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5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)

作者： Sun, Jinmeng Yu, Zhongqing Wang, Haiya Qingdao Univ Sch Data Sci & Software Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781728185750

In order to realize the predictive maintenance of rolling bearings in industry, this paper proposes an online fault diagnosis method for rolling bearings based on three machine learning algorithms. The method mainly includes two steps: establishing a fault diagnosis model and online fault diagnosis. Firstly, preprocess the collected bearing vibration data, and then train and optimize the fault diagnosis model, and finally realize online fault diagnosis. The experimental results show that, compared with the traditional bearing fault diagnosis method, the online fault diagnosis of the bearing by the machine learning method is simpler and has a better diagnosis effect.

关键词： rolling bearing fault diagnosis machine learning algorithm predictive maintenance

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Electricity Corpus Construction based on Data Mining and machine learning algorithm 5

Electricity Corpus Construction based on Data Mining and Mac...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Zhao, Liujun Kong, Weizheng Wang, Chunming State Grid Energy Res Inst CO Ltd Beijing 102209 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

With the exponential increase of the Internet information, it is more difficult to collect and distinguish the useful information online. Corpus plays an important role in information retrieval and recognition. This paper proposes a corpus construction method based on data mining and machine learning for the energy and power industry. Firstly, the information data of the power industry are obtained by web crawler technology. Then they are stored in a database in structured manner. In order to construct a useful power industry corpus, the Long Short-Term Memory - Conditional Random Fields (LSTM-CRF) model are adopted for word segmentation, the Inverse Document Frequency (IDF) feature and Accessor Variety feature are used for new word discovery. The final electricity corpus consists of words, paragraphs and chapters. Based on the information retrieval and knowledge map, the built electricity corpus is demonstrated that is benefit to energy and power research.

关键词： Electricity Corpus Text information Word segmentation Data mining machine learning algorithm

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Applied Difference Techniques of machine learning algorithm and Web-based Management System for Sickle Cell Disease 8

Applied Difference Techniques of Machine learning Algorithm ...

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International Conference on Developments in eSystems Engineering (DeSE)

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiya Keenan, Russell Keight, Robert Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Sch Comp & Math Sci Appl Comp Res Grp Byrom St Liverpool L3 3AF Merseyside England Alder Hey Childrens Hosp Haematol Treatment Ctr Liverpool Paediat Haemophilia Ctr Liverpool L12 2AP Merseyside England

ISBN: (纸本)9781509018611

machine learning algorithm and web-based application systems have played a major role in improving the healthcare organisation in terms of continuous tele-monitoring therapy and maintaining telemedicine management systems. Currently, no intelligent system has been used in terms of managing sickle cell disease. However, this paper presents a system that facilitates a shift from manual methods to automated approach that can offer fast data collection with a reduced error rate. The system will be able to examine patient data and provide a suitable amount of Hydroxycarbamide drugs/liquid for each patient. By using a web-based system, we tend to improve patient welfare and mitigate patient illness before it gets worse over time, particularly with elderly people. The system will forward any critical concerns from the patient;it generates an automatic message to the medical doctors based on web-based platform in order to assist them with optimal decision-making. The initial case study that has been addressed in this project is how to make predictions for sickle cell disease for the amount of dose based on different architectures of machine learning in order to obtain high accuracy and performance. The most significant key for making predictions of the amount of medication is to enable healthcare organisation to provide accurate therapy recommendations based on previous data. The results using ANN showed that the proposed network produces significant improvements using the different evaluation methods. In our experiments, The MLP algorithm produced the best result in terms of the lowest error rates compare with other techniques. The Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error achieved 17887.55, 133.74, 90.20 and 0.1345, respectively.

关键词： Sickle Cell Disease machine learning algorithm Neural Network Web-based System Real-time data E-Health

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Breast Cancer Diagnosis Using Adaptive Voting Ensemble machine learning algorithm

Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machi...

