Статті в журналах: "Machine learning tools"

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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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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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Hussein, Eslam A., Christopher Thron, Mehrdad Ghaziasgar, Antoine Bagula, and Mattia Vaccari. "Groundwater Prediction Using Machine-Learning Tools." Algorithms 13, no. 11 (November 17, 2020): 300. http://dx.doi.org/10.3390/a13110300.

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Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.

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Bosetti, Paolo, Matteo Ragni, and Matteo Leoni. "Modern machine-learning tools for crystallography." Acta Crystallographica Section A Foundations and Advances 73, a2 (December 1, 2017): C562. http://dx.doi.org/10.1107/s2053273317090118.

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Padarian, José, Budiman Minasny, and Alex B. McBratney. "Machine learning and soil sciences: a review aided by machine learning tools." SOIL 6, no. 1 (February 6, 2020): 35–52. http://dx.doi.org/10.5194/soil-6-35-2020.

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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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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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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O’Gorman, Eoin J. "Machine learning ecological networks." Science 377, no. 6609 (August 26, 2022): 918–19. http://dx.doi.org/10.1126/science.add7563.

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Ng, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization." Biotechnology and Bioprocessing 2, no. 9 (November 2, 2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.

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Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

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Sukhoparov, M. E., K. I. Salakhutdinova, and I. S. Lebedev. "Software Identification by Standard Machine Learning Tools." Automatic Control and Computer Sciences 55, no. 8 (December 2021): 1175–79. http://dx.doi.org/10.3103/s0146411621080459.

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Gleyzer, S. V., L. Moneta, and Omar A. Zapata. "Development of Machine Learning Tools in ROOT." Journal of Physics: Conference Series 762 (October 2016): 012043. http://dx.doi.org/10.1088/1742-6596/762/1/012043.

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Yousefi, Jamileh, and Andrew Hamilton-Wright. "Characterizing EMG data using machine-learning tools." Computers in Biology and Medicine 51 (August 2014): 1–13. http://dx.doi.org/10.1016/j.compbiomed.2014.04.018.

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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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12

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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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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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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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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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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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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Mullainathan, Sendhil, and Jann Spiess. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 87–106. http://dx.doi.org/10.1257/jep.31.2.87.

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Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble—and thus where they can be most usefully applied.

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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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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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Coco, Giovanni. "ROUND ABOUT MACHINE LEARNING." Coastal Engineering Proceedings, no. 36v (December 31, 2020): 4. http://dx.doi.org/10.9753/icce.v36v.keynote.4.

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In this talk I will review the use of Machine Learning in studies of coastal morphodynamics. I will discuss a number of problems where ML tools have been used and why it makes sense to use ML methods. I will also outline recent advances in ML algorithms and applications, and discuss possible areas for future research.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/X5QnAdD1-T8

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21

Zhang, Mei. "Evaluation Of Machine Learning Tools For Distinguishing Fraud From Error." Journal of Business & Economics Research (JBER) 11, no. 9 (August 30, 2013): 393. http://dx.doi.org/10.19030/jber.v11i9.8067.

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<p>Fraud and error are two underlying sources of misstated financial statements. Modern machine learning techniques provide a potential direction to distinguish the two factors in such statements. In this paper, a thorough evaluation is conducted evaluation on how the off-the-shelf machine learning tools perform for fraud/error classification. In particular, the task is treated as a standard binary classification problem; i.e., mapping from an input vector of financial indices to a class label which is either error or fraud. With a real dataset of financial restatements, this study empirically evaluates and analyzes five state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines, decision trees, and bagging. There are several important observations from the experimental results. First, it is observed that bagging performs the best among these commonly used general purpose machine learning tools. Second, the results show that the underlying relationship from the statement indices to the fraud/error decision is likely to be non-linear. Third, it is very challenging to distinguish error from fraud, and general machine learning approaches, though perform better than pure chance, leave much room for improvement. The results suggest that more advanced or task-specific solutions are needed for fraud/error classification.</p>

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Dietrich, Bastian, Jessica Walther, Matthias Weigold, and Eberhard Abele. "Machine learning based very short term load forecasting of machine tools." Applied Energy 276 (October 2020): 115440. http://dx.doi.org/10.1016/j.apenergy.2020.115440.

