Готові списки джерел за темами / Machine learning tools

Добірка наукової літератури з теми "Machine learning tools"

Автор: Grafiati
Опубліковано: 6 вересня 2023
Оновлено: 7 вересня 2023

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Статті в журналах з теми "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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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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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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Анотація:
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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3

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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4

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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Анотація:
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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5

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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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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Анотація:
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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9

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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Більше джерел

Дисертації з теми "Machine learning tools"

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Kanwar, John. "Smart cropping tools with help of machine learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74827.

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Анотація:
Machine learning has been around for a long time, the applications range from a big variety of different subjects, everything from self driving cars to data mining. When a person takes a picture with its mobile phone it easily happens that the photo is a little bit crooked. It does also happen that people takes spontaneous photos with help of their phones, which can result in something irrelevant ending up in the corner of the image. This thesis combines machine learning with photo editing tools. It will explore the possibilities how machine learning can be used to automatically crop images in an aesthetically pleasing way and how machine learning can be used to create a portrait cropping tool. It will also go through how a straighten out function can be implemented with help of machine learning. At last, it is going to compare this tools with other software automatic cropping tools.
Maskinlärning har funnits en lång tid. Deras jobb varierar från flera olika ämnen. Allting från självkörande bilar till data mining. När en person tar en bild med en mobiltelefon händer det lätt att bilden är lite sned. Det händer också att en tar spontana bilder med sin mobil, vilket kan leda till att det kommer med något i kanten av bilden som inte bör vara där. Det här examensarbetet kombinerar maskinlärning med fotoredigeringsverktyg. Det kommer att utforska möjligheterna hur maskinlärning kan användas för att automatiskt beskära bilder estetsikt tilltalande samt hur maskinlärning kan användas för att skapa ett porträttbeskärningsverktyg. Det kommer även att gå igenom hur en räta-till-funktion kan bli implementerad med hjälp av maskinlärning. Till sist kommer det att jämföra dessa verktyg med andra programs automatiska beskärningsverktyg.
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2

Nordin, Alexander Friedrich. "End to end machine learning workflow using automation tools." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119776.

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Анотація:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 79-80).
We have developed an open source library named Trane and integrated it with two open source libraries to build an end-to-end machine learning workflow that can facilitate rapid development of machine learning models. The three components of this workflow are Trane, Featuretools and ATM. Trane enumerates tens of prediction problems relevant to any dataset using the meta information about the data. Furthermore, Trane generates training examples required for training machine learning models. Featuretools is an open-source software for automatically generating features from a dataset. Auto Tune Models (ATM), an open source library, performs a high throughput search over modeling options to find the best modeling technique for a problem. We show the capability of these three tools and highlight the open-source development of Trane.
by Alexander Friedrich Nordin.
M. Eng.
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3

Jalali, Mana. "Voltage Regulation of Smart Grids using Machine Learning Tools." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/95962.

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Анотація:
Smart inverters have been considered the primary fast solution for voltage regulation in power distribution systems. Optimizing the coordination between inverters can be computationally challenging. Reactive power control using fixed local rules have been shown to be subpar. Here, nonlinear inverter control rules are proposed by leveraging machine learning tools. The designed control rules can be expressed by a set of coefficients. These control rules can be nonlinear functions of both remote and local inputs. The proposed control rules are designed to jointly minimize the voltage deviation across buses. By using the support vector machines, control rules with sparse representations are obtained which decrease the communication between the operator and the inverters. The designed control rules are tested under different grid conditions and compared with other reactive power control schemes. The results show promising performance.
With advent of renewable energies into the power systems, innovative and automatic monitoring and control techniques are required. More specifically, voltage regulation for distribution grids with solar generation is a can be a challenging task. Moreover, due to frequency and intensity of the voltage changes, traditional utility-owned voltage regulation equipment are not useful in long term. On the other hand, smart inverters installed with solar panels can be used for regulating the voltage. Smart inverters can be programmed to inject or absorb reactive power which directly influences the voltage. Utility can monitor, control and sync the inverters across the grid to maintain the voltage within the desired limits. Machine learning and optimization techniques can be applied for automation of voltage regulation in smart grids using the smart inverters installed with solar panels. In this work, voltage regulation is addressed by reactive power control.
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Viswanathan, Srinidhi. "ModelDB : tools for machine learning model management and prediction storage." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113540.

