Thematische Bibliographien / ML algorithm

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Auswahl der wissenschaftlichen Literatur zum Thema „ML algorithm“

Autor: Grafiati

Veröffentlicht am 29. Juni 2021

Zuletzt aktualisiert am 11. September 2023

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Zeitschriftenartikel zum Thema "ML algorithm"

Sharaff, Aakanksha, und Naresh Kumar Nagwani. „ML-EC2“. International Journal of Web-Based Learning and Teaching Technologies 15, Nr. 2 (April 2020): 19–33. http://dx.doi.org/10.4018/ijwltt.2020040102.

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A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

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JAY, C. B., G. BELLÈ und E. MOGGI. „Functorial ML“. Journal of Functional Programming 8, Nr. 6 (November 1998): 573–619. http://dx.doi.org/10.1017/s0956796898003128.

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We present an extension of the Hindley–Milner type system that supports a generous class of type constructors called functors, and provide a parametrically polymorphic algorithm for their mapping, i.e. for applying a function to each datum appearing in a value of constructed type. The algorithm comes from shape theory, which provides a uniform method for locating data within a shape. The resulting system is Church–Rosser and strongly normalizing, and supports type inference. Several different semantics are possible, which affects the choice of constants in the language, and are used to illustrate the relationship to polytypic programming.

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Nutipalli, Preeti. „Model Construction Using ML for Prediction of Student Placement“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 6 (30.06.2022): 2213–19. http://dx.doi.org/10.22214/ijraset.2022.44273.

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Abstract: “Model construction using ML for prediction of student placement” aims to predict the placement of a student using various performance metrics on the Machine Learning algorithms. Early prediction makes the institutional growth as well as the student to get placed. It helps the student to prepare all the company requirements at early stage and monitors the student performance. Existed work was done on the algorithms like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes. In the proposed work to predict the student placement considered dataset and applied data preprocessing to make the data easier to train the model for prediction using Decision Tree (DT) and XG Boost along with the existing algorithms. Accuracies are calculated using different performance metrics like Accuracy and F1-score, Precision, Recall. The algorithm that worked with the best accuracy is SVM with 91%, and the LR and DT algorithms got 88% accuracy whereas Naïve Bayes got 86% and then the XG Boost stood last with an accuracy of 84%. We are able to make a decision which algorithm is better than other algorithms. Higher accuracy algorithm is mostly preferred to predict the student performance.

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Garduno, Edgar, und Gabor T. Herman. „Superiorization of the ML-EM Algorithm“. IEEE Transactions on Nuclear Science 61, Nr. 1 (Februar 2014): 162–72. http://dx.doi.org/10.1109/tns.2013.2283529.

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Wang, Peng, Weijia He, Fan Guo, Xuefang He und Jiajun Huang. „An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array“. AIMS Mathematics 7, Nr. 4 (2022): 5563–93. http://dx.doi.org/10.3934/math.2022308.

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<abstract><p>The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.</p></abstract>

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Choubey, Shubham. „Diabetes Prediction Using ML“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 6 (30.06.2023): 4209–12. http://dx.doi.org/10.22214/ijraset.2023.54415.

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Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.

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Chen, Haihua, Shibao Li, Jianhang Liu, Yiqing Zhou und Masakiyo Suzuki. „Efficient AM Algorithms for Stochastic ML Estimation of DOA“. International Journal of Antennas and Propagation 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4926496.

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The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.

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Lee, J. H., H. J. Kwon und Y. K. Jin. „Numerically Efficient Implementation of JADE ML Algorithm“. Journal of Electromagnetic Waves and Applications 22, Nr. 11-12 (Januar 2008): 1693–704. http://dx.doi.org/10.1163/156939308786390256.

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Mansour, Mohammad M. „A Near-ML MIMO Subspace Detection Algorithm“. IEEE Signal Processing Letters 22, Nr. 4 (April 2015): 408–12. http://dx.doi.org/10.1109/lsp.2014.2357991.

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Pachouly, Shikha. „Student General Performance Prediction Using ML Algorithm“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 5 (31.05.2023): 7201–9. http://dx.doi.org/10.22214/ijraset.2023.53398.

