Machine Learning | IEEE DataPort

Telugu Handwritten Vowels

A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.

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Energy-efficient indoor localization WiFi-fingerprint dataset

WiFi measurements dataset for WiFi fingerprint indoor localization compiled on the first and ground floors of the Escuela Técnica Superior de Ingeniería Informática, in Seville, Spain. The facility has 24.000 m² approximately, although only accessible areas were compiled.

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File Fragment Type (FFT) - 75 Dataset

This FFT-75 dataset contains randomly sampled, potentially overlapping file fragments from 75 popular file types (see details below). It is the most diverse and balanced dataset available to the best of our knowledge. The dataset is labeled with class IDs and is ready for training supervised machine learning models. We distinguish 6 different scenarios with different granularity and provide variants with 512 and 4096-byte blocks. In each case, we sampled a balanced dataset and split the data as follows: 80% for training, 10% for testing and 10% for validation.

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Security

The Good, The Bad and The Fair: KPIs from Network Elements

Measurements collected from R1 for root cause analyses of the network service states defined from quality and service design perspectives

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Communications

Big Data Machine Learning Benchmark on Spark

We introduce a benchmark of distributed algorithms execution over big data. The datasets are composed of metrics about the computational impact (resource usage) of eleven well-known machine learning techniques on a real computational cluster regarding system resource agnostic indicators: CPU consumption, memory usage, operating system processes load, net traffic, and I/O operations. The metrics were collected every five seconds for each algorithm on five different data volume scales, totaling 275 distinct datasets.

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Nurse Care Activity Recognition Challenge

In an aging population, the demand for nurse workers increases to care for elders. Helping nurse workers make their work more efficient, will help increase elders quality of life, as the nurses can focus their efforts on care activities instead of other activities such as documentation. Activity Recognition can be used for this goal.

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Cyfra 21.1 CEA for both serum and pleural fluid of 168 patients

Malignant pleural effusions (MPEs) are a challenging public health problem, causing significant morbidity and often being the first presenting sign of cancer. Pleural fluid cytology is the most common method used to differentiate malignant from non-malignant effusions. However, its sensitivity reaches 50-70% and depends on the experience of the cytologist, the tumor load, and the amount of fluid tested. Therefore, diagnostic inaccuracy and a high incidence of false negatives may endanger patients with clinical mistreatment and mismanagement.

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Cancer Data

Situational Awareness Assisted MmWave Vehicular Beam Training

Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.

The code assumes Python 3.

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Communications

Footstep analysis using Pressure Sensors

The pressure sensors are represented by black circles, which are located in the three zones of each foot. For the left foot: S1 and S2 cover the forefoot area. S3, S4, and S5 the midfoot area. S6 and S7 the rearfoot or heel area. Similarly, for the right foot: S8 and S9 represent the forefoot area. S10, S11, S12 the midfoot area. S13 and S14 the heel area. The values of each sensor are read by the analog inputs of an Arduino mega 2560.

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Locally Linear Embedding and fMRI feature selection in psychiatric classification

This dataset collection contains eleven datasets used in Locally Linear Embedding and fMRI feature selection in psychiatric classification.

The datasets given in the Links section are reduced subsets of those contained in their respective tar files (a consequence of Mendeley Data's 10GB limitation).

The Linked datasets (not the tar files) contain just the MATLAB file and the resting state image (or block-design fMRI for the MRN dataset), where appropriate.

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