Smart Self-Immolation Prediction Techniques: An Analytical Study for Predicting Suicidal Tendencies Using Machine Learning Algorithms

Modern science and technology development enables humans to control devices using their brains. brain computer interface (BCI) system using EEG (Electroencephalogram) is a non-invasive method that uses brain signals to control robots. Brain control robots can assist people with disabilities to improve their livelihood. In our proposed work to control wheelchair movement for rehabilitation using EEG, electrodes are first placed on the subject's scalp to acquire brain signal activity. After that, the signals got filtered using fast fourier transformation (FFT) method, and then features got extracted using power spectral density (PSD). Random forest is used for the classification of wheelchair movement. For this purpose, a publicly available dataset from Kaggle is used, and an average accuracy of 96.79 is achieved. The proposed architecture has outperformed all the existing ones in its concerned domain; thus, it is suitable, cost-effective, and flexible for the users, which also helps maintain user privacy.

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An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram

Bag

Ghosh

Saha

2022

Design and Control Advances in Robotics

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A Machine Learning Approach Towards Heart Attack Prediction

Ghosh

Sharma

Saha

2022

Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death

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Various types of heart diseases and conditions leading to increasing chance of heart attack have been a serious concern all over the world. Several factors like blood pressure, cholesterol, diabetes, obesity can affect the heart, and thus, those should be monitored regularly to prevent the chance of heart attack in people of different age groups. This chapter at first has analyzed different existing benchmarks of heart attack analysis. Being motivated by the shortcomings of the state-of-the-art literature and to address the challenges, it has introduced support vector machine, the most popular supervised machine learning algorithm to classify the chance of heart attack using a dataset downloaded from Kaggle. The experimental result has been evaluated using different performance metrics, including accuracy, error rate, precision, recall, F1 score. Finally, the performance has been compared with the existing related works also to validate its effectiveness and efficiency in real-time heart attack prediction.

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Smart Self-Immolation Prediction Techniques: An Analytical Study for Predicting Suicidal Tendencies Using Machine Learning Algorithms

Cited by 2 publications

References 48 publications

An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram

An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram

A Machine Learning Approach Towards Heart Attack Prediction

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