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Abstract: We will start to examine and categorise the data once we have obtained the required information using our surveys. This plan involves locating data patterns and trends that can be leveraged to create powerful machine learning models. The accuracy, recall, and precision of these models will then be tested to ensure that they are reliable and effective. They will then be developed using a variety of techniques. This would require meticulous attention to detail when analysing datasets as well as a thorough understanding of the advantages and disadvantages of various machine learning techniques. Our ultimate goal is to develop machine learning (ML) models that accurately predict events using the data we have collected, providing insightful knowledge about our area of interest. We are confident that we can achieve this goal and significantly advance the fields of machine learning and data analysis by carefully planning and carrying out our work. For example, this technique could be used to predict how well students in a particular school district will perform academically. By collecting information on factors such as their emotional state and extracurricular activities in addition to more conventional data like grades and test results, we could develop ML models that precisely forecast which children are at risk of falling behind and how to support them. Which will enable us to identify the variables influencing students' success or contributing to their poor performance. This could make it easier for teachers to provide more individualised support for students and improve their overall academic performance

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Dissertationen zum Thema "ML algorithm"

Krüger, Franz David, und Mohamad Nabeel. „Hyperparameter Tuning Using Genetic Algorithms : A study of genetic algorithms impact and performance for optimization of ML algorithms“. Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42404.

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Maskininlärning har blivit allt vanligare inom näringslivet. Informationsinsamling med Data mining (DM) har expanderats och DM-utövare använder en mängd tumregler för att effektivisera tillvägagångssättet genom att undvika en anständig tid att ställa in hyperparametrarna för en given ML-algoritm för nå bästa träffsäkerhet. Förslaget i denna rapport är att införa ett tillvägagångssätt som systematiskt optimerar ML-algoritmerna med hjälp av genetiska algoritmer (GA), utvärderar om och hur modellen ska konstrueras för att hitta globala lösningar för en specifik datamängd. Genom att implementera genetiska algoritmer på två utvalda ML-algoritmer, K-nearest neighbors och Random forest, med två numeriska datamängder, Iris-datauppsättning och Wisconsin-bröstcancerdatamängd. Modellen utvärderas med träffsäkerhet och beräkningstid som sedan jämförs med sökmetoden exhaustive search. Resultatet har visat att GA fungerar bra för att hitta bra träffsäkerhetspoäng på en rimlig tid. Det finns vissa begränsningar eftersom parameterns betydelse varierar för olika ML-algoritmer.
As machine learning (ML) is being more and more frequent in the business world, information gathering through Data mining (DM) is on the rise, and DM-practitioners are generally using several thumb rules to avoid having to spend a decent amount of time to tune the hyperparameters (parameters that control the learning process) of an ML algorithm to gain a high accuracy score. The proposal in this report is to conduct an approach that systematically optimizes the ML algorithms using genetic algorithms (GA) and to evaluate if and how the model should be constructed to find global solutions for a specific data set. By implementing a GA approach on two ML-algorithms, K-nearest neighbors, and Random Forest, on two numerical data sets, Iris data set and Wisconsin breast cancer data set, the model is evaluated by its accuracy scores as well as the computational time which then is compared towards a search method, specifically exhaustive search. The results have shown that it is assumed that GA works well in finding great accuracy scores in a reasonable amount of time. There are some limitations as the parameter’s significance towards an ML algorithm may vary.

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Mohammad, Maruf H. „Blind Acquisition of Short Burst with Per-Survivor Processing (PSP)“. Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/46193.

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This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the performance of PSP degrades when certain sequences are transmitted. In this thesis, the blind acquisition characteristics of PSP are discussed. The problematic sequences for any joint ML data and channel estimator are discussed from an analytic perspective. Based on the theory of indistinguishable sequences, modifications to conventional PSP are suggested that improve its acquisition performance significantly. The effect of tree search and list-based algorithms on PSP is also discussed. Proposed improvement techniques are compared for different channels. For higher order channels, complexity issues dominate the choice of algorithms, so PSP with state reduction techniques is considered. Typical misacquisition conditions, transients, and initialization issues are reported.
Master of Science

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Deyneka, Alexander. „Metody ekvalizace v digitálních komunikačních systémech“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218963.

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Tato práce je psaná v angličtině a je zaměřená na problematiku ekvalizace v digitálních komunikačních systémech. Teoretická část zahrnuje stručné pozorování různých způsobů návrhu ekvalizérů. Praktická část se zabývá implementací nejčastěji používaných ekvalizérů a s jejich adaptačními algoritmy. Cílem praktické části je porovnat jejich charakteristiky a odhalit činitele, které ovlivňují kvalitu ekvalizace. V rámci problematiky ekvalizace jsou prozkoumány tři typy ekvalizérů. Lineární ekvalizér, ekvalizér se zpětnou vazbou a ML (Maximum likelihood) ekvalizér. Každý ekvalizér byl testován na modelu, který simuloval reálnou přenosovou soustavu s komplexním zkreslením, která je složena z útlumu, mezisymbolové interference a aditivního šumu. Na základě implenentace byli určeny charakteristiky ekvalizérů a stanoveno že optimální výkon má ML ekvalizér. Adaptační algoritmy hrají významnou roli ve výkonnosti všech zmíněných ekvalizérů. V práci je nastudována skupina stochastických algoritmů jako algoritmus nejmenších čtverců(LMS), Normalizovaný LMS, Variable step-size LMS a algoritmus RLS jako zástupce deterministického přístupu. Bylo zjištěno, že RLS konverguje mnohem rychleji, než algoritmy založené na LMS. Byly nastudovány činitele, které ovlivnili výkon popisovaných algoritmů. Jedním z důležitých činitelů, který ovlivňuje rychlost konvergence a stabilitu algoritmů LMS je parametr velikosti kroku. Dalším velmi důležitým faktorem je výběr trénovací sekvence. Bylo zjištěno, že velkou nevýhodou algoritmů založených na LMS v porovnání s RLS algoritmy je, že kvalita ekvalizace je velmi závislá na spektrální výkonové hustotě a a trénovací sekvenci.

