Machine Learning

We created a 2563-image custom dragon fruit image dataset, with 1248 images of raw dragon fruits and 1315 photographs of ripe dragon fruits. The images were taken with the Nikon D5200 DSLR and OnePlus 6's Sony IMX 519 16 megapixel camera. The photographs taken with the DSLR camera had a resolution of 4000 by 6000 pixels, while those taken with the OnePlus6 had a resolution of 3456 by 4608 pixels. They were photographed in natural sunlight. The average temperature during that time was 28°C (84.2°F), with partly sunny skies, 65 percent humidity, and 17 km/h wind speeds.

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We present an improved EfficientDet network module , accomplished by adding attention mechanism, deformable convolution, and multiclass focal loss. The image shows the flowchart of proposed approach.

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It presents bigest Assamese Isolated handwritten numeral dataset that is consist of   35106 numerals samples in grayscale format of uniform size 28x28. The contributors arestudents and Academic professionals.

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This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.

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1840 Views

These are the coefficients of the equivalent velocity increment estimator for minimum-time low-thrust transfers to the geostationary orbit.

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This dataset contains raw captured packet headers from six commercial drones.

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Question Answer Pair dataset for Sentiment Analysis Tasks.

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Non uniformly illuminated Blender simulated and camera captured haze masks.

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The following folder contains the data simulated using the metholodgy described in the paper titled "Federated Learning based Base Station Selection using LiDAR Data"

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Data of cricket bowlers was recorded using wearable IMU sensors. Data were collected from the designed sensor units placed on the thigh and tibia of the front leg of each bowler using a strap. Recorded data include 3-axes accelerometer data and 4-dimensional quaternion data.

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