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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 269 条记录，以下是141-150 订阅

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Detection of Skin Allergy Causing Chemicals in Textile Fibers using Principal Component Analysis and Support Vector machine 21

Detection of Skin Allergy Causing Chemicals in Textile Fiber...

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Proceedings of the 3rd international conference on Intelligent Science and Technology

作者： Jessie R. Balbin Gio Carlo F. Bautista Gella Mae J. Flores Matthew C. Teves School of EECE Mapua UniversityPhilippines Philippines

ISBN: (纸本)9781450388009

This paper presents the use of raspberry pi and an array of sensors to determine the various substances found in textile fabrics, mainly garments bought in fast fashion stores, which can be the cause of skin allergies. The support vector machine and principal component analysis machine learning algorithms are used to identify the type of skin allergy, the chemicals present and the type of fabric that was tested.

关键词： Skin Allergy Support Vector machine pattern recognition Principal Component Analysis Textile Fibers

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A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition Software engineering

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Application of slack variable-optimized SVM on piano teaching reform

Application of slack variable-optimized SVM on piano teachin...

引用

2021 3rd international conference on Applied machine learning and Data Science, ICAMLDS 2021

作者： Li, Tianjiao Shangdong Management University Shandong Province Jinan City China

The data mining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. Based on the advantages of the SVM algorithm, this paper optimizes the slack variables, establishes a teaching quality evaluation model, and takes 500 subjects of the questionnaire survey as data samples by comparing with a variety of classification methods to evluate, and the results show that the accuracy of the SVM model is 92.70%, which proves the effectiveness of the research model. © Published under licence by IOP Publishing Ltd.

关键词： pattern recognition

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

3rd International Conference on Information and Communicatio...

引用

3rd international conference on Information and Communication Technology and Applications, ICTA 2020

ISBN: (纸本)9783030691424

The proceedings contain 55 papers. The special focus in this conference is on Information and Communication Technology and Applications. The topics include: Application of Supervised machine learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems;anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers;Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF;A Conceptual Hybrid Model of Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) for Masquerade Attack Detection;An Automated Framework for Swift Lecture Evaluation Using Speech recognition and NLP;DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal;hausa Intelligence Chatbot System;an Empirical Study to Investigate Data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced Data;a Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value;application of Big Data Analytics for Improving learning Process in Technical Vocational Education and Training;validation of Student Psychological Player Types for Game-Based learning in University Math Lectures;outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score;an Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence;optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm;identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary pattern Features;feature Weighting and Classification Modeling for Network Intrusion Detection Using machine learning Algorithms;comparative Performance Analysis of Anti-virus Software.

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

3rd International Conference on Smart Innovations in Communi...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

ISBN: (纸本)9789811553448

The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet;analysis and prediction of fuel oil in a terminal;an android malware detection method based on native libraries;fire early warning system based on precision positioning technology;the construction of learning resource recommendation system based on recognition technology;review of ultra-low-power cmos amplifier for bio-electronic sensor interface;an enhancement of underwater images based on contrast restricted adaptive histogram equalization for image enhancement;hybridization of social spider optimization (sso) algorithm with differential evolution (de) using super-resolution reconstruction of video images;enhanced convolutional neural network (ecnn) for maize leaf diseases identification;facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm;visibility improvement of hazy image using fusion of multiple exposure images;comparative analysis of consensus algorithms and issues in integration of blockchain with iot;use of hadoop for sentiment analysis on twitter’s big data;enhanced mobility management model for mobile communications;multi-objective-based lion optimization algorithm for relay selection in wireless cooperative communication networks;embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings;prediction of different classes of skin disease using machine learning techniques;feature selection and classification based on swarm intelligence approach to detect malware in android platform;preface;trust-based fuzzy bat optimization algorithm for attack detection in manet;transfer learning: Survey and classification.

