Готові списки джерел за темами / Artificial datasets

Добірка наукової літератури з теми "Artificial datasets"

Автор: Grafiati
Опубліковано: 22 червня 2024

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Статті в журналах з теми "Artificial datasets":

1

Serrano-Pérez, Jonathan, and L. Enrique Sucar. "Artificial datasets for hierarchical classification." Expert Systems with Applications 182 (November 2021): 115218. http://dx.doi.org/10.1016/j.eswa.2021.115218.

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2

Lychev, Andrey V. "Synthetic Data Generation for Data Envelopment Analysis." Data 8, no. 10 (September 27, 2023): 146. http://dx.doi.org/10.3390/data8100146.

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The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.
3

Petráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni, and Jozef Dudiak. "Benford’s Law in Electric Distribution Network." Mathematics 11, no. 18 (September 10, 2023): 3863. http://dx.doi.org/10.3390/math11183863.

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Benford’s law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford’s law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford’s law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford’s law can be applied and that it can extract the dataset’s artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention.
4

Dasari, Kishore Babu, and Nagaraju Devarakonda. "TCP/UDP-Based Exploitation DDoS Attacks Detection Using AI Classification Algorithms with Common Uncorrelated Feature Subset Selected by Pearson, Spearman and Kendall Correlation Methods." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 61–71. http://dx.doi.org/10.18280/ria.360107.

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The Distributed Denial of Service (DDoS) attack is a serious cyber security attack that attempts to disrupt the availability security principle of computer networks and information systems. It's critical to detect DDoS attacks quickly and accurately while using as less computing power as possible in order to minimize damage and cost efficient. This research proposes a fast and high-accuracy detection approach by using features selected by proposed method for Exploitation-based DDoS attacks. Experiments are carried out on the CICDDoS2019 datasets Syn flood, UDP flood, and UDP-Lag, as well as customized dataset. In addition, experiments were also conducted on a customized dataset that was constructed by combining three CICDDoS2019 datasets. Pearson, Spearman, and Kendall correlation techniques have been used for datasets to find un-correlated feature subsets. Then, among three un-correlated feature subsets, choose the common un-correlated features. On the datasets, classification techniques are applied to these common un-correlated features. This research used conventional classifiers Logistic regression, Decision tree, KNN, Naive Bayes, bagging classifier Random forest, boosting classifiers Ada boost, Gradient boost, and neural network-based classifier Multilayer perceptron. The performance of these classification algorithms was also evaluated in terms of accuracy, precision, recall, F1-score, specificity, log loss, execution time, and K-fold cross-validation. Finally, classification techniques were tested on a customized dataset with common features that were common in all of the dataset’s common un-correlated feature sets.
5

Kusetogullari, Huseyin, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, and Johan Hall. "ARDIS: a Swedish historical handwritten digit dataset." Neural Computing and Applications 32, no. 21 (March 29, 2019): 16505–18. http://dx.doi.org/10.1007/s00521-019-04163-3.

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Abstract This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of machine learning methods on several digit datasets is carried out. Additionally, correlation between ARDIS and existing digit datasets Modified National Institute of Standards and Technology (MNIST) and US Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset. Accordingly, convolutional neural network trained on MNIST and USPS and tested on ARDIS provide the highest accuracies $$58.80\%$$ 58.80 % and $$35.44\%$$ 35.44 % , respectively. Consequently, the results reveal that machine learning methods trained on existing datasets can have difficulties to recognize digits effectively on our dataset which proves that ARDIS dataset has unique characteristics. This dataset is publicly available for the research community to further advance handwritten digit recognition algorithms.
6

Morgan, Maria, Carla Blank, and Raed Seetan. "Plant disease prediction using classification algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 257. http://dx.doi.org/10.11591/ijai.v10.i1.pp257-264.

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<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>
7

Saul, Marcia, and Shahin Rostami. "Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets." Health Informatics Journal 28, no. 1 (January 2022): 146045822210871. http://dx.doi.org/10.1177/14604582221087109.

