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Requirements Prediction for Medical Products Based on Named Entity Recognition 3

Requirements Prediction for Medical Products Based on Named ...

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3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Ruan, Peiguang Liu, Ruanchong Guo, Bo Liu, Zhepeng Zhang, Chengying Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Guangdong Pharmaceutical University The First Clinical Medicine School Guangzhou China College of Science and Technology School of Finance and Trade Zhuhai Zhuhai China Shantou University College of Engineering Shantou China Xihua University School of Management Chengdu China

ISBN: (纸本)9798350316674

Medical product demand forecasting is a necessary condition for building a medical intelligent system, and is an important means to alleviate the lack of medical resources, and the form of medical product demand is mainly unstructured text, including the number of medicines, medical record texts. These medical texts contain a large number of medical terms related to the patient health situation and medical terms are the most direct embodiment of medical knowledge. However, existing methods for requirements are concentrated on the neural network or machine learning algorithms, which ignores the tremendous information in the medical terms. In this work, we utilize the named entity recognition method to predict the demand of the medical products. Efficiently identifying these medical terms from medical texts is a key factor in ensuring the high performance of medical intelligence systems. Therefore, the goal of the medical named entity recognition task is to extract medically significant symptoms, medical history, diseases, etc. from medical texts and realize the downstream prediction task. From our extensive experiment simulation and comparison with existing prediction methods, we can conclude the proposed method can achieve the precise prediction accuracy with reasonable computation costs. © 2023 IEEE.

关键词： Computation Costs Medical Product Named Entity Recognition Requirement Prediction

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Performance Comparison of Simple Regression, Random Forest and XGBoost Algorithms for Forecasting Electricity Demand 3

Performance Comparison of Simple Regression, Random Forest a...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Gokce, Muhammet Mustafa Duman, Erkan Turkish Electricity Transmission Corporation Ankara Turkey Faculty of Engineering Firat University Computer Engineering Department Elazig Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Electrical energy is the locomotive of the economy, industry, and development in terms of the development of countries. In order to meet the need during the periods when the energy demand reaches its peak and to prevent the market participants from making economic losses when it is at the lowest level, the closest prediction should be made. Load forecasting is very important in planning the generation, transmission, and management of energy and in pricing electricity in the most appropriate way. Regional, demographic and meteorological variables are effective in the energy production plan. These factors affect the electricity market operated by the system operator in every sense. An energy forecasting plan is needed in order to keep the supply and demand of energy in balance. Today, the use of large data sets has a positive effect on machine learning and artificial neural network training. By using these data sets, very high performances are achieved in modeling. In this study, Turkey's electricity consumption between the years 2018-2021 was modeled using the Linear Regression from supervised learning techniques, Random Forest and XGBoost algorithms from machine learning models. In our study, short-term consumption load forecastings were made hourly, considering the meteorological factors and public holidays in the country, and the forecasting performances of the three different algorithms used were compared. If the study is used, it is foreseen that it will eliminate the energy supply-demand imbalance. © 2022 IEEE.

关键词： machine learning

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Improving Diabetes Mellitus Prediction with MICE and SMOTE for Imbalanced Data 3

Improving Diabetes Mellitus Prediction with MICE and SMOTE f...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Abdullah, Mohammad Nasir Wah, Yap Bee Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Department of Statistics Perak Tapah35400 Malaysia Unitar International University Selangor Petaling Jaya47301 Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Diabetes Mellitus is a chronic disease, and it affects our body nerves, eyes, kidney, and heart if not treated from the early stage. Clinical data faces issue of missing values due to lost in follow up of the patients imbalanced data. The aim of this study is to develop a Diabetes Mellitus prediction model using machine learning (ML) classifiers for an imbalanced dataset missing value. The PIMA Indian Diabetes Dataset was obtained from UCI machine learning repository. Data pre-processing involves data imputation using Multivariate Imputation Chained Equation (MICE) by Permutation Mean Matching method. Then the data was balanced using SMOTE and ROSE method. Results showed that the F-measure of the ML classifiers is higher under SMOTE oversampling technique. The random forest classifier has the highest F-measure (0.8307). The important features identified based on random forest classifier were glucose, BMI, and age. SMOTE technique is a good oversampling technique for imbalanced data. © 2022 IEEE.