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Анотація:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
Building a machine learning model is often an iterative process. Data scientists train hundreds of models before finding a model that meets acceptable criteria. But tracking these models and remembering the insights obtained from them is an arduous task. In this thesis, we present two main systems for facilitating better tracking, analysis, and querying of scikit-learn machine learning models. First, we introduce our scikit-learn client for ModelDB, a novel end-to-end system for managing machine learning models. The client allows data scientists to easily track diverse scikit-learn workflows with minimal changes to their code. Then, we describe our extension to ModelDB, PredictionStore. While the ModelDB client enables users to track the different models they have run, PredictionStore creates a prediction matrix to tackle the remaining piece in the puzzle: facilitating better exploration and analysis of model performance. We implement a query API to assist in analyzing predictions and answering nuanced questions about models. We also implement a variety of algorithms to recommend particular models to ensemble utilizing the prediction matrix. We evaluate ModelDB and PredictionStore on different datasets and determine ModelDB successfully tracks scikit-learn models, and most complex model queries can be executed in a matter of seconds using our query API. In addition, the workflows demonstrate significant improvement in accuracy using the ensemble algorithms. The overall goal of this research is to provide a flexible framework for training scikit-learn models, storing their predictions/ models, and efficiently exploring and analyzing the results.
by Srinidhi Viswanathan.
M. Eng.
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5

Borodavkina, Lyudmila 1977. "Investigation of machine learning tools for document clustering and classification." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8932.

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Анотація:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 57-59).
Data clustering is a problem of discovering the underlying data structure without any prior information about the data. The focus of this thesis is to evaluate a few of the modern clustering algorithms in order to determine their performance in adverse conditions. Synthetic Data Generation software is presented as a useful tool both for generating test data and for investigating results of the data clustering. Several theoretical models and their behavior are discussed, and, as the result of analysis of a large number of quantitative tests, we come up with a set of heuristics that describe the quality of clustering output in different adverse conditions.
by Lyudmila Borodavkina.
M.Eng.
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6

Song, Qi. "Developing machine learning tools to understand transcriptional regulation in plants." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93512.

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Анотація:
Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance. Recent advances of genomic technologies have led to the generation of much genomic data for the model plant, Arabidopsis. To understand gene responses activated by specific external stress signals, these large-scale data sets need to be analyzed to generate new insight of gene functions in stress responses. This poses new computational challenges of mining gene associations and reconstructing regulatory interactions from large-scale data sets. In this dissertation, several computational tools were developed to address the challenges. In Chapter 2, ConSReg was developed to infer condition-specific regulatory interactions and prioritize transcription factors (TFs) that are likely to play condition specific regulatory roles. Comprehensive investigation was performed to optimize the performance of ConSReg and a systematic recovery of nitrogen response TFs was performed to evaluate ConSReg. In Chapter 3, CoReg was developed to infer co-regulation between genes, using only regulatory networks as input. CoReg was compared to other computational methods and the results showed that CoReg outperformed other methods. CoReg was further applied to identified modules in regulatory network generated from DAP-seq (DNA affinity purification sequencing). Using a large expression dataset generated under many abiotic stress treatments, many regulatory modules with common regulatory edges were found to be highly co-expressed, suggesting that target modules are structurally stable modules under abiotic stress conditions. In Chapter 4, exploratory analysis was performed to classify cell types for Arabidopsis root single cell RNA-seq data. This is a first step towards construction of a cell-type-specific regulatory network for Arabidopsis root cells, which is important for improving current understanding of stress response.
Doctor of Philosophy
Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance to plants. Genomics technology has been used in past decade to generate gene expression data under different abiotic stresses for the model plant, Arabidopsis. Recent new genomic technologies, such as DAP-seq, have generated large scale regulatory maps that provide information regarding which gene has the potential to regulate other genes in the genome. However, this technology does not provide context specific interactions. It is unknown which transcription factor can regulate which gene under a specific abiotic stress condition. To address this challenge, several computational tools were developed to identify regulatory interactions and co-regulating genes for stress response. In addition, using single cell RNA-seq data generated from the model plant organism Arabidopsis, preliminary analysis was performed to build model that classifies Arabidopsis root cell types. This analysis is the first step towards the ultimate goal of constructing cell-typespecific regulatory network for Arabidopsis, which is important for improving current understanding of stress response in plants.
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7