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Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset.
8

Gau, Michael-Lian, Huong-Yong Ting, Teck-Hock Toh, Pui-Ying Wong, Pei-Jun Woo, Su-Woan Wo, and Gek-Ling Tan. "Effectiveness of Using Artificial Intelligence for Early Child Development Screening." Green Intelligent Systems and Applications 3, no. 1 (May 9, 2023): 1–13. http://dx.doi.org/10.53623/gisa.v3i1.229.

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This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.
9

GHAFFARI, REZA, IOAN GROSU, DACIANA ILIESCU, EVOR HINES, and MARK LEESON. "DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM." International Journal of Bifurcation and Chaos 23, no. 05 (May 2013): 1330013. http://dx.doi.org/10.1142/s0218127413300139.

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In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers.
10

Pavlov, Nikolay A., Anna E. Andreychenko, Anton V. Vladzymyrskyy, Anush A. Revazyan, Yury S. Kirpichev, and Sergey P. Morozov. "Reference medical datasets (MosMedData) for independent external evaluation of algorithms based on artificial intelligence in diagnostics." Digital Diagnostics 2, no. 1 (April 30, 2021): 49–66. http://dx.doi.org/10.17816/dd60635.

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The article describes a novel approach to creating annotated medical datasets for testing artificial intelligence-based diagnostic solutions. Moreover, there are four stages of dataset formation described: planning, selection of initial data, marking and verification, and documentation. There are also examples of datasets created using the described methods. The technique is scalable and versatile, and it can be applied to other areas of medicine and healthcare that are being automated and developed using artificial intelligence and big data technologies.
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Статті в журналах Дисертації

Дисертації з теми "Artificial datasets":

1

Hilton, Erwin. "Visual datasets for artificial intelligence agents." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119553.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 41).
In this thesis, I designed and implemented two visual dataset generation tool frameworks. With these tools, I introduce procedurally generated new data to test VQA agents and other visual Al models on. The first tool is Spatial IQ Generative Dataset (SIQGD). This tool generates images based on the Raven's Progressive Matrices spatial IQ examination metric. The second tool is a collection of 3D models along with a Blender3D extension that renders images of the models from multiple viewpoints along with their depth maps.
by Erwin Hilton.
M. Eng.
2

Siddique, Nahian A. "PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.

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Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recog­nizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets. The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted from the available sample set through an iterative process of selective under-sampling. Then, the proposed artificial NN comprises of a kernel function optimizer to specifically enhance class boundaries for imbalanced datasets by conformally transforming the kernel functions. Finally, a single hidden layer weighted neural network structure is proposed to train models from the imbalanced dataset. The proposed NN architecture is derived to effectively classify any binary dataset with even very high imbalance ratio with appropriate parameter tuning and sufficient number of processing elements. Effectiveness of the proposed method is tested on accuracy based performance metrics, achieving close to and above 90%, with several imbalanced datasets of generic nature and compared with state of the art methods. The proposed model is also used for classification of a 25GB computed tomographic colonography database to test its applicability for big data. Also the effectiveness of under-sampling, kernel optimization for training of the NN model from the modified kernel gram matrix representing the imbalanced data distribution is analyzed experimentally. Computation time analysis shows the feasibility of the system for practical purposes. This report is concluded with discussion of prospect of the developed model and suggestion for further development works in this direction.
3

Lundberg, Oskar. "Decentralized machine learning on massive heterogeneous datasets : A thesis about vertical federated learning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444639.

