Artificial Intelligence

Dataset consisting of 17 images of Nile Tilapia bred in a circular pond (diameter=10m, deep = 1.5m ) located at the tronconal in Hermosillo, Sonora, México and an aquarium.

From the total of fish, 17 were selected. Each fish was measured with a ruler; the longitude was measured from the beginning of tail to tip and the height was measured from the beginning of the dorsal fin to the bottom, and its weight was measured with a standard balance.






This dataset was initially collected by Mrs Athira P K  with the help of  teachers and students of Rahmania school for handicapped, Kozhikode, Kerala, India. Later the dataset was extended by many other BTech and MTech students with the help of their friends.

MUDRA NITC dataset consists of videos of static and dynamic gestures of Indian sign language. In static gestures mainly static alphabets videos and  preprocessed image frames are included.


In recent years, teaching-learning methods have emerged into a completely new dimension from what used to be a traditional approach. The in-person lectures have been converted into online virtual learning, the traditional record-keeping has been replaced by robust learning management systems which have made the teaching-learning process lot more efficient and convenient.


These are the two lncRNA and protein interactions (LPI) datasets, which collected and built by Zhang et al. and Zheng et al., respectively.

dataset1: Wen Zhang, Qianlong Qu, Yunqiu Zhang, Wei Wang. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions. Neurocomputing 273: 526-534 (2018)


The concept of wellness, as proposed by Halbert L. Dunn, recognizes the importance of multiple dimensions, such as social and mental well-being, in maintaining overall health. Neglecting these dimensions can have long-term negative consequences on an individual's mental well-being. In the context of traditional in-person therapy sessions, efforts are made to manually identify underlying factors that contribute to mental disturbances, as these factors, if triggered, can potentially lead to severe mental health disorders.


Objective: The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science. Methods: By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset.


This dataset investigates the suitability of different filters for Learning-Based Motion Magnification (LBMM) and examines the impact of filter parameters on output results. The study finds that the Butterworth filter produces satisfactory results, while the analysis of IIR filters is unsatisfactory due to computational and memory limitations. Additionally, the efficacy of IIR filters for image processing and the reliability of FIR filters are called into question.


With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track objects in aerial images and videos. To develop and test such algorithms,  datasets of aerial videos captured from drones are essential.



This data set contains three activities of laying up, passing, and shooting of nine professional basketball players, collected and processed by Yu Zhou, Chuanshi Xie, and Yufan Wang.


The dataset contains basketball activity data for nine varsity basketball players of professional skill levels. Each player wore a smart bracelet on their right wrist to record activity data during the event. The smart bracelet contains an accelerometer and gyroscope that collects acceleration and angular velocity information, and it has a sampling frequency of 50 Hz. The basketball activities of the players are laying up, passing and shooting, which are defined as shown in Table 1.