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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20255 条记录，以下是4611-4620 订阅

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Enhancing Road Infrastructure Maintenance Using Deep learning Approach 23th

Enhancing Road Infrastructure Maintenance Using Deep Learnin...

引用

23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Vinothkumar, S. Dhanushya, S. Guhan, S. Krisvanth, P. Kongu Engineering College TamilNadu Erode638060 India

ISBN: (纸本)9783031648359

The physical gathering of info on road damage can be time-consuming and dangerous to people's safety, yet preserving the transportation network is essential for providing secure and lasting transport systems. To address these challenges, this paper presents a novel approach that leverages Unmanned Aerial Vehicles (UAVs) and deep learning approaches can greatly improve the effectiveness and precision of road damage identification. The proposed approach employs the YOLOv8 algorithm, cutting-edge object detection and localization model, for detecting road damage in UAV images. The YOLOv8 model offers improved accuracy compared to previous versions, namely YOLOv4, YOLOv5, and YOLOv7, by using UAVs equipped with high-resolution cameras, info on road damage can be captured efficiently and remotely. On a synthesis of the Indian roadway data and the rdD2022 dataset, this method was trained and tested. The trained YOLOv8 model was used to identify and locate different types of road degradation such cracks, potholes, and surface degradation. The combination of UAV imagery and deep learning algorithms has shown great potential to detect automated road deterioration. This research highlights the advantages of using YOLOv8 for improved accuracy in identifying road damages. This work lays the path for further developments in the field, enabling safer transport systems and more effective road maintenance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Classification of Green Tea Samples Based on Epigallocatechin-3-Gallate Content Using Molecular Imprinting Polymerization Technique 3

Classification of Green Tea Samples Based on Epigallocatechi...

引用

3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Modak, Angiras Das, Debangana Roy, Runu Banerjee Jadavpur University Dept. of Instrumentation and Electronics Engineering Kolkata India Silicon Institute of Technology Dept. of Electronics and Communication Engineering Bhubaneshwar India

ISBN: (纸本)9798350313703

The proposed system implements a machine learning based approach to classify samples of green tea based on the EGCG (Epigallocatechin-3-Gallate) content. The EGCG content in green tea samples are estimated by using the synthesized molecularly imprinted polymer (MIP) electrode. The voltametric responses were obtained using a three-electrode set-up. The data was recorded and pre-processed. The responses obtained after pre-processing are fed for feature selection using UMAP (Uniform Manifold Approximation and Projection) algorithm, in which the number of features is reduced to generate optimized output. Further, the selected features are processed with K-means clustering method, to distinguish the green tea samples. The classification of green tea samples is performed by using XGBoost (Extreme Gradient Boost) algorithm. The use of XGBoost provides fast converging with limited steps, direct route to the least error and simplified calculations to lower computational costs and improve speed. Finally, the efficacy of the proposed system is calculated based on metrics namely, Calinski Harabasz Score, Distortion score and Silhouette score for K-means clustering technique. The efficiency of the introduced XGBoost method is calculated based on the metrics such as accuracy, AUC (Area under Curve), Recall, Precision, F1-score, Kappa and MCC (Matthews Correlation Coefficient) with outcomes of 0.96, 0.99, 0.96, 0.96, 0.96, 0.94 and 0.95. Thus, the outcome of the proposed method denotes the effective performance in classifying the green tea samples. © 2024 IEEE.

关键词： Polymerization

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An intelligent Technique for the Effective Prediction of Monkeypox Outbreak 3

An Intelligent Technique for the Effective Prediction of Mon...