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The need for a method to create a collaborative machine learning model which can utilize data from different clients, each with privacy constraints, has recently emerged. This is due to privacy restrictions, such as General Data Protection Regulation, together with the fact that machine learning models in general needs large size data to perform well. Google introduced federated learning in 2016 with the aim to address this problem. Federated learning can further be divided into horizontal and vertical federated learning, depending on how the data is structured at the different clients. Vertical federated learning is applicable when many different features is obtained on distributed computation nodes, where they can not be shared in between. The aim of this thesis is to identify the current state of the art methods in vertical federated learning, implement the most interesting ones and compare the results in order to draw conclusions of the benefits and drawbacks of the different methods. From the results of the experiments, a method called FedBCD shows very promising results where it achieves massive improvements in the number of communication rounds needed for convergence, at the cost of more computations at the clients. A comparison between synchronous and asynchronous approaches shows slightly better results for the synchronous approach in scenarios with no delay. Delay refers to slower performance in one of the workers, either due to lower computational resources or due to communication issues. In scenarios where an artificial delay is implemented, the asynchronous approach shows superior results due to its ability to continue training in the case of delays in one or several of the clients.
4

Horečný, Peter. "Metody segmentace obrazu s malými trénovacími množinami." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412996.

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The goal of this thesis was to propose an image segmentation method, which is capable of effective segmentation process with small datasets. Recently published ODE neural network was used for this method, because its features should provide better generalization in case of tasks with only small datasets available. The proposed ODE-UNet network was created by combining UNet architecture with ODE neural network, while using benefits of both networks. ODE-UNet reached following results on ISBI dataset: Rand: 0,950272 and Info: 0,978061. These results are better than the ones received from UNet model, which was also tested in this thesis, but it has been proven that state of the art can not be outperformed using ODE neural networks. However, the advantages of ODE neural network over tested UNet architecture and other methods were confirmed, and there is still a room for improvement by extending this method.
5

Woods, Brent J. "Computer-Aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270.

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6

Yasarer, Hakan. "Decision making in engineering prediction systems." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16231.

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Анотація:
Doctor of Philosophy
Department of Civil Engineering
Yacoub M. Najjar
Access to databases after the digital revolutions has become easier because large databases are progressively available. Knowledge discovery in these databases via intelligent data analysis technology is a relatively young and interdisciplinary field. In engineering applications, there is a demand for turning low-level data-based knowledge into a high-level type knowledge via the use of various data analysis methods. The main reason for this demand is that collecting and analyzing databases can be expensive and time consuming. In cases where experimental or empirical data are already available, prediction models can be used to characterize the desired engineering phenomena and/or eliminate unnecessary future experiments and their associated costs. Phenomena characterization, based on available databases, has been utilized via Artificial Neural Networks (ANNs) for more than two decades. However, there is a need to introduce new paradigms to improve the reliability of the available ANN models and optimize their predictions through a hybrid decision system. In this study, a new set of ANN modeling approaches/paradigms along with a new method to tackle partially missing data (Query method) are introduced for this purpose. The potential use of these methods via a hybrid decision making system is examined by utilizing seven available databases which are obtained from civil engineering applications. Overall, the new proposed approaches have shown notable prediction accuracy improvements on the seven databases in terms of quantified statistical accuracy measures. The proposed new methods are capable in effectively characterizing the general behavior of a specific engineering/scientific phenomenon and can be collectively used to optimize predictions with a reasonable degree of accuracy. The utilization of the proposed hybrid decision making system (HDMS) via an Excel-based environment can easily be utilized by the end user, to any available data-rich database, without the need for any excessive type of training.
7

Gusarov, Nikita. "Performances des modèles économétriques et de Machine Learning pour l’étude économique des choix discrets de consommation." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALE001.

