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Proceedings of the 2023 International Joint Conference on Robotics and Artificial Intelligence

作者： Wenqiang Yue Li-an Chen Haiyan Huang School of Electrical Engineering and Automation Xiamen University of Technology China School of Electrical Engineering and Automation Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology China Xiamen Minghan Electric Co. Ltd. China

ISBN: (纸本)9798400707704

With the development of environmental sensor technology, more and more environmental sensors are widely used in various fields, such as environmental monitoring, smart home, smart city and so on. However, due to the complexity and diversity of environmental sensor data, there are a high number of anomalous data in sensor data, which can lead to misjudgment and false positives, compromising data reliability and accuracy. Therefore, the detection method of abnormal data in different dimensions of environmental sensors based on machine learning algorithms were studied in this paper. Firstly, the environmental sensor data of IBRL (Intel Berkeley Research Lab) data set was preprocessed, including data cleaning, data normalization and feature extraction. Then, the simulation experiment was completed using the data set, and the data of different dimensions was detected using machine learning algorithms such as OneClass Support Vector machine algorithm, Local Outlier Factor algorithm, and Isolation Forest algorithm. Finally, through experimental comparison and analysis of the application effects of the above three algorithms in anomaly detection of environmental data in different dimensions, the Isolation Forest algorithm with the best comprehensive detection effect was finally selected for practical engineering projects. In practical engineering projects, maintenance costs can be effectively reduced, project safety and reliability can be improved, and the purpose of improving project efficiency can ultimately be achieved by the proposed method.

关键词： Abnormal data detection Environmental sensor machine learning algorithm

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Elevator dynamic monitoring and early warning system based on machine learning algorithm

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IET NETWORKS 2022年第0期

作者： Zhang, Shuai Yin, Qiangguo Wang, Jinlong Weifang Engn Vocat Coll Dept Informat Engn Weifang Shandong Peoples R China Weifang Engn Vocat Coll Dept Informat Engn Weifang 262500 Shandong Peoples R China

With the rapid progress of modern society, elevator, as a vertical means of transportation in modern high-rise buildings, has become an indispensable part of People's Daily life. Because the elevator is a kind of mechanical equipment with a very high frequency of use, the peak hour will reach 250 times per hour, and because the elevator in the daily operation of the running parts work many times, the operating system is complex, good quality. Different passengers lead to frequent elevator failures. The dynamic monitoring and early warning system is a system which, according to the characteristics of the elevator, monitors the change trend of risk factors in the elevator by collecting relevant information, and evaluates the degree of deviation of various risk states from the early warning line, so as to send early warning signals to the decision-making level and take pre-control countermeasures in advance. machine learning is a multi-domain interdisciplinary discipline, which specialises in studying how computers simulate or realise human learning behaviours to acquire new knowledge or skills and reorganise existing knowledge structures to continuously improve their own performance. This study aims to monitor and warn the elevator dynamics. machine learning algorithm is introduced in this work, and particle swarm optimisation algorithm is used to improve the model. The model is optimised, and the experimental comparison shows that the optimisation of model parameters can further improve the accuracy of elevator load prediction. Then, the deep network prediction model is constructed, and the number of network layers is determined through experiments. Firstly, a multi-layer network is trained in the training model, and this process can be used to extract the indicators of inducing factors, so as to avoid the difficult and time-consuming manual extraction of indicators, and improve the prediction accuracy of the system. After five experiments, the prediction errors of t

关键词： deep confidence network machine learning algorithm monitoring and early warning system particle swarm algorithm

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A modelling approach based on Bayesian networks for dam risk analysis: Integration of machine learning algorithm and domain knowledge

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2022年 71卷

作者： Tang, Xianqi Chen, Anyi He, Jinping Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Wuhan Univ Sch Water Resources & Hydropower Engn Wuhan 430072 Peoples R China

The safety of dams, especially that of earthen dams, is threatened by various uncertain and interrelated risk factors. Consequently, dam risk analysis is vital for dam safety governance and failure prevention. A Bayesian network (BN) is an effective tool for this issue as its excellent ability in representing uncertainty and reasoning. Most previous studies have relied solely on domain knowledge (DK) to establish BN models, leading to inefficient and subjective results when solving complex systems. The increasing observations has improved the viability of using machine learning (ML) to automatically model complex systems. Herein, ML algorithms are used to develop automatic BN models for risk analysis of earthen dams in the USA, which are subsequently modified using DK. The results revealed that the automatic BN models can identify some potential causal relationships that are ignored by DK, whereas some impractical causalities identified in the automatic BN models can be modified by using DK. Moreover, the modified BN model has a better performance in the prediction of earthen dam failure with an average overall accuracy of 84.6%, compared to 80.3% with the automatic BN models, and 76.5% with a manual BN model created using only DK. Using the modified BN models, the three foremost risk factors based on their influence and sensitivity analysis were identified to be extreme flood, malfunction of spillway or gate, and slope instability. Our study highlights that the integration of ML algorithms and DK is an effective approach for developing reliable BN models for dam risk analysis.

