Supervised learning algorithms

Rolling bearing faults have been capturing substantial research attention as they are the root causes of malfunctions in mechatronics systems than any other factors. The detection of rolling bearing faults in the early stage is therefore a mandatory requirement demanded by reliable industrial plants.

15p vithomson 02-07-2024 1 0   Download

Anomaly detection for Internet of things (IoT) networks is a challenging issue due to the huge number of devices that connect to each other and generate huge amounts of data. In this study, we propose a model combining Autoencoder (AE) with classification algorithms to build an endto-end architecture for processing, feature extraction and data classification.

13p viambani 18-06-2024 7 1   Download

Lecture Artificial intelligence: Q learning. This lecture provides students with content including: supervised learning; unsupervised learning; reinforcement learning; utilize the Q matrix;... Please refer to the detailed content of the lecture!

18p codabach1016 03-05-2024 1 0   Download

The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one.

13p viarnault 25-04-2024 1 1   Download

This scholarly research paper addresses the crucial and complex challenge of detecting and categorizing Internet of Things (IoT) botnets through the utilization of machine learning algorithms. The study is focused on conducting meticulous analysis and manipulation of IoT botnet data, with a specific emphasis placed on the widely acknowledged IoT23 dataset.

12p visystrom 22-11-2023 8 5   Download

This course is an introduction to the subject of Artificial Neural Networks and Genetic Algorithms, two very new subjects forming part of Distributed Artificial Intelligence. As you leaf through these notes you will notice that they are full of mathematical equations. The reason is simple: these subjects are inherently mathematical. However the course and assessments are such that it will be possible for you to pass if you do not touch the equations. However if you wish to gain a good pass you must attempt to master the equations.

136p haojiubujain08 01-11-2023 5 2   Download

Why this book on computational intelligence? Need arose from a graduate course, where students do not have a deep background of artificial intelligence and mathematics. Therefore the introductory nature, both in terms of the CI paradigms and mathematical depth. While the material is introductory in nature, it does not shy away from details, and does present the mathematical foundations to the interested reader.

311p haojiubujain07 20-09-2023 6 3   Download

This paper aims to provide a thorough insight into Deep learning (DL) algorithms’ contributions for IoT security, especially on the ways they operate, the benefits and drawbacks and possible applications in IoT security systems as well as illustrates how they are applied to enhance IoT security.

9p vifriedrich 06-09-2023 5 3   Download

This study proposes a distributed classification framework, which adapts supervised SelfOrganizing Maps (SOM) as base learners. The supervised SOM is the integration of the SOM algorithm with the Learning Vector Quantization (LVQ) algorithm, so called SOM-LVQ model. Multiple SOM-LVQ models are created using different feature subsets, each of which represents one different local information source.

6p viannee 02-08-2023 6 4   Download

In this paper, multiple classification algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), logistic regression, Gaussian, Bernoulli, multinomial Naïve Bayes, and linear discriminant analysis were executed on the seismic attributes for lithofacies prediction.

9p vibentley 08-09-2022 14 4   Download

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features.

21p vialfrednobel 29-01-2022 12 0   Download

This study endeavored to build a system that could predict an individual’s personality through SM conversation. Four BIG5 personality items (i.e. Extraversion (EXT), Consciousness (CON), Agreeable (AGR) and Openness to Experiences (OPN) equivalent to the Myers–Briggs Type Indicator (MBTI)) were predicted using six supervised machine learning (SML) algorithms.

10p lazzaro 30-12-2021 11 0   Download

The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods.

17p vijeeni2711 30-06-2021 12 1   Download

In this paper, we propose a semisupervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we also introduce a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms.

19p nguaconbaynhay11 07-04-2021 14 1   Download

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate.

14p viwyoming2711 16-12-2020 10 1   Download

Taxonomic assignment is a crucial step in a metagenomic project which aims to identify the origin of sequences in an environmental sample. Among the existing methods, since composition-based algorithms are not sufficient for classifying short reads, recent algorithms use only the feature of similarity, or similarity-based combined features.

12p vioklahoma2711 19-11-2020 7 1   Download

Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods.

16p vioklahoma2711 19-11-2020 9 2   Download

The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting.

13p vioklahoma2711 19-11-2020 11 0   Download

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources.

13p viconnecticut2711 29-10-2020 12 1   Download

Though clustering algorithms have long history, nowadays clustering topic still attracts a lot of attention because of the need of efficient data analysis tools in many applications such as social network, electronic commerce, GIS, etc. Recently, semi-supervised clustering, for example, semi-supervised K-Means, semi-supervised DBSCAN, semi-supervised graph-based clustering (SSGC) etc., which uses side information to boost the performance of clustering process, has received a great deal of attention.

12p 12120609 23-03-2020 31 2   Download