Nagler, Dylan Jeremy. "SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:12705172.

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Анотація:
This paper compares various methods for automated musical analysis, applying machine learning techniques to gain insight about the Lieder (art songs) of com- poser Franz Schubert (1797-1828). Known as a rule-breaking, individualistic, and adventurous composer, Schubert produced hundreds of emotionally-charged songs that have challenged music theorists to this day. The algorithms presented in this paper analyze the harmonies, melodies, and texts of these songs. This paper begins with an exploration of the relevant music theory and ma- chine learning algorithms (Chapter 1), alongside a general discussion of the place Schubert holds within the world of music theory. The focus is then turned to automated harmonic analysis and hierarchical decomposition of MusicXML data, presenting new algorithms for phrase-based analysis in the context of past research (Chapter 2). Melodic analysis is then discussed (Chapter 3), using unsupervised clustering methods as a complement to harmonic analyses. This paper then seeks to analyze the texts Schubert chose for his songs in the context of the songs’ relevant musical features (Chapter 4), combining natural language processing with feature extraction to pinpoint trends in Schubert’s career.
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8

Parikh, Neena (Neena S. ). "Interactive tools for fantasy football analytics and predictions using machine learning." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100687.

Повний текст джерела
Анотація:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-84).
The focus of this project is multifaceted: we aim to construct robust predictive models to project the performance of individual football players, and we plan to integrate these projections into a web-based application for in-depth fantasy football analytics. Most existing statistical tools for the NFL are limited to the use of macro-level data; this research looks to explore statistics at a finer granularity. We explore various machine learning techniques to develop predictive models for different player positions including quarterbacks, running backs, wide receivers, tight ends, and kickers. We also develop an interactive interface that will assist fantasy football participants in making informed decisions when managing their fantasy teams. We hope that this research will not only result in a well-received and widely used application, but also help pave the way for a transformation in the field of football analytics.
by Neena Parikh.
M. Eng.
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Arango, Argoty Gustavo Alonso. "Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88987.

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Анотація:
Metagenomics has become a reliable tool for the analysis of the microbial diversity and the molecular mechanisms carried out by microbial communities. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. Interpretation of specific information from metagenomic data is especially a challenge for environmental samples as current annotation systems only offer broad classification of microbial diversity and function. Therefore, I developed MetaStorm, a public web-service that facilitates customization of computational analysis for metagenomic data. The identification of antibiotic resistance genes (ARGs) from metagenomic data is carried out by searches against curated databases producing a high rate of false negatives. Thus, I developed DeepARG, a deep learning approach that uses the distribution of sequence alignments to predict over 30 antibiotic resistance categories with a high accuracy. Curation of ARGs is a labor intensive process where errors can be easily propagated. Thus, I developed ARGminer, a web platform dedicated to the annotation and inspection of ARGs by using crowdsourcing. Effective environmental monitoring tools should ideally capture not only ARGs, but also mobile genetic elements and indicators of co-selective forces, such as metal resistance genes. Here, I introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology to provide insights into mobility, co-selection, and pathogenicity. Sequence alignment has been one of the preferred methods for analyzing metagenomic data. However, it is slow and requires high computing resources. Therefore, I developed MetaMLP, a machine learning approach that uses a novel representation of protein sequences to perform classifications over protein functions. The method is accurate, is able to identify a larger number of hits compared to sequence alignments, and is >50 times faster than sequence alignment techniques.
Doctor of Philosophy
Antimicrobial resistance (AMR) is one of the biggest threats to human public health. It has been estimated that the number of deaths caused by AMR will surpass the ones caused by cancer on 2050. The seriousness of these projections requires urgent actions to understand and control the spread of AMR. In the last few years, metagenomics has stand out as a reliable tool for the analysis of the microbial diversity and the AMR. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics, a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. In particular, by the development of computational pipelines to process metagenomics data in the cloud and distributed systems, the development of machine learning and deep learning tools to ease the computational cost of detecting antibiotic resistance genes in metagenomic data, and the integration of crowdsourcing as a way to curate and validate antibiotic resistance genes.
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10