关键词： Dam risk analysis Earthen dam failure Bayesian network machine learning algorithm Domain knowledge Risk factor

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Risk Stratification of Early-Stage Cervical Cancer with Intermediate-Risk Factors: Model Development and Validation Based on machine learning algorithm

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ONCOLOGIST 2021年第12期26卷 E2217-E2226页

作者： Chu, Ran Zhang, Yue Qiao, Xu Xie, Lin Chen, Wei Zhao, Ying Xu, Yintao Yuan, Zeng Liu, Xiaolin Yin, Aijun Wang, Zhiwen Zhang, Qing Yang, Xingsheng Su, Xuantao Kong, Beihua Song, Kun Shandong Univ Qilu Hosp Cheeloo Coll Med Dept Obstet & Gynecol Jinan Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China Jining 1 Peoples Hosp Dept Obstet & Gynecol Jining Shandong Peoples R China Shandong Univ Qilu Hosp Gynecol Oncol Key Lab Jinan Shandong Peoples R China

Background Adjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice. Materials and Methods This retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS). Results A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. The Kaplan-Meier analysis showed significant differences in DFS (p = .001) and OS (p = .011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p > .05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63. Conclusion Two prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of patients with CC with pathological intermediate-risk factors. Evaluation of long-term survival will be needed to corroborate these findings. Implications for Practice The Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. This study developed two prognostic models based

关键词： Cervical cancer Intermediate-risk factor Adjuvant therapy Sedlis criteria Prediction model machine learning algorithm

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Green extraction and optimization of bioactive compounds from Solanum torvum Swartz. using ultrasound-aided solvent extraction method through RSM, ANFIS and machine learning algorithm

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SUSTAINABLE CHEMISTRY AND PHARMACY 2023年 36卷

作者： Petchimuthu, Priya Sumanth, Gorla Buchayyagari Kunjiappan, Selvaraj Kannan, Suthendran Pandian, Sureshbabu Ram Kumar Sundar, Krishnan Kalasalingam Acad Res & Educ Dept Biotechnol Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Informat Technol Krishnankoil 626126 Tamil Nadu India

Biologically important phytocompounds based on total polyphenolic, flavonoid contents and their free radical scavenging potential were completely harvested from Solanum torvum Sw. through ultrasound-aided solvent extraction (UASE) method. The face-centered central composite design (FCCD) in response surface methodology (RSM) was applied to optimize the effective extraction parameters, and adaptive neuro-fuzzy inference system (ANFIS), as well as machine learning (ML) algorithm models were used to validate the optimized extraction parameters for the highest yield of bioactive phytocompounds. The effect of five extraction parameters, namely methanol concentration (X1: 55-65% in v/v with water), ultrasound-intensity (X2: 50-60 W cm-2), temperature (X3: 30-45 degrees C), time (X4: 15-25 min) and particle size (X5: 0.5-1.5 mm) at five levels (very low (-2.37), low (-1), medium (0), high (+1) and very high (+2.37)) were studied for optimized yields of total polyphenolic (TP;(y1)), total flavonoid contents (TF;(y2)) and their free radical scavenging abilities (%DPPH*sc (y3), %ABTS*sc (y4), and %H2O2*sc (y5)). The optimal condition was attained at X1 = 65%, X2 = 60 W cm-2, X3 = 45 degrees C, X4 = 15 min, and X5 = 0.5 mm;under this condition, the highest yields of y1 = 830.677 mg gallic acid equivalent (GAE)/g, y2 = 535.38 mg rutin equivalents (RE)/g, y3 = 75.22%, y4 = 84.12%, and y5 = 85.02% was obtained. Most of the experimental values were well matched with the predicted responses of the RSM model. Further, ANFIS and random forest machine learning algorithm were utilized to compare and validate the optimized extraction parameters. Also, GC-MS and LC-MS were carried out to investigate the presence of various bioactive phytocompounds in the optimized extract.

关键词： Solanum torvum Swartz Response surface methodology ANFIS machine learning algorithm FCCD Ultrasound-aided solvent extraction

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Potential application of generative artificial intelligence and machine learning algorithm in oil and gas sector: Benefits and future prospects

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TECHNOLOGY IN SOCIETY 2024年 79卷

作者： Ochieng, Edward G. Ominde, Diana Zuofa, Tarila British Univ Dubai BUiD Fac Business & Law Project Management Dubai U Arab Emirates Strathmore Univ Strathmore Business Sch Off Ole Sangale Rd Nairobi Kenya Univ Wisconsin Parkside Coll Business Econ & Comp Somers WI USA

With the rapid advancement of technology and societies, the global energy sector now acknowledges that by integrating contemporary digital technologies into their operations and capabilities, can improve their competitive advantage and innovation performance and processes. Moreover, energy operators are also facing a significant undertaking: how to best use and secure large amounts of data that promote sustainable productivity performance and minimise potential threats in the oil and gas value chain and project operations. In view of the foregoing, various facets like Generative Artificial Intelligence (GAI) and machine learning algorithms (MLA) are increasingly gaining popularity within oil and gas sector operations. Thus, we explored how GAI and ML algorithms can enhance oil and gas value chain productivity performance. The Principal Component Analysis (PCA) was employed to identify significant GAI and MLA variables influencing performance in the oil and gas value chain, while Structural Equation Modelling (SEM) was used to test regression equations related to their application. The study found that risk portfolios and profiles can be appraised throughout the value chain by effectively utilising GAI and ML algorithms in upstream, midstream and downstream undertakings. While these findings are noteworthy and have significant implications for current practice, the paper advocates that an array of digital technologies beyond GAI and ML can still be examined during future studies to demonstrate a holistic perspective on how digital transformation can be achieved across the energy sector value and project operations.