关键词： Decision trees

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3rd international conference on Computing Science, Communication and Security, COMS2 2022

3rd International Conference on Computing Science, Communica...

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3rd international conference on Computing Science, Communication and Security, COMS2 2022

ISBN: (纸本)9783031105500

The proceedings contain 22 papers. The special focus in this conference is on Computing Science, Communication and Security. The topics include: Energy Efficient Allocation of Resources in NOMA Based (MU-HCRN) with Perfect Spectrum Sensing;application of Forensic Audio-Video Steganography Technique to Improve Security, Robustness, and Authentication of Secret Data;device to Device Communication over 5G;achieving Energy Efficiency in Life-Logging applications of Internet of Things Using Data Compression Through Incorporation of machine learning and Edge-Cloud Architecture;meta Heuristic Backtracking Algorithm for Virtual machine Placement in Cloud Computing Migration;Motor Imagery EEG Signal Classification Using Deep Neural Networks;development of Smart Sensor for IoT Based Environmental Data Analysis Through Edge Computing;to Identify Malware Using machine learning Algorithms;adaptive Rider Grey Wolf Optimization Enabled Pilot-Design for Channel Estimation in Cognitive Radio;cab Fare Prediction Using machine learning;traffic Flow Prediction Using Deep learning Techniques;machine learning for Classification of DOS Attack in Smart Healthcare Networks;sandbox Environment for Real Time Malware Analysis of IoT Devices;One-to-One Matching for Cooperative Resource Sharing and Communication in CRNs;preface;systematic Review on Various Techniques of Android Malware Detection;Extending WSN Life-Time Using Energy Efficient Based on K-means Clustering Method;Blocking Estimation Using Optimal Guard Channel Policy in GSM 900 System;The Novel Approach of Down-Link Spectral Efficiency Enhancement Using Massive MIMO in Correlated Rician Fading Scenario;Ortho Image Mosaicing and Object Identification of UAV Data.

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application Exploration of Robot Process Automation in Digital Labor Time Management System 3

Application Exploration of Robot Process Automation in Digit...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Fei Li, Jian College of Basic Military and Political Education National University of Defense Technology Hunan Changsha China School of Physics and Electronic Information Engineering Neijiang Normal University Sichuan Neijiang China

In the digital era, Robotic Process Automation (RPA) has become an important technology in the field of information technology. The purpose of this article is to apply RPA technology to the digital labor time management system of enterprises to improve management efficiency and accuracy. This article mainly used data comparison and mathematical model analysis methods to draw conclusions and provide specific solutions. The experimental results showed that the minimum time reduction rate for robot process automation was 19.5%. The RPA solution for digital labor time management system has the characteristics of sustainable development, convenient management, and strong systematicity, which has great promotion and application value. © 2023 The Authors. Published by Elsevier B.V.

关键词： Process control

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Data Security Mining Method for Social Media Users’ Mental Health Status Test Based on machine learning Algorithm 3rd

Data Security Mining Method for Social Media Users’ Mental ...

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3rd EAI international conference on IoT and Big Data Technologies for Health Care, IotCARE 2022

作者： Su, Junyu Liu, Wenjian Zhao, Hanxu Wang, Wennan Shi, Chiyu Faculty of Data Science City University of Macau China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Academy of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9783031335440

Users who frequently use social media can easily lead to changes in their mental health status. In order to accurately predict users’ mental health status, a data mining method for testing social media users’ mental health status is designed based on machine learning algorithms. Perform clustering processing on the test data, calculate the centroid of each cluster and the sum of squared errors of the data set, and obtain the clustering result of the test data;extract the characteristics of social media users’ mental health status, and calculate the abnormal score of the object's mental health status, Use this to distinguish the user's psychological state;build a data mining model based on machine learning algorithms, build a one-dimensional convolutional neural network framework, record the activation functions of the convolutional layer and the pooling layer, and obtain the output value of the fully connected layer;design social media user psychology Health state test data analysis algorithm to obtain a safe mining method for social media users’ mental health state test data. Obtain the best period of social media users’ mental health status observation through experiments. It can be seen from the experimental data that the correlation of the mental health state mining results obtained by this method is higher than 0.91, and the prediction accuracy is higher than 92%, which improves the effectiveness of mental health state prediction. The best observation period is one month to two months before the expected time. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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machine learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： machine learning

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3rd international conference on Recent Advances in Materials, Manufacturing and Thermal Engineering, RAMMTE 2022

3rd International Conference on Recent Advances in Materials...