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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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Byrne, Cian, Thomas Dickson, Marin Lauber, Claudio Cairoli, and Gabriel Weymouth. "Using Machine Learning to Model Yacht Performance." Journal of Sailing Technology 7, no. 01 (May 9, 2022): 104–19. http://dx.doi.org/10.5957/jst/2022.7.5.104.

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Accurate modelling of the performance of a yacht in varying environmental conditions can significantly improve a yachts performance. However, a racing yacht is a highly complex multi-physics system meaning that real-time performance prediction tools are always semi-empirical, leaving significant room for improvement. In this paper we first use unsupervised machine learning to analyse full-scale yacht performance data. The widely documented ORC VPP (ORC, 2015) and the commercial Windesign VPP are compared to the data across a range of wind conditions. The data is then used to train machine learning models. A number of machine learning regression algorithms are explored including Neural Networks, Random Forests and Support Vector Machines and improvements of 82% are obtained compared to the commercial tools. The use of physics- based learning models (Weymouth and Yue, 2013) is explored in order to reduce the amount of data required to achieve accurate predictions. It is found that machine learning models can outperform empirical models even when predicting performance in environmental conditions that have not been supplied to the model as part of the training dataset.

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Maheshwari, Himani, Pooja Goswami, and Isha Rana. "A Comparative Study of Different Machine Learning Tools." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 184–90. http://dx.doi.org/10.26438/ijcse/v7i4.184190.

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Kishore, M. Ram. "A Review on Machine Learning Tools and Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4270–83. http://dx.doi.org/10.22214/ijraset.2022.44888.

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Abstract: Data is evolving as the fuel for the new economy and the future economy. Having the right data saves time. For major businesses, a significant portion of their work is spent gathering data, sorting it, and then analyzing it in various business contexts to derive insights that are beneficial to the firm. The key challenge or the key opportunity to any organization is to be able to convert the data available into intelligent action. The best way to take advantage of this data is to convert into intelligence, specifically intelligent actions. All these cannot be performed effectively when done manually. Using machine learning, it is possible to build a system that continuously learns from historical data, build models which are fed with input data, train those models, and then predict the future when it receives new data. This whole task of building intelligent systems can be boiled down to building better models that can make precise predictions. There are several tools available that can be utilized to construct better models for different algorithms, which simplifies the process of building models seamlessly and easier to comprehend. Building a machine learning system requires a number of steps, including handling various data types, analyzing and preprocessing them, creating neural networks, training the model, evaluating it, and making iterative changes to the model to improve its performance on both training and test datasets. In this paper we will introduce you to various tools that can be used at different phases of developing a strong machine learning model. Additionally, we'll provide case-studies of a few applications, guide you through the tasks the application performs, and explain which machine learning techniques are employed to meet the application's primary goals.

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Chavan, Mr Vikram. "Malware Classification using Machine Learning Algorithms and Tools." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 69–73. http://dx.doi.org/10.22214/ijraset.2021.34353.

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The explosive growth of malware variants poses a major threat to information security. Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. According to numerous increasing of worm malware in the networks nowadays, it became a serious danger that threatens our computers. Networks attackers did these attacks by designing the worms. A designed system model is needed to defy these threats, prevent it from multiplying and spreading through the network, and harm our computers. In this paper, we designed a classification on system model for this issue. The designed system detects the worm malware that depends on the information of the dataset that is taken from website, the system will receive the input package and then analyze it, the Naïve Bayesian classification technique will start to work and begin to classify the package, by using the data mining Naïve Bayesian classification technique, the system worked fast and gained great results in detecting the worm. By applying the Naïve Bayesian classification technique using its probability mathematical equations for both threat data and benign data, the technique will detect the malware and classify data whether it was threat or benign.

