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

共 6844 条记录，以下是671-680 订阅

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Research on the Application of College English Teaching System Using Random Forest 3

Research on the Application of College English Teaching Syst...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Huang, Xiaojing Dou, Anqi Hainan Vocational University Of Science And Technology Foreign Language Teaching Department Haikou China

ISBN: (纸本)9798350318609

With the growth of language research and teaching, people realize that the grammatical competence is a significant thing of the communicative competence. Presently, there are various Artificial Intelligence (AI) assisted educational applications in schools as well as universities. This research utilizes an AI approach integrated with the knowledge recommendation to the system as well as develops an online English teaching system. The system of English teaching is helpful in determining the potential as well as internal connections among the estimation results as well as different factors. In this research, the machine learning (ML) approach of Random Forest (RF) is proposed for the College English Teaching System. The proposed approach initially performed the correlation analysis on the developed estimation data. After that, the integration principles were followed to estimate the coordination among indexes in the estimation of the teaching. The proposed RF approach attains the interaction analysis of 98.2 %, 96.3%, 95.2 %, 97.2 % and 96.2 % in the number of students of 20,40, 60, 80 and 1 00 respectively. © 2024 IEEE.

关键词： machine learning

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Construction of quantitative prediction model of metal mineral resources based on big data mining 3

Construction of quantitative prediction model of metal miner...

引用

3rd international conference on Computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

作者： Fu, Taiyu Chongqing Test Center of Geology and Minerals Chongqing400042 China

ISBN: (纸本)9781510672925

With the vigorous development of big data technology, the application of data mining program to complete quantitative prediction and evaluation of mineral resources has become a new trend in the development of mineral exploration industry and an important way to promote the digital transformation of traditional prospecting technology. In this regard, this paper will combine geological theory analysis and data mining algorithm to build a quantitative prediction model of metal mineral resources, realize the analysis, processing and visual interpretation of deep geoscience data, and provide necessary data science support for actual mineral exploration. The prediction model takes the machine learning algorithm as the core, and relies on the class libraries such as Numpy, Pandas and Sklearn in Python environment to complete the development and training. Combined with the spatial attribute database in ArcGIS, the prediction elements and importance evaluation indicators are obtained, and the final quantitative prediction results are obtained, and the result evaluation is completed. Practice has proved that the quantitative prediction model of mineral resources based on machine learning algorithm has high prediction accuracy, and the visual results can clearly show the high probability mineral resources areas, which has high practical promotion value. © 2024 SPIE.

关键词： Minerals

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

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

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Advancing Road Safety through Driver Drowsiness Detection Using Deep learning Model 3

Advancing Road Safety through Driver Drowsiness Detection Us...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Pandey, Sanjeev Lekha, J. Kadyan, Sweety University Lavasa School of Science Maharashtra Pune India

ISBN: (纸本)9798350372120

Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. © 2024 IEEE.

关键词： Computer vision

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Plant Leaf Disease Detection and Classification Using MobileNetV3 and Wavelet Kernel Extreme learning machine 3

Plant Leaf Disease Detection and Classification Using Mobile...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shruthi, B.S. Narasimha Murthy, M.S. Prakash, Chidurala Sai Riad Al-Fatlawy, Ramy Sundaram, S. Meenakshi Malnad College Of Engineering Department Of Information Science Hassan India Bms Institute Of Technology And Management Department Of Computer Science And Engineering Bangalore India Sr University Department Of Electrical And Electronics Engineering Warangal India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

Detection of plant disease is the most important aspect in agriculture, aimed at identifying and diagnosing disease that affect plants. If the plant disease detected earlier, that helps to avoid risk from damaging plants and the soil. However, there is a difficulty to differentiate the diseased and healthy leaves due to similarity of disease symptoms. To solve this problem, an effective Wavelet Kernel Extreme learning machine (WKELM) classifier is proposed for distinguishing diseased and healthy plants. Images from the PlantVillage dataset are preprocessed using Bilateral filter and then segmented by Adaptive Gaussian Threshold (AGT) to separate the diseased portion from leaf. MobileNetV3 a Convolutional Neural Network (CNN) used for effective feature extraction with the hyper parameters which are tuned using effective Emperor Penguin Optimizer (EPO) and classification is done by the WKELM for accurate results of plant disease. The experimental results of the proposed model performed by the evaluation metrics achieved accuracy of 99.7%, recall of 99.6%, precision of 99.6%, F1-score of 99.7% which is greater than other existing deep learning models like CNN, Transfer learning with Deep CNN models, CNN - Visual Geometry Group (VGG). © 2024 IEEE.

