Posture Recognition Using Ensemble Deep Models under Various Home Environments

This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself.

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A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

Frontiers in Energy Research ◽

10.3389/fenrg.2021.720831 ◽

2021 ◽

Vol 9 ◽

Author(s):

Chen Li ◽

Gaoqi Liang ◽

Huan Zhao ◽

Guo Chen

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Event Detection ◽

Detection Algorithm ◽

Demand Side ◽

Rule Based ◽

Detection Algorithms ◽

Deep Model ◽

Conventional Machine

Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.

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Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210711 ◽

2021 ◽

pp. 1-12

Author(s):

Mukul Kumar ◽

Nipun Katyal ◽

Nersisson Ruban ◽

Elena Lyakso ◽

A. Mary Mekala ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Research Work ◽

Oral Communication ◽

Neural Net ◽

Emotion Classification ◽

Style Transfer ◽

Conventional Machine ◽

Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

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Deep Learning and Medical Imaging

Advances in Healthcare Information Systems and Administration - Intelligent Systems for Healthcare Management and Delivery ◽

10.4018/978-1-5225-7071-4.ch005 ◽

2019 ◽

pp. 101-147

Author(s):

Nourhan Mohamed Zayed ◽

Heba A. Elnemr

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Medical Imaging ◽

Medical Image Analysis ◽

Future Research ◽

Research Directions ◽

Conventional Machine ◽

Future Research Directions ◽

Nested Hierarchy ◽

Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

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Content Noise Detection Model Using Deep Learning in Web Forums

Sustainability ◽

10.3390/su12125074 ◽

2020 ◽

Vol 12 (12) ◽

pp. 5074

Author(s):

Jiyoung Woo ◽

Jaeseok Yun

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Model ◽

Detection Model ◽

Proposed Model ◽

Web Forum ◽

Web Forums ◽

Conventional Machine ◽

Text Features ◽

Deep Learning Model

Spam posts in web forum discussions cause user inconvenience and lower the value of the web forum as an open source of user opinion. In this regard, as the importance of a web post is evaluated in terms of the number of involved authors, noise distorts the analysis results by adding unnecessary data to the opinion analysis. Here, in this work, an automatic detection model for spam posts in web forums using both conventional machine learning and deep learning is proposed. To automatically differentiate between normal posts and spam, evaluators were asked to recognize spam posts in advance. To construct the machine learning-based model, text features from posted content using text mining techniques from the perspective of linguistics were extracted, and supervised learning was performed to distinguish content noise from normal posts. For the deep learning model, raw text including and excluding special characters was utilized. A comparison analysis on deep neural networks using the two different recurrent neural network (RNN) models of the simple RNN and long short-term memory (LSTM) network was also performed. Furthermore, the proposed model was applied to two web forums. The experimental results indicate that the deep learning model affords significant improvements over the accuracy of conventional machine learning associated with text features. The accuracy of the proposed model using LSTM reaches 98.56%, and the precision and recall of the noise class reach 99% and 99.53%, respectively.

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Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study

Machine Learning for Sustainable Development ◽

10.1515/9783110702514-011 ◽

2021 ◽

pp. 175-198

Author(s):

Roopa B. Hegde ◽

Vidya Kudva ◽

Keerthana Prasad ◽

Brij Mohan Singh ◽

Shyamala Guruvare

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Conventional Machine

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Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Nutrients ◽

10.3390/nu12082466 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2466

Author(s):

Gonzalo Colmenarejo

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Adolescent Obesity ◽

The Body ◽

Childhood And Adolescence ◽

Hormonal Changes ◽

Learning Models ◽

Model Complex ◽

Childhood And Adolescent Obesity ◽

Machine Learning Models

The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

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Comparison of deep learning and conventional machine learning methods for classification of colon polyp types

The EuroBiotech Journal ◽

10.2478/ebtj-2021-0006 ◽

2021 ◽

Vol 5 (1) ◽

pp. 34-42

Author(s):

Refika Sultan Doğan ◽

Bülent Yılmaz

Keyword(s):

Machine Learning ◽

Deep Learning ◽

White Light ◽

Narrow Band ◽

Optical Biopsy ◽

Colon Polyps ◽

Tissue Biopsy ◽

Conventional Machine ◽

White Light Imaging

AbstractDetermination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.

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An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

10.36227/techrxiv.13466597.v1 ◽

2020 ◽

Author(s):

Jahnvi Gupta ◽

Nitin Gupta ◽

Mukesh Kumar ◽

Ritwik Duggal

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Patient Monitoring ◽

Elderly Care ◽

Medical Science ◽

Sensor Technology ◽

Human Posture ◽

Posture Recognition ◽

Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

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Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

Sensors ◽

10.3390/s19225046 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5046 ◽

Cited By ~ 2

Author(s):

Lvwen Huang ◽

Han Guo ◽

Qinqin Rao ◽

Zixia Hou ◽

Shuqin Li ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Three Dimensional ◽

Automatic Measurement ◽

The Body ◽

Surface Shape ◽

Body Dimension ◽

Qinchuan Cattle ◽

Body Dimensions ◽

Deep Model

For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention.

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Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives

Methods of Information in Medicine ◽

10.1055/s-0039-1677692 ◽

2019 ◽

Vol 58 (01) ◽

pp. 031-041 ◽

Cited By ~ 4

Author(s):

Sara Rabhi ◽

Jérémie Jakubowicz ◽

Marie-Helene Metzger

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Characteristic Curve ◽

Bayesian Optimization ◽

Gradient Boosting ◽

Support Vector ◽

University Hospitals ◽

Infection Control Practitioner ◽

Medical Reports ◽

Conventional Machine

Objective The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. Methods The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove). Results CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference. Conclusions The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports.

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