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Zhang, Dan [Verfasser]. „Iterative algorithms in achieving near-ML decoding performance in concatenated coding systems / Dan Zhang“. Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1048607224/34.

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Santos, Helton Saulo Bezerra dos. „Essays on Birnbaum-Saunders models“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/87375.

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Nessa tese apresentamos três diferentes aplicações dos modelos Birnbaum-Saunders. No capítulo 2 introduzimos um novo método por função-núcleo não-paramétrico para a estimação de densidades assimétricas, baseado nas distribuições Birnbaum-Saunders generalizadas assimétricas. Funções-núcleo baseadas nessas distribuições têm a vantagem de fornecer flexibilidade nos níveis de assimetria e curtose. Em adição, os estimadores da densidade por função-núcleo Birnbaum-Saunders gene-ralizadas assimétricas são livres de viés na fronteira e alcançam a taxa ótima de convergência para o erro quadrático integrado médio dos estimadores por função-núcleo-assimétricas-não-negativos da densidade. Realizamos uma análise de dados consistindo de duas partes. Primeiro, conduzimos uma simulação de Monte Carlo para avaliar o desempenho do método proposto. Segundo, usamos esse método para estimar a densidade de três dados reais da concentração de poluentes atmosféricos. Os resultados numéricos favorecem os estimadores não-paramétricos propostos. No capítulo 3 propomos uma nova família de modelos autorregressivos de duração condicional baseados nas distribuições misturas de escala Birnbaum-Saunders (SBS). A distribuição Birnbaum-Saunders (BS) é um modelo que tem recebido considerável atenção recentemente devido às suas boas propriedades. Uma extensão dessa distribuição é a classe de distribuições SBS, a qual (i) herda várias das boas propriedades da distribuição BS, (ii) permite a estimação de máxima verossimilhança em uma forma eficiente usando o algoritmo EM, e (iii) possibilita a obtenção de um procedimento de estimação robusta, entre outras propriedades. O modelo autorregressivo de duração condicional é a família primária de modelos para analisar dados de duração de transações de alta frequência. A metodologia estudada aqui inclui estimação dos parâmetros pelo algoritmo EM, inferência para esses parâmetros, modelo preditivo e uma análise residual. Realizamos simulações de Monte Carlo para avaliar o desempenho da metodologia proposta. Ainda, avalia-mos a utilidade prática dessa metodologia usando dados reais de transações financeiras da bolsa de valores de Nova Iorque. O capítulo 4 trata de índices de capacidade do processo (PCIs), os quais são ferramentas utilizadas pelas empresas para determinar a qualidade de um produto e avaliar o desempenho de seus processos de produção. Estes índices foram desenvolvidos para processos cuja característica de qualidade tem uma distribuição normal. Na prática, muitas destas ca-racterísticas não seguem esta distribuição. Nesse caso, os PCIs devem ser modificados considerando a não-normalidade. O uso de PCIs não-modificados podemlevar a resultados inadequados. De maneira a estabelecer políticas de qualidade para resolver essa inadequação, transformação dos dados tem sido proposta, bem como o uso de quantis de distribuições não-normais. Um distribuição não-normal assimétrica o qual tem tornado muito popular em tempos recentes é a distribuição Birnbaum-Saunders (BS). Propomos, desenvolvemos, implementamos e aplicamos uma metodologia baseada em PCIs para a distribuição BS. Além disso, realizamos um estudo de simulação para avaliar o desempenho da metodologia proposta. Essa metodologia foi implementada usando o software estatístico chamado R. Aplicamos essa metodologia para um conjunto de dados reais de maneira a ilustrar a sua flexibilidade e potencialidade.
In this thesis, we present three different applications of Birnbaum-Saunders models. In Chapter 2, we introduce a new nonparametric kernel method for estimating asymmetric densities based on generalized skew-Birnbaum-Saunders distributions. Kernels based on these distributions have the advantage of providing flexibility in the asymmetry and kurtosis levels. In addition, the generalized skew-Birnbaum-Saunders kernel density estimators are boundary bias free and achieve the optimal rate of convergence for the mean integrated squared error of the nonnegative asymmetric kernel density estimators. We carry out a data analysis consisting of two parts. First, we conduct a Monte Carlo simulation study for evaluating the performance of the proposed method. Second, we use this method for estimating the density of three real air pollutant concentration data sets, whose numerical results favor the proposed nonparametric estimators. In Chapter 3, we propose a new family of autoregressive conditional duration models based on scale-mixture Birnbaum-Saunders (SBS) distributions. The Birnbaum-Saunders (BS) distribution is a model that has received considerable attention recently due to its good properties. An extension of this distribution is the class of SBS distributions, which allows (i) several of its good properties to be inherited; (ii) maximum likelihood estimation to be efficiently formulated via the EM algorithm; (iii) a robust estimation procedure to be obtained; among other properties. The autoregressive conditional duration model is the primary family of models to analyze high-frequency financial transaction data. This methodology includes parameter estimation by the EM algorithm, inference for these parameters, the predictive model and a residual analysis. We carry out a Monte Carlo simulation study to evaluate the performance of the proposed methodology. In addition, we assess the practical usefulness of this methodology by using real data of financial transactions from the New York stock exchange. Chapter 4 deals with process capability indices (PCIs), which are tools widely used by companies to determine the quality of a product and the performance of their production processes. These indices were developed for processes whose quality characteristic has a normal distribution. In practice, many of these characteristics do not follow this distribution. In such a case, the PCIs must be modified considering the non-normality. The use of unmodified PCIs can lead to inadequacy results. In order to establish quality policies to solve this inadequacy, data transformation has been proposed, as well as the use of quantiles from non-normal distributions. An asymmetric non-normal distribution which has become very popular in recent times is the Birnbaum-Saunders (BS) distribution. We propose, develop, implement and apply a methodology based on PCIs for the BS distribution. Furthermore, we carry out a simulation study to evaluate the performance of the proposed methodology. This methodology has been implemented in a noncommercial and open source statistical software called R. We apply this methodology to a real data set to illustrate its flexibility and potentiality.

