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Статті в журналах з теми "MACHINE LEARNING TOOL"

Автор: Grafiati

Опубліковано: 26 жовтня 2023

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1

E, Prabhakar, Suresh Kumar V.S, Nandagopal S, and Dhivyaa C.R. "Mining Better Advertisement Tool for Government Schemes Using Machine Learning." International Journal of Psychosocial Rehabilitation 23, no. 4 (December 20, 2019): 1122–35. http://dx.doi.org/10.37200/ijpr/v23i4/pr190439.

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2

Monostori, László. "Learning procedures in machine tool monitoring." Computers in Industry 7, no. 1 (February 1986): 53–64. http://dx.doi.org/10.1016/0166-3615(86)90009-6.

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3

Kokar, Mieczyslaw M., Jerzy Letkowski, and Thomas F. Callahan. "Learning to monitor a machine tool." Journal of Intelligent & Robotic Systems 12, no. 2 (June 1995): 103–25. http://dx.doi.org/10.1007/bf01258381.

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4

Gittler, Thomas, Stephan Scholze, Alisa Rupenyan, and Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning." Journal of Manufacturing and Materials Processing 4, no. 3 (September 2, 2020): 86. http://dx.doi.org/10.3390/jmmp4030086.

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Анотація:

Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.

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5

Baltruschat, Marcel, and Paul Czodrowski. "Machine learning meets pKa." F1000Research 9 (February 13, 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.1.

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Анотація:

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

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6

Baltruschat, Marcel, and Paul Czodrowski. "Machine learning meets pKa." F1000Research 9 (April 27, 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.2.

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Анотація:

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

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7

Finlay, Janet. "Machine learning: A tool to support usability?" Applied Artificial Intelligence 11, no. 7-8 (October 1997): 633–51. http://dx.doi.org/10.1080/088395197117966.

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8

Caté, Antoine, Lorenzo Perozzi, Erwan Gloaguen, and Martin Blouin. "Machine learning as a tool for geologists." Leading Edge 36, no. 3 (March 2017): 215–19. http://dx.doi.org/10.1190/tle36030215.1.

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9

Whitehall, B. L., S. C. Y. Lu, and R. E. Stepp. "CAQ: A machine learning tool for engineering." Artificial Intelligence in Engineering 5, no. 4 (October 1990): 189–98. http://dx.doi.org/10.1016/0954-1810(90)90020-5.

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10

Wang, Zhi‐Lei, Toshio Ogawa, and Yoshitaka Adachi. "A Machine Learning Tool for Materials Informatics." Advanced Theory and Simulations 3, no. 1 (November 18, 2019): 1900177. http://dx.doi.org/10.1002/adts.201900177.

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11

S, Ganeshkumar, Deepika T, and Anandakumar Haldorai. "A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing." Defence Science Journal 72, no. 5 (November 1, 2022): 712–20. http://dx.doi.org/10.14429/dsj.72.17533.

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Анотація:

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems.

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12

Schneckenburger, Max, Luis Garcia, and Rainer Börret. "Machine learning robot polishing cell." EPJ Web of Conferences 215 (2019): 05002. http://dx.doi.org/10.1051/epjconf/201921505002.

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The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. With increasing optic sizes, the stability of the polishing process becomes more and more important. Parameters such as chemical stability of the polishing slurry or tool wear are key elements for a deterministic computer controlled polishing (CCP) process. High sophisticated CCP processes such as magnetorheological finishing (MRF) or the zeeko bonnet polishing process rely on the stability of the relevant process parameters for the prediction of the desired material removal. The aim of this work is to monitor many process-relevant parameters by using sensors attached to the polishing head and to the polishing process. Examples are a rpm and a torque sensor mounted close to the polishing pad, a vibration sensor for the oscillation bearings, as well as a tilt sensor and a force sensor for measuring the polishing pressure. By means of a machine learning system, predictions of tool wear and the related surface quality shall be made. The aim is the detection of the critical influence factors during the polishing process and to have a predictive maintenance system for tool path planning and for tool change intervals.