关键词： Generative artificial intelligence machine learning algorithm Value chain operations Oil and gas Productivity performance Risk management

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Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis

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BMC CANCER 2019年第1期19卷 464-464页

作者： Yu, Lingming Tao, Guangyu Zhu, Lei Wang, Gang Li, Ziming Yi, Jianding Chen, Qunhui Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Dept Radiol 241 Huaihai West Rd Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Dept Pathol Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Ctr Stat Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Affiliated Chest Hosp Shanghai Chest Hosp Ctr Lung Tumor Clin Med Shanghai 200030 Peoples R China

PurposeTo explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature *** with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity >0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA *** prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in *** pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.

关键词： Non-small cell lung cancer (NSCLC) Computed tomography (CT) Radiomics machine learning algorithm

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Applications Research of machine learning algorithm in Translation System

Applications Research of Machine Learning Algorithm in Trans...

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2018 6th International Conference on machinery,Materials and Computing Technology(ICMMCT 2018)

作者： Lu Yang Da Chen Wenxue Wu School of Foreign Languages and Cultures Xihua University School of Science Xihua University

In recent years, machine translation has made outstanding achievements, but there are also the problems such as fuzzy rules, insufficient data and improper order in the machine translation practice, which makes it still unsatisfactory. machine learning can abstract learning features, and establish complex mapping relationship between input and output signals, and effectively improve the problems existing in the translation system. The concepts and common models of machine learning and deep learning are given in this paper. Based on the neural model, the paper analyses the rules, structure and models of machine translation system. According to the requirement of the translation system, this paper develops a translation system based on machine learning, and gives the design process of the pre-processing module, the coding module, the attention module and the decoding module. Practice has proved that the system has a good performance in translation performance.

关键词： machine learning algorithm Deep learning Translation system

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Identification of key genes for IgA nephropathy based on machine learning algorithm and correlation analysis of immune cells

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TRANSPLANT IMMUNOLOGY 2023年第1期78卷 101824-101824页

作者： Chen, Suzhi Li, Yongzhang Wang, Guangjian Song, Lei Tan, Jinchuan Yang, Fengwen Hebei Prov Hosp Chinese Med Dept Nephrol 1 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Hebei Prov Hosp Chinese Med Dept Res Ctr 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Hebei Prov Hosp Chinese Med Dept Androl 389 Zhongshan East Rd Shijiazhuang 050011 Hebei Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Peoples R China Hebei Technol Innovat Ctr TCM Spleen & Kidney Dis Cangzhou Peoples R China

Introduction: The pathogenesis and progression mechanism of Immunoglobulin A nephropathy (IgAN) is not fully understood. There is a lack of panoramic analysis of IgAN immune cell infiltration and algorithms that are more efficient and accurate for screening key pathogenic genes. Methods: RNA sequencing (RNA-seq) data sets on IgAN were downloaded from the Gene Expression Omnibus (GEO) database, including GSE93798, GSE35489, and GSE115857. The RNA-seq data set of kidney tissue as control samples were downloaded from the Genotype-Tissue Expression (GTEx) database. Three machine learning algorithms-weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-were used to identify the key pathogenic gene sets of the IgAN disease. The ssGSEA method was applied to calculate the immune cell infiltration (ICI) of IgAN samples, whereas the Spearman test was used for correlation analysis. The receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of key genes. The correlation between the key genes and ICI was analyzed using the Spearman test. Results: A total of 177 genes were screened out as differentially expressed genes (DEGs) for IgAN, including 135 up-regulated genes and 42 down-regulated genes. The DEGs were significantly enriched in the inflammatory-or immune-related pathways (gene sets). Activating transcription factor 3 (AFT3), C-X-C Motif Chemokine Ligand 6 (CXCL6), and v-fos FBJ murine osteosarcoma viral oncogene homolog B (FOSB) were identified using WGCNA, support vector machine, and LASSO algorithms. These three genes revealed good diagnostic efficacy in the training and test cohorts. The CXCL6 expression positively correlated with activated B cells and memory B cells. Conclusion: ATF3, FOSB, and CXCL6 genes were identified as potential biomarkers of IgAN. These three genes exhibited good diagnostic efficacy for IgAN. We described the landscape of immun

关键词： Immunoglobulin a nephropathy machine learning algorithm ATF3 CXLC6 FOSB

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Multi Strategy machineenglish Translation System based on machine learning algorithm 22

Multi Strategy Machineenglish Translation System based on Ma...

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Proceedings of the 2022 5th International Conference on machine learning and machine Intelligence

作者： Shuang Jiang Jilin Animation Institute 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.

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

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