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12th international conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th international conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-based Prediction of Human Ageing Related Proteins;the Insight of Clustering Algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through internet of Medical Things;image Processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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Experimental Study of a LoRa based Wireless Sensor Network for Low Power Localization in an Outdoor Environment 2

Experimental Study of a LoRa Based Wireless Sensor Network f...

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2nd international conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Tobbal, Abdelhafid Bengherbia, Billel Bouziani, Anis Cherifi, Youcef University Yahia Fares of Medea Research Laboratory in Advanced Electronics Systems Lsea Medea Algeria

ISBN: (纸本)9798350314243

The internet of Things (IoT) and Wireless Sensor Network (WSN) have transformed industries by highlighting their crucial roles in sensing, communication, and data-driven decisions. One paramount feature in WSN is localization, enabling precise positioning of devices in real-world applications. Global Positioning systems (GPS) have long been the mainstay of high-precision localization, but their high energy consumption is a significant obstacle to their seamless integration with low-power devices. In the quest for suitable solutions, Low Power Wide Area Networks (LPWAN) have emerged as key players, providing energy-efficient connectivity. However, these advances raise questions about the localization accuracy within these networks. In this paper, we present the implementation of WSN based on Long Range (LoRa) technology to estimate the position of an object in an outdoor environment. We took advantage of LoRa technology to collect Received signal Strength Indicator (RSSI) measurements over a long distance. An Artificial Neural Network (ANN) is then trained on a collected dataset to estimate the 2-dimensional position of the object. The results show an estimate of the position with an average absolute error of 40.5 meters in an area of 360 meters by 360 meters. A web interface is made with Node-Red to monitor the position of the mobile object. © 2023 IEEE.

关键词： internet of things

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Threat Modeling for ML-based Topology Prediction in Vehicular Edge Computing Architecture

Threat Modeling for ML-based Topology Prediction in Vehicula...

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2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Doan, Hong Hanh Paul, Audri Adhyas Zeindlinger, Harald Zhang, Yiheng Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

The internet of Vehicles (IoV), a network that interlinks vehicles, infrastructure, and assorted entities, serves as a cornerstone for intelligent transportation systems and the emergence of smart cities. Within this context, edge computing has been identified as a critical solution for providing rapid and reliable data processing. Machine Learning (ML) techniques have become essential to IoV activities such as resource allocation and load balancing across mobile edge servers, typified by decentralized services that range from natural language processing to image recognition. The fusion of ML with edge computing within IoV architecture promises enhanced performance, efficiency, and safety. However, this amalgamation also creates challenges related to data privacy, cybersecurity, malfunctioning edge devices, inconsistent network connectivity, human errors, and malicious insiders. Consequently, this paper focuses on modeling security threats within an ML-based edge computing framework for the IoV. We analyze the system provided by the Linux Foundation Edge Akraino Project's Stable Topology Prediction blueprint by employing a hybrid threat modeling technique. Our strategy leverages STRIDE to elicit threats on distinct system elements like vehicle-to-vehicle communication networks, edge networks, and ML models. Subsequently, these threats are consolidated for a comprehensive view using an attack tree. © 2023 IEEE.

关键词： Machine learning

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Flipped Classroom Teaching Mode in College English Teaching based on image Recognition 4th

Flipped Classroom Teaching Mode in College English Teaching ...

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Cheng, Min Du, Jian Nanjing Xiaozhuang University Nanjing211171 China

ISBN: (纸本)9789819911448

With the continuous improvement of the education recording system and artificial intelligence technology, the intelligent education system based on image recognition (IR) technology has achieved rapid development and application. The intelligent IR and analysis system for classroom behavior will analyze and record the behavior action (BA) data in the whole classroom. This work establishes an intelligent IR system using a convolutional neural network (CNN)-based image behavior action recognition algorithm. This system identifies students' behavior actions in an English flipped classroom (FC) to reflect students’ motivation for the English classroom and to gauge the allure of the teacher's lecture to the students. The study's results will provide data reference in the subsequent assessment and analysis of the quality of college English teaching. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image recognition

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Rule Generation for Network Intrusion Detection systems based on Packets-To-Video Transformation 7th

Rule Generation for Network Intrusion Detection Systems Base...

