Machine Learning
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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Recently, combinatorial interaction strategies have a large spectrum as black box strategies for testing software and hardware. This paper discusses a novel adoption of a combinatorial interaction strategy to generate a sparse combinatorial data table (SCDT) for machine learning. Unlike test data generation strategies, in which the t-way tuples synthesize into a test case, the proposed SCDT requires analyzing instances against their corresponding tuples to generate a systematic learning dataset.
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Efficient and realistic tools capable of modeling radio signal propagation are an indispensable component for the effective operation of wireless communication networks. The advent of artificial intelligence (AI) has propelled the evolution of a new generation of signal modeling tools, leveraging deep learning (DL) models that learn to infer signal characteristics.
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The dataset includes 22 projects and 1680 user stories, with the aim of classifying these stories into those suitable for AI implementation and those not recommended for AI implementation. The labeling was done in a group, reaching a consensus on each user story in each project, determining whether it is susceptible to being developed with AI. Thus, each user story was evaluated and assigned a value of 1 if it was considered suitable for AI implementation (this label was named AI), and a value of 0 if it was not (this label was named not-AI).
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The terahertz communications band in the 252 to325 GHz range has been recently explored for its potential to meet the stringent requirements for the emerging sixth generation of wireless communications. However, there are several challenges including noise and nonlinearity that hinder efficient implementations. We aim to address this limitation in terahertz communications through convolutional neural networks (CNN) enhanced by the domain knowledge from traditional Volterra filters.
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We evaluate the performance of our proposed protocol using three benchmark datasets. Each dataset is composed of 25 percent of the local data forming the test dataset and 75 percent of the local data forming the training dataset.
MNIST: The dataset is made up of gray pictures that are digits which are written by hand containing ten different classes which provides 60000 training samples in total.
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The dataset contains 560 different observations each having 1049 absorption data points for cancerous and non-cancerous skin cells. The reflection absorption data were obtained from terahertz metamaterials on top of which the cells are placed. The 560 observations made were for varying size tissue thickness and polarization and incident wave angle
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The dataset contains Game stats for all matches in the League of Legends LEC Spring Playoffs 2024. It has 81 columns and 420 rows. Here is the description of the columns.
Dataset Contents:
● Player: Name of the player.
● Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
● Team: Name of the player's team
● Opponent_Team: Name of the opposing team
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We introduce two novel datasets for cell motility and wound healing research: the Wound Healing Assay Dataset (WHAD) and the Cell Adhesion and Motility Assay Dataset (CAMAD). WHAD comprises time-lapse phase-contrast images of wound healing assays using genetically modified MCF10A and MCF7 cells, while CAMAD includes MDA-MB-231 and RAW264.7 cells cultured on various substrates. These datasets offer diverse experimental conditions, comprehensive annotations, and high-quality imaging data, addressing gaps in existing resources.
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712 Views
Moroccan Dialect Emotion Recognition Dataset is a collection of voice records of people speaking Moroccan dialect in 5 states of emotion: Neutral, Happy, Sad, Angry and Fearful. The dataset has been collected in different Moroccan cities in 2024. Each recorder has 5 records per emotion class. The dataset contains 2000 record. The records are saved in .wav format, which is useful for signal processing with python libraries. The dataset is used for signal processing and emotion recognition using deep Learning models.
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290 Views