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13

Pagadipala srikanth, Pulimamidi sai teja, Borigam Lakshmi prasad, and Veduruvada pavan kalyan. "A comprehensive review of machine learning techniques in computer numerical controlled machines." International Journal of Science and Research Archive 9, no. 1 (June 30, 2023): 627–37. http://dx.doi.org/10.30574/ijsra.2023.9.1.0491.

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Анотація:

Machine learning (ML) is significant advancements in computer science and data processing systems that may be utilized to improve almost all technology-enabled services, goods, and industrial applications. Machine learning is a branch of computer science and artificial intelligence. It emphasizes the use of data and algorithms to mimic the learning process of machines and improve system accuracy. To extend the life of the cutting tools used in machining processes, machine learning algorithms may be used to forecast cutting forces and cutting tool wear. In order to improve productivity during the component production processes, optimized machining parameters for CNC machining operations may be obtained by applying cutting-edge machine learning algorithms. Furthermore, the appearance of Advanced machine learning algorithms can forecast and enhance machinable components to raise the caliber of machinable parts. Machine learning is applied to prediction approaches of energy consumption of CNC machine tools in order to analyses and minimize power usage during CNC machining processes. The use of machine learning and artificial intelligence systems in CNC machine tools is examined in this paper, and future research projects are also suggested in order to offer an overview of the most recent studies on these topics. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in published papers to offer innovative concepts and approaches in applications of machine learning in CNC machine tools.

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14

Wu, Q., E. Liu, Y. H. He, and X. Tang. "Application Research on Extreme Learning Machine in Rapid Detection of Tool Wear in Machine Tools." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012091. http://dx.doi.org/10.1088/1742-6596/2025/1/012091.

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Abstract In order to put an end to the product quality accidents caused by cutting tool breakage or severe wear in machining process, the paper explored an ELM model detection method based on voice recognition. In this paper, firstly it analysed the features of cutting sound signal in time-frequency domain, then discussed the extraction method of tool working state sensitive spectral energy statistical feature based on wavelet packet decomposition, and finally constructed an ELM fast detection model based on sound feature recognition. The experimental results demonstrated that the ELM detection model could achieve higher detection accuracy and faster response time. The simulation results show that the ELM model is effective and applicable in detecting tool wear with the help of sound recognition.

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15

Guile, David. "Machine learning – A new kind of cultural tool? A “recontextualisation” perspective on machine learning + interprofessional learning." Learning, Culture and Social Interaction 42 (October 2023): 100738. http://dx.doi.org/10.1016/j.lcsi.2023.100738.

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16

J., Praveen Gujjar, and Naveen Kumar V. "Google Colaboratory : Tool for Deep Learning and Machine Learning Applications." Indian Journal of Computer Science 6, no. 3-4 (August 31, 2021): 23. http://dx.doi.org/10.17010/ijcs/2021/v6/i3-4/165408.

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17

Mahardika, Rizka. "THE USE OF TRANSLATION TOOL IN EFL LEARNING: DO MACHINE TRANSLATION GIVE POSITIVE IMPACT IN LANGUAGE LEARNING?" Pedagogy : Journal of English Language Teaching 5, no. 1 (July 30, 2017): 49. http://dx.doi.org/10.32332/pedagogy.v5i1.755.

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Анотація:

Translation tools are commonly used for translating a text written in one language (source language) into another language (target language). They are used to help translators in translating big numbers of translation works in effective time. There are three types of translation tools being studied in the article entitled Machine Translation Tools: Tools of the Translator’s Trade written by Peter Katsberg published in 2012. They are Fully Automated Machine Translation (or FAMT), Human Aided Machine Translation (or HAMT) and Machine Aided Human Translation (or MAHT). Katsberg analyzed how each translation tool works, the naturality and approriateness of its translation and the compatibility of using it. In this digital era, translation tools are not only popular among translators but also among EFL learners. Beginning with the use of portable dictionary such as Alfalink and expanding to the more sopisticated translation tool such as Google Translate. Some novice learners usually use this translation tools in doing their task without recorrecting the translation result. This happens perhaps because they do not have enough background knowledge to evaluate the translation result. Thus, it will be better when the learners have good mastery in basic English and train them to be aware in evaluating the result from translation tools. On the other words, Human Aided Machine Translation may be the wise choice to do translation task effectively and efficiently particularly in managing the time.

