Artificial Intelligence
The select_mmsi.xls file is the MMIS number were selected. The information include the gear type of each vessel, the startdate and enddate of random selected 5000 position points. The txt files are the raw information of each position points for all vessel.
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The data collection was carried out over several months and across several cities including but not limited to Quetta, Islamabad and Karachi, Pakistan. Ultimately, the number of images collected as part of the Pakistani dataset were, albeit in a very small quantity. The images taken were also distributed across the classes unevenly, just like the German dataset. All the 359 images were then manually cropped to filter out the unwanted image background data. All the images were sorted into folders with names corresponding to the label of the images.
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This is a supplementary data file, providing the data used to evaluate the performance of our 3D fully convolutional neural network. This network removes reverberation noise from ultrasound channel data. This dataset is simulated ultrasound channel data, simulated in Field II Pro, and has artificial reverberation and thermal noise added. This dataset will be linked to our publication, once it is accepted.
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Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).
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food recognition
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We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.
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In this work, physical parameter‐based modeling of small signal parameters for a metal‐semiconductor field‐effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function.
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IEEE Access "A Process-aware memory compact-device model using long-short term memory"
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Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.
Many of the investigator working on disease detection in Basil leaves where the following diseases occur
1) Gray Mold
2) Basal Root Rot, Damping Off
3) Fusarium Wilt and Crown Rot
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Description:
This repository contains the datasets used as part of the OC2 lab's work on Student Performance prediction and student engagement prediction in eLearning environments using machine learning methods.
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