Random fully connected layered 1D CNN for solving the Z-bus loss allocation problem

Alkan

Concurrency and Computation

2022

Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder with symptoms of attention deficit, hyperactivity and impulsivity, with a prevalence of 8%-12% worldwide. Various studies have revealed that there may be a relationship between ADHD and temperament traits in the etiology of ADHD, which has a multifactorial etiology. According to our knowledge, there is no study in the literature that determines the use of machine learning methods in diagnosing ADHD uses a data set created with temperament characteristics. Different methods were used in this first study. The study included 60 ADHD patients and 60 control group children. The test scores of these children were collected from the Department of Child and Adolescent Psychiatry, Kahramanmaraş Sütçü İmam University Medical Faculty, after obtaining the necessary ethics committee permission. ADHD diagnosis was made according to DSM-5 classification. According to temperament characteristics, the highest classification success in ADHD diagnosis was calculated as 92.5% in decision tree method. Long short term memory (LSTM), one of the deep learning methods, achieved 88% classification success. The success of both methods is quite high and they have been compared with some ADHD classification studies in the literature.

Section: Methodsmentioning

confidence: 99%

Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features

Alkan

Concurrency and Computation

2022

Pattern Recognition Letters

“…The feature

is then passed to two fully-connected layers [22] . The first FCL contains 500 neurons, and the last FCL contains

neurons, where

stands for the number of classes.…”

Section: Methodsmentioning

confidence: 99%

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

Zhang

et al. 2021

Background COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day. Method This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap. Results The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%. Conclusion Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.

“…The features from the experimental dataset are extracted by the first two convolutional and pooling layers. Finally, the fully connected layer is used for classification [39]. The graphical view of simple CNN architecture has shown in Fig.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

Kumar

Suresh

2023

Multimed Tools Appl

The recognition of human activities has become a dominant emerging research problem and widely covered application areas in surveillance, wellness management, healthcare, and many more. In real life, the activity recognition is a challenging issue because human beings are often performing the activities not only simple but also complex and heterogeneous in nature. Most of the existing approaches are addressing the problem of recognizing only simple straightforward activities (e.g. walking, running, standing, sitting, etc.). Recognizing the complex and heterogeneous human activities are a challenging research problem whereas only a limited number of existing works are addressing this issue. In this paper, we proposed a novel Deep-HAR model by ensembling the Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for recognizing the simple, complex, and heterogeneous type activities. Here, the CNNs are used for extracting the features whereas RNNs are used for finding the useful patterns in time-series sequential data. The activities recognition performance of the proposed model was evaluated using three different publicly available datasets, namely WISDM, PAMAP2, and KU-HAR. Through extensive experiments, we have demonstrated that the proposed model performs well in recognizing all types of activities and has achieved an accuracy of 99.98%, 99.64%, and 99.98% for simple, complex, and heterogeneous activities respectively.