The objective of this paper is to propose well-suited machine learning models to predict throughput such as artificial neural networks (ANN), k-Nearest Neighbours (KNN) regression, random forest regression, and graph neural networks (GNN).

This paper proposes a framework for a robotic arm coupled with a wheelchair, to aid amputees in accessing physical objects. The framework comprises primarily the software modules with a hardware prototype that provides an environment to test the functionality. The hardware makes use of a stereo vision camera to provide a video feed of the physical objects present in front of the wheelchair, in addition to obtaining a series of three dimensional point clouds corresponding to the video feed. The hardware includes a provision for two revolute joints offering pitch and yaw motion, located on a moving chair.

The major cause of deaths around the world is cardiovascular disease. Arrhythmia is one such disease in which the heart beats in an abnormal rhythm or rate. The detection and classification of various types of cardiac arrhythmia is a challenging task for doctors. If it’s not done accurately or not done on time, the patient’s life can be at a great risk, as few arrhythmias are serious, and some can even cause potentially fatal symptoms. This paper illustrates an effective solution to help doctors in the critical diagnosis of various types of cardiac arrhythmias. To classify the type of arrhythmia, the patient might be suffering from, the solution utilizes a variety of machine learning algorithms. UCI machine learning repository dataset is used for training and testing the model. Implementing the solution can provide a much-needed early diagnosis that proves to be critical in saving many human lives.

This paper presents a method to automate the identification of scenes from Indian mythology in works of art such as paintings and line drawings. Artificial neural networks were used to detect mythological characters, animals, landscapes and weapons in the input image to aid scene detection. The mythological texts associated with the image were used to discern the strength of the relationships between characters and build a Character Association Graph, which was used to improve the predictions made by the neural networks. These predictions and a graph-based algorithm were used to map the input image to a set of likely scenes. Experiments were carried out on images from the ancient Indian epic, The Ramayana, with prediction accuracy of 76% on high definition (HD) images and 64% on non-HD images.

This paper proposes a low-cost novel EEG based BCI prototype to detect if an on-site worker is sleep-deprived or not elegantly. The worker is required to wear a modified safety helmet with an innocuously placed signal acquisition device and it’s paraphernalia that does not hinder the worker’s activities. A few time and frequency domain features have been derived from the collected data to recognize sleep deprivation of workers. The smart helmet communicates with a local server within radio range. The server runs a random forest classifier algorithm to classify if the worker is sleep deprived or not and alerts the supervisor if necessary. A single Inertial Measurement Unit (IMU) sensor is utilized to detect if the worker has fallen down. The entire setup is supported by an android application that keeps the supervisor up-to-date on the statuses of the workers. A classification accuracy as high as 98% for the helmet based EEG setup was obtained through in-house live experiments upon sleep-deprived subjects.

This paper outlines a system that combines the existing trivial traffic signal lights with sensors, which are capable of synchronizing with each other, and take certain decisions on the switching of lights as per the given set of conditions.