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Blood eosinophil count combined with asthma history could predict chronic rhinosinusitis with nasal polyp recurrence

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Acta oto-laryngologica 2021年第3期141卷 279-285页

作者： Xiaoyan Wang Yifan Meng Hongfei Lou Kuiji Wang Chengshuo Wang Luo Zhang Department of Otolaryngology Head and Neck Surgery Beijing TongRen Hospital Capital Medical University Beijing China. Research Unit of Diagnosis and Treatment of Chronic Nasal Diseases Chinese Academy of Medical Sciences Beijing China. Beijing Key Laboratory of Nasal Diseases Beijing Institute of Otolaryngology Beijing China. Department of Allergy Beijing TongRen Hospital Capital Medical University Beijing China.

BACKGROUND:The use of non-invasive clinical markers for predicting CRS recurrence is still not well investigated. OBJECTIVE:The aim of this study was to investigate the comprehensive effects of non-invasive clinical markers on the recurrence of CRS with nasal polyps (CRSwNP). MATERIALS AND METHODS:A total of 346 consecutive CRSwNP patients undergoing endoscopic functional sinus surgery were recruited. The demographic characteristics and clinical parameters were recorded. machine learning algorithm were used for evaluating the predictive value of asthma history and blood eosinophils percentage. RESULTS:Finally, 313/346 patients completed the study. The average follow-up time was 24 months after the first surgery. For the CRSwNP with asthma patients, the blood eosinophils percentage cut-off value was 3.7%. However, for the CRSwNP without asthma patients, the blood eosinophils percentage cut-off value was high, at 6.9%. CONCLUSION:Combined asthma history and blood eosinophils percentage can predict CRSwNP recurrence, while asthma history can reduce the threshold of blood eosinophils percentage to predict CRSwNP recurrence. SIGNIFICANCE:For the CRS patients, combined asthma history and blood eosinophils percentage can predict recurrence, while asthma history can reduce the threshold of blood eosinophils percentage to predict recurrence.

关键词： Asthma blood eosinophils percentage chronic rhinosinusitis with nasal polyps endoscopic sinus surgery machine learning algorithm prediction recurrence

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Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme

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ENGINEERING FAILURE ANALYSIS 2020年 114卷

作者： Jiang, Fengyuan Dong, Sheng Ocean Univ China Coll Engn Qingdao 266100 Shandong Peoples R China

Platform falling object collision on offshore pipelines are catastrophic to the environment and economy. Based on finite element analysis and machine learning algorithms, a quantitative analysis model is proposed to quantify failure risk. To consider the uncertainties and nonlinear effects in the collision events, the Latin Hypercube Sampling technique and the finite element simulation is coupled to draw the sample space. Then four machine learning models are developed and the prediction abilities in the pipeline response are compared. The genetic programming shows the best performance with the relative absolute error of 0.04-0.05, which is integrated into Monte Carlo Simulation to complete the risk analysis. This quantitative analysis model is verified with a method and indicates good consistency and potential in considering nonlinear effects and pipe-soil interactions. Effects of related factors on failure risk are examined, including seabed flexibility, burial depth, acceptable criterion, and sensibility of basic variables. Compared with the method recommended by the Det Norkske Veritas, the proposed model can account for the seabed flexibility effect, and the failure risk declined by 23.6%. The increase in burial depth affects risk reduction significantly but is limited under a strict criterion. The fitting equations of burial depth and failure probabilities as well as different acceptable criteria are proposed for safety design. Sensibility analysis of the basic variables reveals that the quality of wall thickness and pipeline diameter are important to failure risk.

关键词： Offshore pipelines Quantitative risk analysis machine learning algorithm Impact loading Pipe-soil interaction

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Flexible Piezoelectric Acoustic Sensors and machine learning for Speech Processing

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ADVANCED MATERIALS 2020年第35期32卷 e1904020页

作者： Jung, Young Hoon Hong, Seong Kwang Wang, Hee Seong Han, Jae Hyun Trung Xuan Pham Park, Hyunsin Kim, Junyeong Kang, Sunghun Yoo, Chang D. Lee, Keon Jae Korea Adv Inst Sci & Technol Dept Mat Sci & Engn 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn 291 Daehak Ro Daejeon 34141 South Korea

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.

关键词： acoustic sensors flexible piezoelectrics machine learning algorithm speech processing

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Research on Strategic Choice of Enterprise Innovation under the Shared Economy

Frontiers in Economics and Management

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Frontiers in Economics and Management 2021年第11期2卷 263-270页

作者： Shangjie Leng

[Purposes] The main purpose of this article is to collect micro‐blog comments for emotional analysis and get the emotional tendency of the audience. [Method] First, collecting one favorite video comments in micro‐blog as corpus. Then, Pycharm software is used to process the data, that is, word2vec is used to generate the word vector. Finally, through machine learning method to further analyze the accuracy of data to get the conclusion. [Findings] When the processed data is calculated by machine learning algorithm, the corresponding accuracy is obtained, which confirms that the research is also suitable for sentiment analysis of short video comments on micro‐blog. However, it also can be found that the limitation of the study is that it is not complete to determine emotional orientation only by accuracy rate. [Conclusions] In general, this topic is for the emotional analysis of short video comments on micro‐blog. Through the research, it can also be got the emotional tendency of the audience, which shows that in practice, the technology of emotional analysis is suitable for most fields.

