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A Novel Ensemble learning Method for Online learning Scenarios 4

A Novel Ensemble Learning Method for Online Learning Scenari...

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4th IEEE international conference on Electronics Technology, ICET 2021

作者： Liu, Ye Cao, Weipeng Liu, Yiwen Zou, Weidong Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of International Exchange Shenzhen China Beijing Institute of Technology School of Automation Beijing China

ISBN: (纸本)9781728176734

Online Sequential Extreme learning machine (OS-ELM) is a modeling algorithm for data stream mining that has attracted much attention in recent years. OS-ELM has extremely fast training speed and good generalization ability, however, because it uses random input weights for the model initialization and these parameters remain unchanged throughout the training process, which causes instability in the performance of the model. To alleviate this problem, we propose an improved OS-ELM algorithm based on the ensemble learning mechanism, which we call ELOS-ELM. ELOS-ELM uses multiple initialization methods to construct diverse base online learners, and the prediction results of the model are determined by voting by these base models. In this way, the final model can avoid misjudgments caused by the knowledge blind zone of a single base model. Extensive experimental results on eight benchmarks show that ELOS-ELM can effectively improve the stability and generalization ability of the OS-ELM. © 2021 IEEE.

关键词： E-learning

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Early Detection and Prevention of Hypoglycemic Episodes in Diabetics Using Random Forest Algorithm

Early Detection and Prevention of Hypoglycemic Episodes in D...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Pinnapureddy Manasa Maragoni Mahendar K Raghavendar Satyanarayana Nimmala Ravikumar Ch Dokku Durga Bhavani Department of CSE Neil Gogte Institute of Technology Hyderabad India Department of CSE Teegala Krishna Reddy Engineering College Hyderabad India Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of CSE Sreenidhi University Hyderabad India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Individuals with diabetes, especially those using insulin or other antidiabetic medications, can experience hypoglycemia, a disease marked by abnormally low blood glucose levels. Hypoglycemia is often characterized by blood glucose levels falling below 70 mg/dL. Prompt recognition of hypoglycemia is crucial to prevent severe health problems. this study presents a Random Forest-Based Model for the early detection of hypoglycemia. the model was created with a dataset derived from Continuous Glucose Monitoring (CGM) systems and clinical data, comprising 1,500 records. the dataset comprises essential aspects including real-time glucose measurements, insulin levels, alcohol intake, meal timing, physical activity, and demographic information. the dataset was divided into 65% for training and 35% for testing, with absent values rectified by mean substitution. the Random Forest algorithm was utilized, and its efficacy was assessed by measures including accuracy, precision, recall, and F1-score. the suggested model exhibited enhanced performance, exceeding that of current machine learning classifiers. the Random Forest model adeptly detects hypoglycemic episodes and addresses imbalanced data with remarkable precision and recall. Integrating alcohol use as a variable further augments the model's ability to consider lifestyle factors affecting hypoglycemia

关键词： Continuous glucose monitoring Training machine learning algorithms data models Diabetes Classification algorithms Timing Insulin Random forests Blood

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Parkinson's Disease Detection Using data mining Models: A Comparative Study

Parkinson's Disease Detection Using Data Mining Models: A Co...

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data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Reem Jamal Majed Rashid Moahmmed Al-Heddi Ahmed M. Zeki Big Data Science and Analytics College of Science University of Bahrain Sakhir Bahrain Department of Information System College of Information Technology University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

data mining techniques have emerged in the healthcare industry over the past years in determining the occurrences and development of diseases. this paper aim is to conduct a comparative study between three data mining models in predicting Parkinson’s Disease using biological voice measurements dataset. the three models that are evaluated are: Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector machine (SVM). data oversampling using Synthetic Minority Oversampling Technique (SMOTE) method was used to balance the data and feature selection was applied to select the most relevant attributes. the CRISP-DM framework is used to perform the analysis using Weka machine learning tool. the results showed that the KNN model obtained the best accuracy of 94.11% on balanced dataset.

关键词： Support vector machines Industries Radio frequency Analytical models Biological system modeling Nearest neighbor methods Feature extraction

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Using EEG data to Classify Hand Motor Imagery in Stroke Patients

Using EEG Data to Classify Hand Motor Imagery in Stroke Pati...

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Electronic Information Engineering and Computer Technology (EIECT), international conference on

作者： Yiwei Shen Junting Liu Yuxuan Yang Wenjun Xie Hefei University of Technology School of Software Hefei Anhui

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

EEG-driven brain-controlled prosthetics can greatly help people with movement disorders in their daily lives. Still, the task of accurately translating intentions into expected actions in the brain remains a technical challenge. In recent years, as a branch of machine learning, Deep Neural Networks (DNN) have made great progress in improving the recognition and classification ability of computer vision tasks. this study evaluate the information representation ability of 2 machine learning models and 16 NN models and their variants. the experiment is conducted on an open source EEG dataset of hemiplegic stroke patients, and we evaluate the thematic and cross-thematic performance of the above algorithm. We find that a single-layer GRU network remained an optimal choice in subject subject classification because it is able to effectively reduce model overfitting. For cross-subject analysis, some deep-learning models outperform all other methods due to the availability of training data and can function effectively in cross-subject decoding tasks. Independent Component Analysis (ICA) processing is also proved to be effective for improving the accuracy of classification. these improvements will provide important information for researchers in the field of Brain-computer interface (BCI) to select data processing strategies appropriate for their research tasks, it also lays a foundation for the future research on the interaction between the characteristics of bioelectric signals and the structure of neural networks.