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Cette thèse est une étude interdisciplinaire de la modélisation discrète des choix, abordant à la fois les techniques d'économétrie et d'apprentissage automatique (ML) appliquées à la modélisation des choix individuels de consommation. La problématique découle de points de contact insuffisants entre les utilisateurs (économistes et ingénieurs) et les analystes des données, qui poursuivent différents objectifs, bien qu'ils utilisent des techniques similaires. Pour combler cet écart interdisciplinaire, ce travail propose un framework unifié pour l'analyse des performances du modèle. Il facilite la comparaison des techniques d'analyse des données sous différentes hypothèses et transformations.Le framework conçu convient à une variété de modèles économétriques et ML. Il aborde la tâche de comparaison des performances du point de vue de la procédure de recherche, incorporant toutes les étapes affectant potentiellement les perceptions des performances. Pour démontrer les capacités du framework, nous proposons une série de 3 études appliquées. Dans ces études, la performance du modèle est explorée face aux changements de: (1) la taille et l'équilibre de l'échantillon, résultant de la collecte de données; (2) les changements de la structure des préférences au sein de la population, reflétant des hypothèses comportementales incorrectes; et (3) la sélection du modèle, directement liée à la perception des performances
This thesis is a cross-disciplinary study of discrete choice modeling, addressing both econometrics and machine learning (ML) techniques applied to individual choice modeling. The problematic arises from insufficient points of contact among users (economists and engineers) and data scientists, who pursue different objectives, although using similar techniques. To bridge this interdisciplinary gap, the PhD work proposes a unified framework for model performance analysis. It facilitates the comparison of data analysis techniques under varying assumptions and transformations.The designed framework is suitable for a variety of econometrics and ML models. It addresses the performance comparison task from the research procedure perspective, incorporating all the steps potentially affecting the performance perceptions. To demonstrate the framework’s capabilities we propose a series of 3 applied studies. In those studies the model performance is explored face to the changes in (1) sample size and balance, resulting from data collection; (2) changes in preferences structure within population, reflecting incorrect behavioral assumptions; and (3) model selection, directly intertwined with the performance perception
8

Matsumoto, Élia Yathie. "A methodology for improving computed individual regressions predictions." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-12052016-140407/.

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This research proposes a methodology to improve computed individual prediction values provided by an existing regression model without having to change either its parameters or its architecture. In other words, we are interested in achieving more accurate results by adjusting the calculated regression prediction values, without modifying or rebuilding the original regression model. Our proposition is to adjust the regression prediction values using individual reliability estimates that indicate if a single regression prediction is likely to produce an error considered critical by the user of the regression. The proposed method was tested in three sets of experiments using three different types of data. The first set of experiments worked with synthetically produced data, the second with cross sectional data from the public data source UCI Machine Learning Repository and the third with time series data from ISO-NE (Independent System Operator in New England). The experiments with synthetic data were performed to verify how the method behaves in controlled situations. In this case, the outcomes of the experiments produced superior results with respect to predictions improvement for artificially produced cleaner datasets with progressive worsening with the addition of increased random elements. The experiments with real data extracted from UCI and ISO-NE were done to investigate the applicability of the methodology in the real world. The proposed method was able to improve regression prediction values by about 95% of the experiments with real data.
Esta pesquisa propõe uma metodologia para melhorar previsões calculadas por um modelo de regressão, sem a necessidade de modificar seus parâmetros ou sua arquitetura. Em outras palavras, o objetivo é obter melhores resultados por meio de ajustes nos valores computados pela regressão, sem alterar ou reconstruir o modelo de previsão original. A proposta é ajustar os valores previstos pela regressão por meio do uso de estimadores de confiabilidade individuais capazes de indicar se um determinado valor estimado é propenso a produzir um erro considerado crítico pelo usuário da regressão. O método proposto foi testado em três conjuntos de experimentos utilizando três tipos de dados diferentes. O primeiro conjunto de experimentos trabalhou com dados produzidos artificialmente, o segundo, com dados transversais extraídos no repositório público de dados UCI Machine Learning Repository, e o terceiro, com dados do tipo séries de tempos extraídos do ISO-NE (Independent System Operator in New England). Os experimentos com dados artificiais foram executados para verificar o comportamento do método em situações controladas. Nesse caso, os experimentos alcançaram melhores resultados para dados limpos artificialmente produzidos e evidenciaram progressiva piora com a adição de elementos aleatórios. Os experimentos com dados reais extraído das bases de dados UCI e ISO-NE foram realizados para investigar a aplicabilidade da metodologia no mundo real. O método proposto foi capaz de melhorar os valores previstos por regressões em cerca de 95% dos experimentos realizados com dados reais.
9

Gualandi, Giacomo. "Analisi di dataset in campo finanziario mediante reti neurali LSTM." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19623/.

