Data-driven audiogram classifier using data normalization and multi-stage feature selection.

Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients' audiograms. Configuring the hearing aid is done by modifying the designed filters' gains to match the patient's audiogram.

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver.

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life.

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building.

In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or "smellprint" emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model.

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN).

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ.