Machine Learning

Mulan , a sourceforge net multi-target dataset available in www.openml.org. Despite the numerous interesting applications of MTR, there are only few publicly available datasets of this kind - perhaps because most applications are industrial - and most experimental evaluations of MTR methods are based on a limited amount of datasets. For this study, much effort was made for the composition of a large and diverse collection of benchmark MTR datasets.

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Ulos is one of the traditional fabrics originating from North Sumatra, Indonesia. Ulos has various motifs and distinctive colors, which reflect the culture and philosophy of the Batak people. In this study, we collected a dataset of color ulos images with high pixel quality. The purpose of this research is to analyze the visual characteristics of ulos, such as pattern, color, and explore the potential utilization of this dataset for various applications, such as cultural preservation, product development, and visual analysis.

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We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.

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This study used a benchmark dataset, applying different embedding like LASER and FastText to capture contextual information, which was combined to create a new hybrid embedding. This hybrid embedding was fed to machine-learning (ML) and deep learning (DL) classifiers.

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As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.

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Smart grid, an application of Internet of Things (IoT) is modern power grid that encompasses power and communication network from generation to utilization. Home Area Network (HAN), Field or Neighborhood Area Network (FAN/NAN) and Wide Area network (NAN) using Wireless LAN and Wireless/Wired WAN protocols are employed from generation to utilization . Advanced Metering Infrastructure, a utilization side infrastructure facilitates communication between smart meters and the server where energy efficient protocols are mandate to support smart grid .

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This dataset consists of 737 documents from the BBC Sport website, corresponding to sports news articles in five topical areas from 2004-2005. The class labels are divided into five categories: athletics, cricket, football, rugby, and tennis. The datasets have been pre-processed using the Porter stemming algorithm, stop-word removal, and filtering out terms with low frequency (count < 3).

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The dataset contains two types of articles fake and real News. This dataset was collected from realworld sources; the truthful articles were obtained by crawling articles from Reuters.com (News website). As for the fake news articles, they were collected from different sources. The fake news articles were collected from unreliable websites that were flagged by Politifact (a fact-checking organization in the USA) and Wikipedia. The dataset contains different types of articles on different topics, however, the majority of articles focus on political and World news topics.

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These are tight pedestrian masks for the thermal images present in the KAIST Multispectral pedestrian dataset, available at https://soonminhwang.github.io/rgbt-ped-detection/

Both the thermal images themselves as well as the original annotations are a part of the parent dataset. Using the annotation files provided by the authors, we develop the binary segmentation masks for the pedestrians, using the Segment Anything Model from Meta.

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The fifteen publicly available datasets from the UCI Machine Learning Library are selected for experiments. The selected datasets has no missing values, therefore, normalization is applied solely to the original datasets. For nominal data, it has been discretized and compressed to the range of 0 to 1. Similarly, for continuous data that does not fall within the range of 0 to 1, normalization has been applied.

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