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Con il presente elaborato si è esplorato il campo della data analytics. È stato analizzato un dataset relativo all' andamento storico del titolo di borsa di una società, i cui dati sono stati manipolati in modo tale da renderli compatibili per un loro utilizzo in una applicazione di Machine Learning. Si sono approfondite le reti neurali artificiali LSTM e con esse si è creato un modello che permettesse di effettuare delle predizioni sui valori futuri del titolo. Infine sono state valutate le differenze tra i valori predetti e quelli reali assunti dal titolo di borsa.
10

Mattiussi, Vlad. "Una Rassegna di Dataset e Applicazioni Innovative di Intelligenza Artificiale per Affrontare la Pandemia da COVID19." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21844/.

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Il machine learning e la computer vision hanno avuto rilevanti sviluppi negli ultimi anni, compiendo progressi in molti settori. L’IA ha contribuito ad affrontare la pandemia di coronavirus (COVID-19). La scienza e la tecnologia hanno contribuito in modo significativo all’attuazione di queste politiche in questo caotico periodo senza precedenti. Ad esempio, i robot vengono utilizzati negli ospedali per fornire cibo e medicine ai pazienti con coronavirus o i droni vengono utilizzati per disinfettare strade e spazi pubblici I ricercatori di informatica, d’altra parte, sono riusciti a rilevare precocemente i pazienti infettivi utilizzando tecniche in grado di elaborare e comprendere i dati di imaging medico come immagini a raggi X e scansioni di tomografia computerizzata (CT). Tutte queste tecniche computazionali fanno parte dell’intelligenza artificiale, che è stata applicata con successo in vari campi.
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Книги з теми "Artificial datasets":

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Shi, Feng. Learn About Artificial Neural Networks in Python With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497093.

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Mountantonakis, M. Services for Connecting and Integrating Big Numbers of Linked Datasets. IOS Press, Incorporated, 2021.

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Chirimuuta, Mazviita. The Development and Application of Efficient Coding Explanation in Neuroscience. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0009.

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In the philosophy of neuroscience, much attention has been paid to mechanistic causal explanations, both in terms of their theoretical virtues, and their application in potential therapeutic interventions. Non-mechanistic, non-causal explanatory models, it is often assumed, would have no role to play in any practical endeavors. This assumption ignores the fact that many of the non-mechanistic explanatory models which have been successfully employed in neuroscience have their origins in engineering and applied sciences, and are central to many new neuro-technologies. This chapter examines the development of explanations of lateral inhibition in the early visual system as implementing an efficient code for converting photoreceptor input into a data-compressed output from the eye to the brain. Two applications of the efficient coding approach are considered: in streamlining the vast datasets of current neuroscience by offering unifying principles, and in building artificial systems that replicate vision and other cognitive functions.
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Villez, Kris, Daniel Aguado, Janelcy Alferes, Queralt Plana, Maria Victoria Ruano, and Oscar Samuelsson, eds. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789061154.

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In recent years, the wastewater treatment field has undergone an instrumentation revolution. Thanks to increased efficiency of communication networks and extreme reductions in data storage costs, wastewater plants have entered the era of big data. Meanwhile, artificial intelligence and machine learning tools have enabled the extraction of valuable information from large-scale datasets. Despite this potential, the successful deployment of AI and automation depends on the quality of the data produced and the ability to analyze it usefully in large quantities. Metadata, including a quantification of the data quality, is often missing, so vast amounts of collected data quickly become useless. Ultimately, data-dependent decisions supported by machine learning and AI will not be possible without data readiness skills accounting for all the Vs of big data: volume, velocity, variety, and veracity. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems provides recommendations to handle these challenges, and aims to clarify metadata concepts and provide advice on their practical implementation in water resource recovery facilities. This includes guidance on the best practices to collect, organize, and assess data and metadata, based on existing standards and state-of-the-art algorithmic tools. This Scientific and Technical Report offers a great starting point for improved data management and decision making, and will be of interest to a wide audience, including sensor technicians, operational staff, data management specialists, and plant managers. ISBN: 9781789061147 (Paperback) ISBN: 9781789061154 (eBook) ISBN: 9781789061161 (ePub)

Частини книг з теми "Artificial datasets":

1

Arbelaitz, Olatz, Ibai Gurrutxaga, Javier Muguerza, and Jesús María Pérez. "Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets." In Advances in Artificial Intelligence, 111–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40643-0_12.