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18

Schad, Jörg, Rajiv Sambasivan, and Christopher Woodward. "Arangopipe, a tool for machine learning meta-data management." Data Science 4, no. 2 (October 13, 2021): 85–99. http://dx.doi.org/10.3233/ds-210034.

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Анотація:

Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.

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19

Douglas, Michael R. "Machine learning as a tool in theoretical science." Nature Reviews Physics 4, no. 3 (February 14, 2022): 145–46. http://dx.doi.org/10.1038/s42254-022-00431-9.

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20

Zhao, Weixiang, Abhinav Bhushan, Anthony Santamaria, Melinda Simon, and Cristina Davis. "Machine Learning: A Crucial Tool for Sensor Design." Algorithms 1, no. 2 (December 3, 2008): 130–52. http://dx.doi.org/10.3390/a1020130.

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21

Chandrinos, Spyros K., Georgios Sakkas, and Nikos D. Lagaros. "AIRMS: A risk management tool using machine learning." Expert Systems with Applications 105 (September 2018): 34–48. http://dx.doi.org/10.1016/j.eswa.2018.03.044.

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22

Harvey, Neal, and Reid Porter. "User-driven sampling strategies in image exploitation." Information Visualization 15, no. 1 (November 13, 2014): 64–74. http://dx.doi.org/10.1177/1473871614557659.

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Both visual analytics and interactive machine learning try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human–computer interaction. This article focuses on one aspect of the human–computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data are to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications, but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this article, we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools toward local minima that have lower error than tools trained with all of the data. In preliminary experiments, we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

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23

Waghmode, Prof R. T. "Hard Disk Failure Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4459–64. http://dx.doi.org/10.22214/ijraset.2023.52604.

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Abstract: Failure of Hard Disk is a term most companies and people, fear about. People get concerned regarding data loss. Therefore, predicting the failure of the HDD is an important and to ensure the storage security of the data center. There exist a system named, S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. Our project will be predicting the failure of hard drive whether it will fail or not. This prediction will be based on Machine Learning algorithm. S.M.A.R.T. values of hard disk will be extracted from external tool.

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24

Yan, Wei, Chenxun Lu, Ying Liu, Xumei Zhang, and Hua Zhang. "An Energy Data-Driven Approach for Operating Status Recognition of Machine Tools Based on Deep Learning." Sensors 22, no. 17 (September 1, 2022): 6628. http://dx.doi.org/10.3390/s22176628.

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Анотація:

Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent requirement to improve the fault diagnosis ability of machine tools. Through the identification of the operating state (OS) of the machine tools, defining the time point of machine tool failure and the working energy-consuming unit can be assessed. In this way, the fault diagnosis time of the machine tool is shortened and the fault diagnosis ability is improved. Aiming at the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional OS recognition methods, a deep learning method based on data-driven machine tool OS recognition is proposed. Various power data (such as signals or images) of CNC machine tools can be used to recognize the OS of the machine tool, followed by an intuitive judgement regarding whether the energy-consuming units included in the OS are faulty. First, the power data are collected, and the data are preprocessed by noise reduction and cropping using the data preprocessing method of wavelet transform (WT). Then, an AlexNet Convolutional Neural Network (ACNN) is built to identify the OS of the machine tool. In addition, a parameter adaptive adjustment mechanism of the ACNN is studied to improve identification performance. Finally, a case study is presented to verify the effectiveness of the proposed approach. To illustrate the superiority of this method, the approach was compared with traditional classification methods, and the results reveal the superiority in the recognition accuracy and computing speed of this AI technology. Moreover, the technique uses power data as a dataset, and also demonstrates good progress in portability and anti-interference.

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25

Jany Shabu, S. L., Rohan Loganathan Reddy, V. Maria Anu, L. Mary Gladence, and J. Refonaa. "Machine Learning Based Malicious Android Application Detection." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3468–72. http://dx.doi.org/10.1166/jctn.2020.9212.

