Artificial Intelligence

Solar-powered insecticidal lamps have been widely used in agricultural pest control systems, where stable 4G connectivity is critical for real-time transmission of multi-source field data (soil parameters, pest images, and environmental metrics). However, the lack of reliable 4G signal strength datasets in agricultural scenarios, especially under rainfall conditions that cause signal degradation, poses a great challenge to deployment planning and network reliability.
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This dataset comprises 2 million synthetic samples generated using the Variational Autoencoder-Generative Adversarial Network (VAE-GAN) technique. The dataset is designed to facilitate cardiovascular disease prediction through various demographic, physical, and health-related attributes. It contains essential physiological and behavioral indicators that contribute to cardiovascular health.
Dataset Description The dataset consists of the following features:
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This dataset comprises 2 million synthetic samples generated using the Variational Autoencoder-Generative Adversarial Network (VAE-GAN) technique. The dataset is designed to facilitate cardiovascular disease prediction through various demographic, physical, and health-related attributes. It contains essential physiological and behavioral indicators that contribute to cardiovascular health.
Dataset Description The dataset consists of the following features:
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In recent years, the fusion of artificial intelligence and semantic web technologies has paved the way for innovative approaches to managing and utilizing information. With the growing demand for structured gastronomical data, there is a need for well-defined ontologies that facilitate recipe organization, ingredient classification, nutritional insights, and personalized diet recommendations. The dataset presents a multilingual recipe ontology and knowledge graph, capturing critical relationships between ingredients, nutrition, cooking actions, and recipe planning.
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MagNet is a large-scale dataset designed to enable researchers modeling magnetic core loss using machine learning to accelerate the design process of power electronics. The dataset contains a large amount of voltage and current data of different magnetic components with different shapes of waveforms and different properties measured in the real world. Researchers may use these data as pairs of excitations and responses to build up dynamic magnetic models or calculate the core loss to derive static models.
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Large Vision-Language Models (LVLMs) struggle with distractions, particularly in the presence of irrelevant visual or textual inputs. This paper introduces the Irrelevance Robust Visual Question Answering (IR-VQA) benchmark to systematically evaluate and mitigate this ``multimodal distractibility". IR-VQA targets three key paradigms: irrelevant visual contexts in image-independent questions, irrelevant textual contexts in image-dependent questions, and text-only distractions.
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Agriculture is the backbone of Mizoram’s state economy as the majority of the people use agriculture and its allied sector as their livelihood. According to the 2011 census, more than 50% of the people are still engaged in agriculture and its related activities. Jhum cultivation or shifting cultivation is the primary farming pattern in the state.
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The dataset consists of two primary files: dataset.json and analysis_script.ipynb. The dataset.json file contains structured records of AI-assisted psychological therapy sessions, including emotion recognition, NLP techniques, cognitive behavioral therapy (CBT) patterns, hypnotherapy data, user feedback, and therapy outcomes. The analysis_script.ipynb Jupyter Notebook provides data preprocessing, visualization, and statistical analysis of therapy session outcomes.
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The datasets include six publicly datasets for dynamic graph analysis: UCI captures social interactions among UC Irvine students; Digg records user interactions on the news-sharing website; Email-Eu-core details email communications in a European research institution; ia-contacts-dublin tracks human contacts in Dublin; sx-mathoverflow and sx-askubuntu are two temporal networks datasets formed from user activities on StackOverflow.
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This work presents a dataset based on multiple network and service metrics (KPIs and KQIs), the latest providing the E2E conditions of video on demand service. Particularly, the dataset also includes an attack situation where an attacker injects traffic into the network. In total, there are 3600 samples, with different configurations of Physical Resource Blocks and cell gain, from sessions of 60 seconds.
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