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IEEMA IEEE Engineer Infinite Conference

作者： Khuriwal, Naresh Mishra, Nidhi Poornima Univ Jaipur Dept Comp Engn Jaipur Rajasthan India

ISBN: (纸本)9781538611388

According to Breast Cancer Institute (BCI), Breast Cancer is one of the most dangerous type of diseases that is very effective for women in the world. As per clinical expert detecting this cancer in its first stage helps in saving lives. As per *** offers individualized guides for more than 120 types of cancer and related hereditary syndromes. For detecting breast cancer mostly machine learning techniques are used. In this paper we proposed adaptive ensemble voting method for diagnosed breast cancer using Wisconsin Breast Cancer database. The aim of this work is to compare and explain how ANN and logistic algorithm provide better solution when its work with ensemble machine learning algorithms for diagnosing breast cancer even the variables are reduced. In this paper we used the Wisconsin Diagnosis Breast Cancer dataset. When compared to related work from the literature. It is shown that the ANN approach with logistic algorithm is achieved 98.50% accuracy from another machine learning algorithm.

关键词： Breast Cancer Neural Network Logistic machine learning algorithm WDBC Dataset

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MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF machine learning algorithm IN LULC CLASSIFICATION IN THE LARGE AREA USING SENTINEL-2 AND LANDSAT-8 DATASETS OF DEHRADUN AS A TEST CASE 24

MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF MACHINE ...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Srivastava, Anubhava Bharadwaj, Shruti Dubey, Rakesh Sharma, Vinarma Bhushan Biswas, Susham Rajiv Gandhi Inst Petr Technol Dept Comp Sci & Engn Amethi Uttar Pradesh India

In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency's "Sentinel" and American Earth observation satellite" landsat" missions have been used in various remote sensing *** Earth Engine (GEE) is such a tool that publicly allow the use of these available datasets, there is a large amount of available data in GEE, which are being used for computing and analysing purpose. In this article, we compare the classification performance of four supervised machine learning algorithms: Classification and Regression Tree (CART), Random forests (RF), Gradient tree boosting (GTB), Support vector machines (SVM). The study area is located at 30.3165 degrees N, 78.0322 degrees E near the Himalayan foothills, with four land-use land-cover (LULC) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 and LANDSAT-8 covering spring, summer, autumn, and winter conditions. Here we collected a total of 2084 sample points in which 536, 506, 505, 540 points belong to urban, water, forest and agriculture points respectively. which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, for accuracy measurement we use confusion and Cohen's kappa calculation *** have calculated CART (Accuracy 93.52% and Kappa coefficient 91.36%), Random Forest (Accuracy 95.86% and Kappa coefficient 94.48%),Gradient Tree Boost (Accuracy 95.33% and Kappa coefficient 93.37%),Support Vector machine (Accuracy 73.54% and Kappa coefficient 76.28%) for Landsat 8 data sets and CART (Accuracy 89.24% and Kappa coefficient 85.64%), Random Forest (Accuracy 91.45% and Kappa coefficient 88.59%),Gradient Tree Boost (Accuracy 87.71% and Kappa coefficient 83.58

关键词： Google Earth Engine Landsat Sentinel machine learning algorithm Classification

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Advance Flood Detection and Notification System based on Sensor Technology and machine learning algorithm 22

Advance Flood Detection and Notification System based on Sen...

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22nd International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiva Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Appl Comp Res Grp Sch Comp & Math Sci Byrom St Liverpool L3 3AF Merseyside England

ISBN: (纸本)9781467383530

Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.

关键词： Flood detection system Sensor network Short message service Global communication and mobile system Real-time data machine learning algorithm

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Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy

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Frontiers of Nursing 2021年第3期8卷 209-221页

作者： Li-Li Wei Yue-Shuai Pan Yan Zhang Kai Chen Hao-Yu Wang Jing-Yuan Wang Department of Nursing The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China Intensive Care Unit The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China Research Institute for Mathematics and Interdisciplinary Sciences Qingdao University of Science and TechnologyQingdaoShandong 266061China Department of Critical Care and Respiratory Medicine The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China

Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early ***:This study identified indicators related to GDM through a literature review and expert *** women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively *** on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was ***:We obtained 4806 analyzable data from 1625 pregnant *** these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set *** of F1 and F2 was used for training the random forest *** overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 *** explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression *** trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of *** addition,there are cer tain requirements for the propor tions of negative and posi

关键词： early prediction gestational diabetes mellitus machine learning algorithm random forest regression

