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检索条件"任意字段=2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024"

共 2274 条记录，以下是31-40 订阅

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A Novel Elm Optimization and Its Application in iot Intrusion Detection 5th

A Novel Elm Optimization and Its Application in IoT Intrusio...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Maseno, Elijah M. Wang, Zenghui Univ South Africa Dept Comp Sci Florida South Africa Univ South Africa Dept Elect & Smart Syst Engn ZA-1709 Florida South Africa

ISBN: (纸本)9789819770038;9789819770045

Globally, the Internet of Things (iot) has grown exponentially in the last few years. The integration of numerous iot devices enhances connectivity but also exposes them to various cyber-attacks. With the increase of complex and intelligent cyber-attacks, the traditional methods of cyber-attack detection, such as anomaly and misuse intrusion detection systems (IDS), have proven ineffective in combating emerging cyber-attacks. Scholars have suggested that the existing IDS's performance can be enhanced by integrating machine learning methods. This study explored the integration of ELM (Extreme learning machine) with NSGA-II (Non-Dominated Sorting Genetic Algorithm II). Our empirical analysis reveals that ELM outperforms other algorithms regarding learning rate, usability, and effectiveness of generalization. NSGA-II provides an equilibrium between exploration and exploitation, successfully managing the trade-offs present in multi-objective optimization problems to identify a range of excellent solutions along the Pareto front. Most of the existing works on ELM optimization focuses on improving one input parameter. This study explored the integration of ELM with NSGA-II for both input weights and hidden neurons. The model was trained and tested using two datasets namely iot_ToN network and UNSWNB15 datasets. This research used TON_iot Windows dataset to further test the capacity of the model to identify novel attacks. Using the UNSWNB15 and iot_ToN network datasets, respectively, the model's accuracy was 0.68 and 0.65. In addition, the model recorded an accuracy 0.768 using TON_iotWindows dataset, demonstrating the potential of the model in detection of new forms of attacks. This study acknowledges the ongoing evolution of challenges in iot security and the need for continuous innovation to stay ahead of emerging threats in the future. The contributions of this study can be explored further to develop reliable intrusion detection solutions for the dynamic iot landscape

关键词： Artificial Intelligence (AI) Internet of Things (iot) Cyber-Attack Intrusion Detection System (IDS) Evolutionary Algorithm Optimization machine learning

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3rd international conference on Internet of Everything, IoECon 2024

3rd International Conference on Internet of Everything, IoEC...

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3rd international conference on Internet of Everything, IoECon 2024

ISBN: (纸本)9783031844256

The proceedings contain 10 papers. The special focus in this conference is on Internet of Everything. The topics include: Parallelism Everywhere: The Internet of Everything and Parallel computing;ioV Simulation Architecture for Software-Defined Vehicular Fog Network Orchestration;toward Modeling of Flooding Attacks Targeting Massive iot Networks;using the Internet of Everything for machine-learning-Based Computer System Design and Optimization;enhancing Urban Freight Delivery: A machine learning Approach to Predicting Delivery Speed;soil-Based Thermoelectric Energy Harvesting System for iot Devices;a Methodology for Measuring Selected Performance Parameters of smartphone-Based Vehicle-to-Pedestrian and Pedestrian-to-Infrastructure Communication;wordPress Architecture Modernization Projects.

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Proceedings - 2024 20th international conference on Distributed computing in smart Systems and the Internet of Things, DCOSS-iot 2024

Proceedings - 2024 20th International Conference on Distribu...

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20th Annual international conference on Distributed computing in smart Systems and the Internet of Things, DCOSS-iot 2024

ISBN: (纸本)9798350369441

The proceedings contain 112 papers. The topics discussed include: federated learning assisted model for android malware detection using gannet optimization algorithm;a deep q-learning framework for enhanced QoE and energy optimization in fog computing;traffic data augmentation using GANs for ITS;EdgeBoost: confidence boosting for resource constrained inference via selective offloading;FedLSF: federated local graph learning via specformers;an efficient approach for merging multidimensional blockchains in mobile iot;predicting solar-harvested energy for resource-constrained iot devices using machine learning;tremor detection in Parkinson’s disease from wearable data: a comparative study of centralized learning versus federated learning;DNA-inspired image encryption: leveraging chaos for enhanced security;and obscured wildfire flame detection by spatio-temporal analysis of smoke patterns using frame-wise transformers.

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Harnessing iot and Robotics for Comprehensive Human Activity Recognition in smart Buildings 5th

Harnessing IoT and Robotics for Comprehensive Human Activity...

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5th 5th Olympus - international conference on Supply Chain

作者： Toupas, Petros Tsamis, Georgios Zotou, Dimitra Giakoumis, Dimitrios Votis, Konstantinos Tzovaras, Dimitrios Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9783031693434;9783031693441

The incorporation of Internet of Things (iot) devices and robotics into smart building environments has introduced new opportunities for human activity recognition (HAR). The paper introduces an innovative approach that combines iot sensors and robotic data to create a complete dataset for Human Activity Recognition (HAR) in smart building settings. The system utilises a synchronised data collection technique to gather information from fixed iot sensors, wearables, and smart devices, as well as from mobile robots. The dataset obtained includes a wide variety of sensor modalities, such as accelerometers, gyroscopes, microphones, motion sensors, smart plugs, and cameras (thermal, RGB-D). A proposed strategy is presented to tackle the difficulties of multimodal data fusion and classification by fusing all sensor data into a single machine learning model. This study lays the groundwork for future works into Human Activity Recognition (HAR) systems, which involve the integration of multi-sensor settings, sophisticated data fusion algorithms, context-sensitive identification, and tailored support for individuals working or residing in smart environments. The setup of the sensors and use cases of this study were conducted in a smart home environment. Nevertheless, the integrated system can be tailored to suit the requirements of any smart building.