Schildt, Alexandra, and Jenny Luo. "Tools and Methods for Companies to Build Transparent and Fair Machine Learning Systems." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279659.

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Анотація:
AI has quickly grown from being a vast concept to an emerging technology that many companies are looking to integrate into their businesses, generally considered an ongoing “revolution” transforming science and society altogether. Researchers and organizations agree that AI and the recent rapid developments in machine learning carry huge potential benefits. At the same time, there is an increasing worry that ethical challenges are not being addressed in the design and implementation of AI systems. As a result, AI has sparked a debate about what principles and values should guide its development and use. However, there is a lack of consensus about what values and principles should guide the development, as well as what practical tools should be used to translate such principles into practice. Although researchers, organizations and authorities have proposed tools and strategies for working with ethical AI within organizations, there is a lack of a holistic perspective, tying together the tools and strategies proposed in ethical, technical and organizational discourses. The thesis aims to contribute with knowledge to bridge this gap by addressing the following purpose: to explore and present the different tools and methods companies and organizations should have in order to build machine learning applications in a fair and transparent manner. The study is of qualitative nature and data collection was conducted through a literature review and interviews with subject matter experts. In our findings, we present a number of tools and methods to increase fairness and transparency. Our findings also show that companies should work with a combination of tools and methods, both outside and inside the development process, as well as in different stages of the machine learning development process. Tools used outside the development process, such as ethical guidelines, appointed roles, workshops and trainings, have positive effects on alignment, engagement and knowledge while providing valuable opportunities for improvement. Furthermore, the findings suggest that it is crucial to translate high-level values into low-level requirements that are measurable and can be evaluated against. We propose a number of pre-model, in-model and post-model techniques that companies can and should implement in each other to increase fairness and transparency in their machine learning systems.
AI har snabbt vuxit från att vara ett vagt koncept till en ny teknik som många företag vill eller är i färd med att implementera. Forskare och organisationer är överens om att AI och utvecklingen inom maskininlärning har enorma potentiella fördelar. Samtidigt finns det en ökande oro för att utformningen och implementeringen av AI-system inte tar de etiska riskerna i beaktning. Detta har triggat en debatt kring vilka principer och värderingar som bör vägleda AI i dess utveckling och användning. Det saknas enighet kring vilka värderingar och principer som bör vägleda AI-utvecklingen, men också kring vilka praktiska verktyg som skall användas för att implementera dessa principer i praktiken. Trots att forskare, organisationer och myndigheter har föreslagit verktyg och strategier för att arbeta med etiskt AI inom organisationer, saknas ett helhetsperspektiv som binder samman de verktyg och strategier som föreslås i etiska, tekniska och organisatoriska diskurser. Rapporten syftar till överbrygga detta gap med följande syfte: att utforska och presentera olika verktyg och metoder som företag och organisationer bör ha för att bygga maskininlärningsapplikationer på ett rättvist och transparent sätt. Studien är av kvalitativ karaktär och datainsamlingen genomfördes genom en litteraturstudie och intervjuer med ämnesexperter från forskning och näringsliv. I våra resultat presenteras ett antal verktyg och metoder för att öka rättvisa och transparens i maskininlärningssystem. Våra resultat visar också att företag bör arbeta med en kombination av verktyg och metoder, både utanför och inuti utvecklingsprocessen men också i olika stadier i utvecklingsprocessen. Verktyg utanför utvecklingsprocessen så som etiska riktlinjer, utsedda roller, workshops och utbildningar har positiva effekter på engagemang och kunskap samtidigt som de ger värdefulla möjligheter till förbättringar. Dessutom indikerar resultaten att det är kritiskt att principer på hög nivå översätts till mätbara kravspecifikationer. Vi föreslår ett antal verktyg i pre-model, in-model och post-model som företag och organisationer kan implementera för att öka rättvisa och transparens i sina maskininlärningssystem.
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Книги з теми "Machine learning tools"