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FECCHIO, PIETRO. „High-precision measurement of the hypertriton lifetime and Λ-separation energy exploiting ML algorithms with ALICE at the LHC“. Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968462.

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Garg, Anushka. „Comparing Machine Learning Algorithms and Feature Selection Techniques to Predict Undesired Behavior in Business Processesand Study of Auto ML Frameworks“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285559.

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In recent years, the scope of Machine Learning algorithms and its techniques are taking up a notch in every industry (for example, recommendation systems, user behavior analytics, financial applications and many more). In practice, they play an important role in utilizing the power of the vast data we currently generate on a daily basis in our digital world.In this study, we present a comprehensive comparison of different supervised Machine Learning algorithms and feature selection techniques to build a best predictive model as an output. Thus, this predictive model helps companies predict unwanted behavior in their business processes. In addition, we have researched for the automation of all the steps involved (from understanding data to implementing models) in the complete Machine Learning Pipeline, also known as AutoML, and provide a comprehensive survey of the various frameworks introduced in this domain. These frameworks were introduced to solve the problem of CASH (combined algorithm selection and Hyper- parameter optimization), which is basically automation of various pipelines involved in the process of building a Machine Learning predictive model.
Under de senaste åren har omfattningen av maskininlärnings algoritmer och tekniker tagit ett steg i alla branscher (till exempel rekommendationssystem, beteendeanalyser av användare, finansiella applikationer och många fler). I praktiken spelar de en viktig roll för att utnyttja kraften av den enorma mängd data vi för närvarande genererar dagligen i vår digitala värld.I den här studien presenterar vi en omfattande jämförelse av olika övervakade maskininlärnings algoritmer och funktionsvalstekniker för att bygga en bästa förutsägbar modell som en utgång. Således hjälper denna förutsägbara modell företag att förutsäga oönskat beteende i sina affärsprocesser. Dessutom har vi undersökt automatiseringen av alla inblandade steg (från att förstå data till implementeringsmodeller) i den fullständiga maskininlärning rörledningen, även känd som AutoML, och tillhandahåller en omfattande undersökning av de olika ramarna som introducerats i denna domän. Dessa ramar introducerades för att lösa problemet med CASH (kombinerat algoritmval och optimering av Hyper-parameter), vilket i grunden är automatisering av olika rörledningar som är inblandade i processen att bygga en förutsägbar modell för maskininlärning.

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Protzenko, Jonathan. „Mezzo : a typed language for safe effectful concurrent programs“. Paris 7, 2014. http://www.theses.fr/2014PA077159.

