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检索条件"任意字段=1st International Conference on Machine Learning Algorithms, ICMLA 2024"

共 3767 条记录，以下是11-20 订阅

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data Processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data Processing, AAIMB 2023

ISBN: (纸本)9783031730641

The proceedings contain 51 papers. The special focus in this conference is on Advances in Artificial Intelligence and machine learning in Big Data Processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile Robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – Image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through Computer Vision and Deep learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance Images Using CNN;diagnosis of Parkinson’s Disease Using machine learning and Deep learning Techniques;a Survey on Deep learning Based Human Activity Recognition System;a Deep learning Approach for Non - invasive Body Mass Index Calculation;Early-stage Detection of Alzheimer’s Disease Using MRI Scans with Deep learning;penguin Search Optimization with Deep learning Based Cybersecurity Malware Spectrogram Image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using machine learning and Deep learning algorithms: A Review;URL Phishing Detection Using Deep learning and machine learning Techniques;enhanced Disease Recognition and Classification in Black Gram Plant Leaves Using Deep learning;Ensemble Deep learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet Data Using Hybrid Deep learning Techniques;computer Vision to Animal Footprint Classification Based on Deep learning Model;Speech Emotion Recognition Using CNN Classifier Based on Deep learning Model;face Detection and Recognition for Criminal Identification System Using Deep learning.

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on machine learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

引用

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on machine learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of stake;Maximum Likelihood Estimators on MCMC Sampling algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machine learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An

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Preface to Geometry-grounded Representation learning and Generative Modeling (GRaM) Workshop

Proceedings of Machine Learning Research

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Proceedings of machine learning Research 2024年 251卷 1-6页

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1st international Workshop on Trustworthy AI – Integrating learning, Optimization and Reasoning, TAILOR 2020 held as a part of European conference on Artificial Intelligence, ECAI 2020

1st International Workshop on Trustworthy AI – Integrating ...

引用

1st international Workshop on Trustworthy AI – Integrating learning, Optimization and Reasoning, TAILOR 2020 held as a part of European conference on Artificial Intelligence, ECAI 2020

ISBN: (纸本)9783030739584

The proceedings contain 23 papers. The special focus in this conference is on Integrating learning, Optimization and Reasoning. The topics include: Towards Automated GDPR Compliance Checking;towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic algorithms;an Analysis of Regularized Approaches for Constrained machine learning;safe learning and Optimization Techniques: Towards a Survey of the state of the Art;a Causal Framework for Understanding Optimisation algorithms;uncertainty Quantification and Calibration of Imitation learning Policy in Autonomous Driving;synthesising Reinforcement learning Policies Through Set-Valued Inductive Rule learning;modelling Agent Policies with Interpretable Imitation learning;value-Alignment Equilibrium in Multiagent Systems;election Manipulation on Social Networks with Messages on Multiple Candidates Extended Abstract;process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language;underestimation Bias and Underfitting in machine learning;AI-Supported Innovation Monitoring;Semi-supervised Co-ensembling for AutoML;transparent Adaptation in Deep Medical Image Diagnosis;Two to Trust: AutoML for Safe Modelling and Interpretable Deep learning for Robustness;Consensus for Collaborative Creation of Risk Maps for COVID-19;Guided-LORE: Improving LORE with a Focused Search of Neighbours;interactive Natural Language Technology for Explainable Artificial Intelligence;Hybrid AI: The Way Forward in AI by Developing Four Dimensions;towards Certifying Trustworthy machine learning Systems.

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machine learning Models for daily AQI Prediction: An In-depth Analysis 1

Machine Learning Models for daily AQI Prediction: An In-dept...

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1st international conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Narkhede, Gaurav Deore, Ajit Kolte, Bhavika Dr Vishwanath Karad Mit World Peace University Department of Electrical and Electronics Engineering Pune India Dr Vishwanath Karad Mit World Peace University Department of Physics Pune India

ISBN: (纸本)9798350376517

The increase in pollutant concentrations in the atmosphere is one of the most important environmental challenges that must be addressed. Researchers employed a range of cutting- edge machine learning prediction algorithms to anticipate pollution. This study contrasts four different daily AQI pre- diction algorithms: Extra Tree Regressor, Random Forest Regressor, Light Gradient Boosting machine and K-Nearest Neighbor. The assessment criteria used in this work to evaluate the models are RMSE, MAE and MAPE. © 2024 IEEE.

关键词： Adversarial machine learning

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Novel L1-Based Neural Gas Clustering algorithms 23

Novel L1-Based Neural Gas Clustering Algorithms

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23rd IEEE international conference on machine learning and Applications, icmla 2024

作者： Cavalcanti, Nico L. De Carvalho, Francisco De A. T. Universidade Federal de Pernambuco - Ufpe Centro de Informática - CIn Av. Jorn. Anibal Fernandes s/n Cidade Universitaria PE Recife50.740 -540 Brazil

ISBN: (纸本)9798350374889

Clustering algorithms of the Neural Gas (NG) type take into consideration the dissimilarities between prototypes in the original input space. It has been successfully applied in vector quantization, topology creation as well as clustering. NG algorithms conventionally are based on the squared Euclidean distance or L2 distance, which has several known setbacks (not robust to noise and outliers). Our goal is to introduce new NG clustering algorithms (online and batch) based on the L1 distance (more robust to noise and outliers). We propose three Neural Gas algorithms based on the L1 distance using two different algorithms to find the optimal prototypes and compare them with another well-known clustering algorithm. Given the experiments performed, the proposed methods showed a competitive performance. Preliminary results indicate that research on Neural Gas algorithms based on L1 distance is promising. © 2024 IEEE.

