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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Sanxiang Xiao Yihui Deng Ming Li School of Computer Science Guangzhou College of Applied Science and Technology Guangzhou Guangdong China Experimental Training Center Guangzhou College of Applied Science and Technology Guangzhou Guangdong China School of Computer Science Guangzhou City Construction College Guangzhou Guangdong China

The development of the Internet has brought convenience to people, but also caused security risks. How to establish a protection system that can not only effectively prevent computer network(CN)s from being attacked by hackers and viruses, but also effectively protect user privacy, real-time and stability has become an urgent problem to be solved. In this paper, the system performance test and analysis, including system test environment, system test content and so on. This paper mainly analyzes the system performance and improvement maethods of the network information security protection system(SPS) under relevant algorithms by means of experimental simulation and data comparison. Experimental data show that the recall rate after incremental training can reach more than 90%. More than 97% of website operation files are recovered in a short time. In order to further improve the performance of the system, a wider range of web pages should be selected for testing.

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Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data 3

Predicting Heart Disease Using Machine Learning Techniques o...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Baddah, Waheeb Qasem, Hamzah Ali Alsabry, Ayman Hussein Obaid, Abdulrahman Mohammed Abdo Mohammed, Awadh Hanash, F.E. Azal University for Human Development Department of Information Technology Sana'a Yemen International University of Technology Twintech 21 September University of Medical and Applied Sciences Sana'a Yemen International University of Technology Twintech Department of Computer Science Sana'a Yemen Medical Information Technology 21 September University of Medical and Applied Sciences Sana'a Yemen Biomedical Equipments Technology 21 September University of Medical and Applied Sciences Sana'a Yemen International University of Technology Twintech Sana'a Yemen

ISBN: (纸本)9798350305333

heart disease is a prevalent cause of mortality worldwide, and the ability to identify and prevent this ailment at an early stage is crucial for enhancing patient outcomes. Recently, researchers have focused on utilizing Machine-Learning (ML) techniques to predict risk factors and the occurrence of heart disease. This study aims to identify the optimal parameters for accurately predicting the occurrence of heart disease through the fine-tuning of ML models' parameters. This study incorporated a blend of datasets from Cleveland, Hungary, Switzerland, and Long Beach V, which consisted of patients' medical histories and laboratory test outcomes. The collected data were employed to predict the occurrence of heart disease through diverse ML algorithms, such as KNN, decision tree, and SVM. Bayesian optimization was also utilized to fine-tune the hyperparameters of the ML algorithms for heart disease prediction. The findings demonstrated that the ML models achieved high accuracy in predicting the occurrence of heart disease, with the optimized KNN model achieving the highest accuracy rate of 100%. © 2023 IEEE.

关键词： Forecasting

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on Information technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Dimitrios Meimetis Ioannis Daramouskas Vaios Lappas Isidoros Perikos Vaggelis Kapoulas Michael Paraskevas Department of Aerospace Science & Technology NKUA Athens Greece Computer Technology Institute and Press “Diophantus” Patras Greece Applied Mechanics Lab UoP Patras Greece Computer Engineering and Informatics Department UoP Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.

关键词： Simulation Autonomous aerial vehicles Aircraft navigation Sensor systems Robustness Real-time systems Trajectory Safety Sensors Drones

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Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

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Journal of computer and Communications 2023年第10期11卷 82-100页

作者： Chimezie Calistus Ogharandukun Martin Abdullahi Monday Essien Joe Department of Electrical/Electronic and Computer Engineering Veritas University Abuja Nigeria Department of Pure and Applied Physics Veritas University Abuja Nigeria Department of Computer Science Air Force Institute of Technology Kaduna Nigeria Department of Computer and Information Technology Veritas University Abuja Nigeria

This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency. The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.

关键词： Multi-Core Processing Distributed Computing Event-Driven Modelling Discrete Event Simulation Data Analysis and Visualization

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Large Kernel Convolution Application for Land Cover Change Detection of Remote Sensing Images

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SSRN 2024年

作者： Huang, Junqing Yuan, Xiaochen Lam, Chan-Tong Ke, Wei Huang, Guoheng Faculty of Applied Sciences Macao Polytechnic University 999078 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510000 China

In land cover change detection tasks, extracting universal features of changing targets is crucial for achieving precise detection results. A larger receptive field helps the model capture these universal features of changing targets. Although Large Kernel Convolution has been widely used in the field of computer vision, its potential in land cover change detection of remote sensing images has not been fully explored. To address this, a novel Re-parameterization Large kernel Convolution Network for Change Detection (CD-RLKNet) is proposed. CD-RLKNet utilizes Spatial and Temporal Adaptive Fusion Module to preliminarily extract spatial-temporal features from bi-temporal remote sensing images, resulting in a coarse-grained fused feature map. Features Assimilation Assistant Module extracts independent features from land cover information of each temporal, serving as auxiliary information for fine-grained features. Bi-temporal Features Integration Module utilizes large kernel convolution to extract bi-temporal land cover features with a larger receptive field, capturing fine-grained differences in these features. Experiments have been conducted on SYSU-CD, LEVIR-CD and GVLM-CD datasets, and results show that the proposed CD-RLKNet achieves IoU values of 0.6882, 0.8294 and 0.7729, respectively, surpassing the compared SOTA models. The code of CD-RLKNet can be achieved from https://***/juncyan/***. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Identifying Travel Modes Using GPS Trajectories: A Short Review

Identifying Travel Modes Using GPS Trajectories: A Short Rev...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Samer Murrar Mahmoud H. Qutqut Fatima Al-Haj Faculty of Information Technology Applied Science Private University Amman Jordan Faculty of Computer Science University of New Brunswick Fredericton NB Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Intelligent Transportation Systems (ITS) depend on precisely identifying travel modes from GPS data, which is crucial for optimizing traffic management, enhancing road safety, and making informed urban planning decisions. The ubiquity of mobile devices has led to a rapid increase in GPS data collection, which challenges practical data analysis. This paper evaluates the performance of three sophisticated deep learning frameworks for classifying travel modes from GPS trajectories: the Convolutional Encoder-Recurrent Conditional Random Field (ConvEncoder-CRFR), the Convolutional Neural Network (CNN) ensemble with Discrete Wavelet Transform (DWT), and the Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN). This analysis delves into each method's capabilities to distinguish between various travel modes, such as walking, biking, bus, car, and train. Moreover, it emphasizes the broader implications of these techniques in advancing ITS functionality and contributing to developing more intelligent and sustainable urban environments.

关键词： Performance evaluation Reviews Urban planning Transportation Transforms Road safety Mobile handsets Discrete wavelet transforms Convolutional neural networks Global Positioning System

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Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

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computer Systems science & Engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan S.Ramesh S.Janupriya Sangsoon Lim Seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer Science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data Science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated *** proposed CSM-FFDNN model involves two major processes namely FE and *** proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework 2

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Abdellatif Mohamed, Tamer Mohamed Almatarneh, Sattam Al-Mashagbeh, Malak Aljaidi, Mohammad School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350373363

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score. © 2023 IEEE.

关键词： Intrusion detection

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Resource Allocation in Cloud Computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). Cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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