Artificial Intelligence
Data was acquired using data aquisiton interface in a laboratory on a flow control unit. The data has been transformed into two excel spreadsheets which was later used in Matlab. This dataset also consists of three Matlab codes. First one is the code for the experiment in which the ANN models were developed. Second Matlab code is the code for data importing from the excel spreadsheets and the third Matlab code is the data preparation code for the Simulink purposes in order to test the models.
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With the rapid advancement of large language models (LLMs), Model-as-a-Service (MaaS) has emerged as a powerful paradigm, enabling providers to deliver pre-trained models, computational resources, and database management within a unified platform.
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New technology solutions including tablets and advanced applications exist in modern classrooms across Indonesia but typically they miss the core educational objectives. The process of elementary school children memorizing the emoticon "happy" represents a lack of comprehension while high school students find themselves overwhelmed by IoT data and teachers work to adapt to AI-based educational requirements.
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The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention.
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The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6 viral diseases, and 3 mite-induced diseases, categorized into 97 classes.
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The data of ROSMAP dataset have been preprocessed and dimensionally reduced in the original research, thus we did not perform further preprocessing on it. For SCZ dataset, we firstly removed features with more than 50% missing or 0 expression values for all omics sets. Log transformation was then utilized to normalize omics expression values, and the Z-score method was used to standardize all features of each sample in every omics sets. Only samples presented in both omics sets and label set were retained in the dataset of analysis.
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This dataset supports the LookCursor AI project, which implements eye-tracking-based cursor control using OpenCV and Dlib. The primary file included is shape_predictor_68_face_landmarks.dat, a pre-trained model used to detect and map 68 facial landmarks essential for tracking eye movements. The dataset enables accurate facial feature detection, which is critical for cursor movement based on eye gaze.
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**PDBBindv2016** | Binding Affinity Regression | Benchmark Evaluation (Effectiveness) | Each sample in the PDBBind v2016 dataset is a complex, but we extracted the sequence data with substantial information loss to yield a protein-ligand sequence pair. We maintained the same split setting used in a previous study, where the refined set (excluding the core set) is treated as training (train.csv) and validation (valid.csv) sets, while the core set (complexes with the highest resolution) is treated as the test set (test.csv).
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The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
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467 Views
With the increasing uncertainties introduced by intermittent renewable energy sources, as a critical decision-making tool for power system operations, security-constrained unit commitment (SCUC) provides an efficient solution for economically and robustly responding to the changes in the power system operating state. In this study, a graph reinforcement learning (GRL)-based approach is proposed to address the day-ahead SCUC problem, incorporating alternating current (AC) power flow constraints.
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