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Neuroevolutionary Transfer learning for Time Series Forecasting 18th

Neuroevolutionary Transfer Learning for Time Series Forecas...

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Proceedings of the 18th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Vellinger, Aymeric Torres, J.F. Divina, Federico Vanhoof, Wim Pablo de Olavide University Sevilla Spain University of Namur Namur Belgium

ISBN: (纸本)9783031425288

In this paper, we propose a neuroevolution technique specifically designed for evolving LSTM networks. The proposed technique uses a grammar-based approach to evolve LSTM neural networks for time series prediction tasks, and is based on a previous technique which was designed in order to evolve CNN networks. We use transfer learning in order to reduce the computational time of our approach. We have compared results obtained with other state of the art time series forecasting techniques on twenty time series, which contains data generated by sensors placed on a number of Iberian pigs. Results obtained confirm the effectiveness of the strategy proposed in this work. Overall, we showcase the potential of our proposal in producing precise and efficient deep learning models for time series prediction, as well as the adaptability of transfer learning to new datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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Efficient Stochastic Approaches for Multidimensional Integrals in Bayesian Statistics 12th

Efficient Stochastic Approaches for Multidimensional Integra...

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12th international conference on Large-Scale Scientific Computations (LSSC)

作者： Todorov, Venelin Dimov, Ivan Bulgarian Acad Sci Inst Math & Informat Dept Informat Modeling Acad Georgi Bonchev StBlock 8 Sofia 1113 Bulgaria Bulgarian Acad Sci Inst Informat & Commun Technol Dept Parallel Algorithms Acad G Bonchev StBlock 25 A Sofia 1113 Bulgaria

ISBN: (纸本)9783030410322;9783030410315

A fundamental problem in Bayesian statistics is the accurate evaluation of multidimensional integrals. A comprehensive experimental study of quasi-Monte Carlo algorithms based on Sobol sequence combined with Matousek linear scrambling and a comparison with adaptive Monte Carlo approach and a lattice rule based on generalized Fibonacci numbers has been presented. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing high dimensional integrals. It is a crucial element since this may be important to be estimated in order to achieve a more accurate and reliable interpretation of the results in Bayesian statistics which is foundational in applications such as machine learning.

关键词： Monte Carlo and quasi-Monte Carlo algorithms Multidimensional integrals Bayesian statistics machine learning

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Authentication of Vincent van Gogh’s Work 19th

Authentication of Vincent van Gogh’s Work

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19th international conference on Computer Analysis of Images and Patterns, CAIP 2021

作者： David, Lucas Oliveira Pedrini, Helio Dias, Zanoni Rocha, Anderson Institute of Computing University of Campinas Campinas Brazil

ISBN: (纸本)9783030891305

Increasing digital art has, without a doubt democratized the access to art content to the public at large. It has had, however, resulted in an inadvertent growing number of forgeries and misinformation around art content. In this work, we employ convolutional network-based strategies to identify and classify art-related digital artifacts. Firstly, van Gogh paintings are used to explore and refine strategies capable of discriminating the brushstroke pattern of van Gogh. We achieve significant performance improvements over the VGDB-2016 dataset, while also increasing class-balanced accuracy when compared to previous results in the same set. In a second phase, we collect two new sets and perform cross-dataset evaluation to demonstrate that our solution generalizes well to painting recaptures with varying resolution, sizes and sources, while performing fairly well against unseen high-resolution scans of paintings. Finally, we propose a strategy for painting authentication that combines results of multiple recaptures. © 2021, Springer Nature Switzerland AG.

关键词： machine learning

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soft Network Organisation Towards Future Distributed ML-based Sensing Systems 28

Soft Network Organisation Towards Future Distributed ML-base...

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28th international conference on software, Telecommunications and Computer Networks (softCOM)

作者： Cichon, Krzysztof Poznan Univ Technol PUT Poznan Poland

ISBN: (纸本)9789532900996

The article analyses efficient information detection by a network of sensors. It is assumed that every sensor collects local information, however, with the application of clustering in the network, it is possible to efficiently exchange knowledge within the network. In particular, the local knowledge about a licensed signal is acquired by sensors, and it is then exchanged and transferred to global knowledge. To this end, two types of clustering are analysed: hard K-means clustering and soft K-means. In the latter case, the crucial knowledge about stiffness is important to perform an efficient exchange of information. The analysis of desired stiffness values has been carried out with simulation experiments where the energy usage and reporting efficiency were compared for various sizes and types of clusters. Moreover, the quality of global knowledge is also shown in the simulation results.