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The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.

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26

Tyler, Neil. "Machine Learning to Improve Software Quality." New Electronics 53, no. 10 (May 26, 2020): 8. http://dx.doi.org/10.12968/s0047-9624(22)61253-7.

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27

Su, Moting, Zongyi Zhang, Ye Zhu, Donglan Zha, and Wenying Wen. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods." Energies 12, no. 9 (May 3, 2019): 1680. http://dx.doi.org/10.3390/en12091680.

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Анотація:

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

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28

Yousif, Thanaa Hasan, Nahla Ali Tomah, and Marwa Jaleel Mohsin. "Machine learning-based diagnosis of eye-diseases." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 2 (November 1, 2023): 787. http://dx.doi.org/10.11591/ijeecs.v32.i2.pp787-795.

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Анотація:

<span>Over the last several years, artificial intelligence (AI) has been substantially utilized in image processing and classification. Several tools are accessible for visualizing, training, and pre-processing image data. One such tool is orange, which has several pre-processing modules and a particular add-on for image processing methods in addition to excellent data visualization. The tool (version 3.32.0) was used in the suggested study to give a comparative and predictive analysis using several classification models. Three main models have been used to train and predict the three groups image eye diseases. The results were compared based on some criteria, including area-under-a-curve (AUC), the accuracy of classification (CA), F1 score, precision, and recall. These models include K-nearest neighbour (KNN), logistic regression (LR), artificial neural networks (ANN) and stacking model. The stacking model, which is a novel model, is also presented in this work by concatenating the output of the parallel form of ANN and KNN models with the LR model. The best performance belonged to the Stacking model, which offers the best detection and prediction results.</span>

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29

Wei, Chen Lung, Hsin Yu Cheng, Chi Yuang Yu, and Yung Chou Kao. "Development of a Virtual Milling Machining Center Simulation System with Switchable Modular Components." Applied Mechanics and Materials 479-480 (December 2013): 343–47. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.343.

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Анотація:

The application of traditional three-axis milling machine center is very popular and the related application technology is also much matured resulting in mechanical components to be machined with good quality. Machine tool has therefore become an inevitable facility in precision manufacturing. Furthermore, the pursuit of higher precision machining has thus demanding five-axis machine tool to be adopted owing to its flexibility and capability in machining more precise mechanical components in shorter time. However, one of the key factors for the popularity in smooth introduction of five-axis machine tool would be based on a very user friendly learning and teaching environment. This is partly because two more rotational axes in a five-axis machine tool could generate very complex toolpath movement that is out of the imagination of a general operator. Furthermore, the price of an industrial five-axis machine tool is not normally affordable by an educational institute; to the worse, the maintenance cost is also very high. There is very high risk for a novice to collide during the learning process and this will generally cause big worry of a teacher. This paper aims for the development of a virtual machining center simulation system with switchable modular components to ease the learning process in getting acquainted with a five-axis machine tool. A five-axis machine tool consists generally of two modules: (1) CNC controller and Operation panel, and (2) machine tool hardware. The developed system will provide the novice with four CNC controller with operation human machine interface (HMI), and three typical types of five-axis machine tool, Head-Head (HH), Head-Table (HT), and Table-Table (TT), are also supported. The developed modularized and switchable machining center simulation system has been successfully developed and is very helpful to both learner and teacher

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30

Zarouq Eshkanti, Mohammed A. El, and S. C. Ng. "Backdoor Detection Using Machine Learning." Journal of Engineering & Technological Advances 2, no. 1 (2017): 2–13. http://dx.doi.org/10.35934/segi.v2i1.2.