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Zyablikov, D. A. "APPLICATION OF MACHINE LEARNING METHODS IN MARKETING TOOLS." Прогрессивная экономика, no. 6 (2021): 36–43. http://dx.doi.org/10.54861/27131211_2021_6_36.

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Veena, Dr S., T. Shankari, S. Sowmiya, and M. Varsha. "A SURVEY ON TOOLS USED FOR MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 04, no. 09 (January 30, 2020): 116–19. http://dx.doi.org/10.33564/ijeast.2020.v04i09.012.

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Lo Vercio, Lucas, Kimberly Amador, Jordan J. Bannister, Sebastian Crites, Alejandro Gutierrez, M. Ethan MacDonald, Jasmine Moore, et al. "Supervised machine learning tools: a tutorial for clinicians." Journal of Neural Engineering 17, no. 6 (December 22, 2020): 062001. http://dx.doi.org/10.1088/1741-2552/abbff2.

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Baron, Jason M., Danielle E. Kurant, and Anand S. Dighe. "Machine Learning and Other Emerging Decision Support Tools." Clinics in Laboratory Medicine 39, no. 2 (June 2019): 319–31. http://dx.doi.org/10.1016/j.cll.2019.01.010.

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Demirhan, Ayşe. "Neuroimage-based clinical prediction using machine learning tools." International Journal of Imaging Systems and Technology 27, no. 1 (March 2017): 89–97. http://dx.doi.org/10.1002/ima.22213.

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Guañuna, Gabriel, Santiago Chamba, Nelson Granda, Jaime Cepeda, Diego Echeverría, and Walter Vargas. "Voltage Stability Margin Estimation Using Machine Learning Tools." Revista Técnica "energía" 20, no. 1 (July 27, 2023): 1–8. http://dx.doi.org/10.37116/revistaenergia.v20.n1.2023.570.

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Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.

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Argüelles, Carlos R., and Santiago Collazo. "Galaxy Rotation Curve Fitting Using Machine Learning Tools." Universe 9, no. 8 (August 16, 2023): 372. http://dx.doi.org/10.3390/universe9080372.

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Galaxy rotation curve (RC) fitting is an important technique which allows the placement of constraints on different kinds of dark matter (DM) halo models. In the case of non-phenomenological DM profiles with no analytic expressions, the art of finding RC best-fits including the full baryonic + DM free parameters can be difficult and time-consuming. In the present work, we use a gradient descent method used in the backpropagation process of training a neural network, to fit the so-called Grand Rotation Curve of the Milky Way (MW) ranging from ∼1 pc all the way to ∼105 pc. We model the mass distribution of our Galaxy including a bulge (inner + main), a disk, and a fermionic dark matter (DM) halo known as the Ruffini-Argüelles-Rueda (RAR) model. This is a semi-analytical model built from first-principle physics such as (quantum) statistical mechanics and thermodynamics, whose more general density profile has a dense core–diluted halo morphology with no analytic expression. As shown recently and further verified here, the dark and compact fermion-core can work as an alternative to the central black hole in SgrA* when including data at milliparsec scales from the S-cluster stars. Thus, we show the ability of this state-of-the-art machine learning tool in providing the best-fit parameters to the overall MW RC in the 10−2–105 pc range, in a few hours of CPU time.

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Kang, Rachael, Esa M. Rantanen, and Eric A. Youngstrom. "Machine Learning in Healthcare: Two Case Studies." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 774–78. http://dx.doi.org/10.1177/1071181322661518.

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Machine learning (ML) is making significant inroads into the field of medicine. It can be used as a preventative measure by predicting a patient’s diagnosis and introducing early treatment to prevent adverse outcomes or lessen their impact. However, despite many demonstrated advantages of machine learning tools in health-care, their performance assessment remains partial at best. In particular, human interactions with machine learning tools in clinical settings remain poorly researched. This review examined machine learning tools in two important areas, sepsis diagnosis and suicide prediction. However, our exploration into the use of machine learning in sepsis and suicide prediction turned up no thorough human factors analyses of provider interactions with their machine learning tools, suggesting a critical research gap waiting to be filled.

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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.

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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.