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3rd international conference on Recent Advances in Materials, Manufacturing and Thermal Engineering, RAMMTE 2022

ISBN: (纸本)9789811985164

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in Materials, Manufacturing and Thermal Engineering. The topics include: application of machine learning Approach in Internal Combustion Engine: A Comprehensive Review;experimental Validation of Damage Detection in Concrete Beams Under Impact Load Using Piezo Sensors;experimental Investigation for Enhancing Surface Quality in 3D Printing Process Using Non-planar Layer Method;smart System for Monitoring the Toxic Gases in Sewerage System via Wireless Network;Scrutinizing the Enablers of Flexible Manufacturing Competence of Organizations Using DEMATEL Approach;design, Material, and Performance Study of Modified Solar Cooker;on Identifying the Suitable Substrate Medium for Induction Heating-Based Metal Wire Additive Manufacturing;kinetic Analysis of Phoenix Dactylifera and Phyllanthus Emblica Seeds Through Thermogravimetric Analyser: Determination of Activation Energy;Efficacy of ANN and ANFIS as an AI Technique for the Prediction of COF at Finger Pad Interface in Manipulative Tasks;revisiting the Recent Advancements in the Design and Performance of Solar Greenhouse Dryers;application of Multi-Criteria Decision-Making Tool for Choosing Right Biogas Plants: Process Controllability, Suitability, and Cost Perspectives in the Indian Context;mathematical Modeling and Simulation of Dual Fuel Cycle Using Natural Gas and Diesel/Biodiesel;laser-Induced Spark Ignition of Methane-Air Mixtures in Constant Volume Combustion Chamber;application of Industrial High-Performance Waste and Cigarette Filter in Thermal Insulation;experimental Study of Mechanical and Thermal Properties of Nano-carbon Areca Fiber Powder Reinforced Epoxy Composites;numerical Study on Momentum and Heat Transfer Phenomenon from a Sphere Under Force Convection Environment;preface.

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Impact of Pollutants on Temperature Change and Forecasting Temperature of US Cities 1

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Kankaria, Tanmaay Krishna, Bandla Vaibhav Teja, Duppanapudi Surya Dinesh Chandra Gupta Kolipakula, D.V.S. Sujee, R. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811978746

ISBN: (纸本)9789811978739

Planet Earth has endured extensive climatic changes throughout history. According to the 2018 IPCC report, human activities have accounted for 0.8 °C to 1.2 °C of global warming above pre-industrial levels. Since 1901, the global average temperature has surged by an average rate of 255 K per decade. With the rise in levels of air pollutants, temperatures have altered the chemical composition of the atmosphere. However, current reports and theories only point out the relations between this temperature change and greenhouse gases. By exploiting pollution and temperature datasets in a machine learning model, we will scrutinize the correlations amongst the radical elements causing a surge in temperature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Exploration of Performance of Dynamic Branch Predictors used in Mitigating Cost of Branching 3

Exploration of Performance of Dynamic Branch Predictors used...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Ambashankar, Akash Chandrasekar, Ganesh Charan, A.R. Sankar, S. Kcg College of Technology Department of Cse Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Today, Branch Predictors have become a sophisticated component in all modern processors. While contemporary predictors have good accuracy, the cost of a misprediction is quite high. Hence, active research is being conducted towards improving the performance of branch predictors, while reducing the resources consumed. Modern computer architectures increasingly rely on speculation to boost instruction-level parallelism (ILP). As the pipelines deepen and instructions issued per cycle increase, the accurate branch prediction mechanisms become more and more important in increasing the processor performance. This study explores some branch predictors and compares them on the basis of their performance on certain benchmarks applications. © 2022 IEEE.

关键词： machine learning

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