关键词： Multi-Sensor Fusion Data Collection Robotics

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international conference on computing and machine learning, CML 2024

International Conference on Computing and Machine Learning, ...

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international conference on computing and machine learning, CML 2024

ISBN: (纸本)9789819778386

The proceedings contain 37 papers. The special focus in this conference is on computing and machine learning. The topics include: ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection for Feature Reduction;Enhancing Path Planning with Obstacles Via a Combined Dijkstra-LbCS Methodology;revitalising Volunteerism: The Transformative Influence of Artificial Intelligence in Volunteer Management;optimizing the U-Net Model for Segmenting the Lung Opacity Regions in Chest Radiographs;intelligent Surveillance Camera System Based on Object Tracking;data-Efficient Training for Effective Paraphrase Retrieval Techniques Using Language Models to Identify Research Gaps;Enhancing iot Anomaly Detection with DBSCAN—A Data-Driven Approach;Enhancing Efficiency and Functionality of Voice-Controlled Cars Through NLP Techniques and Additional Features;ensembling of Performance Metrics in Credit Risk Assessment Using machine learning Analytics;GUARDING THE GATE: Innovative Solutions for Third-Party App Vulnerabilities;prediction of Compressive Strength of Geopolymer Concrete via machine learning Techniques;Personalized Stress Mitigation Through EEG Based Stress Classification and Music Recommendation;detection and Prediction of Alzheimer’s Disease Using Rule Induction Method;Heart Disease Prediction Using Bagging QSVC Algorithm;loopholes in Security Implementation of Cloud and iot Environment: Case Study of a Recent Security Incident;speckle Noise Removal from Three-Dimensional Ultrasound Images of Ovaries;Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration and Treatment Identification;rainfall Prediction in Diverse Indian Regions Using machine learning Approaches;climacteric Fruit Skin Defect Region Segmentation by Boundary Identification Using Morphologically Enhanced Watershed;Decoding Sentiments: Harnessing the Power of NLP for Comparative Analysis of ML Algorithms.

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8th international conference on Information Science, Communication and computing, ISCC 2024

8th International Conference on Information Science, Communi...

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8th international conference on Information Science, Communication and computing, ISCC 2024

The proceedings contain 24 papers. The special focus in this conference is on Information Science, Communication and computing. The topics include: The Aiot Ward Management and Health Monitori...

ISBN: (纸本)9789819617807

The proceedings contain 24 papers. The special focus in this conference is on Information Science, Communication and computing. The topics include: The Aiot Ward Management and Health Monitoring System based on Aliyun iot Platform;blockchain-Based Verifiable Multi-Keyword Ciphertext Retrieval Scheme;a Secure and Efficient Signcryption Scheme Applying in iot Scenarios;a Distraction Mining Network for Polyp Segmentation;ORLQ: A Link Quality-Based Routing Protocol for UWSN;A Method for Lightweight DDoS Attack Detection Based on LightGBM;smart Contract Vulnerability Detection Method Based on Node Feature Augmentation Graph Convolutional Network;smart Contract Vulnerability Detection Method Based on Edge-Deactivation Graph Convolutional Network;an Exhaustive Survey on the Methods and Applications of Graph Neural Networks;assessment of Forecast Algorithms of Aircraft Icing in Southwestern China;Research on Topic Mining Based on LDA Model;from Text to Emotion: Applying Fine-Tuning Techniques to Large Language Models for Sentiment Analysis of Children’s Reading;fine-Grained Sketch-Based Image Retrieval Using a Contrastive learning Framework;radiometric Difference Measurement Method Based on Infrared Thermal Imager;automatic Measurement of Knee Joint Space Width on Anteroposterior X-ray Images Based on U2-Net;detection of Hip-Knee-Ankle Angle in Lower Limb X-rays Based on Multi-Classification U2-Net (Angles to Angle);target Association of Heterogeneous Sensors Based on Attribute-Position Fusion;verification and Testing of Chinese Information Technology Application Innovation Network Security for the Tobacco Industry;Fine-Grained Image Classification Based on BAM Attention Mechanism and PPM Pyramid Enhancement Methodological Studies;multimodal machine Translation Based on Text-Image Aggregation;adaptive Multi-scale Fusion Transformer for Image Super-Resolution.

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An Intelligent Optimization Technique Of Automatic Speech Recognition For smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th international conference on Research in computing at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and machine learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (iot) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm smart homes Speech recognition

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Partial Distributed Deep learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： smart manufacturing

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Proceedings of the 2024 international conference on Innovative computing, Intelligent Communication and smart Electrical Systems, ICSES 2024

Proceedings of the 2024 International Conference on Innovati...

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2024 international conference on Innovative computing, Intelligent Communication and smart Electrical Systems, ICSES 2024

ISBN: (纸本)9798331543617

The proceedings contain 396 papers. The topics discussed include: development of an iot enabled smart children tracking and monitoring system using map location services;HMLM: an intelligent artificial intelligence assisted strategy to identify UPI frauds based on hybrid Markov learning methodology;data-driven insights into automotive customer complaints: a machine learning approach to predictive analytics;integrating predictive analytics and deep learning for vehicle safety incident forecasting;analyzing the health status of people in rural areas using machine learning algorithms;machine learning based approach using hand gestures for mouse and video control;experimental analysis of artificial intelligence powered adaptive learning methodology using enhanced deep learning principle;a comprehensive analysis of image processing methods for agricultural product quality control;and convolutional neural network-based multi-fruit classification and quality grading with a Gradio interface.

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Performance Evaluation of Intrusion Detection Systems on the TON_iot Datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (iot) technology develops so quickly, security issues with iot devices have come to light. iot is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making iot ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_iot Datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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