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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是311-320 订阅

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Predicting Road Accident Severity and Identifying High-Risk Areas Using Tree-based learning: A Case Study from the UK 3

Predicting Road Accident Severity and Identifying High-Risk ...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Akarsha, D.P. Mohan, S.G. Nagendra Bharadwaj, D.G. Pandu Kumar, A.N. Manoj, V.S. Nitte Meenakshi Institute of Technology Department of Information Sciences and Engineering Bengaluru India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528201

Road accidents are a pressing global issue, often resulting in injuries ranging from minor to fatal. This study leverages machine learning algorithms to predict the severity and location of road accidents, with the aim of enhancing road safety measures. Utilizing accident data from the UK, we developed predictive models that classify the severity of accidents on a scale from 1 to 5, where 1 represents the least severe and 5 represents the most severe outcomes. The model's predictions have been integrated into a user-friendly application, providing visual insights into accident severity and highlighting high-risk areas on a map. By identifying and visualizing accident-prone locations, this study offers a valuable tool for authorities to implement targeted interventions, potentially reducing the frequency and impact of road accidents. The findings underscore the potential of machine learning in proactive road safety management, contributing to efforts that could save lives and minimize the societal costs associated with road accidents. © 2024 IEEE.

关键词： Adaptation models Visualization Road accidents machine learning algorithms Costs machine learning Predictive models Road safety data models Frequency measurement

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Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biomarker Detection Using machine learning Algorithms 3

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biom...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Balaji, P. Devasena, D. Rithick, P. Karthi, T. Rishmitha, K. Roopa, R. Sri Ramakrishna Engineering College Dept. Of. Electronics and Instrumentation Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Dept. of Robotics and Automation Engineering Tamilnadu Coimbatore India Kongu Engineering College Dept. of Automobile Engineering Erode India Science Arjun College of Technology Dept. of Artificial Intelligence and Data Tamilnadu Coimbatore India

ISBN: (纸本)9798331528201

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices. © 2024 IEEE.

关键词： Polydimethylsiloxane

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Optimizing Travel Itineraries: A Multi-Objective machine learning Model Approach 3

Optimizing Travel Itineraries: A Multi-Objective Machine Lea...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Roy, X Samuel Issac, Titus Terrance, J Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: Artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools. © 2024 IEEE.

关键词： Multilayer neural networks

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Unsupervised Classification of Remotely Sensed High resolution Images using RP-CNN 13

Unsupervised Classification of Remotely Sensed High resoluti...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Fallahreyhani, Maedeh Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Fac Elect Image Proc & Informat Analysis Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

In recent years, various deep learning frameworks have been developed for the classification of remotely sensed images. However, the network models proposed in these frameworks exhibit high complexity and do not yield high classification accuracy when applied to unlabeled scenarios. This paper introduces a Multi spectral image (MSI) classification approach that combines the random patches network with self-supervised branch (RPSS) to extract informative deep features. The proposed method involves convolving image bands with random patches to obtain multi-level deep features. Subsequently, we use panchromatic image (PAN) to extract spatial features. The MS spectral features, the derived RPSS features and spatial features then merged to classify the MSI using a support vector machine (SVM) classifier. The experimental results on real remotely sensed images have been presented.

关键词： Deep learning Multi spectral data Unsupervised classification

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Ensemble-based machine learning Classification for Early Detection of Autism Spectrum Disorder 3

Ensemble-based Machine Learning Classification for Early Det...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subhash, Ambika Rani Ashwin Kumar, U.M. School of Computer Science and Engineering REVA University Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9798331527549

A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the UCI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Precision Agriculture with machine learning & IoT: SVR and DTR Comparative Analysis 3

Enhancing Precision Agriculture with Machine Learning & IoT:...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Ifty, Rashedul Arefin Hossain, Afif Ismail, Md. Saim, Mohammad Abu Huraira International Islamic University Chittagong Kumira Department of Computer Science and Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9798350388282

In many nations, including Bangladesh, the importance of agricultural revenue and employment has dwindled over time. A sizable section of the population works directly in the agricultural industry, typically depending on past crop cultivating expertise. Because of this lack of accurate agricultural information, crop selection may be poor. Furthermore, agricultural institutions rely heavily on manual data, which is insufficient for accurate crop selection and output forecasts. Precision farming remains a distant dream. Using multiple regression methods, this research analyzes the most efficient way for agricultural production prediction. There has been little study into using machine learning methods integrating with IoT to estimate agricultural production. This research also incorporates multiple regression techniques on the obtained dataset using machine learning algorithms. The fundamental goal of this study is to improve the predictability of optimum crop selections, mitigate food issues, and encourage intelligent information prediction analysis in agriculture. This strategy is intended to help farmers make better informed and profitable agricultural choices. data from Bangladesh Bureau of Statistics & Bangladesh Meteorological Department are used, which spans 43 years. This study takes into account one important crop - Aman rice. Furthermore, this research takes into account seven environmental characteristics, including monthly averages of Bangladesh's hottest and lowest temperatures, yearly rainfall, cloud cover, wind speed, relative humidity, and strong sunlight and proposed an IoT model for data collection. The characteristics of optimum control for Bangladesh's primary crop agroecosystems are used. The Decision Tree Regression technique is proposed in this study for agricultural crop selection & yield prediction. © 2024 IEEE.

