scholarly journals Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Zhou
Keyword(s):  
Deep Learning ◽  
Functional Imaging ◽  
Head Model ◽  
Topographic Map ◽  
Human Beings ◽  
Head Surface ◽  
Combining Data ◽  
Intelligent Technology ◽  
3D Cnn ◽  
Virtual Data

When facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology and computer science. Therefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. Firstly, the EEG forward model is established according to functional magnetic resonance imaging (fMRI), and the transfer matrix from the signal source at the cerebral cortex to the head surface electrode is obtained. Therefore, the activation results of fMRI emotional experiment can be mapped to the three-layer head model to obtain the EEG topographic map reflecting the degree of emotional correlation. Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. Experimental data analysis shows that, compared with traditional methods, the proposed method can retain emotion related EEG signals to a greater extent and obtain a higher accuracy of emotion five classifications under the same feature dimension.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Deep Learning-based Framework for Smart Sustainable Cities

2019 ◽  
Vol 15 (4) ◽  
pp. 76-107
Author(s):  
Nagarathna Ravi ◽  
Vimala Rani P ◽  
Rajesh Alias Harinarayan R ◽  
Mercy Shalinie S ◽  
Karthick Seshadri ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Deep Learning ◽  
Air Pollutants ◽  
Human Life ◽  
Deep Belief Network ◽  
Motor Vehicles ◽  
Human Beings ◽  
Long Term Effects ◽  
Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

2020 ◽  
Vol 30 (06) ◽  
pp. 2050032
Author(s):  
Wei Feng ◽  
Nicholas Van Halm-Lutterodt ◽  
Hao Tang ◽  
Andrew Mecum ◽  
Mohamed Kamal Mesregah ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Disease Process ◽  
Disease Classification ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Tool ◽  
3D Cnn

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

scholarly journals Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children

2021 ◽  
Vol 15 ◽  
Author(s):  
Sreevalsan S. Menon ◽  
K. Krishnamurthy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Disruptive Behavior ◽  
Behavior Disorders ◽  
Child Behavior Checklist ◽  
Disruptive Behavior Disorders ◽  
Fmri Data ◽  
Typically Developing ◽  
Oppositional Defiant ◽  
3D Cnn

Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108–131 months who were enrolled in the Adolescent Brain Cognitive Development Study. Children were grouped based on the presence of DBDs (n = 550) and typically developing (n = 550); assessments were based on the scores from the Child Behavior Checklist and on the Schedule for Affective Disorders and Schizophrenia for School-age Children-Present and Lifetime version for DSM-5. The diffusion, structural, and resting-state functional magnetic resonance imaging (rs-fMRI) data were used as input data to the 3D CNN. The model achieved 72% accuracy in classifying children with DBDs with 70% sensitivity, 72% specificity, and an F1-score of 70. In addition, the discriminative power of the classifier was investigated by identifying the cortical and subcortical regions primarily involved in the prediction of DBDs using a gradient-weighted class activation mapping method. The classification results were compared with those obtained using the three neuroimaging modalities individually, and a connectome-based graph CNN and a multi-scale recurrent neural network using only the rs-fMRI data.

scholarly journals A Medical Service Application Based on 3D-CNN and Knowledge Graph

2021 ◽  
Vol 2078 (1) ◽  
pp. 012048
Author(s):  
Jiasheng Ni
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Knowledge Graph ◽  
Medical Response ◽  
Learning Techniques ◽  
3D Cnn ◽  
Lung Cancer Detection

Abstract Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.

scholarly journals Deep learning models for classification of gases detected by sensor arrays of artificial nose

2019 ◽  
Author(s):  
Ismael Araujo ◽  
Juan Gamboa ◽  
Adenilton Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Classification Problems ◽  
Artificial Nose ◽  
Learning Techniques

To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

scholarly journals Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanghao Jin ◽  
Yixin Hu ◽  
Yuming Jiao ◽  
Junfang Wen ◽  
Qingzeng Song
Keyword(s):  
Deep Learning ◽  
Local Environment ◽  
Learning Model ◽  
Human Beings ◽  
Training Samples ◽  
Local Probability ◽  
Validation Set ◽  
Deep Learning Model ◽  
New Framework ◽  
Better Than

Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

scholarly journals Applying Deep Learning to Specific Learning Disorder Screening

2020 ◽  
Author(s):  
Nuriel Shalom Mor ◽  
Kathryn L Dardeck
Keyword(s):  
Deep Learning ◽  
Learning Disorder ◽  
Human Beings ◽  
Negative Consequences ◽  
Operating Characteristics ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Specific Learning Disorder ◽  
Deep Cnn ◽  
Specific Learning

Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.

scholarly journals Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion

2021 ◽  
Vol 11 (18) ◽  
pp. 8663
Author(s):  
Wen Chen ◽  
Chengwei Ju ◽  
Yanzhou Li ◽  
Shanshan Hu ◽  
Xi Qiao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Natural Environment ◽  
Stem Node ◽  
Combining Data ◽  
Real Time Detection ◽  
Data Expansion ◽  
Sugarcane Stem ◽  
The Impact ◽  
Detection Speed

The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.

Sign in / Sign up

Export Citation Format

Share Document