engineering BMS College of Engineering Bangalore India

ISBN: (纸本)9789811636899

The existing Intrusion Detection Systems which are mostly designed for detecting the particular form of Intrusion for Wireless Sensor Network (WSN) has many restrictions for different types of attacks and network structures. A novel strategy for intrusion detection based on knowledge has to be applied where the attacks are prevented from creating deviation of normal features and also from various other aggregated shapes. Multiple types of attacks should be detected over different structures of network. Every year, security is provided to the networks such that intrusions are prevented, spending around more than billions of dollars, all over the world. Among them, few disruptions that occur for vital systems are considered as the most serious type of threat mainly in areas like hospitals, military, banks and other critical applications. Firstly, clusters are discovered in the features of the network using mean shift unsupervised clustering algorithm, in the phase of training. In the next stage, the revealed clusters are generalized as an anomaly, if there is definite amount of deviation taken at the preliminary stage from normal cluster which are captured during the initial stage of training, with occurrence of no attacks. Thus, as samples are traced in different stages, the threats need to be averted, with many solutions possible, and one of the best solutions possible is to design a model of Intrusion Detection System (IDS), with the approach of Bayesian and Hidden Markov Network. In the proposed framework, the IDS are designed with different processing levels of training and testing based on connection records. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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An overview of Cooperative Spectrum Sensing based on machine learning Techniques

An overview of Cooperative Spectrum Sensing based on Machine...

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international conference on Electronics, Control, Optimization and Computer science (ICECOCS)

作者： Chaymae GATTOUA Otman CHAKKOR Fouad AYTOUNA New Technology Trends(NTT) National School of Applied Sciences - Tetuan Tetuan Morocco Remote Sensing & Geographic Information System (RS-GIS)) National School of Applied Sciences - Tetuan Tetuan Morocco

ISBN: (数字)9781728169217

ISBN: (纸本)9781728169224

Cognitive Radio is an intelligent wireless communication system able of learning from the environment. It allows reusing of the radio resources available by users called Secondary Users without creating harmful interference with licensed users. Spectrum Sensing is the important function in Cognitive Radio technology. The application of machine learning techniques for Spectrum Sensing has a drawn interest in the literature. In this paper, we present an overview on machine learning techniques for Cooperative Spectrum Sensing. We compare three machine learning techniques, (Naive Bayes, SVM and Multilayer Perceptron) with traditional cooperative Spectrum Sensing techniques (Maximum Ratio Combining MRC, OR rule and And rule), under AWGN channel and Rayleigh channel. The performance evaluated using ROC curve, and the results demonstrate that MRC is the optimum technique under AWGN channel and SVM with Gaussian kernel function is the optimum machine learning technique under Rayleigh channel.

关键词： Sensors Support vector machines machine learning Wideband Feature extraction data models Cognitive radio

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