Machine Learning
The Tunnel Cable Fire dataset is derived experimentally, this dataset contains images of cable flames at different stages, different cable layers, and different wind speeds, with a special focus on computer vision tasks such as fire detection and segmentation. These images have been enhanced with mosaic data for a total of 1812 datasets, including single and double layer cable fire images in the case of no wind and wind speed of 2.7m/s.
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AI was begun to increasingly part of EFL education introducing novelties, issues and opportunities. Current studies explore many possibilities, such as employing federated learning as a protection of data privacy and the implementation of ChatGPT in multilingual learning. This article offers comprehensive analysis of how AI could transform pedagogy, improve writing skills and motivate students through evaluating the novelty, existing voids, need, and implications of many the most promising studies.
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This dataset contains human motion data collected using inertial measurement units (IMUs), including accelerometer and gyroscope readings, from participants performing specific activities. The data was gathered under controlled conditions with verbal informed consent and includes diverse motion patterns that can be used for research in human activity recognition, wearable sensor applications, and machine learning algorithm development. Each sample is labeled and processed to ensure consistency, with raw and augmented data available for use.
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This dataset extends the standard Myers-Briggs Type Indicator (MBTI) dataset, widely available on Kaggle, by incorporating advanced data augmentation techniques leveraging GPT-based Transformers. The augmentation addresses inherent class imbalance and data sparsity issues in the original dataset, significantly enriching the volume and diversity of textual samples while maintaining linguistic and contextual fidelity to the MBTI personality types.
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This dataset was developed using the MOBATSim simulator in MATLAB 2020b, designed to mimic real-world autonomous vehicle (AV) environments. It focuses on providing high-quality data for research in anomaly detection and cybersecurity, particularly addressing False Data Injection Attacks (FDIA). The dataset includes comprehensive sensor information, such as speed, rotational movements, positional coordinates, and labelled attack data, enabling supervised learning.
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- The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
- Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.
- The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.
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The dataset was specifically created to address the need for violence detection in surveillance systems. It consists of self-recorded videos simulating different types of violent activities relevant to college environments. The dataset is organized into four distinct classes:
Slap
Punch
Kick
Group Violence
Others - Over Crowding, Loitering, Assault, Abuse
Each video is labeled according to its corresponding class to facilitate supervised learning for violence detection models.
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277 Views
All multimodal recommendation datasets used in the manuscript Enhancing Robustness and Generalization Capability for Multimodal Recommender Systems via Sharpness-Aware Minimization (BSAM), which includes five Amazon datasets. Each dataset includes both visual and textual modalities. Baby, Sports, Clothing, Pet, and Office from Amazon. All the datasets comprise textual and visual features in the form of item descriptions and images. Our data preprocessing methodology follows the approach outlined in the MMRec Framework.
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All multimodal recommendation datasets used in the manuscript Enhancing Robustness and Generalization Capability for Multimodal Recommender Systems via Sharpness-Aware Minimization (BSAM), which includes five Amazon datasets. Each dataset includes both visual and textual modalities. Baby, Sports, Clothing, Pet, and Office from Amazon. All the datasets comprise textual and visual features in the form of item descriptions and images. Our data preprocessing methodology follows the approach outlined in the MMRec Framework.
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The proper evaluation of food freshness is critical to ensure safety, quality along with customer satisfaction in the food industry. While numerous datasets exists for individual food items,a unified and comprehensive dataset which encompass diversified food categories remained as a significant gap in research. This research presented UC-FCD, a novel dataset designed to address this gap.
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96 Views