关键词： Convolutional neural networks

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Fatty Liver Disease Prediction Through machine learning 3

Fatty Liver Disease Prediction Through Machine Learning

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Mathur, Siddharth Karodi, Priyansh Dhanare, Ritesh SVKM's NMIMS MPSTME Department of Information Technology Shirpur India

ISBN: (纸本)9798350396157

Fatty Liver Disease (FLD) is a medical condition that poses potential health risks and can affect individuals of various age groups, irrespective of demographic factors. The field of machine learning (ML) has experienced considerable progress in the analysis of medical data, facilitating the discovery of concealed information that plays a vital role in the diagnosis of fatty liver disease (FLD). Liver disorders, such as Fatty Liver Disease, exert a significant influence on the population, necessitating substantial research focus. The main aim of this study is to utilize a blend of machine learning algorithms, specifically XGBoost (XGB), Naive Bayes (NB), Random Forest (RF), and an Artificial Neural Network (ANN), to effectively forecast the incidence of fatty liver disease. To accomplish this goal, the research employed a machine learning methodology and proceeded to train the chosen algorithms (XGBoost, Naive Bayes, Random Forest, and ANN) using appropriate datasets for FLD prediction. The accuracy score and confusion matrix were used to evaluate the performance of the classification algorithm. This made it possible to get a full picture of both its accuracy and its ability to classify. The study exhibited effective utilization of machine learning algorithms to accurately forecast the incidence of fatty liver disease. The accuracy score and confusion matrix were found to be useful metrics for evaluating the algorithms' precision in detecting instances of FLD. The results underscore the potential of machine learning algorithms in accurately predicting FLD, thereby enabling early detection and enhanced management. The augmentation of prediction accuracy and personalized approaches to addressing the complexities of fatty liver disease can be achieved through additional validation and exploration of advanced technologies, including imaging techniques and the integration of multi-omics data. Further investigation in this field exhibits potential for the development of m

关键词： Neural networks

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A Signal Processing and machine learning Framework for Enhanced TDS Simulation 3

A Signal Processing and Machine Learning Framework for Enhan...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shuvo, Shuvendu Pal Pal Shibazee, Shirshendu Khuki, Arifa Akter Das, Chaitee Paul, Goutam Mita, Mitaly Paul Khulna University of Engineering and Technology Dept. of CE Khulna9203 Bangladesh Bangladesh University of Engineering and Technology Dept. of EEE Dhaka1000 Bangladesh Khulna University of Engineering and Technology Dept. of MSE Khulna9203 Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of Pharmacy Gopalganj8100 Bangladesh Khulna University Dept. of Economics Khulna9208 Bangladesh

ISBN: (纸本)9798331534400

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features;hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes. © 2024 IEEE.

关键词： Empirical mode decomposition

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Heart Disease Prediction Using machine learning Algorithms: Performance Analysis 3

Heart Disease Prediction Using Machine Learning Algorithms: ...

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

作者： Saha, Subrata Rahman, Md. Motinur Suki, Tahmid Tamrin Alam, Md. Mahbub Alam, Md. Shah Haque, Mohammod Abu Sayid Bangladesh Atomic Energy Commission Institute of Electronics Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078. © 2024 IEEE.

关键词： Forecasting

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Automated Detection of Brain Tumor Using Advanced Deep learning Models 3

Automated Detection of Brain Tumor Using Advanced Deep Learn...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Uniyal, Manas Saini, Chirag Gupta, Amit Gupta, Richa Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dehradun India

ISBN: (纸本)9798350354171

This paper describes how to use deep learning (CNN) to detect brain tumors in medical images, solve tumor differentiation problems, and detect distributed network complexity. Four CNN architectures and two complementary models (MobileNet) were evaluated for their performance in brain tumor classification. The study used a variety of data sets from brain MRI scans that included different tumor types, sizes, and locations. Pre-processing techniques such as image normalization and scaling increase the power of the model. Each CNN is designed and trained with tests that evaluate accuracy, sensitivity, specificity, and computational efficiency. Comparative analysis identifies strengths and weaknesses and provides insight into the suitability of the model for real clinical applications. © 2024 IEEE.

关键词： Contrastive learning

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