One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental data is time and resource consuming in practice, necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novel approach is presented in this work, where a multi-ion-sensing emulator is designed to explain the response of an ion-sensing array in a mixed-ion environment.

The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. Most of the earlier works in this area are based on computer vision. However, mostly these works are limited in providing real time solution for the detection activities. Therefore, we are currently working toward the Internet of Things (IoT) based solution for the human posture recognition.

Vehicular networks have various characteristics that can be helpful in their inter-relations identifications. Considering that two vehicles are moving at a certain speed and distance, it is important to know about their communication capability. The vehicles can communicate within their communication range. However, given previous data of a road segment, our dataset can identify the compatibility time between two selected vehicles. The compatibility time is defined as the time two vehicles will be within the communication range of each other.

INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. To overcome this problem, we have created a dataset named FruitsGB (Fruits Good/Bad) dataset.