Artificial Intelligence
The fitness landscape of optimization problems significantly impacts the performance of metaheuristic optimization algorithms. While no algorithm performs well on all problems, identifying the most suitable one for a specific problem can be achieved by extracting features from the landscape. However, these features are often extracted before optimization, disregarding valuable knowledge collected during the optimization process. Moreover, existing algorithm selection methods rely on a single algorithm, limiting flexibility and missing potentially better options.
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Iron Ore Powder Dataset: The mineral powder dataset is utilized to predict the moisture content present in the mineral powder. Images of the mineral powder at different moisture levels exhibit variations in texture, which are influenced by the moisture content and significantly affect the surface morphology of the powder. Specifically, this variation is evident in the changes in texture and surface features.
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Smart Home Automation (SHA) has significantly improved homes’ convenience, comfort, security, and safety. It has gained widespread use due to its intelligent monitoring and quick response capabilities. The current state of SHA enables effective monitoring and motion detection. However, false notifications remain a significant challenge, as they can cause unnecessary alarms in intrusion detection systems. To address this, we propose an intelligent model for a smart home security system that uses computer vision techniques to detect trespasser movement near the boundary wall.
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The Metaverse Gait Authentication Dataset (MGAD) is a large-scale gait dataset designed for biometric authentication in virtual environments.
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This dataset comprises Channel State Information (CSI) data collected from WiFi signals in six indoor environments, specifically designed for research in indoor intrusion detection. The dataset captures fine-grained variations in wireless signals caused by human, which are indicative of potential intrusions. CSI data, extracted from commercial WiFi chipsets, provides detailed amplitude and phase information across subcarriers, enabling robust detection of subtle environmental changes.
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Legal analysis utilizing natural language processing and machine learning technologies is a difficult undertaking that has recently sparked the interest of many academics and industries. Using a human-annotated dataset summarized into colloquial Thai from Supreme Court decisions, this work investigates a different combination of NLP, ML, and rule-based techniques for accurate legal case analysis as per Thai law, especially property-related offences, with the intuition to imitate the lawyer's cognitive process.
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The process of allocating the intestate inheritance among the statutory heirs is sophisticated yet occurs regularly. Many scholars have attempted to develop automated allocation systems to tackle this High task. However, most amply existing systems rely on conventional form-based input, which may overwhelm the general users. Furthermore, no existing system concerning intestate inheritance allocation according to the Civil and Commercial Code of Thailand is publicly available.
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This dataset is used to evaluate the effectiveness of the Growing-MoE learning framework. The dataset contains tasks across computer vision (CV) and natural language processing (NLP). The dataset includes CV tasks such as CIFAR, ImageNet, Cars, and Flowers, as well as NLP tasks including English Wikipedia and GLUE benchmark.
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We introduce a dataset comprised of energy consumption data from smart meters in French households, capturing detailed, disaggregated time series for various home appliances. This dataset covers a six-month period with a one-minute sampling rate across five different households. The objective of this dataset is to support the development of models that learn disentangled representations of time series energy data, which can significantly enhance model generalization across both in-distribution and out-of-distribution scenarios.
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This dataset contains audio recordings sourced from more than 57 TV shows provided by the Saudi Broadcasting Authority. The total number of hours published for these recordings is ~667 hours. The recordings are in Arabic, the majority are in Saudi dialects, and some are in other dialects. To enhance the usage of SADA, the dataset is split into training, validation, and testing sets. Each of validation and testing sets is around 10 hours in audio segments length while training set is 418 hours.
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