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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461-470 订阅

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Evaluation and Comparison of Ten machine learning Classification Models Based on the Mobile Users Experience 3

Evaluation and Comparison of Ten Machine Learning Classifica...

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

作者： Hu, Haoxiang Zhou, Zhexuan Shandong University School of Mathematics Shandong Jinan250100 China

ISBN: (纸本)9798350316674

machine learning prediction is a statistical prediction model based on computer algorithms, which has been very popular with the development of AI. It is the most commonly used tool in big data prediction and data mining. To establish an effective machine learning model and get better prediction accuracy, we must choose a suitable algorithm based on actual problems and then fully improve the model. This article lists ten commonly used machine learning models and train and fit these classification models in Python based on the data set provided by the 2022 MathorCup Big data Competition. Meanwhile, by establishing feature engineering and choosing the best hyperparameter combination, we got the best performance of the model's prediction. The result shows that among the ten machine learning models, the CatBoost Classifier had the highest accuracy 0.7849, while the XGBoost Classifier had the highest macro-F1 score 0.4898;after hyperparameter tuning, the best macro-F1 score of XGBoost Classifier could attain 0.5216. © 2023 IEEE.

关键词： F1-score Feature engineering Hyperparameter Tuning machine learning Model Comparison XGBoost Classifier

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An Interpretable machine learning Approach for Identification of the Risk Factors of Early-Stage Overweight and Obesity 3

An Interpretable Machine Learning Approach for Identificatio...

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

作者： Roy, Priyanka Tipu, Amrita Das HSTU Department of CSE Dinajpur5200 Bangladesh

ISBN: (纸本)9798350388282

The increasing overweight and obesity rates pose a significant risk to public health globally, as it is closely associated with various diseases and higher rates of morbidity and mortality. Prompt and effective intervention in the early stages is therefore crucial. Furthermore, identifying obesity risk factors is essential to combat the growing rates of several secondary health problems such as cardiovascular disease, type II diabetes, and other comorbidities. This study aims to unveil the key factors playing a remarkable role in shaping this situation, utilizing the immense potential of machine learning methods. The proposed pipeline implements feature selection using Recursive Feature Elimination to effectively reduce the dimensionality of the dataset. The extracted subset of the original secondary dataset is then employed to train different state-of-the-art machine learning classifiers, including Logistic Regression, Random Forest, Decision Trees, along with the proposed novel stacking ensemble model. The proposed model outperforms other models, achieving a noteworthy accuracy of 99.58% with an AUC score of 1.0, when combined with the SMOTE-ENN balancing technique. Additionally, model explainable tool SHAP is used to rank the features according to their individual contribution, providing a more in-depth explanation of how each feature impacts the model outcome. By leveraging advanced machine learning algorithms and extensive datasets, this approach can significantly improve early-stage obesity prediction by uncovering its underlying causes. © 2024 IEEE.

关键词： Decision trees

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3rd international conference on Internet of Everything, IoECon 2024

3rd International Conference on Internet of Everything, IoEC...

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3rd international conference on Internet of Everything, IoECon 2024

ISBN: (纸本)9783031844256

The proceedings contain 10 papers. The special focus in this conference is on Internet of Everything. The topics include: Parallelism Everywhere: The Internet of Everything and Parallel Computing;ioV Simulation Architecture for Software-Defined Vehicular Fog Network Orchestration;toward Modeling of Flooding Attacks Targeting Massive IoT Networks;using the Internet of Everything for machine-learning-Based Computer System Design and Optimization;enhancing Urban Freight Delivery: A machine learning Approach to Predicting Delivery Speed;soil-Based Thermoelectric Energy Harvesting System for IoT Devices;a Methodology for Measuring Selected Performance Parameters of Smartphone-Based Vehicle-to-Pedestrian and Pedestrian-to-Infrastructure Communication;wordPress Architecture Modernization Projects.

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Emerging Frontiers in Disease Prediction: Leveraging ML Classification Algorithms for Brain Stroke 3

Emerging Frontiers in Disease Prediction: Leveraging ML Clas...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Singh, Preet Ramkumar, Kr Mittal, Sonam Hasija, Taniya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

The area of biomedical and clinical has been continuously influenced by Artificial Intelligence (AI) and machine learning (ML), with the help of predictive technology and determining relationships between different variables. This paper aims to find the classification of different labelled parameters of brain stroke to get the results of the possibility a patient would face a stroke or not. A brain stroke is a condition under which the supply of blood is affected in the brain and leads to hemorrhagic conditions. This study suggests an early prognosis of stroke disorders by the utilization of various machine learning methods, taking into account factors such as hypertension, obesity, cardiovascular disease, mean blood sugar level, habit of smoking, earlier stroke, and age. The different Supervised ML algorithms used in this categorical analysis are Logistic Regression (LR), K-Nearest Neighbor (KNN), RandomForest Classifier and AdaBoost. Amongst all the best-performing models, KNN has an accuracy of 97%. The correct predictions of the model made it viable to take precautions in advance and undertake a healthy lifestyle for a long sustainable life. © 2024 IEEE.

关键词： Self-supervised learning

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Detection of False data Injection Attacks in Smart Power Grid Using data Balancing-Based machine learning Algorithms 3

Detection of False Data Injection Attacks in Smart Power Gri...