关键词： Sentiment Analysis Micro‐blog Short Video machine learning algorithm Data Preprocessing

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Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on machine learning

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Procedia Manufacturing 2020年 48卷 1009-1019页

作者： Jiwoong Lee Inwoong Noh Sang Ik Jeong Yongho Lee Sang Won Lee Dept. of Mechanical Engineering Sungkyunkwan University 2066 Seobu-ro Jangan-gu Suwon-si Gyenggi-do 16419 Republic of Korea School of Mechanical Engineering Sungkyunkwan University 2066 Seobu-ro Jangan-gu Suwon-si Gyenggi-do 16419 Republic of Korea

This paper focuses on the real-time online monitoring and diagnosis framework for the angular misalignment of the robot spot-welding system, which can result in significant quality degradation of a weld nugget such as porosity. The data-driven approach is applied by installing the voltage and current sensors, collecting the associated mass data and processing them under normal and abnormal (angular misalignment) conditions. Two categories of features are extracted from the dynamic resistance (DR) and the voltage and current ones that are decomposed by wavelet transform (WT). The DR features are extracted from the DR profile and some critical features are selected by a t-test methodology. In the case of the WT-based features, the critical ones are selected by a max-relevance and min-redundancy (mRMR) and a sequential backward selection (SBS) wrapper. Consequently, three types of critical feature sets, such as DR features, WT features, and hybrid features combining those, are prepared to train machine learning-based models. Support vector machine (SVM) and probabilistic neural network (PNN) are applied to establish the diagnosis models, and the diagnostic accuracy and robustness are evaluated. Finally, the software for the on-line monitoring and diagnosis for angular misalignment of robot spot-welding system is developed and demonstrates its real-time applicability in an industrial site.

关键词： Robot spot-welding Angular misalignment On-line monitoring diagnosis Data-driven approach Dynamic resistance (DR) Wavelet transform (WT) machine learning algorithm

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Mortality Prediction using machine learning Techniques: Comparative Analysis 9

Mortality Prediction using Machine Learning Techniques: Comp...

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IEEE 9th International Conference on Advanced Computing (IACC)

作者： Verma, Akash Goyal, Shreya Thakur, Shridhar Kumar Gupta, Archit Gupta, Indrajeet Bhilai Inst Technol Dept CSE Durg India Natl Inst Technol Dept CSE Jalandhar Punjab India Galgotias Univ Dept Comp Sci Greater Noida India ABES Engn Coll Dept Comp Sci Ghaziabad India Bennett Univ Dept CSE Greater Noida India

ISBN: (纸本)9781728143927

In recent past, data mining, artificial intelligence, and machine learning have gained enormous attention to improve hospital performance. In some hospitals, medical personals want to improve their statists by decreasing the number of patients dying in the hospital. The research is focused on the mortality prediction of measurable outcomes, including the risk of complications & length of hospital stay. The duration spent in the hospital of the patient plays an important role both for patients & healthcare providers, influenced by numerous factors. LOS (length of stay) in critical care has great importance, both to the patient experience as well as the cost of care and is influenced by the complex environmental factors of the Hospitals. LOS is a parameter that is used to identify the extremity of illness & health-related resource utilization. This paper provides the improved prediction rate that a patient survives or dies in the range of length of stay in the hospital. It also anchors the analytical methods for the length of stay and mortality prediction.

关键词： machine learning algorithm Feature Scaling Feature Extraction Neural Networks Logistics Regression Support Vector machine

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machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2020年第1期14卷 713-725页

作者： Jumin, Ellysia Zaini, Nuratiah Ahmed, Ali Najah Abdullah, Samsuri Ismail, Marzuki Sherif, Mohsen Sefelnasr, Ahmed EI-Shafie, Ahmed Univ Tenaga Nas Coll Engn Dept Civil Engn Selangor Darul Ehsan Malaysia Univ Tenaga Nas Inst Energy Infrastruct IEI Selangor Darul Ehsan Malaysia Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Air Qual & Environm Res Grp Terengganu Malaysia Univ Malaysia Terengganu Fac Sci & Marine Environm Terengganu Malaysia Univ Malaysia Terengganu Inst Trop Biodivers & Sustainable Dev Terengganu Malaysia United Arab Emirates Univ Natl Water Ctr NWC Al Ain U Arab Emirates United Arab Emirates Univ Coll Engn Civil & Environm Engn Dept Al Ain U Arab Emirates Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia

