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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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An intelligent assessment method of power information intrusion tolerance based on machine learning 3

An intelligent assessment method of power information intrus...

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3rd international conference on computer Communication and Network Security, CCNS 2022

作者： Huang, M.L. Guo, S.X. Song, W.J. Liao, Z.G. Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangdong Zhuhai China

ISBN: (纸本)9781510660113

An intelligent evaluation method of power information intrusion tolerance based on machine learning is proposed to solve the problems of selection of evaluation indexes, poor evaluation accuracy and efficiency in the evaluation method of power information intrusion tolerance. An intelligent evaluation system of power information intrusion tolerance is established, and an intelligent evaluation model is established by using random forest algorithm. The random forest algorithm determines the evaluation weight according to the quantitative value of the index, sets the value of the intrusion risk function, and obtains the intelligent evaluation results of intrusion tolerance. The method studied is applied to the comparative evaluation experiment of the survivability situation of the power information physical system. The evaluation mean square deviation of the intelligent evaluation method based on machine learning is less than 0.1, the average time is 140.72ms, and the evaluation efficiency is increased by 42.51%. At 1000min, the reliability value is still 0.46, which has practical significance. © 2022 SPIE.

关键词： Efficiency

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Sentiment Analysis on Online learning for Higher Education During Covid-19 3

Sentiment Analysis on Online Learning for Higher Education D...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Remali, Nor Ain Syafiqah Shamsuddin, Mohd Razif Abdul-Rahman, Shuzlina Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia Research Initiative Group of Intelligent Systems Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Distance learning has become a necessary option to the education system for almost every nation because of the COVID-19 outbreak. Due to the closure of educational institutions, which creates obstacles to students' learning in the current COVID 19 pandemic environment, the role of information technology has gained momentum. With the current technology advancements, online learning is made possible for everyone. Although this method appears to be helpful to students and instructors, the effectiveness depended on several factors. For instance, the availability of internet services and the economic capacity of the users may affect the user experience of online learning. The student's reaction is desperately needed to enhance the university's effectiveness and acquire insight into student wants. However, it is difficult to collect and evaluate all the text data on social media, particularly Twitter. In this research, the analysis regarding perception students towards online learning is done through the Natural Language Processing technique which is sentiment analysis. The aim for this study is to determine the terms or keywords used for online learning and identify which machine learning classifiers work best with a large dataset. This research used a Twitter dataset that consisted of 38,602 tweets posted between 23rd July and 14th August 2020. Firstly, pre-processed the data to remove irrelevant tweets. Second, classify the tweet into three classes namely positive, negative, and neutral using rapid miner. Consequently, several machine learning classifiers are trained using different techniques which are Support Vector machine (SVM), Naïve Bayes (NB), Decision Tree (DCT), and Random Forest (RF). Support Vector machine classifier with a percentage split 80:20 using VADER Lexicon managed to get the highest accuracy which is approximately 90.41%. Finally, the result of the sentiment analysis has been shown using Power BI data visualization for better understanding. For fut

关键词： Students

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3rd international Workshop on Active Inference, IWAI 2022

3rd International Workshop on Active Inference, IWAI 2022

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3rd international Workshop on Active Inference, IWAI 2022

ISBN: (纸本)9783031287183

The proceedings contain 25 papers. The special focus in this conference is on Active Inference. The topics include: Active Inference Successor Representations;learning Policies for Continuous Control via Transition Models;attachment Theory in an Active Inference Framework: How Does Our Inner Model Take Shape?;capsule Networks as Generative Models;home Run: Finding Your Way Home by Imagining Trajectories;a Novel Model for Novelty: Modeling the Emergence of Innovation from Cumulative Culture;active Inference and Psychology of Expectations: A Study of Formalizing ViolEx;AIXI, FEP-AI, and Integrated World Models: Towards a Unified Understanding of Intelligence and Consciousness;intention Modulation for Multi-step Tasks in Continuous Time Active Inference;Interpreting Systems as Solving POMDPs: A Step Towards a Formal Understanding of Agency;learning Generative Models for Active Inference Using Tensor Networks;a Worked Example of the Bayesian Mechanics of Classical Objects;a Message Passing Perspective on Planning Under Active Inference;efficient Search of Active Inference Policy Spaces Using k-Means;value Cores for Inner and Outer Alignment: Simulating Personality Formation via Iterated Policy Selection and Preference learning with Self-World Modeling Active Inference Agents;deriving Time-Averaged Active Inference from Control Principles;disentangling Shape and Pose for Object-Centric Deep Active Inference Models;object-Based Active Inference;knitting a Markov Blanket is Hard When You are Out-of-Equilibrium: Two Examples in Canonical Nonequilibrium Models;spin Glass Systems as Collective Active Inference;mapping Husserlian Phenomenology onto Active Inference;the Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference;world Model learning from Demonstrations with Active Inference: application to Driving Behavior.

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Research on the Conservation of Historical Buildings Based on Digital 3D Reconstruction 3

Research on the Conservation of Historical Buildings Based o...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Yan Chen, Jiating Xiamen Institute of Technology Fujian Xiamen361021 China University of the East Manila1008 Philippines

As a cultural carrier, historical buildings embody the humanistic atmosphere of different periods and regions, and contain a profound cultural heritage, being the crystallization of the wisdom of the Chinese nation for thousands of years. At present, when repairing and protecting existing historic buildings, without complete construction information to maintain the "authenticity"they contain, any conservation action may cause irreversible secondary damage, making the restoration of historic buildings particularly difficult. Compared to traditional conservation methods, digital conservation has the advantages of efficiency, accuracy, three-dimensionality, realistic reproduction and preventable simulation, and the application of digital technology can improve the overall level of conservation of historic buildings. Therefore, for the better conservation of historic buildings, this paper summarises a feasible and realistic approach to the digital conservation of historical buildings by collecting information on Building A, using software to numerically simulate the 3D model for virtual restoration, and using digital technology for virtual restoration research. © 2023 The Authors. Published by Elsevier B.V.