1

Khosrowpour, Mehdi, and Information Resources Management Association. Machine learning: Concepts, methodologies, tools and applications. Hershey, PA: Information Science Reference, 2012.

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Learning computer numerical control. Albany, NY: Delmar Publishers, 1992.

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Cost-sensitive machine learning. Boca Raton, FL: CRC Press, 2012.

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Eibe, Frank, and Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.

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Castiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.

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Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.

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Pardalos, Panos M., Stamatina Th Rassia, and Arsenios Tsokas, eds. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.

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Witten, I. H. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, Calif: Morgan Kaufmann, 2000.

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Srinivasa, K. G., G. M. Siddesh, and S. R. Manisekhar, eds. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5.

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National Institute of Standards and Technology (U.S.), ed. Manufacturing technology learning modules: Sharing resources for school outreach. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Частини книг з теми "Machine learning tools"

1

Geetha, T. V., and S. Sendhilkumar. "Machine Learning: Tools and Software." In Machine Learning, 103–25. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-5.

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Sekar, Maris. "Storytelling Tools." In Machine Learning for Auditors, 173–79. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8051-5_18.

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Alvo, Mayer. "Tools for Machine Learning." In Statistical Inference and Machine Learning for Big Data, 277–327. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06784-6_11.

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Vasudevan, Shriram K., Nitin Vamsi Dantu, Sini Raj Pulari, and T. S. Murugesh. "Tools and Pre-requisites." In Machine Learning with oneAPI, 56–75. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003393122-5.

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Vasudevan, Shriram K., Nitin Vamsi Dantu, Sini Raj Pulari, and T. S. Murugesh. "More Intel Tools for Enhanced Development Experience." In Machine Learning with oneAPI, 181–92. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003393122-12.

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Scutari, Marco, and Mauro Malvestio. "Tools for Developing Pipelines." In The Pragmatic Programmer for Machine Learning, 247–62. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429292835-10.

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Li, Xuekui, Lei Chen, Yi Shi, and Ping Cui. "Learning Parameter Analysis for Machine Reading Comprehension." In Simulation Tools and Techniques, 485–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72792-5_39.

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Etaati, Leila. "Overview of Microsoft Machine Learning Tools." In Machine Learning with Microsoft Technologies, 355–58. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3658-1_20.

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Smith, Einar. "Scientific Machine Learning with PyTorch." In Introduction to the Tools of Scientific Computing, 359–410. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16972-4_16.

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Etaati, Leila. "Deep Learning Tools with Cognitive Toolkit (CNTK)." In Machine Learning with Microsoft Technologies, 287–302. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3658-1_17.

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Тези доповідей конференцій з теми "Machine learning tools"

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Raboudi, Khaoula, and Abdelkader Ben Saci. "Machine learning for optimized buildings morphosis." In DTUC '20: Digital Tools & Uses Congress. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3423603.3424057.

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Kaytan, Mustafa, and Ibrahim Berkan Aydilek. "A review on machine learning tools." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090257.