引用

3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Mir, Azka Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350322125

Monkey pox is a viral disease that spreads from animals especially monkey to human beings. Monkey pox outbreak has been increasing at a concerning rate. The outbreak of monkey pox has infected several people around the world. The extent and intensity of the disease can be determined by the occurrence of the symptoms. The objective of this paper is to predict monkeypox virus so that outbreak can be administered before monkeypox looms as a viral health hazard. The monkeypox case has been classified as confirmed, discarded and suspected. This paper uses a supervised machine learning model to predict the status of monkey pox case. To diagnose monkeypox virus case, clinical parameters are required. The selected dataset contains the parameters of monkey pox virus from April 2022 onwards. It is necessary to predict the monkey pox outbreak before it effects more valuable lives. For the purpose of this paper, supervised machine learning techniques have been used to determine the performance of the dataset through experimental analysis. The experiment has been performed using various classifiers such as Decision tree, Naïve Bayes etc. to compare the accuracy rate. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 93.51% using K-NN classifier with k=5 neighbors. Rapid miner platform is used for the application of the machine learning tools and techniques for the purpose of this research. This paper highlights the effective machine learning steps for the development of highly accurate model using machine learning techniques on monkey pox outbreak dataset. © 2023 IEEE.

关键词： Viruses

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Mental Confusion Prediction in E-learning Contexts with EEG and Machine learning 3rd

Mental Confusion Prediction in E-Learning Contexts with EE...

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3rd international conference on Novel and intelligent Digital Systems, NiDS 2023

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031440960

In cognitive science, the term confusion is used to capture the decline in learners’ cognitive ability, which affects their ability to think, solve a problem, learn and understand. Unlike classroom education, in online e-learning contexts (e.g., in Massive Open Online Courses (MOOC)), confusion hinders the smooth evolution of the learning process from the learners’ side, as the educator can’t immediately interact with the students to restore cognitive equilibrium. This paper presents a Machine learning (ML) based approach by comparing several classifiers that were trained and tested exploiting Electroencephalogram (EEG) data (namely, band power, attention and mediation features) acquired by the MindSet device to efficiently distinguish "Confused" from "Not-Confused" subjects. In particular, J48 was the dominant model reaching an optimal performance with accuracy, precision and recall equal to 99.9% and an Area Under the Curve (AUC) of 100%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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An Approach to Ensemble Simplified ResNet Models-Using Egyptian Hieroglyphs data Set 3

An Approach to Ensemble Simplified ResNet Models-Using Egypt...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Yan, Zixiang Jin, Kecheng Hou, Jingkun Zhang, Pu Nanjing Tech University Nanjing211816 China Nanjing Hankai Academy Nanjing210031 China School of Inteligence Science and Technology University of Science and Technology Beijing Beijing100083 China Austin Friars St Monica's School Etterby Scaur Cumbria CarlisleCA3 9PB United Kingdom

ISBN: (纸本)9798350316674

Since the inception of computer vision, recognizing text, including both printed and handwritten characters, has posed a significant challenge for traditional machine learning models. However, only a handful of these models have focused on ideographical scripts. Similarly, while mathematical solutions to common neural network problems such as degradation and computational complexity have been studied, possible shortcuts are often overlooked. This paper presents an innovative approach by introducing a novel ensemble network called "SparrowNet"and compares it to ResNet18 and GoogLeNet V2. Using an open-source Egyptian hieroglyph dataset, the paper focuses on evaluating the efficiency and accuracy of different networks using various performance metrics. Additionally, the paper discusses the advantages of the two existing models and explains the reasons behind the modifications made to SparrowNet. Through this research, it has been demonstrated that the use of 1x1 convolutions can significantly improve speed and reduce computational complexity, while a voting mechanism can be a novel solution to prevent degradation, and the modified network is capable to solve this particular character recognition task. © 2023 IEEE.