关键词： Signal detection machine learning clustering network organisation

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Electricity Load and Price Forecasting Using Enhanced machine learning Techniques 13th

Electricity Load and Price Forecasting Using Enhanced Machin...

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13th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Bano, Hamida Tahir, Aroosa Ali, Ishtiaq Khan, Raja Jalees ul Hussen Haseeb, Abdul Javaid, Nadeem COMSATS Univ Islamabad Islamabad 44000 Pakistan Sardar Bhadur Khan Women Univ Quetta Quetta 87300 Pakistan

ISBN: (纸本)9783030222635;9783030222628

The exponential increase in electricity generation and consumption pattern are the two main issues in the wholesale markets. To handle these issues different machine learning techniques are used for load and price prediction in the research field. The wholesale utilities provide real-time data of load and price for the better prediction of electricity generation purposes. The New York Independent System Operator (NY-ISO) is one of the utility which provide electricity to different counties like United States, Canada and Israel. In this paper, hourly data of 2016-2017 is used for the forecasting process of load and price of New York City. Feature selection and extraction are used to achieve important features. The feature selection is done by two techniques Classification and Regression Tree (CART) and Recursive Feature Elimination (RFE) and Feature extraction by using Singular Value Decomposition (SVD). The Multiple Layer Perceptron (MLP), Support Vector machine (SVM) and Logistic Regression (LR) classifiers are separately used for forecasting purposes of electricity load and price. Further enhance these three techniques EMLP, ESVM and ELR to take more accurate results for electricity load and price forecasting.

关键词： Forecasting

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Vector borne disease outbreak prediction by machine learning

Vector borne disease outbreak prediction by machine learning

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2020 international conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2020

作者： Raizada, Sandali Mala, Shuchi Shankar, Achyut Amity University Amity School of Engineering and Technology Department of Computer Science Noida India

ISBN: (纸本)9781728172132

Vector Borne Disease is a form of illness which is caused by parasites, viruses and bacteria. The infection is transferred through blood-feeding arthropods such as mosquitoes, fleas ticks etc. Every year from diseases such as yellow fever, Malaria more than 700, 000 deaths occur. These diseases are most common in tropical and subtropical areas and affect the underprivileged populations. Deep learning an essential part of Artificial Intelligence provides an uncanny power to systems to construct a complex network using layers of perceptrons which mimic the human neurons. This network Combined with algorithms of machine learning may serve as one of the most powerful tool in healthcare to classify and analyze huge amount of medical data and predict future trends through Supervised learning. The paper we focused on effective prediction of the vector borne disease outbreak (Multiclass Classification) of three diseases (Chikungunya, Malaria, Dengue) across the Indian-subcontinent. We have examined and refined our model over data collected across India in 2013-2017. We have put forward a Convolutional Neural Network outbreak risk prediction algorithm using contrasting data. To our finest understanding, none of the previous works have centered on contrasting data in area of analysis of medical data. The prediction accuracy of our suggested CNN algorithm is 88%. © 2020 IEEE.

关键词： Healthcare machine learning

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A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label 15th

A Deep Self-learning Classification Framework for Incomplete...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Luo, Mengzhen Shi, Xiaoyu Ji, Qianqian Shang, Mingsheng He, Xianbo Tao, Weiguo The Computer School of China West Normal University NanchongSichuan637002 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

ISBN: (纸本)9783030325909

The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studies using machine learning methods focus on classification the medical patents with single label. However, the classification of medical patents is a multi-label task, and the available data are always incomplete with losing the part information of patents. In this paper, we propose a deep self-learning classification framework that can deal with the incomplete medical patterns with multi-label issue. It consists of a text processor and patent classifier. For the text processor, a professional medical text thesaurus is built via GloVe method, which can learn more specialized vocabulary. For the patent classifier, we adopt a bidirectional long short term memory (Bi-LSTM) model to construct our patent classifier, which can learn the hidden knowledge from medical patents and associate with appropriate labels to medical patents automatically. Furthermore, an advanced focal loss function is design to further improve the classification accuracy. Experiments on the Thomson Reuters dataset demonstrate that our proposed method outperform the other existing methods in terms of precision and recall, when dealing with incomplete medical patents with multi-label issue.. © 2020, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Towards smart agriculture: A deep learning based phenotyping scheme for leaf counting

Towards smart agriculture: A deep learning based phenotyping...