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Owen, Steven. "Working with CUBIT's Machine Learning Tools." In Proposed for presentation at the Cubit 200 virtual class held October 19-20, 2021 in online, . US DOE, 2021. http://dx.doi.org/10.2172/1892152.

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Agarwal, Namita, and Saikat Das. "Interpretable Machine Learning Tools: A Survey." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308260.

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Abid, Saad Bin, Vishal Mahajan, and Levi Lucio. "Machine Learning for Learnability of MDD tools." In The 31st International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2019. http://dx.doi.org/10.18293/seke2019-050.

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TAGLIAFERRI, ROBERTO, FRANCESCO IORIO, FRANCESCO NAPOLITANO, GIANCARLO RAICONI, and GENNARO MIELE. "INTERACTIVE MACHINE LEARNING TOOLS FOR DATA ANALYSIS." In Proceedings of the 6th International Workshop on Data Analysis in Astronomy “Livio Scarsi”. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812779458_0030.

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Ramírez-Amaro, K., and J. C. Chimal-Eguía. "Machine Learning Tools to Time Series Forecasting." In 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session MICAI. IEEE, 2007. http://dx.doi.org/10.1109/micai.2007.42.

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Obulesu, O., M. Mahendra, and M. ThrilokReddy. "Machine Learning Techniques and Tools: A Survey." In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2018. http://dx.doi.org/10.1109/icirca.2018.8597302.

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Sworna, Zarrin Tasnim, Anjitha Sreekumar, Chadni Islam, and Muhammad Ali Babar. "Security Tools’ API Recommendation Using Machine Learning." In 18th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011708300003464.

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Klumbytė, Goda, Claude Draude, and Alex Taylor. "Critical Tools for Machine Learning: Situating, Figuring, Diffracting, Fabulating Machine Learning Systems Design." In CHItaly '21: 14th Biannual Conference of the Italian SIGCHI Chapter. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3464385.3467475.

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Звіти організацій з теми "Machine learning tools"

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Harris, Philip. Physics Community Needs, Tools, and Resources for Machine Learning. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1873720.

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Teodoridis, Florenta, Jino Lu, and Jeffrey Furman. Mapping the Knowledge Space: Exploiting Unassisted Machine Learning Tools. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30603.

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Cary, Dakota, and Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, November 2020. http://dx.doi.org/10.51593/2020ca003.

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Анотація:
Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.
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Cao, Larry. I. Machine Learning and Data Science Applications in Investments. CFA Institute Research Foundation, March 2023. http://dx.doi.org/10.56227/23.1.7.

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Experts from Robeco, Goldman Sachs Global Investment Research, and Neuberger Berman detail AI and big data tools to augment your existing investment processes. Understand the landscape and choose approaches fitting your strategic priorities.
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Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20190042.

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This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.
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Rduner, Tim G. J., and Helen Toner. Key Concepts in AI Safety: Specification in Machine Learning. Center for Security and Emerging Technology, December 2021. http://dx.doi.org/10.51593/20210031.

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Анотація:
This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.
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Lu, Michael, and Tina Gibson. Development of Predictive Tools for Anti-Cancer Peptide Candidates using Generative Machine Learning Models. Journal of Young Investigators, May 2021. http://dx.doi.org/10.22186/jyi.39.5.60-64.

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8

Ma, Zhegang, Han Bao, Sai Zhang, Min Xian, and Andrea Mack. Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1847070.

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Gates, Allison, Samantha Guitard, Jennifer Pillay, Sarah A. Elliott, Michele P. Dyson, Amanda S. Newton, and Lisa Hartling. Performance and Usability of Machine Learning for Screening in Systematic Reviews: A Comparative Evaluation of Three Tools. Agency for Healthcare Research and Quality (AHRQ), November 2019. http://dx.doi.org/10.23970/ahrqepcmethmachineperformance.

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Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Robustness and Adversarial Examples. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20190041.

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Анотація:
This paper is the second installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces adversarial examples, a major challenge to robustness in modern machine learning systems.
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