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2

Khangamwa, Gift, Terence van Zyl, and Clint J. van Alten. "Towards a Methodology for Addressing Missingness in Datasets, with an Application to Demographic Health Datasets." In Artificial Intelligence Research, 169–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22321-1_12.

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Cerquides, Jesús, Maite López-Sánchez, Santi Ontañón, Eloi Puertas, Anna Puig, Oriol Pujol, and Dani Tost. "Classification Algorithms for Biomedical Volume Datasets." In Current Topics in Artificial Intelligence, 143–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881216_16.

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4

Sebé, Francesc, Josep Domingo-Ferrer, and Agustí Solanas. "Noise-Robust Watermarking for Numerical Datasets." In Modeling Decisions for Artificial Intelligence, 134–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_14.

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5

García-Álvarez, David, Javier Lara Hinojosa, and Francisco José Jurado Pérez. "Global Thematic Land Use Cover Datasets Characterizing Artificial Covers." In Land Use Cover Datasets and Validation Tools, 419–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_21.

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AbstractThe mapping of artificial covers at a global scale has received increasing attention in recent years. Numerous thematic global Land Use Cover (LUC) datasets focusing on artificial surfaces have been produced at increasingly high spatial resolutions and using methods that ensure improved levels of accuracy. In fact, there are several long time series of maps showing the evolution of artificial surfaces from the 1980s to the present. Most of them allow for change detection over time, which is possible, thanks to the high level of accuracy at which artificial surfaces can be mapped and because transitions from artificial to non-artificial covers are very rare. Global thematic LUC datasets characterizing artificial covers usually map the extent or percentage of artificial or urban areas across the world. They do not provide thematic detail on the different uses or covers that make up artificial or urban surfaces. Unlike other general or thematic LUC datasets, those focusing on artificial covers make extensive use of radar data. In several cases, optical and radar imagery have been used together, as each source provides complementary information. Global Urban Expansion 1992–2016 and ISA, which were produced at a spatial resolution of 1 km, are the coarsest of the nine datasets reviewed in this chapter. ISA provides information on the percentage of impervious surface area per pixel. The GHSL edition of 2014 and the GMIS at 30 m also provide sub-pixel information, whereas all the other datasets reviewed here only map the extent of artificial/impervious/urban areas. Most of the datasets reviewed in this chapter were produced at a spatial resolution of 30 m. This is due to the extensive use of Landsat imagery in the production of these datasets. Landsat provides a long, high-resolution series of satellite imagery that enables effective mapping of the evolution of impervious surfaces at detailed scales. Of the datasets produced at 30 m, Global Urban Land maps artificial covers for seven different dates between 1980 and 2015, while GHSL does the same for five different dates between 1987 and 2016, although the map for the last date was produced at 20 m. GUB maps the extent of urban land for seven dates between 1990 and 2018 and was produced together with GAIA, which provides an annual series of maps for the period 1985–2018. HBASE, GMIS and GISM, also at 30 m, are only available for one reference year. The same is true of GUF and WSF, which were produced as part of the same effort to map global artificial surfaces as accurately as possible. They provide the most detailed datasets up to date, with spatial resolutions of 12 m (GUF) and 10 m (WSF). Future updates of WSF will produce a consistent time series of global LC maps of artificial areas from the 1980s to the present. It aims to be the longest, most detailed, most accurate dataset ever produced on this subject.
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Kim, Minho, Hyunjin Yoo, and R. S. Ramakrishna. "Cluster Validation for High-Dimensional Datasets." In Artificial Intelligence: Methodology, Systems, and Applications, 178–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30106-6_18.