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Анотація:

This research aims to design a backdoor detection technique by using machine learning approach. The implication of this research is to ensure a more effective backdoor detection in network platforms. Security measures against these problems include the use of software programs such as backdoor detection programs. One technique is machine learning (ML); that is a set of tools by which a machine can learn new concepts and new patterns based on a history of learned patterns. The work concentrated on application backdoors which are embedded within the code of a legitimate application. The proposed program in this research is an improvement to backdoor detection based on machine learning techniques. It is developed in Java and employs both supervised and unsupervised methods in the WEKA tool. This helps improving the detection compared to previous methods such fuzzy logic. The results of the experiments have proven that the proposed program is better at detecting backdoors than fuzzy logic when valuated with similar data set. This proves that combining K-Nearest Neighbour and Naive Bayes algorithms is better than using Fuzzy Logic method. The program encountered less false positives and detected all backdoors in the dataset.

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31

Chiu, Yu-Cheng, Po-Hsun Wang, and Yuh-Chung Hu. "The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning." Machines 9, no. 9 (August 30, 2021): 184. http://dx.doi.org/10.3390/machines9090184.

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Анотація:

Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle.

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32

Mohammadi, Yousef, Mohammad Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, and Krzysztof Matyjaszewski. "Intelligent Machine Learning: Tailor-Making Macromolecules." Polymers 11, no. 4 (April 1, 2019): 579. http://dx.doi.org/10.3390/polym11040579.

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Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important ‘inverse’ engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.

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33

Koteluk, Oliwia, Adrian Wartecki, Sylwia Mazurek, Iga Kołodziejczak, and Andrzej Mackiewicz. "How Do Machines Learn? Artificial Intelligence as a New Era in Medicine." Journal of Personalized Medicine 11, no. 1 (January 7, 2021): 32. http://dx.doi.org/10.3390/jpm11010032.

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With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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34

Koteluk, Oliwia, Adrian Wartecki, Sylwia Mazurek, Iga Kołodziejczak, and Andrzej Mackiewicz. "How Do Machines Learn? Artificial Intelligence as a New Era in Medicine." Journal of Personalized Medicine 11, no. 1 (January 7, 2021): 32. http://dx.doi.org/10.3390/jpm11010032.

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Анотація:

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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35

van de Lande, Lara S., Athanasios Papaioannou, and David J. Dunaway. "Geometric morphometrics aided by machine learning in craniofacial surgery." Journal of Orthodontics 46, no. 1_suppl (April 8, 2019): 81–83. http://dx.doi.org/10.1177/1465312519840030.

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Geometric morphometrics aided by machine learning provide detailed and accurate statistical models of facial form. They promise to be extremely effective tools in surgical planning and assessment; however, a clinical tool to use this information is still to be created.

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36

Dekermanjian, Jonathan, Wladimir Labeikovsky, Debashis Ghosh, and Katerina Kechris. "MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools." Metabolites 11, no. 10 (October 2, 2021): 678. http://dx.doi.org/10.3390/metabo11100678.

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The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

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37

Woo, Myung, Brooke Alhanti, Sam Lusk, Felicia Dunston, Stephen Blackwelder, Kay S. Lytle, Benjamin A. Goldstein, and Armando Bedoya. "Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned." Journal of Personalized Medicine 10, no. 3 (August 27, 2020): 104. http://dx.doi.org/10.3390/jpm10030104.

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There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

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38

Sobha Rani, Naguri, and Naguri Divya Sruthi. "Automatic Portrait Image Cropping using Machine Learning Models." International Journal of Scientific Methods in Engineering and Management 01, no. 01 (2023): 73–86. http://dx.doi.org/10.58599/ijsmem.2023.1107.

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For a long time, machine learning is an application spanning from a wide variety of subjects – from vehicles to data extraction. When you take an image of your cell phone, the picture is a little tangy. It’s simple. It often happens that people take random pictures using phones, which may end up in a corner of the frame. This work blends computer study with tools for photo editing. It will explore the options of how to automatically create photos with aesthetic pleasure through machine learning and how to create a portrait cutting tool. It also explores how to use machine learning to incorporate a streamlined function. Finally, the tools will be compared to other automated machine cropping tools.

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39

Fertig, Alexander, Lukas Grau, Marius Altmannsberger, and Matthias Weigold. "TOOL CONDITION MONITORING AND TOOL DEFECT DETECTION FOR END MILLS BASED ON HIGH-FREQUENCY MACHINE TOOL DATA." MM Science Journal 2021, no. 5 (November 3, 2021): 5160–66. http://dx.doi.org/10.17973/mmsj.2021_11_2021174.