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Haner Kırğıl, Elif Nur, and Tülin Erçelebi Ayyıldız. "Predicting Software Cohesion Metrics with Machine Learning Techniques." Applied Sciences 13, no. 6 (March 15, 2023): 3722. http://dx.doi.org/10.3390/app13063722.

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The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.

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Lopes, Bárbara Gabrielle C. O., Liziane Santos Soares, Raquel Oliveira Prates, and Marcos André Gonçalves. "Contrasting Explain-ML with Interpretability Machine Learning Tools in Light of Interactive Machine Learning Principles." Journal on Interactive Systems 13, no. 1 (November 21, 2022): 313–34. http://dx.doi.org/10.5753/jis.2022.2556.

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The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated by the model, they will not use them. Furthermore, the human role is often not properly considered in the development of ML systems. In this article, we present the design, implementation and evaluation of Explain-ML, an Interactive Machine Learning (IML) system for Explainable Machine Learning that follows the principles of Human-Centered Machine Learning (HCML). We assess the user experience with the Explain-ML interpretability strategies, contrasting them with the analysis of how other IML tools address the IML principles. To do so, we have conducted an analysis of the results of the evaluation of Explain-ML with potential users in light of principles for IML systems design and a systematic inspection of three other tools – Rulematrix, Explanation Explorer and ATMSeer – using the Semiotic Inspection Method (SIM). Our results generated positive indicators regarding Explain-ML and the process that guided its development. Our analyses also highlighted aspects of the IML principles that are relevant from the users’ perspective. By contrasting the results with Explain-ML and SIM inspections of the other tools we were able to identify common interpretability strategies. We believe that the results reported in this work contribute to the understanding and consolidation of the IML principles, ultimately advancing the knowledge in HCML.

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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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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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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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Renushe, Prof Archana, Rutuja Kesare, Rohini Kumbhar, and Pooja Kumbhar. "Gear Defect Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2906–8. http://dx.doi.org/10.22214/ijraset.2023.50279.

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bstract: This proposed work introduces the application that is used to perceive the condition of system equipment. In machines, equipment is a very important component on every occasion it is damaged then it immediately affects on gadget's reliability. The objective of this project is to increase a system that shows the type of tools or condition of tools used to locate whether or not it's miles faulty or non-faulty manner. Convolution neural network technique might be used to locate the situation of tools and This approach is accomplished via picture processing. It takes the photograph manually from datasets or via the live digicam. The actual result of this Proposed work is to show the pick-out gear picture in which the form of gear is defective or non-defective. We explored and in comparison various CNN networks for item detection using real facts furnished by the consumer. The surface illness detection and counting the variety of equipment teeth manually isn't always correct so to clear up the trouble.

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Rahman, Reza Aulia, Mohammad Faishol Erikyatna, and Achmad Fauzan Hery Soegiharto. "Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning." Journal of Mechanical Engineering Science and Technology (JMEST) 6, no. 2 (November 15, 2022): 85. http://dx.doi.org/10.17977/um016v6i22022p085.

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Towards industry 4.0, monitoring the degradation of machine tools’ components becomes a key feature so that smooth productivity is achieved. To preserve the functionality and performance of the machine tools, proper maintenance activities must be planned and carried out. V-belt is important component in machine tools that transmits power from the electric motor spindle in order to machine to work and cut desired material properly. The purpose of this research is to develop a predictive maintenance system for v-belt milling machine Krisbow 31N2F using machine learning. The machine learning algorithm models using multiple and simple linear regression algorithm was developed in an open-source program. The test results show that the machine learning model has a high accuracy value in both the training data and the testing data. The multiple linear regression model has MSE value of 5.8830x10-6 and MAE value of 0.002. The Simple linear regression model has an MSE value of 0.0004x10-6 and MAE value of 0.162. The results shows that the use of the linear regression algorithm as a support for determining the prediction of RUL v-belt milling machine model 31N2F (BS) is successfully carried out.

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Burgard, Tanja, and André Bittermann. "Reducing Literature Screening Workload With Machine Learning." Zeitschrift für Psychologie 231, no. 1 (February 2023): 3–15. http://dx.doi.org/10.1027/2151-2604/a000509.