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7

Ferreira, Carlos Abreu, João Gama, and Vítor Santos Costa. "Sequential Pattern Mining in Multi-relational Datasets." In Current Topics in Artificial Intelligence, 121–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14264-2_13.

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Shamaev, Dmitry. "Synthetic Datasets and Medical Artificial Intelligence Specifics." In Data Science and Algorithms in Systems, 519–28. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21438-7_41.

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Ewen, Nicolas, Tamer Abdou, and Ayse Bener. "Applications of Feature Selection Techniques on Large Biomedical Datasets." In Advances in Artificial Intelligence, 543–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18305-9_57.

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10

Wang, Xiaoguang, Stan Matwin, Nathalie Japkowicz, and Xuan Liu. "Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets." In Advances in Artificial Intelligence, 174–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_15.

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Тези доповідей конференцій з теми "Artificial datasets":

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Da Silva, Ronnypetson, Valter M. Filho, and Mario Souza. "Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12141.

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Many works that apply Deep Neural Networks (DNNs) to Speech Emotion Recognition (SER) use single datasets or train and evaluate the models separately when using multiple datasets. Those datasets are constructed with specific guidelines and the subjective nature of the labels for SER makes it difficult to obtain robust and general models. We investigate how DNNs learn shared representations for different datasets in both multi-task and unified setups. We also analyse how each dataset benefits from others in different combinations of datasets and popular neural network architectures. We show that the longstanding belief of more data resulting in more general models doesn’t always hold for SER, as different dataset and meta-parameter combinations hold the best result for each of the analysed datasets.
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Ghafourian, Sarvenaz, Ramin Sharifi, and Amirali Baniasadi. "Facial Emotion Recognition in Imbalanced Datasets." In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120920.

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The wide usage of computer vision has become popular in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This paper tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on augmented datasets including CK+, EMOTIC, and KDEF dataset samples. After modifying our dataset, using SMOTETomek approach, we observe improvement over the default method.
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Fang, Yili, Chaojie Shen, Huamao Gu, Tao Han, and Xinyi Ding. "TDG4Crowd:Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/333.

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In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorithm on different datasets often show contradicting conclusions. For example, on the RTE dataset, Dawid and Skene model performs significantly better than Majority Voting, while on the LableMe dataset, the experiments give the opposite conclusion. It is challenging to obtain comprehensive and balanced datasets at a low cost. To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. Using Kullback Leibler divergence and Kolmogorov–Smirnov test, the experiment results show the superior of our method compared with others. Aggregation algorithms also perform more consistently on the synthetic datasets generated using our method.
4

He, Shuai, Yongchang Zhang, Rui Xie, Dongxiang Jiang, and Anlong Ming. "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/132.

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Challenges in image aesthetics assessment (IAA) arise from that images of different themes correspond to different evaluation criteria, and learning aesthetics directly from images while ignoring the impact of theme variations on human visual perception inhibits the further development of IAA; however, existing IAA datasets and models overlook this problem. To address this issue, we show that a theme-oriented dataset and model design are effective for IAA. Specifically, 1) we elaborately build a novel dataset, called TAD66K, that contains 66K images covering 47 popular themes, and each image is densely annotated by more than 1200 people with dedicated theme evaluation criteria. 2) We develop a baseline model, TANet, which can effectively extract theme information and adaptively establish perception rules to evaluate images with different themes. 3) We develop a large-scale benchmark (the most comprehensive thus far) by comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets. Our work offers the community an opportunity to explore more challenging directions; the code, dataset and supplementary material are available at https://github.com/woshidandan/TANet.
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"DATAZAPPER: GENERATING INCOMPLETE DATASETS." In 1st International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001660700690076.

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6

Abdo, Thiago, and Fabiano Silva. "Iterative machine learning applied to annotation of text datasets." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18268.