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Cette thèse décrit comment obtenir de plus fortes garanties de sûreté pour les programmes en utilisant Mezzo, un langage de programmation inspiré par ML, et muni d'un système de types novateur. Les programmes écrits en Mezzo bénéficient de plus fortes garanties, com¬parés à des programmes équivalents écrits dans un dialecte de ML: absence de séquencements critiques (« race conditions »), suivi des changements d'états au travers du système de types, et une notion de possession qui facilite le raisonnement modulaire et la compréhension des programmes. Mezzo n'est pas la premier langage à s'attaquer à cet objectif louable : une première partie s'efforce donc de situer Mezzo dans son contexte, en présentant des travaux emblématiques de la recherche en langages de programmation, travaux qui ont constitué des sources d'inspiration ou ont servi de points de comparaison. Une seconde partie présente le langage. Tout d'abord, au travers d'une riche palette d'exemples, qui permettent d'illustrer les fonctionnalités du lan¬gage ainsi que les gains de sûreté qui en découlent. Puis, dans une partie suivante, de manière formelle, en détaillant les différentes règles qui gouvernent le système de types de Mezzo. Mezzo n'existe pas seulement sur le papier : une dernière partie décrit la manière dont le lan¬gage est implémenté, en formalisant les algorithmes utilisés dans le typeur et en détaillant les techniques utilisées pour déterminer la validité d'un programme
The present dissertation argues that better programming languages can be designed and implemented, so as to provide greater safety and reliability for computer programs. I sustain my daims through the example of Mezzo, a programming language in the tradition of ML, which I co-designed and implemented. Programs written in Mezzo enjoy stronger properties than programs written in traditional ML languages: they are data-race free; state changes can be tracked by the type system; a central notion of ownership facilitates modular reasoning. Mezzo is not the first attempt at designing a better programming language; hence, a first part strives to position Mezzo relative to other works in the literature. I present landmark results in the field, which served either as sources of inspiration or points of comparison. The subsequent part is about the design of the Mezzo language. Using a variety of examples, I illustrate the language features as well as the safety gains that one obtains by writing their programs in Mezzo. In a subsequent part, I formalize the semantics of the Mezzo language. Mezzo is not just a type system that lives on paper: the fmal part describes the implementation of a type-checker for Mezzo, by formalizing the algorithms that I designed and the various ways the type-checker ensures that a program is valid

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Tade, Foluwaso Olunkunle. „Receiver architectures for MIMO wireless communication systems based on V-BLAST and sphere decoding algorithms“. Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/6400.

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Modern day technology aspires to always progress. This progression leads to a lot of research in any significant area of improvement. There is a growing amount of end-users in the wireless spectrum which has led to a need for improved bandwidth usage and BER values. In other words, new technologies which would increase the capacity of wireless systems are proving to be a crucial point of research in these modern times. Different combinations of multiuser receivers are evaluated to determine performance under normal working conditions by comparing their BER performance charts. Multiple input, multiple output (MIMO) systems are incorporated into the system to utilise the increased capacity rates achievable using the MIMO configuration. The effect of MIMO on the technologies associated with modern day technological standards such as CDMA and OFDM have been investigated due to the significant capacity potentials these technologies normally exhibit in a single antenna scenario. An in-depth comparison is established before comparison is made with a conventional maximum likelihood (ML) detector. The complexity of the ML detector makes its realization evaluated in such a manner to achieve the same or near ML solution but with lower computational complexity. This was achieved using a proposed modification of the Schnorr-Euchner Sphere decoding algorithm (SE-SDA). The proposed sphere decoder (P-SD) adopts a modification of the radius utilised in the SE-SDA to obtain a near ML solution at a much lower complexity compared to the conventional ML decoder. The P-SD was configured to work in different MIMO antenna configurations. The need for the highest possible data rates from the available limited spectrum led to my research into the multi-user detection scenario and MIMO.

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PIROZZI, MICHELA. „Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms“. Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/272311.

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Con una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.
With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.

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Bücher zum Thema "ML algorithm"

Tiwari, Manoj Kumar, Madhu Ranjan Kumar, Rofin T. M. und Rony Mitra, Hrsg. Applications of Emerging Technologies and AI/ML Algorithms. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1019-9.

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Amilevičius, Darius, Andrius Utka, Aistė Meidutė und Jūratė Ruzaitė. DIGIRES COVID-19 ML Dataset v.1. Vytauto Didžiojo universitetas, 2023. http://dx.doi.org/10.7220/20.500.12259/252155.

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DIGIRES COVID-19 ML dataset v.1 is a tab-separated (.tsv) file prepared for training machine learning algorithms. The training dataset was compiled from various internet public Lithuanian media sources. It contains 351 records and has the following attributes: "Title": the title of a news article "Text": the text of the article "Label": a label that marks the article as 1: unreliable; 0: reliable 1) "unrealiable" marks articles, which were identified by professional fact checkers as fake news; 2) "reliable" marks trustworthy articles. Classes Labels Word tokens Reliable: 175 67902 Unreliable: 176 118747 Total 351 186649.

