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TEXT2KG 2024 and DQMLKG 2024 - Joint Proceedings of the 3rd international Workshop One Knowledge Graph Generation from Text and data Quality Meets machine learning and Knowledge Graphs, co-located with the Extended Semantic Web conference, ESWC 2024

TEXT2KG 2024 and DQMLKG 2024 - Joint Proceedings of the 3rd ...

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Joint of the 3rd international Workshop One Knowledge Graph Generation from Text and data Quality Meets machine learning and Knowledge Graphs, TEXT2KG 2024 and DQMLKG 2024

The proceedings contain 17 papers. The topics discussed include: open knowledge base canonicalization: techniques and challenges;incorporating type information into zero-shot relation extraction;generating e-commerce related knowledge graph from text: open challenges and early results using LLMs;towards dataset for extracting relations in the climate-change domain;on constructing biomedical text-to-graph systems with large language models;fine-tuning vs. prompting: evaluating the knowledge graph construction with LLMs;Battalogy: empowering battery data management through ontology-driven knowledge graph;leveraging language models for generating ontologies of research topics;towards harnessing large language models as autonomous agents for semantic triple extraction from unstructured text;knowledge graphs for digital transformation monitoring in social media;and moving from tabular knowledge graph quality assessment to rdF triples leveraging ChatGPT.

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12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping Study.

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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

3rd International Conference on Machine Learning, Internet o...

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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

ISBN: (纸本)9789819939312

The proceedings contain 52 papers. The special focus in this conference is on machine learning, Internet of Things and Big data. The topics include: Smart Skin-Proto: A Mobile Skin Disorders Recognizer Model;machine learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks;real Time Air-Writing and Recognition of Tamil Alphabets Using Deep learning;a Fuzzy Logic Based Trust Evaluation Model for IoT;supervised learning Approaches on the Prediction of Diabetic Disease in Healthcare;solar Powered Smart Home Automation and Smart Health Monitoring with IoT;seasonal-Wise Occupational Accident Analysis Using Deep learning Paradigms;MLFP: machine learning Approaches for Flood Prediction in Odisha State;vision-Based Cyclist Travel Lane and Helmet Detection;machine learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf;design and Experimental Analysis of Spur Gear–A Multi-objective Approach;Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques;Computer Vision and Image Segmentation: LBW Automation Technique;a Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity;hybrid Clustering-Based Fast Support Vector machine Model for Heart Disease Prediction;forecasting and Analysing Time Series data Using Deep learning;intelligent Blockchain: Use of Blockchain and machine learning Algorithm for Smart Contract and Smart Bidding;Weed Detection in Cotton Production Systems Using Novel YOLOv7-X Object Detector;smart Healthcare System Management Using IoT and machine learning Techniques;Automatic Code Clone Detection Technique Using SDG;Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System;simulated Design of an Autonomous Multi-terrain Modular Agri-bot;customer Segmentation Analysis Using Clustering Algorithms.

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

3rd International Conference on Machine Learning and Big Dat...

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

The proceedings contain 150 papers. The topics discussed include: analysis of communication strategy of Guzheng art based on artificial intelligence;research on the health detection and seismic performance evaluation of high-rise buildings;application of ecological energy saving design in building reconstruction project;computer network communication security encryption system based on ant colony optimization algorithm;an analysis of data mining techniques in software engineering database design;intelligent analysis algorithm for hidden danger identification of intelligent network monitoring system from the perspective of big data;human behavior recognition of video surveillance system based on neural network;application of AI intelligent learning system in multimedia demonstration;and application of artificial intelligence technology in power system stability assessment.

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3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

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3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine learning, Image Processing, Network Security and data Sciences. The topics include: Analyzing Wearable data for Diagnosing COVID-19 Using machine learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep learning and machine learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer learning;classification of Electrocardiogram Signal Using Hybrid Deep learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine learning Methods;fault Diagnosis in Wind Turbine Blades Using machine learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care;a Novel Smartphone-Based Human Activity Recognition Using Deep learning in Health care;An Enhanced Deep learning Approach for Smartphone-Based Human Activity Recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep learning Techniques;prediction of the Reference Evapotranspiration data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine learning Techniques;deep Transfer learning and Intelligent Item Packing in Retail Management;preface.

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

3rd International Conference on Data Science, Machine Learni...