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7th EAI international conference on Smart Grid and internet of Things, SGIoT 2023

作者： Cheng, Sheng-Tzong Cheng, Yu-Ling Cheung, Ka-Chun National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783031559754

Modern intrusion detection systems utilize machine learning to identify network anomalies. Traditional static rules may not be sufficient to combat emerging attacks, making it critical to adopt a dynamic approach for keeping intrusion detection rules up-to-date. This study introduces an intelligent rule generator with a packet encoding method to represent packets into images, a vision model to encode the images, and a video captioning model, mapping image features to textual descriptions, thereby generating rules suitable for network intrusion detection systems. The results of our simulated data experiments show that our classification model has a higher accuracy than others and is capable of generating rules. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Network security

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An Approach for CT image Conversion Using Filtering based on Quaternion Mathematics 13th

An Approach for CT Image Conversion Using Filtering Based o...

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13th international conference on image Processing and Communications, IP and C 2023, 13th international conference on Computer Recognition systems, CORES 2023

作者： Nasr, Mahmoud Piórkowski, Adam Brzostowski, Krzysztof El-Samie, Fathi E. Abd Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology A. Mickiewicza 30 Av Cracow30-059 Poland New Cairo11845 Egypt Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Manoufia University Menouf Egypt

ISBN: (纸本)9783031416293

Computed tomography (CT) images are widely used in medical examination applications. They are constructed from the raw data using different kernels. However, due to the technology used, the software finds problems with image segmentation in the presence of noise or blurred edges. In this paper, we use quaternion mathematics for image de-noising by applying the bilateral filtering algorithm to reconstruct a pure soft image from the images reconstructed with sharp kernels, without using the raw data. Our output is compared to reconstructed images with different denoising techniques, such as non-local means and wavelet denoising. The reconstructed images using other techniques are assessed using structural index similarity (SSIM) and peak signal-to-noise ratio (PSNR). Results show high similarity by comparing the outputs of sharp kernels to that of the B30f. Furthermore, the results show that the proposed work can efficiently increase the similarity between the images reconstructed with hard and soft kernels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image denoising

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A Low Cost and Power Efficient Real-time Traffic based Urban Street Lighting System using Smart Sensors and IoT 10

A Low Cost and Power Efficient Real-time Traffic based Urban...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Shaik, Shafi Keerthana, M. Sai Sindhu, M. Rakeshram, K. Veekshitha, C. Begum, Muskan B V Raju Institute of Technology ECE Telangana Narsapur India

ISBN: (纸本)9798350353068

Both in highway and urban scenarios, street lighting is essential to improve visibility and lower the accident rate at night. However, it uses a lot of energy, with recent increases in global utilization from 13% to 15% This enormous worldwide energy use is a result of excessive energy consumption, which annually produces roughly 3900 tons of CO2 emissions. Additionally, standard constant illumination lighting systems raise electricity costs and may be responsible for as much as 60% of them. Further, according to the India-USAID statistics, by 2027, the usage of streetlights globally is expected to reach about 363 million. Thus, may lead to an increase in power consumption and Carbon components in the future with existing approaches. Therefore, in this paper, an internet of Things Enabled Smart Street Lighting System for Highway and Urban infrastructures is proposed. To this end, the proposed model makes use of a low-cost, low-power Passive Infrared (PIR) sensor for effective lighting control and has dimming capabilities during any movement of humans or vehicles in or out of sensor range. In addition, a Light Dependent Resistor (LDR) sensor is incorporated into the system for automatic detection of the environment brightness levels and turning on/off the streetlights on roads. For energy-saving lighting options, Sodium Lamps are replaced with Light-Emitting Diodes to lower the environmental effect while ensuring safety. Further, sensor data is analyzed and exchanged with each other over the internet. Finally, the values are stored in the cloud through Thingspeak. © 2024 IEEE.