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The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.
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Carvalho, Nathan F., André A. Gonçalves, and Ana C. Lorena. "Collecting Meta-Data from the OpenML Public Repository." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234286.

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In Machine Learning (ML), selecting the most suitable algorithm for a problem is a challenge. Meta-Learning (MtL) offers an alternative approach by exploring the relationships between dataset characteristics and ML algorithmic performance. To conduct a MtL study, it is necessary to create a metadataset comprising datasets of varying characteristics and defying the ML algorithms at different levels. This study analyzes the information available in the OpenML public repository for building such meta-datasets, which provides a Python API for easy data importation. Assessing the content currently available in the platform, there is still no extensive meta-feature characterization for all datasets, limiting their complete characterization.
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Kim, Seul ki, Kwihoon Kim, and Taeyoung Kim. "Development of AI Educational Datasets Library Using Synthetic Dataset Generation Method." In 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2023. http://dx.doi.org/10.1109/icaiic57133.2023.10067000.

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9

Mayo, Michael, and Quan Sun. "Evolving artificial datasets to improve interpretable classifiers." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900238.

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10

Bhattacharjya, Debarun, Tian Gao, Nicholas Mattei, and Dharmashankar Subramanian. "Cause-Effect Association between Event Pairs in Event Datasets." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/167.

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Causal discovery from observational data has been intensely studied across fields of study. In this paper, we consider datasets involving irregular occurrences of various types of events over the timeline. We propose a suite of scores and related algorithms for estimating the cause-effect association between pairs of events from such large event datasets. In particular, we introduce a general framework and the use of conditional intensity rates to characterize pairwise associations between events. Discovering such potential causal relationships is critical in several domains, including health, politics and financial analysis. We conduct an experimental investigation with synthetic data and two real-world event datasets, where we evaluate and compare our proposed scores using assessments from human raters as ground truth. For a political event dataset involving interaction between actors, we show how performance could be enhanced by enforcing additional knowledge pertaining to actor identities.

Звіти організацій з теми "Artificial datasets":

1

Johra, Hicham, Martin Veit, Mathias Østergaard Poulsen, Albert Daugbjerg Christensen, Rikke Gade, Thomas B. Moeslund, and Rasmus Lund Jensen. Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau542153983.

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The aim of this technical report is to provide a description and access to labelled image and video datasets of human faces that have been generated for different indoor visual comfort and glare visual discomfort situations. These datasets have been used to train and test a computer-vision artificial neural network detecting glare discomfort from images of human faces.
2

Cerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/b7qz5fpva9dar469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
3

Cerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/x92cmhdrxsvu7469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
4

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/n8rzaz6kghskt469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
5

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/atz7nxsz9afbm469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
6

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/w5nn12uvjosgd469.

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This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
7

Chahal, Husanjot, Sara Abdulla, Jonathan Murdick, and Ilya Rahkovsky. Mapping India’s AI Potential. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20200096.

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With its massive information technology workforce, thriving research community and a growing technology ecosystem, India has a significant stake in the development of artificial intelligence globally. Drawing from a variety of original CSET datasets, the authors evaluate India’s potential for AI by examining its progress across five categories of indicators pertinent to AI development: talent, research, patents, companies and investments, and compute.
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Arnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, February 2021. http://dx.doi.org/10.51593/20200064.

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In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.
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Musser, Micah, Rebecca Gelles, Catherine Aiken, and Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, April 2023. http://dx.doi.org/10.51593/20210071.

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Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.
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Chahal, Husanjot, Helen Toner, and Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, September 2021. http://dx.doi.org/10.51593/20200075.

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Conventional wisdom suggests that cutting-edge artificial intelligence is dependent on large volumes of data. An overemphasis on “big data” ignores the existence—and underestimates the potential—of several AI approaches that do not require massive labeled datasets. This issue brief is a primer on “small data” approaches to AI. It presents exploratory findings on the current and projected progress in scientific research across these approaches, which country leads, and the major sources of funding for this research.

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