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Abstract. In our era of accelerated accumulation of knowledge, the manual screening of literature for eligibility is increasingly becoming too labor-intensive for summarizing the current state of knowledge in a timely manner. Recent advances in machine learning and natural language processing promise to reduce the screening workload by automatically detecting unseen references with a high probability of inclusion. As a variety of tools have been developed, the current review provides an overview of their characteristics and performance. A systematic search in various databases yielded 488 eligible reports, revealing 15 tools for screening automation that differed in methodology, features, and accessibility. For the review on the performance of screening tools, 21 studies could be included. In comparison to sampling records randomly, active screening with prioritization approximately halves the screening workload. However, a comparison of tools under equal or at least similar conditions is needed to derive clear recommendations.

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Milakovic, Adrian, Drazen Draskovic, and Bosko Nikolic. "Visual Simulator for Mastering Fundamental Concepts of Machine Learning." Applied Sciences 12, no. 24 (December 17, 2022): 12974. http://dx.doi.org/10.3390/app122412974.

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Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems.

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Walker, David. "Using machine learning to enhance operator performance." APPEA Journal 60, no. 2 (2020): 681. http://dx.doi.org/10.1071/aj19163.

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Machine learning is a powerful tool to analyse very large datasets. Although machine learning has been used for many years in other areas, such a social media, its value to process industry has only recently been realised. Operators interact with control systems in the same way people interact with social media and, as such, many of the algorithms that have been developed for modelling human interaction are applicable to industrial process operations. Recently, control systems companies have been developing analytical tools to leverage the vast amount of data collected in control systems over many years. These tools enable operations to understand the efficiency of their processes and procedures, identify gaps in their standard operating procedures and measure operator capability. This analysis assists in the improvement of procedures, highlights areas where further training is required and identifies opportunities for procedure automation. This has led to considerable improvements in operation performance, resulting in improved production and reduced downtime. This paper describes what machine learning is, how it can be applied to operation performance, the benefits this provides and possible applications in other areas of the industry.

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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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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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Reich, Yoram. "Modelling engineering information with machine learning." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 2 (April 1996): 171–74. http://dx.doi.org/10.1017/s0890060400001487.

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Since the inception of research on machine learning (ML), these techniques have been associated with the task of automated knowledge generation or knowledge reorganization. This association still prevails, as seen in this issue. When the use of ML programs began to attract researchers in engineering design, different existing tools were used to test their utility and gradually, variations of these tools and methods have sprung up. In many cases, the use of these tools was based on availability and not necessarily applicability. When we began working on ML in design, we attempted to follow a different path (Reich, 1991a; Reich & Fenves, 1992) that led to the design of Bridger (Reich & Fenves, 1995), a system for learning bridge synthesis knowledge. Subsequent experiences and further reflection led us to conclude that the process of using ML in design requires careful and systematic treatment for identifying appropriate ML programs for executing the learning tasks we wish to perform (Reich, 1991b, 1993a). Another observation was that the task of creating or reorganizing knowledge for real design tasks is outside the scope of present ML programs. Establishing the practical importance of ML techniques had to start by addressing engineering problems that could benefit from present ML programs.

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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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Thomas, Philip S., Bruno Castro da Silva, Andrew G. Barto, Stephen Giguere, Yuriy Brun, and Emma Brunskill. "Preventing undesirable behavior of intelligent machines." Science 366, no. 6468 (November 21, 2019): 999–1004. http://dx.doi.org/10.1126/science.aag3311.

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Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause harm to humans—is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

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Molina, Mario, and Filiz Garip. "Machine Learning for Sociology." Annual Review of Sociology 45, no. 1 (July 30, 2019): 27–45. http://dx.doi.org/10.1146/annurev-soc-073117-041106.

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Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition to serving similar purposes in sociology, machine learning tools can speak to long-standing questions on the limitations of the linear modeling framework, the criteria for evaluating empirical findings, transparency around the context of discovery, and the epistemological core of the discipline.

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