关键词： Street lighting

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6th EAI international conference on Advanced Hybrid Information Processing, ADHIP 2022

6th EAI International Conference on Advanced Hybrid Informat...

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6th EAI international conference on Advanced Hybrid Information Processing, ADHIP 2022

ISBN: (纸本)9783031288661

The proceedings contain 109 papers. The special focus in this conference is on Advanced Hybrid Information Processing. The topics include: Design of Remote Video Surveillance System based on Cloud Computing;physical Movement Monitoring Method of College Physical Education Students based on Genetic Algorithm;research on Large Data Mining for Online Education of Mobile Terminal based on Block Chain technology;Separation Algorithm of Fixed Wing UAV Positioning signal based on AI;research on Hybrid Maintenance Cost Prediction of Smart Grid based on Multi-dimensional Information;infrared image Face Recognition Method based on signal Interference technology;remote Tutoring System of Ideological and Political Course based on Mobile Client;operational Risk Prevention and Control Monitoring of Smart Financial System based on Deep Learning;remote Monitoring Method of Athlete Training Intensity based on Mobile internet of Things;research on Equipment Management System of Smart Hospital based on Data Visualization;Design of Pond Water Quality Monitoring System based on Mobile UAV technology;Design of Information Consultation System for the Whole Process of Construction Engineering based on BIM technology;the Mobile Teaching Method of Law Course based on Wireless Communication technology;design of Financial and Economic Monitoring System based on Big Data Clustering;recommendation Method of College English Online Mobile Teaching Resources based on Big Data Mining Algorithm;Design of Electronic Communication Power Monitoring System based on GPRS technology;online Interactive Platform for College English Intensive Reading Teaching based on Cloud Service;intelligent Platform for College English Blended Teaching based on Mobile Learning;preface.

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9th EAI international conference on IoT as a Service, IoTaaS 2023

9th EAI International Conference on IoT as a Service, IoTaaS...

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9th EAI international conference on IoT as a Service, IoTaaS 2023

ISBN: (纸本)9783031705069

The proceedings contain 33 papers. The special focus in this conference is on IoT as a Service. The topics include: Highway Obstacle Recognition based on Improved YOLOv7 and Defogging Algorithm;cloud Gaming Resource Management Platform based on Edge Intelligence;hardware-Efficient Polar Decoder for 5G internet of Things Communication;Design and Optimization of a Solar-Powered IRS and Relay Assisted MEC System;highway image Enhancement Method based on Jade;method and Practice of Borehole Group Detection in Borehole Drilling based on Six-Axis Sensing;Deep Reinforcement Learning-based Channel and Power Allocation in Multibeam LEO Satellite systems;CSI-based signal Reconstruction for WiFi Localization;an Indoor Navigation Algorithm Using Multi-dimensional Euclidean Distance and the Adaptive Particle Filter;Research on the PID Controllers Algorithm of the Quad Rotor;based on the Nonlinear State Equations of 6-DOF Robot with Application to Autonomous Flight;a Study on Collaborative Lane Change Decision Making of Multi-automated Vehicles based on Deep Graph Reinforcement Learning;a Short Term Traffic Prediction Model based on Deep Capture of Temporal Periodic Drift;multi-agent Reinforcement Learning for Cooperative On-Ramp Merging of Connected Automated Vehicles;SVM Multi-class Classification Method for Device Identification Using Eye Diagram Parameters;users Collaborative Computing for Hierarchical Federated Learning based on Incentive Mechanism;spatio-Temporal Traffic Prediction of Wireless Communication Network based on Multi-source Data;flying Ad-Hoc Network Routing Protocol based on Backward Algorithm;A Novel HPSO-IGWO Algorithm for Rapidly Searching Optimal Fire Rescue Paths based on IoT Architecture;A Hybrid WOA-MTCNN Algorithm for Accurate Face Detection based on IoT Architecture.

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6th EAI international conference on Advanced Hybrid Information Processing, ADHIP 2022

6th EAI International Conference on Advanced Hybrid Informat...

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6th EAI international conference on Advanced Hybrid Information Processing, ADHIP 2022

ISBN: (纸本)9783031287862

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