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

ISBN: (纸本)9789811959356

The proceedings contain 85 papers. The special focus in this conference is on data Science, machine learning and Applications. The topics include: Design of QCA-Based XOR/XNOR Structures;Design of QCA-Based 1-Bit Magnitude Comparator;a Novel Multimodal Anatomical Medical Image Fusion Using Structure Extraction;Parametric Analysis for Channel Estimation in Massive MIMO Systems with 1-Bits ADCs;skin Cancer Classification Using Deep learning;image Dehazing Using Improved Dark Channel and Vanherk Model;A Novel Bayesian Fusion Model for IR and Visible Images;Retinal Boundary Segmentation in OCT Images Using Active Contour Model;Spectral Efficiency for Multi-bit and Blind Medium Estimation of DCO-OFDM Used Vehicular Visible Light Communication;An Efficient Retinal Layer Segmentation Based on Deep learning Regression Technique for Early Diagnosis of Retinal Diseases in OCT and FUNDUS Images;Design of QCA-Based BCD Adder;crop Yield Prediction Using Deep learning;preface;road Accident Detection and Indication System;real-Time Tweets Streaming and Comparison Using Naïve Bayes Classifier;smart Shopping Trolley for Billing System;a Survey on IoT Protocol in Real-Time Applications and Its Architectures;safe Characteristic Signature Systems with Different Jurisdiction Using Blockchain in E-Health Records;web-Based Trash Segregation Using Deep learning Algorithm;home Automation Using Face Recognition for Wireless Security;Hybrid-Network Intrusion Detection (H-NID) Model Using machine learning Techniques (MLTs);impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis;Several Routing Protocols, Features and Limitations for Wireless Mesh Network (WMN): A Review;a Deep Meta-model for Environmental Sound Recognition;design of Progressive Monitoring Overhead Water Tank.

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Using machine learning to Predict Student Success in Undergraduate engineering Programs 3

Using Machine Learning to Predict Student Success in Undergr...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： DeJong, Brian P. Karadogan, Ernur Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Undergraduate engineering programs are typically considered some of the most challenging as their curricula require students to have an aptitude for math, science, and engineering. The resources (time, effort, funds) required to finish an engineering degree is substantial. Therefore, it is imperative that the engineering students are supported with well-informed academic guidance as early in their education as possible so that these resources can be used most effectively. Analytical and data-driven methods such as machine learning techniques can be used to inform this guidance process by predicting student success based on features such as individual traits and academic performance. In that direction, we investigated the effectiveness of using machine learning in predicting engineering student success based on academic performance in core math, physics, and engineering courses in three undergraduate engineering programs. The data categories selected for training and testing of the machine learning models in this study are common to most engineering programs nationwide and can be customized in a straightforward manner for other engineering disciplines. The methodology and results outlined in this preliminary study shows promise for predicting degree and cumulative GPA in our three engineering programs.

关键词： machine learning undergraduate engineering education student success prediction

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Research on the outlier value analysis algorithm of water conservancy engineering quality detection based on machine learning 24

Research on the outlier value analysis algorithm of water co...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Chu, Dongdong Wang, Xiaoyong Zou, Dan Jiangsu Hydraulic Research Institute Jiangsu Nanjing China Yangzhou City Urban River Management Office Jiangsu Nanjing China

ISBN: (纸本)9798400718120

This study aims to improve the accuracy of the quality detection of water conservancy projects, and to identify and analyze the outliers in the detection data by introducing machine learning algorithms. A variety of technical techniques, including data preprocessing, feature extraction, supervised learning and unsupervised learning, were used to construct and optimize the outlier detection model. The experimental results show that the machine learning-based detection method significantly improves the accuracy and efficiency of outlier identification, especially when using deep learning models. The research conclusion points out that the machine learning algorithm has obvious advantages in processing complex and massive monitoring data, and can be widely used in the quality monitoring and management of water conservancy projects, providing a strong guarantee for the engineering safety. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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machine learning for Enhanced Weather Forecasting in Urban Environments 3

Machine Learning for Enhanced Weather Forecasting in Urban E...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Bandi, Nagavindhya Chenchulekhana Gnana Swathika, O.V. School of Electrical Engineering Vellore Institute of Technology Chennai India Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350354379

This paper aims to revolutionize weather forecasting by exploring the potential of machine learning algorithms to achieve more accurate predictions. The focus is to leverage historical patterns and real-time meteorological data to enhance the precision of short-term and long-term weather forecasts. Through strategic deployment of IoT -enabled weather sensors and sophisticated data analysis techniques, the objective is to demonstrate the efficacy of machine learning in improving weather prediction accuracy, paving the way for more reliable forecasting methods in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic optimization of PID control software parameters based on machine learning 3

Automatic optimization of PID control software parameters ba...

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3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Gu, Deli Harbin Inst Informat Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9798350366105;9798350366099

In order to understand the method of automatic parameter optimization for PID control software, research on automatic parameter optimization for PID control software based on machine learning has been proposed. PID parameter tuning and optimization have always been an important issue in the control field, and appropriate PID parameters can enable the system to have good control effects. Since Ziegler and Nichols proposed the PID parameter tuning method, many algorithms have been used for PID parameter tuning optimization. machine learning algorithms have asymptotic convergence and are a global optimization algorithm that converges to the global optimal solution with probability I. This article focuses on machine learning algorithms and uses MATLAB to optimize the design of PID parameters. This method can make the entire optimization process simple, accurate, and improve computational efficiency.

关键词： machine learning PID Automatic optimization

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