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(Editor), Emden R. Gansner, und John H. Reppy (Editor), Hrsg. The Standard ML Basis Library. Cambridge University Press, 2002.

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Capellman, Jarred. Hands-On Machine Learning with ML. NET: Getting Started with Microsoft ML. NET to Implement Popular Machine Learning Algorithms in C#. Packt Publishing, Limited, 2020.

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Lanham, Micheal. Learn Unity ML-Agents - Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games. Packt Publishing, 2018.

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Amunategui, Manuel. Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud. Apress, 2018.

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Knox, Jason. Machine Learning for Beginners: A Beginner's Guide to Start Out Your Journey with Data Science, Artificial Intelligence, ML and Its Algorithms, Deep Learning and Neural Networks from Scratch. Independently Published, 2019.

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Buchteile zum Thema "ML algorithm"

Mao, Xinyu, Shubo Ren und Haige Xiang. „Reduced ML-DFE Algorithm“. In Recent Advances in Computer Science and Information Engineering, 177–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25769-8_26.

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McAllester, David. „A Logical Algorithm for ML Type Inference“. In Rewriting Techniques and Applications, 436–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44881-0_31.

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Chen, Weijie, Daniel Krainak, Berkman Sahiner und Nicholas Petrick. „A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging“. In Machine Learning for Brain Disorders, 705–52. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_23.

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AbstractThis chapter presents a regulatory science perspective on the assessment of machine learning algorithms in diagnostic imaging applications. Most of the topics are generally applicable to many medical imaging applications, while brain disease-specific examples are provided when possible. The chapter begins with an overview of US FDA’s regulatory framework followed by assessment methodologies related to ML devices in medical imaging. Rationale, methods, and issues are discussed for the study design and data collection, the algorithm documentation, and the reference standard. Finally, study design and statistical analysis methods are overviewed for the assessment of standalone performance of ML algorithms as well as their impact on clinicians (i.e., reader studies). We believe that assessment methodologies and regulatory science play a critical role in fully realizing the great potential of ML in medical imaging, in facilitating ML device innovation, and in accelerating the translation of these technologies from bench to bedside to the benefit of patients.

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Kuriakose, Neenu, und Uma Devi. „MQTT Attack Detection Using AI and ML Algorithm“. In Pervasive Computing and Social Networking, 13–22. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_2.

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Preston, Lauren, und Shivashankar. „Sub-exponential ML Algorithm for Predicting Ground State Properties“. In Computational Science – ICCS 2023, 56–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36030-5_5.

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Jamshidian, Mortaza. „An EM Algorithm for ML Factor Analysis with Missing Data“. In Lecture Notes in Statistics, 247–58. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1842-5_13.

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Graniero, Paolo, und Marco Gärtler. „Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis“. In Machine Learning for Cyber Physical Systems, 53–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_6.

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AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant.

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Elsayed, Samir A. Mohamed, Sanguthevar Rajasekaran und Reda A. Ammar. „ML-DS: A Novel Deterministic Sampling Algorithm for Association Rules Mining“. In Advances in Data Mining. Applications and Theoretical Aspects, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31488-9_18.

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Pupo, Oscar Gabriel Reyes, Carlos Morell und Sebastián Ventura Soto. „ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning“. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 528–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41827-3_66.

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Moriya, Kentaro, und Takashi Nodera. „Breakdown-Free ML(k)BiCGStab Algorithm for Non-Hermitian Linear Systems“. In Computational Science and Its Applications – ICCSA 2005, 978–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424925_102.

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Konferenzberichte zum Thema "ML algorithm"

Kaur, Gurjit, Raaghav Raj Maiya und Ritvik Bharti. „Ml-Powered Cache Replacement Algorithm“. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT). IEEE, 2022. http://dx.doi.org/10.1109/i2ct54291.2022.9824712.

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Bhattacharyya, Santosh, Donald H. Szarowski, James N. Turner, Nathan J. O'Connor und Timothy J. Holmes. „ML-blind deconvolution algorithm: recent developments“. In Electronic Imaging: Science & Technology, herausgegeben von Carol J. Cogswell, Gordon S. Kino und Tony Wilson. SPIE, 1996. http://dx.doi.org/10.1117/12.237475.

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Kanatani, Kenichi, und Yasuyuki Sugaya. „Compact algorithm for strictly ML ellipse fitting“. In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761605.

