Classification of Short-time Single-lead ECG Recordings Using Deep Residual CNN

2019 ◽  
Author(s):  
Areej Kharshid ◽  
Haikel S. Alhichri ◽  
Ridha Ouni ◽  
Yakoub Bazi
Keyword(s):  
Short Time

scholarly journals DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heni Sulistiani ◽  
Ahmad Ari Aldino
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Grant Program ◽  
C4.5 Algorithm ◽  
Long Time ◽  
The Right ◽  
Short Time ◽  
Tuition Fee

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system.

scholarly journals Luxatio lentis in dogs: a case report

2008 ◽  
Vol 52 (No. 5) ◽  
pp. 213-222
Author(s):  
M. Saroglu ◽  
O.D. Erdikmen ◽  
O. Guzel ◽  
D. Aydin
Keyword(s):  
Case Report ◽  
Small Animal ◽  
Intraocular Inflammation ◽  
The Other ◽  
Lens Extraction ◽  
Posterior Direction ◽  
Short Time ◽  
Anterior Direction ◽  
Lens Luxation

The material of the present study was composed of 30 eyes with luxatio lentis occurring in 20 dogs. Unilateral lens luxation was determined in a half of the animals while bilateral luxation was determined in the other half. Lenses in 19 of the 30 eyes were luxated in the anterior direction (63.3%), three were luxated in the posterior direction (10%) and eight were subluxated (26.6%). The distribution of patient dogs based on the breeds showed that the incidence of lens luxation was higher in Terrier, Cocker Spaniel and crossbreed dogs compared to the other breeds. The results of etiological classification of the diseases which are generally seen in older dogs (on average 7.2 years old) were as follows: congenital in two patients, primary luxation in four patients, and secondary luxation in 14 patients. Secondary luxations diagnosed in 14 animals were determined to have developed as a result of trauma in two dogs, glaucoma in one dog, uveitis in one dog, and senile degeneration and/or cataract in 10 dogs. Bilateral (two dogs) and unilateral (five dogs) intracapsullar lens extraction (ICLE) was applied to these animals. The primary disease was to be kept under control by treating those with secondary lens luxation for uveitis or glaucoma. A severe progressive intraocular inflammation developed in one patient. Enucleation was conducted on this patient due to buphthalmus developing in a short time. These results may be helpful to small animal veterinarians dealing with this disease which results in blindness unless early diagnosis and surgical treatment are conducted.

scholarly journals Moving Towards a Finer Way of Light-Cured Resin-Based Restorative Dental Materials: Recent Advances in Photoinitiating Systems Based on Iodonium Salts

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4093
Author(s):  
Monika Topa ◽  
Joanna Ortyl
Keyword(s):  
Biomedical Applications ◽  
Dental Materials ◽  
Dental Composites ◽  
Iodonium Salts ◽  
Photoinduced Polymerization ◽  
Low Energy Consumption ◽  
Restorative Dental Materials ◽  
Phosphine Derivatives ◽  
Short Time

The photoinduced polymerization of monomers is currently an essential tool in various industries. The photopolymerization process plays an increasingly important role in biomedical applications. It is especially used in the production of dental composites. It also exhibits unique properties, such as a short time of polymerization of composites (up to a few seconds), low energy consumption, and spatial resolution (polymerization only in irradiated areas). This paper describes a short overview of the history and classification of different typical monomers and photoinitiating systems such as bimolecular photoinitiator system containing camphorquinone and aromatic amine, 1-phenyl-1,2-propanedione, phosphine derivatives, germanium derivatives, hexaarylbiimidazole derivatives, silane-based derivatives and thioxanthone derivatives used in the production of dental composites with their limitations and disadvantages. Moreover, this article represents the challenges faced when using the latest inventions in the field of dental materials, with a particular focus on photoinitiating systems based on iodonium salts. The beneficial properties of dental composites cured using initiation systems based on iodonium salts have been demonstrated.