关键词： Crops

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Design and Simulation of Automobile Maintenance Detection System Based on machine learning Model 3

Design and Simulation of Automobile Maintenance Detection Sy...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Han, Yangfan Wang, Weiyin Harbin Huade University Harbin Heilongjiang150025 China

ISBN: (纸本)9798350376173

With the rapid development of automobile industry, the demand for automobile maintenance testing technology is increasing. Traditional experience-based fault diagnosis methods often show limitations in the face of complex automobile systems and a large number of sensor data. Therefore, this study proposes an automobile maintenance detection system that integrates machine learning model, aiming at improving the accuracy and efficiency of fault detection in automobile maintenance process. The system uses the sensor data of automobile components as input, and fuses them through machine learning model to realize automatic identification and classification of different fault types. In order to verify the performance of the system, a large number of experiments are carried out in the simulation environment, and the results show that the designed system has good classification effect and generalization ability under different fault types and normal conditions. The results of this study are expected to promote the application of intelligent technology in the field of automobile maintenance and provide strong support for the actual automobile maintenance work. © 2024 IEEE.

关键词： Automotive industry

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machine learning for Nonlinear System Identification: Kernels and ANN 3

Machine Learning for Nonlinear System Identification: Kernel...

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3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Fateh, Rachid Darif, Anouar Pouliquen, Mathieu Frikel, Miloud Safi, Said Ibn Zohr University Faculty of Applied Sciences Department of Computer Sciences Ait Melloul Morocco Sultan Moulay Slimane University LIMATI Laboratory Beni Mellal Morocco Normandie University LIS Laboratory-UR 7478 UNICAEN ENSICAEN Caen France

ISBN: (纸本)9798331522056

Modeling and identifying nonlinear systems are crucial challenges in many scientific and technical fields. This study examines the employment of machine learning for identifying non-linear systems, focusing specifically on two approaches: kernel methods and artificial neural networks (ANN). Kernel methods leverage nonlinear data transformations to model complex relationships by kernel trick, while ANN offers flexibility in modeling interactions between variables. We examine how these two approaches can effectively identify nonlinear systems output, highlighting their respective advantages and disadvantages. Case Experiments illustrate how well these methods perform across different system identification scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Energy-Efficient Development of ML-Enabled Systems: A data-Centric Approach 24

Energy-Efficient Development of ML-Enabled Systems: A Data-C...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Omar, Rafiullah Univ LAquila Laquila Italy

ISBN: (纸本)9798400705915

As the integration of machine learning (ML) models becomes pervasive in software systems, the associated energy costs have emerged as a critical concern. This research delves into the energy efficiency of ML-enabled systems, focusing on the ML component itself and its impact on the overall system. Our primary emphasis is on the data-centric approach, particularly in the context of feature selection and handling concept drift, and how these energy-efficient components affect the overall energy consumption of ML-enabled systems. In our initial investigation, we explored feature selection methods and identified significant variations in their energy consumption. This led us to delve deeper into understanding how different techniques for scoring features contribute to the overall energy footprint of ML models. Subsequently, we are examining the impact of changes in data distribution, often referred to as concept drift, on model accuracy and the associated energy costs. Our empirical experiments will reveal insights into energy-efficient strategies for handling concept drift, a crucial aspect of maintaining ML-enabled systems. We will compare various methods and their effectiveness in mitigating the adverse effects of concept drift while keeping energy consumption in check. The findings from our research contribute to the development of sustainable and energy-efficient ML models within the broader context of software engineering. Lastly, we will compare how different alternatives of ML components in ML-enabled systems affect the overall energy consumption of ML-enabled systems.

关键词： Energy consumption run-time performance scoring methods environmental impact

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Development of an Augmented Reality Application for Algorithms learning in Lima 3

Development of an Augmented Reality Application for Algorith...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Gejaño, Anthuanet Ortega, Eduardo Subauste, Daniel Universidad Peruana de Ciencias Aplicadas Faculty of Engineering Lima Peru

ISBN: (纸本)9798350354171

Every day, more and more companies adopt digital transformation, so more IT professionals are needed. However, very few people study technology careers. This is particularly true because adolescents have difficulties in problem-solving since school, which is essential to becoming a technology professional. This document presents the development of an augmented reality mobile application designed to help adolescents learn to build algorithms entertainingly. In addition, the impact on learning was evaluated through a case study of 100 adolescents. The results demonstrated that the application helped to learn algorithms, as well as, improve the students' logical reasoning and problem-solving skills. © 2024 IEEE.

关键词： Adversarial machine learning

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