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Kathiravan, M., K. Hari Priya, S. Sreesubha, A. Irumporai, V. Sukesh Kumar und Vishnu Vardhan Reddy. „ML Algorithm-Based Detection of Leaf Diseases“. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2022. http://dx.doi.org/10.1109/icssit53264.2022.9716430.

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Jiali Mao, Hongying Jin, Mingdong Li und Jia Li. „ML-KNN algorithm based on frequent item sets“. In 2012 First National Conference for Engineering Sciences (FNCES). IEEE, 2012. http://dx.doi.org/10.1109/nces.2012.6543910.

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Pei Jung Chung und Bohme. „Recursive EM algorithm for stochastic ML DOA estimation“. In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005325.

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Chung, Pei Jung, und Johann F. Bohme. „Recursive EM algorithm for stochastic ML DOA estimation“. In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745287.

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Wu, Luo, Liu An und Bin Liu. „An Iterative ML-based Carrier Frequency Estimation Algorithm“. In 2006 International Conference on Communication Technology. IEEE, 2006. http://dx.doi.org/10.1109/icct.2006.341700.

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Yachuan Bao und Baoguo Yu. „A MAI cancellation algorithm with near ML performance“. In 2015 IEEE International Conference on Communication Software and Networks (ICCSN). IEEE, 2015. http://dx.doi.org/10.1109/iccsn.2015.7296153.

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Seghouane, Abd-Krim. „An iterative projections algorithm for ML factor analysis“. In 2008 IEEE Workshop on Machine Learning for Signal Processing (MLSP) (Formerly known as NNSP). IEEE, 2008. http://dx.doi.org/10.1109/mlsp.2008.4685502.

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Berichte der Organisationen zum Thema "ML algorithm"

Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin und Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, Dezember 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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Gungor, Osman, Imad Al-Qadi und Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, Oktober 2021. http://dx.doi.org/10.36501/0197-9191/21-034.

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The Federal Aviation Administration instrumented four concrete slabs of a taxiway at the John F. Kennedy International Airport to collect pavement responses under aircraft and environmental loading. The study started with developing preprocessing scripts to organize, structure, and clean the collected data. As a result of the preprocessing step, the data became easier and more intuitive for pavement engineers and researchers to transform and process. After the data were cleaned and organized, they were used to develop two prediction models. The first prediction model employs a Bayesian calibration framework to estimate the unknown material parameters of the concrete pavement. Additionally, the posterior distributions resulting from the calibration process served as a sensitivity analysis by reporting the significance of each parameter for temperature distribution. The second prediction model utilized a machine-learning (ML) algorithm to predict pavement responses under aircraft and environmental loadings. The results demonstrated that ML can predict the responses with high accuracy at a low computational cost. This project highlighted the potential of using ML for future pavement design guidelines as more instrumentation data from future projects are collected to incorporate various material properties and pavement structures.

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Armenta, Mikaela Lea. Summit on HPC ML Algorithms and Human Systems Summary. Office of Scientific and Technical Information (OSTI), Oktober 2018. http://dx.doi.org/10.2172/1481533.

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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani und S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. The bounds of the area where earthquakes were located were between 55.5°N-60.0°N and 119.8°W-123.5°W. The earthquakes in the catalogue were initially detected by machine learning models, then reviewed by an analyst to confirm correct identification, and finally located using the Non-Linear Location (NonLinLoc) algorithm. Two distinct sub-areas within the bounds consider different periods to supplement what was not covered in previously published reports - the Northern Montney Trend (NMT) is covered from 2017 to 2020 while the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) is covered from 2019 to 2020. The two sub-areas are distinguished by the BC Oil & Gas Commission (BCOGC) due to differences in their geographic location and geology. The catalogue was produced by picking arrival phases on continuous seismic waveforms from 51 stations operated by various organizations in the region. A total of 17,908 events passed our quality control criteria and are included in the final catalogue. Comparably, the routine Canadian National Seismograph Network (CNSN) catalogue reports 207 seismic events - all events in the CNSN catalogue are present in our catalogue. Our catalogue benefits from the use of enhanced station coverage and improved methodology. The total number of events in our catalogue in 2017, 2018, 2019, and 2020 were 62, 47, 9579 and 8220, respectively. The first two years correspond to seismicity in the NMT where poor station coverage makes it difficult to detect small magnitude events. The magnitude of completeness within the KSMMA (ML = ~0.7) is significantly smaller than that obtained for the NMT (ML = ~1.4). The new catalogue is released with separate files for origins, arrivals, and magnitudes which can be joined using the unique ID assigned to each event.

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Lewis, Cannada, Clayton Hughes, Simon Hammond und Sivasankaran Rajamanickam. Using MLIR Framework for Codesign of ML Architectures Algorithms and Simulation Tools. Office of Scientific and Technical Information (OSTI), Januar 2021. http://dx.doi.org/10.2172/1764336.

