scholarly journals An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ashwin Belle ◽  
Rosalyn Hobson Hargraves ◽  
Kayvan Najarian
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Physiological Signal ◽  
Cognitive Attention ◽  
Primary Focus ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Signal Processing Techniques

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

scholarly journals Automatic Identification of Algal Community from Microscopic Images

2013 ◽  
Vol 7 ◽  
pp. BBI.S12844 ◽  
Author(s):  
Natchimuthu Santhi ◽  
Chinnaraj Pradeepa ◽  
Parthasarathy Subashini ◽  
Senthil Kalaiselvi
Keyword(s):  
Image Processing ◽  
Machine Learning Algorithms ◽  
Algal Community ◽  
Automatic Identification ◽  
Object Boundary ◽  
Algal Communities ◽  
Processing Application ◽  
Segmentation Methods ◽  
Processing Techniques

A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.

scholarly journals Computational classification of animals for a highway detection system

2021 ◽  
Vol 58 ◽  
pp. e174951
Author(s):  
Denis Sato ◽  
Adroaldo José Zanella ◽  
Ernane Xavier Costa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Road Safety ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

scholarly journals OPTIMIZED CLASSIFICATION OF DE-NOISED ECG SIGNAL

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Akhil K V ◽  
Georgy Roy ◽  
Merene Joseph ◽  
Dr. Therese Yamuna Mahesh
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Feed Forward Neural Network ◽  
Physiological Signal ◽  
The Given

ECG signal is a physiological signal mainly used for the diagnosis of abnormalities in the functioning of the heart. There are limitations in detecting the nonlinearities due to the presence of noises in the ECG signal. In our work, the de-noised signal coefficients obtained from different de-noising methods are optimized for reducing the error and redundancy, and are then classified as normal or abnormal signals. The ECG signal is obtained from the PhysioBank dataset and the MIT-BIH arrhythmia database. The two methods used are the Stationary Wavelet Transform (SWT) and the Discrete Wavelet Transform (DWT). The optimization is done using Cuckoo Search (CS) algorithm and the classification is performed by Feed Forward Neural Network using back propagation (FFBP). The performances are evaluated in terms of standard metrics namely, Mean Square Error (MSE) and Signal to Noise Ratio(SNR). The results suggest that although SWT performs better than other de-noising techniques, the two methods correctly classify the given ECG signal of a monitored patient as a normal or abnormal signal.

scholarly journals Five Typical Stenches Detection Using an Electronic Nose

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2514 ◽  
Author(s):  
Wei Jiang ◽  
Daqi Gao
Keyword(s):  
Electronic Nose ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Odor Concentration ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Components Analysis ◽  
Olfactory Organs

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

scholarly journals Cumulant Features based Classification of Brain MR Images using ANN and LS-SVM Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4008-4012
Keyword(s):  
Brain Tumour ◽  
Automatic Classification ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mr Images ◽  
Mr Image ◽  
Linear Discriminant

Automatic classification of magnetic resonance (MR) brain images using machine learning algorithms has a significant role in clinical diagnosis of brain tumour. The higher order spectra cumulant features are powerful and competent tool for automatic classification. The study proposed an effective cumulant-based features to predict the severity of brain tumour. The study at first stage implicates the one-level classification of 2-D discrete wavelet transform (DWT) of taken brain MR image. The cumulants of every sub-bands are then determined to calculate the primary feature vector. Linear discriminant analysis is adopted to extract the discriminative features derived from the primary ones. A three layer feed-forward artificial neural network (ANN) and least square based support vector machine (LS-SVM) algorithms are considered to compute that the brain MR image is either belongs to normal or to one of seven other diseases (eight-class scenario). Furthermore, in one more classification problem, the input MR image is categorized as normal or abnormal (two-class scenario). The correct classification rate (CCR) of LS-SVM is superior than the ANN algorithm thereby the proposed study with LS-SVM attains higher accuracy rate in both classification scenarios of MR images.

scholarly journals Intrusion Detection System on KDD’99 Dataset with Imbalanced Classes

2021 ◽  
Vol 11 (2) ◽  
pp. 35-38
Author(s):  
Anupam Agrawal ◽  
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Geometric Mean ◽  
Principal Component ◽  
Great Accuracy ◽  
Machine Learning Algorithms ◽  
Loop Model ◽  
Imbalanced Classes ◽  
Machine Readable

The paper describes a method of intrusion detection that keeps check of it with help of machine learning algorithms. The experiments have been conducted over KDD’99 cup dataset, which is an imbalanced dataset, cause of which recall of some classes coming drastically low as there were not enough instances of it in there. For Preprocessing of dataset One Hot Encoding and Label Encoding to make it machine readable. The dimensionality of dataset has been reduced using Principal Component Analysis and classification of dataset into classes viz. attack and normal is done by Naïve Bayes Classifier. Due to imbalanced nature, shift of focus was on recall and overall recall and compared with other models which have achieved great accuracy. Based on the results, using a self optimizing loop, model has achieved better geometric mean accuracy.

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