关键词： Drones

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DDoS Attack Detection using Enhanced Long-Short Term Memory with Hybrid machine learning Algorithms 3

DDoS Attack Detection using Enhanced Long-Short Term Memory ...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sinthuja, M. Suthendran, K. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Department of Information Technology India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The Internet of Things (IoT) environment's heterogeneous components make the distributed denial-of-service (DDoS) attack a security challenge. The dependency on the network topology is one of the various drawbacks of the DDoS detection techniques currently in use. In this research work, a new variant of long-short term memory (LSTM) is proposed to detect DDoS attacks in the IoT environment. The proposed method uses a new hybrid method for optimizing the parameters of LSTM based on bacterial foraging optimization (BFO) with a firefly algorithm (FA) called BFOFA-LSTM. The performance of comparison algorithms is examined using two types of datasets including BoT-IoT and CICDDoS2019 and four different performance indicators are considered such as sensitivity, specificity, accuracy, and F-Measure. The proposed BFOFALSTM detection method produced a high detection rate when compared with other literature algorithms. © 2022 IEEE.

关键词： Long short-term memory

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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2023 international conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of machine learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

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State-of-the-art video recognition of human action under natural scenes 3

State-of-the-art video recognition of human action under nat...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Yi, Yang Mo, Qian Chen, Yihua School of Information and Intelligence Engineering Guangzhou Xinhua University Guangdong Guangzhou China

ISBN: (纸本)9781510661240

Human action video recognition focuses on discussing the theories and new algorithms to make computers understand the meanings of variety videos, such as those from TV, sports or family videos, and recently it plays an important role in advanced human-machine interface, security monitor, video retrieval and so on. The step-by-step process of traditional computational algorithm for human action recognition from videos are described in this paper, and some important deep learning models are also addressed by which some advantages and disadvantages of each approach in different tasks are analysed. Finally, some current trends and prospects of video recognition models are concluded. © 2022 SPIE.

关键词： Deep learning

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machine learning-Based Solutions for Securing IoT Systems Against Multilayer Attacks 3rd

Machine Learning-Based Solutions for Securing IoT Systems Ag...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Al Sukhni, Badeea Manna, Soumya K. Dave, Jugal Manoj Zhang, Leishi School of Engineering Technology and Design Canterbury Christ Church University Canterbury KentCT1 1QU United Kingdom Directorate of Research and Publications Rashtriya Raksha University Lavad India

ISBN: (纸本)9783031431395

IoT systems are prone to security attacks from several IoT layers as most of them possess limited resources and are unable to implement standard security protocols. This paper distinguishes multilayer IoT attacks from single-layer attacks and investigates their functioning. For developing a robust and efficient IDS (intrusion detection system), we have trained a few machine learning (ML) approaches such as NB, DT, and SVM using three standard sets of IoT datasets (Bot-IoT, ToN-IoT, Edge-IIoTset). Instead of using all features, the ML models are trained with similar features of multilayer IoT attacks to use optimal computational power and minimum number of features in the training dataset. The NB model achieves an accuracy of 57%–75%, while the DT model achieves an accuracy of 93%–100%. The outcome of the two ML models reveals that training with similar features possesses a higher accuracy level. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multilayers

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Predicting Schizophrenia from fMRI Using Deep learning 1

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kardani, Shail Sharma, Raghav Sharma, Abhishek Deparment of Computer Science Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Deparment of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Schizophrenia is a complex and multifaceted disorder that is still not fully understood. However, many processes are known to drive its onset, relapse, symptomatology, and therapy. One possible explanation for Schizophrenia is that the person has difficulty using proper analytic methods to deal with the complexity and variety of the condition. Many AI algorithms have been designed and developed over time specifically for Neuroinformatics. These algorithms have been a go-to for many researchers and scientists, even for those in the medical field. Because of these AI-powered algorithms, Scientists are able to detect various diseases, whether they are related to brain activity or any other organs. Deep learning is yet another field of machine learning which is made by the inspiration of how neural networks are connected inside our brain. These are more flexible than the Scikit-Learn-based machine learning models as these Deep Neural Network models don’t often require data preprocessing at initial stages. Not only this fact, but also they are used in various places, for example, processing of unstructured data, including texts and images, feature extraction, etc. In this study, we have attempted to detect Schizophrenia from resting-state functional magnetic resonance images using trained Deep Neural Networks as well as Scikit-Learn-based state-of-the-art machine learning models. For the dataset, we have utilized the public COBRE dataset available in the nilearn library, along with some public datasets from OpenfMRI. The Deep learning models detected Schizophrenia with a reliable performance compared to Scikit-Learn-based machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance

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Research on text classification of telecom user query 3

Research on text classification of telecom user query

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Liu, Zhenhua Beijing Research Institute China Telecom Co. Ltd. Beijing China

ISBN: (纸本)9781510661240

Text classification plays a more and more important role in many practical applications. However, there are a large number of unevenly distributed but important data about the complaints of Chinese telecom users. How to efficiently and accurately implement these telephone consultation contents with only a small number of samples to specific departments and improve the user experience has become an urgent problem to be solved. In this paper, we propose a multi model-based machine learning method-telecom textCNN predictor which can effectively improve the accuracy and recall of the model on the long tail data, and automatically mark the text, so as to accurately locate the class of the problem. The results show that our model is helpful for business personnel to quickly identify the type of complaint business, conduct targeted business acceptance and user services, and improve user perception and produces better accuracy than the SVM model. © 2022 SPIE.

关键词： Deep learning

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