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Proximity Estimation with BLE RSSI and UWB Range Using machine learning algorithm 13

Proximity Estimation with BLE RSSI and UWB Range Using Machi...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Debnath, Satinath O'Keefe, Kyle Department of Geomatics Engineering CalgaryAB Canada

ISBN: (纸本)9798350320114

Measuring the distance between two indoor mobile phones has recently become important in the context of pandemic contact tracing. Mobile phone location has improved as new technologies are added to smartphones. Ultra-wideband (UWB) ranging radios capable of centimetre-level ranging accuracies have been included in some flagship smartphone models beginning in 2019 but remain uncommon. The ranging radios compute distances using two-way time of flight approaches, providing up to centimetre-level accuracy in line-of-sight (LOS) conditions. Other mobile phones in the market today use Bluetooth Low Energy (BLE) to estimate proximity between two Bluetooth-enabled devices using the Received Signal Strength Indicator (RSSI). However, this technology suffers from drawbacks since the varying propagation environment inhibits stable RSSI. This paper presents a method where a subset of users are carrying high-end UWB-enabled smartphones. These users collect precise UWB ranges in addition to BLE RSSI. A classical machine learning (ML) algorithm is then trained with UWB ranges as truth values to estimate distances based on RSSI. A random forest ML technique is described for RSSI to distance modeling. The majority of the users, with only BLE, then estimate their relative distances using the trained model. The model was trained using UWB and BLE observations collected in an empty room environment. The model was then tested using RSSI measurements from the same empty and a second room containing typical office furniture. When testing on the training environment, 213 test samples were used. In the second office environment 200 RSSI samples were used to estimate distance in various locations. © 2023 IEEE.

关键词： BLE RSSI distance estimation machine learning algorithm UWB range

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Prediction Accuracy Improvement for Cardiovascular Diseases Using machine learning algorithm 10

Prediction Accuracy Improvement for Cardiovascular Diseases ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Debnarth, Sanjoy Kumar Malik, Swati Kaur, Gaganpreet Bagchi, Susama Soomro, Abdul Majid Naeem, Awad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National College of Business Administration & Economics Department of Computer Science Multan Pakistan

ISBN: (纸本)9798350382471

Worldwide Cardiovascular diseases (CVDs) are one of the most common causes of death. Multiple ML algorithms are employed in this work to address the CVD problem. The technique used in this study employs several machine-learning algorithms to predict CVD. To predict cardiac sickness, data mining approaches such as Decision Trees (DT), Naive Bayes (NB), Support Vector machine (SVM), Logistic Regression (LR), Random Forest (RF), Ada Boosting, Gradient Boosting, and Bagging algorithms have been utilized. Using Base learners and Ensemble classifiers, this study tried to predict the presence of heart disease. Bagging and Gradient Boosting Classifiers had the highest accuracy, with 81.3% and 81.3%, respectively. © 2023 IEEE.

关键词： Bagging Boosting Cardiovascular diseases Classifiers machine learning algorithm

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Multi Strategy machineenglish Translation System based on machine learning algorithm 5

Multi Strategy Machineenglish Translation System based on Ma...

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5th International Conference on machine learning and machine Intelligence, MLMI 2022

作者： Jiang, Shuang Jilin Animation Institute Jilin Province Changchun China

ISBN: (纸本)9781450397551

The advent of the big data era has brought unprecedented data to machine translation, which is particularly important for data-driven models and algorithms. The degree of public quotation of source text is becoming more and more difficult to understand, but the overall translation performance is improving. On this basis, the multi Strategy machineenglish translation (MSMT) system based on MLA is designed in this paper. Firstly, the MSMT system and machineenglish translation process are analyzed, and the Interactive MSMT system is discussed. Then the neural network translation model of machine learning is proposed. In order to improve the performance of the MSMT system based on MLA proposed in this paper, The results show that in the case of full-text retrieval, the time of English translation is greatly increased, and these time are basically spent in finding and matching these sentences. The sentences that cannot be translated due to the failure of feature index can be translated by the Multi Strategy machine English translation system based on MLA proposed in this paper, which verifies the rationality of the translation system proposed in this paper. © 2022 ACM.

关键词： English Translation machine learning algorithm Multi Strategy machine Translation System Design

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