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Detecting distributed DoS attacks in autonomous vehicles external environment using machine learning techniques

Detecting distributed DoS attacks in autonomous vehicles ext...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： A. El-Ghamry M. Elhoseny Faculty of Computers and Information Mansoura University Mansoura Egypt

The use of autonomous vehicles rapidly growing, especially in smart cities. Autonomous vehicles exchange information with an external network through wireless communication, and hence security and privacy challenges become a major challenge for all user of autonomous vehicles. Attackers try to exploit the vulnerabilities in the wireless network that connect autonomous vehicles these by performing different types of attacks such as denial of service attack (DDOS) and injection attacks. To mitigate the catastrophic effect of these attacks, intrusion detection process is a must. In this paper, A novel intrusion-detection method is proposed to detect distributed denial of service attack (DDOS) in the wireless network of autonomous vehicles. In this method, the wireless network intrusion detection system (WNIDS) is applied on “CICDDoS2019” dataset for training and testing our model. First the dataset is cleaned and the most important features are selected using recursive feature elimination method which decrease the number of features to only 8 features. The reduced dataset is tested by two machine learning classifiers (Random Forest (RF) and Stochastic Gradient Descent (SGD)). The evaluation results showed that both RF and SGD classifiers can be used to detect DDOS attack with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Relying only on 8 features only make the proposed method to be applicable to any IoT application such as internet of autonomous vehicles.

关键词： computer network security gradient methods Internet of Things learning (artificial intelligence) mobile robots pattern classification random forests support vector machines

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The impact of feature quality on SDN traffic learning and classification

The impact of feature quality on SDN traffic learning and cl...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： A. Alzu'bi J. Khrais N. Ennab A. Abuarqoub Department of Computer Science Jordan University of Science and Technology Irbid Jordan Cardiff School of Technologies Cardiff Metropolitan University Cardiff UK

Network traffic classification is crucial for facilitating sophisticated network management. Software-defined networks (SDN) provide a featured architecture where the control and data planes are decoupled to enable dynamic configuration. The control plane functionality is implemented in a programmable centralized controller that enables effective use of data-driven based on network traffic classification using efficient machine learning (ML) algorithms. However, there is a lack of standardized benchmarking criteria agreed by the SDN community to evaluate the techniques proposed to collect and classify the network traffic features. This paper presents an empirical analysis to investigate the impact of SDN traffic features on the performance of ML-based classifiers. This study has been conducted using a publicly available SDN traffic dataset. The procedure of feature engineering has also been comprehensively evaluated through three approaches of feature selection, which are low variance filter, high correlation filter, and backward feature elimination. The experimental results show that the performance of ML classifiers is highly driven by the quality traffic features fed to them; the highest quality the traffic features are the best accuracy is achieved.

关键词： computer network security learning (artificial intelligence) pattern classification software defined networking telecommunication control telecommunication network management telecommunication traffic

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Outbreak Prediction of COVID-19 3rd Wave in Asia 4th

Outbreak Prediction of COVID-19 3rd Wave in Asia

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4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： Sharma, Avinash Yadav, Dharminder Reen, L.S. Chandra, Umesh Mullana Haryana Ambala133207 India Department of Computer Science and Technology Glocal University UP Saharanpur India Seth Jai Parkash Institute of Information Technology Yamuna Nagar Haryana Radaur India Department Computer Science and Technology Banda University of Agriculture and Technology UP Banda India

ISBN: (纸本)9789811950360

Coronavirus is a pandemic for whole world and infected more than 200 countries of the world. Spreading of coronavirus started from china at the end of December and within three months, it infected whole world. Coronavirus is belonging to beta coronavirus family. Common symptoms of coronavirus are fever, dry cough, fatigue, and respiratory-related problem. This paper tries to study the infection rate of coronavirus in Asian countries, rest of world countries, and overall world countries. Asia is largest continent of the world which contains approximate 50 countries and highest contributes in GDP. Population of Asian countries 446.27 crore, that is 60 percentage of whole world population and covers 30 percent geographical area of world. China and India are the most populated countries in Asia. Total confirmed cases 2,056,051, recovered cases 502,045, and death cases 134,177 in the world. Scholar also divides the Asia continent into six regions such East, South, Central, North, Southeast and Western Asia for better understanding infection of coronavirus. This paper analyzes the confirmed cases, recovered cases, and death cases in Asia and understands the infection pattern date wise and countries wise. machine learning algorithm is used for prediction infection in the world and also predicts future infection rate and death rate in Asian countries and world. Prophet is used for future prediction of confirmed and death cases in the Asian countries. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Graph Neural Networks for Functional Connectivity Analysis: Uncovering Neural Network Disruptions in Parkinson’s Disease

Graph Neural Networks for Functional Connectivity Analysis: ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning pattern recognition Biological neural networks Diseases

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Research on short term stock selection strategy based on machine learning

Research on short term stock selection strategy based on mac...

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international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Yanzheng Ren School of Mechatronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665417914

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the next 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： Analytical models machine learning algorithms Computational modeling machine learning Mathematical models Data models pattern recognition

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