关键词： Hands Accuracy Convolution Computational modeling Stroke (medical condition) Brain modeling data models Electroencephalography Decoding Convolutional neural networks

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Decoding of Auditory Imagination Activity Based on machine learning Methods

Decoding of Auditory Imagination Activity Based on Machine L...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Wanly Ma Xuegang Tang Qiuqin Xia Yiwei Xu Weitong Gao Jin Gu Southwest Jiaotong University Chengdu China

the decoding of brain signals has important research value and is also full of challenges. In recent years, the fMRI technology is more and more widely used in the brain signals decoding because of its high spatial resolution and non-invasive characteristic. the existing brain region template provides a powerful tool for brain region decoding research, and the obtained functional connectivity matrix quantifies the correlation between brain regions. the development of pattern recognition technology has created a favorable technical foundation for the study of fMRI data, including a variety of mature machine learning methods such as support vector machine. In this paper, the fMRI data of 24 healthy subjects was measured when they performed the auditory imagination under different sound and scene conditions, then the functional connectivity matrices of brain regions were obtained and to be classified by several pattern recognition methods, such as optimized support vector machine, naive Bayes, and logistic regression. At last, the classification methods were compared according to the classification accuracy. Compared with related works, Methods of data acquisition and the accuracy of the results are better than previous job. the method can help psychologists and neuroscientists perform the brain signal decoding with high efficiency and quality. At the same time, the research is helpful to reveal the neural mechanism of auditory imagination and auditory perception, deepen our understanding of human brain auditory information processing, help the computer to simulate human hearing, and use the correlation of brain signals to help the deaf and mute people to carry out related activities, which has good social benefits.

关键词： Support vector machines Analytical models Image recognition Correlation Computational modeling machine learning Functional magnetic resonance imaging

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Prediction of ROP Method Based on Online machine learning and Multi-source data Preprocessing Technology 4

Prediction of ROP Method Based on Online Machine Learning an...

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4th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Yu, Yang Cui, Meng Huang, Kai Luo, Lei Zhang, Xiuling Li, Hui CNPC Engineering Technology RD Company Limited Beijing China Baoji Oilfield Machinery Co.Ltd Baoji China

ISBN: (纸本)9781665428392

the drilling operation is a large scale, technically complex and systematic project, which has always taken the top position of investment in oil exploration and development. Speed and efficiency are the primary goals of drilling engineering. Using information technology to optimize drilling parameters can reduce the complexity of accidents, improve the time efficiency of drilling, and significantly shorten the drilling construction cycle and save exploration and development costs. Forecasting drilling rate of penetration (ROP) is an essential component of drilling optimization. this paper introduces a machine learning-based permeability prediction method and its application effects. In response to the drilling rate of penetration (ROP) problem, a drilling rate of penetration (ROP) model based on an integrated learning algorithm is designed and implemented by mining the historical data collected from a specific block. Meanwhile, this approach is compared with traditional machine learning algorithms such as SVM, LR and KNN. the experimental results indicate that the algorithm has better accuracy and applicability than other methods, which can provide a scientific and reliable reference for improving drilling rate of penetration (ROP) and technical support for realizing intelligent drilling. © 2021 IEEE.

关键词： learning algorithms

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Smart Insole Gait Analysis

Smart Insole Gait Analysis

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Palak Seth Varun Gadi Premalatha G Department of Data Science and Business Systems School of Computing S.R.M Institute of Science and Technology Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Wearable technology has transformed sports performance and health monitoring with real-time biomechanical testing. It is feasible to wear smart insoles with accelerometers and pressure sensors that allow gait analysis through the measurement of critical parameters like joint angles, stride length, ground reaction forces, and foot patterns. An intelligent insole system that tracks gait data, identifies gait deviations from ideal kinematics, and offers movement correction feedback. thus introducing a novel approach for continuous gait monitoring and real-time correction. the system is coupled with machine learning algorithms to evaluate biomechanical parameters and optimize motion effectiveness. Experimental results show that the system can detect 93% gait anomalies at 150ms latency and offer real-time feedback, showing the system to prevent injury and optimize performance. the proposed approach is advantageous to sports individuals, rehabilitation patients, and fitness consumers by way of cost-effective and

关键词： Biomechanics Tracking Prevention and mitigation Real-time systems Biomedical monitoring Wearable devices Injuries Gait recognition Monitoring Sports

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using data mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial recognition;crime data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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A Two-Stage Method for Fine-Grained DNS Covert Tunnel Behavior Detection 4th

A Two-Stage Method for Fine-Grained DNS Covert Tunnel Behavi...

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4th international conference on Science of Cyber Security (SciSec)

作者： Wang, Bingxu Xiong, Gang Fu, Peipei Gou, Gaopeng Qin, Yingchao Li, Zhen Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783031175510;9783031175503

DNS protocol is one of the most important protocols on the Internet. Network firewall and intrusion detection system generally do not block the DNS protocol, and a large number of malwares use DNS protocol as the covert tunnel for remote control. therefore, it is very important to enhance the detection of DNS covert tunnel. At present, the researches always select closed data set to detect different tunnel tools and can not distinguish the specific attack behaviors such as download in the tunnel. this paper proposes a two-stage method for DNS covert tunnel behavior identification, which mainly identifies the DNS covert tunnel traffic and the different behaviors in the tunnel in a real-world network environment. In the first stage, by measuring and analyzing a large number of DNS traffic, the core feature threshold method is proposed to distinguish normal and tunnel traffic. the second stage mainly analyzes five kinds of DNS tunnel behaviors and classifies them with machine learning methods. Finally, more than 50 millions normal DNS sessions traffic is collected from the real network environment, and tunnel traffic is produced by DNS covert tunnel tools. through experiments, using threshold method, the precision and recall rate of DNS covert tunnel detection reach 99%, and time processing efficiency is much higher than machine learning methods. In terms of behavior recognition, the precision and recall of five behaviors reach to 93% and 94%.

关键词： DNS covert tunnel Traffic behavior machine learning

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

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