关键词： batch neural gas clustering l1 distance median

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An Empirical Evaluation of Ensemble machine learning Techniques for Chronic Kidney Disease Prophecy 1

An Empirical Evaluation of Ensemble Machine Learning Techniq...

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1st international conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Reddy, Karra Varun Srinivas, Paruchuri Chaithanya Siddhu, Yarra Sai Bilal, Mohammad Rout, Saroja Kumar Department of Information Technology Hyderabad India

ISBN: (纸本)9798350361780

The most intriguing task in life is vaticination in the medical field. Generally, Chronic Kidney Disease (CKD) is defined by a patient's urine abnormalities, structural abnormalities, or bloodied excretory, level of waste products. This detection can be achieved by applying machine learning algorithms like Random Forest, Decision Tree, KNN, and SVM on patient information to predict the occurrence of chronic kidney disease. This research focuses on the detection of kidney disease using a combination of multiple algorithms to improve accuracy and reliability. The dataset containing clinical features related to kidney diseases is preprocessed, and various machine-learning processes are applied individually. To enhance classification performance, an ensemble technique is employed, combining the predictions of these algorithms using voting or weighted averaging. The machine learning algorithms provide better operation of the clinical data and accurate vaticination of the habitual order complaints. © 2024 IEEE.

关键词： Decision trees

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Advanced Detection of Malicious URLs using machine learning : A Comparative Analysis of SVM, Random Forest, and Logistic Regression 1

Advanced Detection of Malicious URLs using Machine Learning ...

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1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kesarwani, Vartika Rajesh, E. Galgotias University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

With the escalating threats in the digital landscape, recognizing bad URLs has grown to be a paramount concern for ensuring cybersecurity. This research introduces an innovative approach leveraging machine learning algorithms, specifically Support Vector machine (SVM), Random Forest, and Logistic Regression, for proactive detection of malicious URLs. The study gives a comparative evaluation of these algorithms, highlighting their efficacy in discerning malicious URLs from legitimate ones. The suggested framework achieves an astounding degree of precision of 96.6% using Random Forest, demonstrating its robustness in addressing the evolving challenges in web security. The study adds to the field by offering insights into the execution of various machine learning models, paving the path to improved cybersecurity measures. © 2024 IEEE.

关键词： Adversarial machine learning

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Challenges in applying AI techniques in WSN for network lifetime optimization-A review 1st

Challenges in applying AI techniques in WSN for network life...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Kaur, Manpreet Singh, Jaspreet Sharma, Himanshu Inderprastha Engineering College Ghaziabad India Sharda University Greater Noida India Noida Institute of Engineering and Technology Greater Noida India

ISBN: (纸本)9781032874036

The intend of this paper is to provide an extensive review of communication protocols utilised in Wireless Sensor Networks utilizing advanced machine learning (ML) methods to improve network lifetime. These approaches would involve the application of deep learning algorithm to analyse the routing algorithms. This unique survey focuses on leveraging Artificial Intelligence, particularly machine learning, to develop algorithms capable of automatically detecting and predicting the weak links. The intention of the survey is to provide an extensive review of all the flat and hierarchical routing protocols developed for the improvement of Quality of Service(QoS). © 2025 the Author(s).

关键词： Adversarial machine learning

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Exploring machine learning for Faster Mapping and Scheduling of Automotive Applications on ADAS Platforms 23

Exploring Machine Learning for Faster Mapping and Scheduling...

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23rd IEEE international conference on machine learning and Applications, icmla 2024

作者： sterzinger, Rafael Koch, Wolfgang Hoch, Ralph Institute of Computer Technology Tu Wien Vienna Austria

ISBN: (纸本)9798350374889

In this work, we address the challenge of efficient task mapping and scheduling in Advanced Driver Assistance Systems (ADAS), which are becoming increasingly complex. For this, we explore Mixed-Integer Programming (MIP) combined with machine learning (ML) techniques as an alternative to previous work using heuristic algorithms such as simulated annealing or genetic algorithms. Our key contributions include: (1) A simplified MIP formulation of the problem with a novel load-balancing objective. (2) Employing Bayesian optimization to expedite the solving process by finding a better MIP solver configuration, reducing worst-case solving time by 65%. (3) ML-driven decision variable prediction via a Graph Convolutional Network, which is able to fix decision variables with an average precision of up to 77 %, allowing for better branching decisions during the solving process. Experimental results demonstrate the potential for faster solving times, highlighting the value of further integrating machine learning with MIP for advanced ADAS scheduling. © 2024 IEEE.

关键词： Advanced driver assistance systems

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