关键词： Deep learning Egyptian Hieroglyphs Ensemble learning GoogLeNet ResNet

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VR-based Silent Reading and Rosenberg Tests: Machine-learning Approach to Identify learning Disorders 3

VR-based Silent Reading and Rosenberg Tests: Machine-Learnin...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Materazzini, Michele Morciano, Gianluca Alcalde-Llergo, José Manuel Yeguas-Bolivar, Enrique Zingoni, Andrea Taborri, Juri University of Tuscia Dept. of Economics Engineering Society and Business Organization Viterbo Italy University of Córdoba Córdoba Spain

ISBN: (纸本)9798350378009

This study explores the intersection of self-esteem assessment, specific learning disorders (SLDs), and emerging technologies, particularly virtual reality (VR) and machine learning (ML). Through this scope, the research involved the development of novel VR-based tools for administering silent reading and Rosenberg tests, designed to simulate real-world academic challenges and social interactions. A sample of 40 Italian university students, including those diagnosed with SLDs and a control group, participated in the study. data collected from VR were analyzed using ML algorithms, with a focus on dyslexia detection. The findings underscored the critical role of self-esteem in academic performance and overall well-being for individuals with SLDs. ML models, particularly support vector machines (SVM), demonstrated efficacy in identifying dyslexic tendencies based on data from VR. This multidisciplinary approach contributes to advancing personalized intervention strategies for individuals with SLDs, emphasizing early diagnosis and targeted support systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Traffic Sign Recognition Based on Improved VGG16 Algorithm 3

Traffic Sign Recognition Based on Improved VGG16 Algorithm

引用

3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Wang, Jianxia Jiang, Shan Zhou, Wanzhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China

ISBN: (纸本)9781510667563

The detection and recognition of traffic signs acts an extremely crucial role in driverless technology and assisted driving systems. In view of the low recognition rate of traffic signs, this paper improves the neural network based on the existing VGG16 network architecture, including adding BN layer, adopting batch normalization algorithm and adding attention mechanism. The GTSRB data set is enhanced by scaling and random rotation. The data is preprocessed by image graying and image normalization. In this paper, the model recognition rate reached 99.34%, which is of great value to enhance the recognition accuracy of traffic signs and promote the development of intelligent transportation. © 2023 SPIE.

关键词： Traffic signs

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intelligent Overcurrent and Distance Relay Coordination in Power Networks 3

Intelligent Overcurrent and Distance Relay Coordination in P...

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3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Majhi, Abhilash Asit Kumar Singh, Manohar Ray, Papia Veer Surendra Sai University of Technology Department of Electrical Engineering Burla Sambalpur India Punjab Engineering College Deemed University Chandigarh Department of Electrical Engineering India

ISBN: (纸本)9798350319972

The increased deployment of distributed generators (DGs) in distribution networks has brought new challenges to power system protection coordination. With multiple generation sources supplying the distribution network, the conventional protective devices that were designed for centralized generation may not be sufficient to ensure proper protection coordination in case of fault. This research provides an overview of the impact of distributed generations (DGs) on protection coordination in distribution networks, as well as an analysis of the different approaches that can be used to select and coordinate protection systems in the presence or absence of DGs. The proposed approach for selecting the optimal overcurrent (O/C) relays for protection coordination takes into account various relay characteristics and uses the Teaching learning Based Optimization (TLBO) algorithm to determine the best combination of relays for achieving optimal coordination. Optimization of the zones of distance relays is also done and proper coordination time interval (CTI) of 0.2s or more is maintained. The optimization results are tested and validated in Electrical Transient Analyzer Program (ETAP). The effectiveness of the proposed method for achieving optimal coordination in practical power system networks like IEEE-9 bus test system is demonstrated through simulation results, which show that the method can produce feasible and effective solutions. © 2023 IEEE.

关键词： Distributed power generation

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A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels 3

A Latent Dirichlet Allocation Technique for Opinion Mining o...

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3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Afaq, Anam Gaur, Loveleen Singh, Gurmeet Amity University Amity International Business School Noida India The University of the South Pacific School of Business and Management Suva Fiji

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling. © 2022 IEEE.

关键词： Sentiment analysis

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Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine 3

Analyzing Suicide and Contributing Factors in Thailand Using...

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3rd IEEE international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Wongnongtaey, Kanticha Srisomboon, Krissana Sutivong, Daricha Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350333862

The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people's demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice. © 2023 IEEE.

关键词： Decision trees

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