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2020 international conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2020

作者： Hati, Anirban Jyoti Ranjan Singh, Rajiv National Institute of Electronics and Information Technology Kolkata Kolkata India Department of Electronics and Communication Engineering Presidency University Bengaluru India

ISBN: (纸本)9781728172132

Plant phenotyping is a smart technique in which plant features data is collected and analyzed using computer vision, robotics and machine learning techniques to increase agricultural production. We propose a leaf segmentation and leaf counting technique based on learning without using the denotation of the leaf center and the data on the plant segmentation given in the LCC CVPPP 2017 dataset. After required segmentation, noise removal and enhancement, as well as the transformation of leaf pixel data, a deep neural network architecture based on Alexnet, was used on a total of 783 plant images by dividing the dataset into 70% for training, 15% for validation and 15% for testing. The result thus obtained showed significant improvement based on four evaluation parameters such as Count Difference, Absolute Count Difference, Percentage of Agreement and Mean Square Error when compared with contemporary works. © 2020 IEEE.

关键词： Mean square error

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Unsupervised Clustering Algorithm as Region of Interest Proposals for Cancer Detection Using CNN 3rd

Unsupervised Clustering Algorithm as Region of Interest Prop...

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international conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Gogineni, Ajay K. Kishore, Raj Raj, Pranay Naik, Suprava Sahu, Kisor K. IIT Bhubaneswar Sch Elect Sci Bhubaneswar 752050 India IIT Bhubaneswar Virtual & Augmented Real Ctr Excellence Bhubaneswar 752050 India All India Inst Med Sci Dept Radiodiag Bhubaneswar 751019 India IIT Bhubaneswar Sch Minerals Met & Mat Engn Bhubaneswar 752050 India

ISBN: (纸本)9783030372187;9783030372170

Deep learning methods are now getting lot of attention due to their success in many fields. Computer-aided bio-medical image analysis systems act as a tool to assist medical practitioners in correct decision making. Use of Deep learning algorithms to predict Cancer at early stage was highly promoted by Kaggle Data Science Bowl 2017 competition and Cancer Moonshot Initiative. In this article, we have proposed a novel combination of unsupervised machine learning tool which is modularity optimization based graph clustering method and Convolutional Neural Networks (CNN) based architectures for lung cancer detection. The unsupervised clustering method helps in reducing the complexity of CNN by providing Region of Interest (ROI) proposals. Our CNN model has been trained and tested on LUNA 2016 dataset which contains Computed Tomography (CT) scans of lung region. This method provides an approximate pixel-wise segmentation mask along with the class label of the ROI proposals.

关键词： machine learning Graph clustering Modularity CNN ROI CT scan Cancerous nodules

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Addressing Low Dimensionality Feature Subset Selection: ReliefF(-k) or Extended Correlation-Based Feature Selection(eCFS)? 14th

Addressing Low Dimensionality Feature Subset Selection: Reli...

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14th international conference on soft computing Models in Industrial and Environmental Applications (SOCO)

作者： Tallon-Ballesteros, Antonio J. Cavique, Luis Fong, Simon Univ Huelva Dept Elect Comp Syst & Automat Engn Huelva Spain Univ Aberta Lisbon Portugal Univ Macau Dept Comp & Informat Sci Taipa Macao Peoples R China

ISBN: (纸本)9783030200558;9783030200541

This paper tackles problems where attribute selection is not only able to choose a few features but also to achieve a low performance classification in terms of accuracy compared to the full attribute set. Correlation-based feature selection (CFS) has been set as the baseline attribute subset selector due to its popularity and high performance. Around hundred data sets have been collected and submitted to CFS;then the problems fulfilling simultaneously the conditions: (a) a number of selected attributes lower than six and (b) a percentage of selected attributes lower than a forty per cent, have been tested onto two directions. Firstly, in the scope of data selection at the feature level, an advanced contemporary approach have been conducted as well as some options proposed in a prior work. Secondly, the pre-processed and initial problems have been tested with some sturdy classifiers. Moreover, this work introduces a new taxonomy of feature selection according to the solution type and the followed way to compute it. The test bed comprises seven problems featured by a low dimensionality after the CFS application, three out of them report a single selected attribute, another one with two extracted features and the three remaining data sets with four or five retained attributes;additionally, the initial feature set is between six and twenty-nine and the complexity of the problems, in terms of classes, fluctuates between two and twenty-one, throwing averages of sixteen and around five for both aforementioned properties. The contribution concluded that the advanced procedure (extended CFS) is suitable for problems where only one or two attributes are selected by CFS;for data sets with more than two selected features the baseline method is preferable to the advanced one, although the considered feature ranking method achieved intermediate results.

关键词： machine learning Correlation Feature subset selection Feature ranking Extended feature subset selection CFS

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