Breast Cancer Detection Via Wavelet Energy and Support Vector Machine

© 2018 IEEE. Breast cancer as one of the most feared killers of women, there are still no effective means of prevention and treatment on it. However, the popularity of its research continues to rise in academic field. The traditional medical diagnosis is mainly by observing the patient's symptoms to confirm the variety of diseases, but the efficiency is undesirable, and the scientific contribution is poor. At present, due to the dramatical development of the application of machine learning in data detection, the application of computer technology in disease diagnosis has become a new and effective means. This paper used the wavelet energy to extract features of breast cancer, then established a breast cancer predicting model, while re-use data grouping function of support vector machine (SVM), then algorithm would accurately distinguish the characteristics of the data among benign malignant tumors. So, the accuracy of intelligent diagnosis in breast cancer has be improved, and proven to be better than two state-of-the-art approaches

Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches

Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review

An efficient system that is capable to detect driver fatigue is urgently needed to help avoid road crashes. Recently, there has been an increase of interest in the application of electroencephalogram (EEG) to detect driver fatigue. Feature extraction and signal classification are the most critical steps in the EEG signal analysis. A reliable method for feature extraction is important to obtain robust signal classification. Meanwhile, a robust algorithm for signal classification will accurately classify the feature to a particular class. This paper concisely reviews the pros and cons of the existing techniques for feature extraction and signal classification and its fatigue detection accuracy performance. The integration of combined entropy (feature extraction) with support vector machine (SVM) and random forest (classifier) gives the best fatigue detection accuracy of 98.7% and 97.5% respectively. The outcomes from this study will guide future researchers in choosing a suitable technique for feature extraction and signal classification for EEG data processing and shed light on directions for future research and development of driver fatigue countermeasures

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Human memory stores various information and events that can be retrieved when needed. Many factors can influence memory performance which either provide positive or negative feedback. This research investigates the effect of audiovisual stimulation on adult memory based on electroencephalography (EEG) analysis. Sixty college students are participating in this experimental study. They must memorize visual assessment at two different levels in Mozart's Sonata music and white noise stimulation. During memorizing duration, the EEG machine records brain electrical activity based on 10–20 electrode placement. The collected raw brain signals are processed using the wavelet-based method. The stationary wavelet transform (SWT) is used for artifact elimination, whereas discrete wavelet transform (DWT) is applied to obtain alpha, beta, theta, and gamma rhythms. The time–frequency domain features are collected from the EEG signals to discover the influence of audiovisual stimulation. The findings showed a different increasing and decreasing trend of mean, standard deviation, and peak-to-peak EEG signal amplitude before and after audiovisual stimulation exposure. The theta and alpha rhythms showed the most influence with the highest relative power. Suppression of relative gamma and beta power is vital for improving visual information processing and attention level. Memorizing in audio stimulation has suppressed the relative alpha, beta, theta, and gamma power, leading to better visual memorizing ability. The white noise stimulation provides more influence on adult visual memory