Artificial Intelligence
This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.
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The dataset contains integrating renewable energy sources into power grids, emphasizing the need for advanced data-driven optimization models for optimal power flow problems. The dataset, which includes comprehensive details on both load and generator buses, covering active and reactive power measurements and voltage magnitudes and angles for the modified IEEE 39 bus system with wind power integration, is ideally suited for data-driven power system analysis studies. The dataset was generated for a part of the experiments.
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The 4-inch and 2-inch dataset are generated using random mover poses with single square coils. The sample sizes are 8 million and 9.6 million for the 4-inch and 2-inch datasets, respectively. The input and label data are min-max normalized to range between zero and one. The 20.6 million dataset file is created using the same wrench model (GT model), which has infeasible mover poses. The infeasible mover poses are scruntinized for the 4-inch and 2-inch dataset to make sure the PM is above the top surface of the coil.
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This study assesses the viability of employing GPT-4, an artificial intelligence model, for generating personalized exercise therapies for knee osteoarthritis patients amidst increasing demand for rehabilitation services and constrained resources, especially in developing and underdeveloped countries. Fifty patients with symptomatic knee osteoarthritis, selected randomly from a group of one hundred and thirtytwo diagnosed between January 1st, 2023, and May 1st, 2023, received personalized exercise programs from GPT- 4.
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The dataset comprises many variables like area, production, season, minimum humidity, maximum humidity, minimum temperature, maximum temperature, district, crop name which impact the agricultural output of different crops in the region of Bangladesh. Surveys were conducted in various areas of Bangladesh to gather data on different types of crops. The primary aim of this collection is to facilitate research in the domain of precision agriculture.
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A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.
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CT RECIST response, as measured by the change of tumor diameter, can accurately reflect objective response rate for advanced NSCLC patients. However, there exists obvious discordant between CT RECIST response and prognostic indicators. Thus, our study aimed to identify a new CT RECIST response indicator at the early treatment stage to reflect the prognosis more accurately.We studied 916 tumor lesions obtained through deep learning and found that the shape of the lesions was irregular.
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We utilized Digital Ocean's cloud service, setting up three Linux virtual machines, each with 1vCPU, 1GB of memory, and a 10GB disk. The architecture included an API gateway for routing requests to a stateless application service backed by a database for storing application data. The application operates the service under a fluctuating workload generated by a load-testing script to simulate real-world usage scenarios. The target source or the application service is integrated with Prometheus, a monitoring tool for gathering system metrics.
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This study presents an automated approach for the generation of graphs from hand-drawn electrical circuit diagrams, aiming to streamline the digitization process and enhance the efficiency of traditional circuit design methods. Leveraging image processing, computer vision algorithms, and machine learning techniques, the system accurately identifies and extracts circuit components, capturing spatial relationships and diverse drawing styles.
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