With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.
This paper considers an application of phase-only digital encryption to the three-pass protocol leading to a new 'nokey-exchange algorithm'. After providing a study on the theoretical background to the method, an algorithm is presented on a step-by-step basis together with three examples of cryptanalysis. A prototype MATLAB function is provided for validation of the approach and for further development by interested readers.
Mobile Applications (Apps) offer numerous advantages related to entertainment, communication, monitoring and sensing to name a few. In this study, a Gyroscope Explorer Apps is employed for data gathering of azimuth, pitch, and roll. The mobile phone is carried by Lego Mindstorms (EV3), in which it travels the ladder into the different angles: 4.13°, 7.77°, 10.81°, and 12.80°. The data collected was classified into eight classes: 4.13°uphill,
Worldwide improvements in the quality of life highlight immense need to have a remote health monitoring system that can provide critical biomedical data. This paper presents a low-cost health monitoring system, forming part of the Internet of Things (IoT), which aims at continuous, 24/7 monitoring of elderly people and disabled people. The system is implemented with a variety of sensors, for example, temperature, heart rate, and movement measurements, to observe a person's status. Doctors may also prescribe this system with a specific number and type of sensor, depending on a patient's condition. In a case study, three sensors measured the status of a person during the day. The measurements reflected the actions of the person as he/she relaxed or was active, in addition to monitoring his/her state of health. The observed data were recorded in a database that can be displayed by authorized caregivers. Results witnessed the efficacy of the proposed system. The proposed system finds enormous potential in giving remote healthcare facilities, especially to unaccompanied older adults.
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