High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on machine learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different machine learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R-2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

关键词： Ozone concentration prediction machine learning algorithm ozone precursors Boosted Decision Tree Regression neural network linear regression Pearson Correlation

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Multi-criteria decision analysis for regional-scale flood susceptibility mapping in Kerala state, India

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DISCOVER APPLIED SCIENCES 2025年第6期7卷

作者： Kendagannaswamy, M. S. Roopa, C. K. Harish, B. S. Mukesh, M. S. JSS Sci & Technol Univ Dept Informat Sci & Engn Mysuru 570006 Karnataka India

Floods in the southern Indian state of Kerala are the most frequent and deadliest types of natural disasters, leading to large economic losses and having killed over 410 people since June 2018. Despite Kerala's flood vulnerability and due to its intense annual rainfall, existing flood prediction approaches often fail to provide accurate and localized risk assessments. Various machine learning (ML) approaches offer promising results but there is a critical gap in applying these advanced ML algorithms along with systematized decision-making frameworks that would work well for Kerala's specific geographical and climatic conditions. This research aims to develop flood susceptibility maps for Kerala using a spatial flood database integrated within the ArcGIS interface. An efficient framework integrating Light Gradient Boosting machine (LightGBM) learning models with the Analytic Hierarchy Process (AHP) is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. The resultant map is validated using the Area Under Curve approach, which showed good accuracy of 0.948 and 0.763 for the LightGBM and AHP methods, respectively. The result indicates that the use of machine learning algorithm resulted better reliability of flood susceptibility maps. This study's findings apply to local authorities to maintain their preparedness and response efforts as well as minimize flood consequences.

关键词： Flood susceptibility Analytical hierarchy process Susceptibility Remote sensing GIS machine learning algorithm

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Primum non nocere: In silico prediction of adverse drug reactions of antidepressant drugs

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COMPUTATIONAL TOXICOLOGY 2021年 18卷

作者： Gunes, Serdar Sinan Yesil, Cagri Gurdal, Enise Ece Korkmaz, Emin Erkan Yarim, Mine Aydin, Ahmet Sipahi, Hande Yeditepe Univ Fac Pharm Dept Toxicol Istanbul Turkey Yeditepe Univ Fac Engn Dept Comp Engn Istanbul Turkey Yeditepe Univ Fac Pharm Dept Pharmaceut Chem Istanbul Turkey Yeditepe Univ Fac Pharm Dept Pharmaceut Toxicol TR-34755 Istanbul Turkey

Early and accurate prediction of adverse drug reactions (ADRs) by computer-aided methods is extremely important for decreasing health threats, along with preventing product withdrawal after clinical trials which need long time and high cost. In this study, we built a model to predict 329 known ADRs of 27 approved antidepressant drugs, and we examined three machine learning algorithms (multilayer perceptron, k-nearest neighbour and support vector machine) in order to find which one is better for this duty. We integrated the known ADRs of a drug with the drug's chemical structures and biological properties, including drug targets, enzymes and transporters for prediction of ADR in our predictive model. In addition, 329 known ADRs were grouped according to their system organ classes (SOCs) and compared the performances of ADR predictions. This assessment, based on a ten-fold cross-validation, showed that the multilayer perceptron (MLP) algorithm surpassed the others. 248 out of 329 ADRs were successfully predicted. The best performing attribute group was the chemical plus biological, followed by the chemical and biological. The mean AUC values are 0.623, 0.676, and 0.695 for the feature groups of chemical, biological, and chemical plus biological, respectively. Among ADRs of the 21 SOCs, only ADRs belonging to 5 SOCs were predicted successfully by chemical, biological or combined attributes. Also, the ADRs related to withdrawal of indalpine, pheniprazine, medifoxamine, zimelidine and amineptine were successfully predicted by our model constructed using MLP with the CfsSubsetEval and BestFirst, based on chemical properties of the approved antidepressants. The results showed the external validity of our approach, predicting satisfactory amount of previously known ADRs from the literature. In conclusion, the proposed approach is an efficient and promising tool for predicting ADRs of antidepressants.

关键词： Adverse drug reactions Antidepressants Computational toxicology In silico machine learning algorithm Toxicity

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A Computer-aided Diagnosis System for Glioma Grading using Three Dimensional Texture Analysis and machine learning in MRI Brain Tumour 3

A Computer-aided Diagnosis System for Glioma Grading using T...

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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Al-Zurfi, Ahmed Naser Meziane, Farid Aspin, Rob Univ Salford Sch Comp Sci & Engn Salford Lancs England Univ Kufa Fac Engn Dept Commun Engn Kufa Iraq

ISBN: (纸本)9781728135786

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computer-aided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading.

关键词： Glioma Grading Grey level co-occurrence matrix Single and ensemble classification system machine learning algorithm

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