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3rd international conference on Advanced Electrical engineering, ICAEE 2024

作者： Gasmi, Younes Bali, Noureddine Karim, Atif Belagoune, Soufiane Usthb Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350349351

The state estimation technique in intelligent power systems is critical for estimating state variables based on appropriate measurement data. Nevertheless, this technique is exposed to cyber-attacks, the best known of which is the false data injection attack (FDIA), which poses a danger to advanced energy systems. This work describes a method for detecting fraudulent attacks using machine learning techniques that include Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). Different oversampling approaches have been used to tackle the imbalanced dataset issue including the Random Oversampling Technique (ROT), Synthetic Minority Oversampling Technique (SMOTE) and Borderline SMOTE. The approach was applied to well-known Oak Ridge National Laboratory datasets. Various measures were used to analyze the efficiency of this study, including accuracy, F1-score, precision and recall. © 2024 IEEE.

关键词： Phasor measurement units

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machine learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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Short-term photovoltaic power prediction study based on similar day clustering and machine learning 3

Short-term photovoltaic power prediction study based on simi...

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3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhao, Qian Guo, Feng Wang, Ting Liu, Yuankai Linyi Univ Linyi Shandong Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Aiming at the challenge of short-term prediction caused by the stochasticity and uncertainty of power fluctuations in PV power systems, this paper proposes a new method for short-term prediction of PV power based on k-means++ clustering, empirical modal decomposition of fully adaptive noise ensemble (ICEEMDAN), Improved Red-tailed Hawk Optimization Algorithm (IRTH), and Least Squares Support Vector machine (LSSVM). First, the k-means++ algorithm is used to classify the historical data into weather types such as sunny, cloudy and rainy days;second, ICEEMDAN decomposes the raw PV power data into a number of intrinsic modal functions IMFs;IRTH optimizes the kernel and penalty parameters of the LSSVM model with the aim of solving the sensitivity problem of the traditional LSSVM in the parameter selection. The experimental results show that the prediction model proposed in this paper exhibits better prediction accuracy in the short-term prediction of PV power generation.

关键词： Photovoltaic power prediction Similar day clustering ICEEMDAN Red-tailed eagle algorithm Least squares support vector machine

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A Review on Papaya Leaf and Fruit Disease Classification Techniques 3

A Review on Papaya Leaf and Fruit Disease Classification Tec...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rank, Yashkumar Sutariya, Kruti Computer Science and Engineering Department Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

The rising occurrence of diseases affecting papaya leaves and fruits presents considerable obstacles to agricultural yield and food safety. This review article sought to offer a thorough overview of the contemporary methods employed in the categorization of papaya diseases. The main impetus for this research was the necessity to advance precision agriculture through enhanced detection and management of diseases. The objective of this study was to evaluate and integrate different methods, including those leveraging ML and DL, used in disease classification. Research focused on summarizing current literature, identifying the most effective techniques, and highlighting areas that need more exploration. This review serves as a valuable resource for researchers and professionals in agricultural technology, offering insights into the strengths and weaknesses of various classification approaches. © 2024 IEEE.

关键词： Deep learning machine learning Papaya Disease Transfer learning Vision Transformers

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Optimizing Client Selection for Federated learning: A PPO-Based Method 3

Optimizing Client Selection for Federated Learning: A PPO-Ba...

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3rd international conference on Big data, Information and Computer Network, BDICN 2024

作者： Azhati, Baheti Li, Xianda Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350363821

Federated learning (FL), as a distributed machine learning approach, is widely recognized and applied in various fields due to its advantages such as multi-party participation, privacy protection, and reduced communication burden. However, in the context of edge networks, the performance of FL is challenged by the heterogeneity of device resources and data. This underscores the crucial importance of client selection. In our paper, we address this challenge by considering factors like model accuracy and device communication latency, constructing a multi-objective trade-off optimization problem. We formulate this problem as a Markov Decision Process (MDP) and we propose a proximal policy optimization (PPO)-based node selection algorithm. This algorithm aims to obtain an optimal strategy for effectively addressing the trade-off problem by selecting an ideal set of clients for model aggregation. Simulation results demonstrate that our method reduces model training latency and improves accuracy, exhibiting resilience against problematic clients. © 2024 IEEE.

关键词： Federated learning

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Research on News Text Classification Based on BERT-BiLSTM-TextCNN-Attention 24

Research on News Text Classification Based on BERT-BiLSTM-Te...

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3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Wang, Jia Li, Zongting Ma, Chenyang Dalian Polytech Univ Dalian 116034 Liaoning Peoples R China Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116034 Liaoning Peoples R China

ISBN: (纸本)9798400718212

Traditional machine learning models are difficult to capture complex features and contextual relationships. While a singular deep learning architecture surpasses machine learning in text processing, it falls short of encompassing the entirety of textual information [5]. Enter a novel approach: a news text classifier built upon BERT-BiLSTM-TextCNN-Attention. This model employs BERT's pre-trained language models to delve into text content. It then channels this data into a BiLSTM layer, capturing sequence nuances and long-term dependencies for comprehensive semantic insight. Following this, the output moves through a TextCNN layer, effectively capturing local semantic cues through convolution. The model culminates with attention mechanisms that highlight pivotal text attributes, refining feature vectors for the Softmax layer's classification. The experimentation utilized a subset of the THUCNews Chinese news text dataset. Results indicate that the BERT BiLSTM TextCNN Attention model achieved 96.48% accuracy, outperforming other benchmarks. This underscores its superiority in handling Chinese news text classification and validating its prowess in extracting deep semantic nuances and crucial local features from the text.

关键词： Deep learning text classification natural language processing neural network

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