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Irudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman und Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, Oktober 2004. http://dx.doi.org/10.32747/2004.7587221.bard.

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Rapid detection of pathogens and hazardous elements in fresh fruits and vegetables after harvest requires the use of advanced sensor technology at each step in the farm-to-consumer or farm-to-processing sequence. Fourier-transform infrared (FTIR) spectroscopy and the complementary Raman spectroscopy, an advanced optical technique based on light scattering will be investigated for rapid and on-site assessment of produce safety. Paving the way toward the development of this innovative methodology, specific original objectives were to (1) identify and distinguish different serotypes of Escherichia coli, Listeria monocytogenes, Salmonella typhimurium, and Bacillus cereus by FTIR and Raman spectroscopy, (2) develop spectroscopic fingerprint patterns and detection methodology for fungi such as Aspergillus, Rhizopus, Fusarium, and Penicillium (3) to validate a universal spectroscopic procedure to detect foodborne pathogens and non-pathogens in food systems. The original objectives proposed were very ambitious hence modifications were necessary to fit with the funding. Elaborate experiments were conducted for sensitivity, additionally, testing a wide range of pathogens (more than selected list proposed) was also necessary to demonstrate the robustness of the instruments, most crucially, algorithms for differentiating a specific organism of interest in mixed cultures was conceptualized and validated, and finally neural network and chemometric models were tested on a variety of applications. Food systems tested were apple juice and buffer systems. Pathogens tested include Enterococcus faecium, Salmonella enteritidis, Salmonella typhimurium, Bacillus cereus, Yersinia enterocolitis, Shigella boydii, Staphylococus aureus, Serratiamarcescens, Pseudomonas vulgaris, Vibrio cholerae, Hafniaalvei, Enterobacter cloacae, Enterobacter aerogenes, E. coli (O103, O55, O121, O30 and O26), Aspergillus niger (NRRL 326) and Fusarium verticilliodes (NRRL 13586), Saccharomyces cerevisiae (ATCC 24859), Lactobacillus casei (ATCC 11443), Erwinia carotovora pv. carotovora and Clavibacter michiganense. Sensitivity of the FTIR detection was 103CFU/ml and a clear differentiation was obtained between the different organisms both at the species as well as at the strain level for the tested pathogens. A very crucial step in the direction of analyzing mixed cultures was taken. The vector based algorithm was able to identify a target pathogen of interest in a mixture of up to three organisms. Efforts will be made to extend this to 10-12 key pathogens. The experience gained was very helpful in laying the foundations for extracting the true fingerprint of a specific pathogen irrespective of the background substrate. This is very crucial especially when experimenting with solid samples as well as complex food matrices. Spectroscopic techniques, especially FTIR and Raman methods are being pursued by agencies such as DARPA and Department of Defense to combat homeland security. Through the BARD US-3296-02 feasibility grant, the foundations for detection, sample handling, and the needed algorithms and models were developed. Successive efforts will be made in transferring the methodology to fruit surfaces and to other complex food matrices which can be accomplished with creative sampling methods and experimentation. Even a marginal success in this direction will result in a very significant breakthrough because FTIR and Raman methods, in spite of their limitations are still one of most rapid and nondestructive methods available. Continued interest and efforts in improving the components as well as the refinement of the procedures is bound to result in a significant breakthrough in sensor technology for food safety and biosecurity.

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Marra de Artiñano, Ignacio, Franco Riottini Depetris und Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, Juli 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.

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Chen, Z., S. E. Grasby, C. Deblonde und X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.

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The objective of this study is to search for features and indicators from the identified geothermal resource sweet spot in the south Mount Meager area that are applicable to other volcanic complexes in the Garibaldi Volcanic Belt. A Landsat 8 multi-spectral band dataset, for a total of 57 images ranging from visible through infrared to thermal infrared frequency channels and covering different years and seasons, were selected. Specific features that are indicative of high geothermal heat flux, fractured permeable zones, and groundwater circulation, the three key elements in exploring for geothermal resource, were extracted. The thermal infrared images from different seasons show occurrence of high temperature anomalies and their association with volcanic and intrusive bodies, and reveal the variation in location and intensity of the anomalies with time over four seasons, allowing inference of specific heat transform mechanisms. Automatically extracted linear features using AI/ML algorithms developed for computer vision from various frequency bands show various linear segment groups that are likely surface expression associated with local volcanic activities, regional deformation and slope failure. In conjunction with regional structural models and field observations, the anomalies and features from remotely sensed images were interpreted to provide new insights for improving our understanding of the Mount Meager geothermal system and its characteristics. After validation, the methods developed and indicators identified in this study can be applied to other volcanic complexes in the Garibaldi, or other volcanic belts for geothermal resource reconnaissance.

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