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In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data.

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40

Mamledesai, Harshavardhan, Mario A. Soriano, and Rafiq Ahmad. "A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning." Applied Sciences 10, no. 20 (October 19, 2020): 7298. http://dx.doi.org/10.3390/app10207298.

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Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.

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41

Asif, Muhammad, Hang Shen, Chunlin Zhou, Yuandong Guo, Yibo Yuan, Pu Shao, Lan Xie, and Muhammad Shoaib Bhutta. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions." Sustainability 15, no. 10 (May 19, 2023): 8298. http://dx.doi.org/10.3390/su15108298.

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Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

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42

Shin, Seung-Jun, Young-Min Kim, and Prita Meilanitasari. "A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes." Processes 7, no. 10 (October 14, 2019): 739. http://dx.doi.org/10.3390/pr7100739.

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The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

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43

Hanuschkin, Alexander, Jürgen Schorr, Christian Krüger, and Steven Peters. "Machine Learning as an Analysis Tool in Engine Research." ATZelectronics worldwide 16, no. 1-2 (January 2021): 44–47. http://dx.doi.org/10.1007/s38314-020-0574-7.

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44

KUTSENOGIY, P. K., V. K. KALICHKIN, A. L. PAKUL, and S. P. KUTSENOGIY. "MACHINE LEARNING AS A TOOL FOR CROP YIELD FORECAST." Rossiiskaia selskokhoziaistvennaia nauka, no. 1 (2021): 72–75. http://dx.doi.org/10.31857/s2500262721010178.

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45

MU, Dianfang, Xianli LIU, Caixu YUE, Qiang LIU, Zhengyan BAI, Steven Y. LIANG, and Yunpeng DING. "On-line tool wear monitoring based on machine learning." Journal of Advanced Manufacturing Science and Technology 1, no. 2 (2021): 2021002. http://dx.doi.org/10.51393/j.jamst.2021002.

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46

Anderson, Don D. "Machine Translation As a Tool in Second Language Learning." CALICO Journal 13, no. 1 (January 14, 2013): 68–97. http://dx.doi.org/10.1558/cj.v13i1.68-97.

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The current major Machine Translation (MT) evaluation effort, funded by the Advanced Research Projects Agency (ARPA), shows that when compared to expert human translators, MT systems perform only about 65% as well on the average. In this paper it is argued that despite their overall poor performance, MT software can be used as a powerful focal point to improve second language (L2) skills. The paper describes the evaluation of Computronics Corporation's Targumatik (Hebrew-->English), a PC-based MT system running under DOS, and shows how each problem and potential obstruction to learning can be overcome by means of discovery procedures using a set of tools and procedures called the 'learning algorithm.'

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47

Zhou, Bo, Tongtong Tian, Jibin Zhao, and Dianhai Liu. "Tool-path continuity determination based on machine learning method." International Journal of Advanced Manufacturing Technology 119, no. 1-2 (November 1, 2021): 403–20. http://dx.doi.org/10.1007/s00170-021-08156-2.

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48

Cockshott, Anne. "Reservoir computing, a machine learning tool for robust forecasting." Scilight 2021, no. 51 (December 17, 2021): 511107. http://dx.doi.org/10.1063/10.0009049.

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49

Kutsenogiy, P. K., V. K. Kalichkin, A. L. Pakul, and S. P. Kutsenogiy. "Machine Learning as a Tool for Crop Yield Prediction." Russian Agricultural Sciences 47, no. 2 (March 2021): 188–92. http://dx.doi.org/10.3103/s1068367421020117.

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50

SAKUMA, Taishi, Kotaro YAMADA, Toshiki HIROGAKI, Eiichi AOYAMA, and Hiroyuki KODAMA. "Data mining from tool catalog introducing machine learning method." Proceedings of The Manufacturing & Machine Tool Conference 2018.12 (2018): A20. http://dx.doi.org/10.1299/jsmemmt.2018.12.a20.

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