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

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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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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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A No-Code Approach to Classifying Neurological Disorders Using Orange data Mining 3

A No-Code Approach to Classifying Neurological Disorders Usi...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Davidraj, Yabez Elavarasan, S. Vivek, T. Munirathinam, Ramesh Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798331523893

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer's disease, Huntington's disease, motor neuron disease, and Pick's disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis. © 2025 IEEE.

关键词： Neurodegenerative diseases

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AnoLSTM-A Deep learning Approach for Test Cases Prioritization 3

AnoLSTM-A Deep Learning Approach for Test Cases Prioritizati...

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3rd international conference on machine learning and data engineering, icmlde 2024

作者： Choudhury, Tapas Kumar Behera, Mousumi Dash, Sanjit Kumar Pani, Subhendu Kumar Mishra, Jibitesh Biju Patnaik University of Technology Odisha Rourkela India Odisha University of Technology and Research Odisha Bhubaneswar India

In a continuous integration environment, code is often merged into the mainline codebase. As a result, regression testing is essential for ensuring that modifications to the primary codebase do not adversely affect the performance of the software's current characteristics. Test case prioritization (TCP) is a regression-based approach for reordering test cases in a test suite to enhance fault detection performance and shorten test case execution time. In this study, we put forward the AnoLSTM-TCP (Anomaly-Based Long Short-Term Memory Test Case Prioritisation) method to prioritize test cases. Our strategy leverages historical test case data, including features such as duration, execution of the test cases, and list of previous test results to automatically learn patterns and prioritize test cases effectively. Our proposed method goal is to increase the rate at which faults are discovered in test cases. For our experiment, we use an industrial dataset from ABB Robotics Norway (IOFROL and Paint Control). The evaluation metric we used to analyze the effectiveness of our model is APFD values. We compare our novel work with previous works of Random, ROCKET, RETECS, Deepgini, DeepOrder, and HyLSTMTCP. Finally, our experimental results show that AnoLSTM-TCP significantly achieves higher APFD values of 0.85, and 0.90 on IOFROL, and Paint Control datasets which are higher than existing approaches. © 2025 The Author(s).

关键词： Integration testing

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Explainable Diabetic Retinopathy Detection Using EnlightenGAN Images with Deep learning Technique 3

Explainable Diabetic Retinopathy Detection Using EnlightenGA...

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3rd international conference on machine learning and data engineering, icmlde 2024

作者： Kannan, M. Nikitha, S. Baig, Thouqeer Akshay, S. Mukhopadhyay, Adwitiya Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka India

Diabetic Retinopathy (DR) is considered one of the most serious causes of visual impairment and permanent blindness in the world, causing damage to the optic nerve head without any prior clinical sign. Colored retinal images are the most often utilized imaging data in the medical sector for screening and diagnosing eye disorders, and fundus cameras are commonly employed in clinical research to collect these images. Due to a number of reasons, such as exposure discomfort, incorrect equipment parameter setting, improper operation, and a lack of proficiency on the part of various medical professionals during the image-acquisition process, there are many poor-quality retinal scans in the current database. The main purpose of this research is to perform early detection of DR by examining different levels of Proliferative Diabetic Retinopathy (PDR) and Nonproliferative Retinopathy (NPDR) images using deep learning algorithms. This work recommends employing EiGAN (EnlightenGenerative Adversarial Network) to enhance the quality of the fundus images. The ROI-based segmentation and subsequent classification of the quality-enhanced fundus images are accomplished with the help of the proposed Convolutional Transfer learning Neural Network (CTLNN). The dataset was obtained from Precious Optics, Mysuru, India. The image dataset contains 1920 samples of PDR and NPDR fundus retinal images. Among these, there are multiple classes under NPDR, such as level-1 (healthy), level-2 (mild), level-3 (moderate), and level-4(severe), respectively. This research shows that the methods such as transfer learning and Efficient Net-B7 design to achieve robustness and efficiency. The methodology observes a significant increase across a range of performance indicators through a review of pre and post-enhancement findings. © 2025 The Author(s).

关键词： Clinical research

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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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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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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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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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