scholarly journals Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

2020 ◽  
Vol 10 (9) ◽  
pp. 3097
Author(s):  
Dmitry Kaplun ◽  
Alexander Voznesensky ◽  
Sergei Romanov ◽  
Valery Andreev ◽  
Denis Butusov
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Feature Space ◽  
Roc Curves ◽  
Harmonic Wavelets ◽  
Intelligence System ◽  
Stationary Signals ◽  
Harmonic Wavelet ◽  
Short Time

This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

scholarly journals Teletherapie und Telemedizin in Deutschland

2021 ◽  
Vol 27 (02) ◽  
pp. 111-118
Author(s):  
L. Mörsdorf ◽  
U. Beushausen
Keyword(s):  
Continuity Of Care ◽  
Speech Therapy ◽  
Service Providers ◽  
Healthcare Providers ◽  
Speech Disorders ◽  
Keywords Telemedicine ◽  
German Speaking ◽  
Short Time ◽  
Psychotherapeutic Care

Zusammenfassung Die durch die WHO vorgenommene Einstufung des Coronavirus als Pandemie hat Kliniker herausgefordert, die Kontinuität der medizinischen Versorgung aufrechtzuerhalten. Die Teletherapie wurde so innerhalb kürzester Zeit zum wichtigsten Mittel der Leistungserbringer, die diese zuvor nie oder nur sparsam genutzt hatten. Im Bereich der ärztlichen sowie der psychotherapeutischen Versorgung wurde parallel zum ersten Lockdown seit dem 01. April 2020 die zunächst bestehende Begrenzung der telemedizinischen Behandlung von 20 % der gesamten Behandlungen einer Praxis aufgehoben. Auch im Bereich der Heilmittelerbringer wurde phasenweise eine Erlaubnis zur Durchführung von Videotherapien erteilt. Die Studienlage zu diesem Thema weist darauf hin, dass die Wirksamkeit der logopädischen Teletherapie durchaus gegeben ist, wie am Beispiel der Sprachtherapie bei neurologisch bedingten Sprachstörungen im deutschsprachigen Raum im Artikel dargestellt wird, auch wenn weiterhin Studien in diesem Bereich erforderlich sind. Die Teletherapie sollte im Heilmittelbereich ebenso dauerhaft ermöglicht werden wie die teletherapeutische Versorgung durch Psychologen oder die elemedizinische Versorgung durch Ärzte. Auf diese Weise könnte eine Quattro-win-Situation für Leistungsträger, -finanzierer, -erbringer sowie für die Leistungsempfänger entstehen. Schlüsselwörter: Telemedizin, Teletherapie, Online-Sprachtherapie, Covid-19-Pandemie Abstract The WHO's classification of the coronavirus as a pandemic challenged clinicians to maintain continuity of care. Thus, in a very short time, teletherapy became the most important tool for healthcare providers who had never used it before, or had used it only sparingly. Parallel to the first lockdown, beginning on April 01, 2020, the existing limit on telemedicine treatment in medical and psychotherapeutic care – 20% of the total number of treatments at a practice – was lifted. Also, in the area of remedy providers, permission was given in phases for the execution of video therapies. The studies on this topic indicate that logopedic teletherapy is clearly effective, as shown in the example of speech therapy for neurologically-related speech disorders in German-speaking countries in the article, even if further studies are needed. Teletherapy should be made permanently possible in the field of remedies, just as teletherapy care by psychologists, or telemedicine treatment by physicians, are possible. This would create a “quattro-win” situation for service providers, financiers, providers and recipients. Keywords: telemedicine, teletherapy, online speech therapy, covid-19 pandemic

scholarly journals Efficient Commercial Classification of Agricultural Products using Convolutional Neural Networks

2021 ◽  
Vol 10 (4) ◽  
pp. 353
Author(s):  
Ali Jebelli ◽  
Rafiq Ahmad
Keyword(s):  
Detection System ◽  
High Accuracy ◽  
Agricultural Products ◽  
The Other ◽  
Internet Connection ◽  
Product Image ◽  
Short Time ◽  
Speed And Accuracy ◽  
Chemical Materials

<p>Agricultural products, as essential commodities, are among the most sought-for items in superstores. Barcode is usually utilized to classify and regulate the price of products such as ornamental flowers in such stores. However, the use of barcodes on some fragile agricultural products such as ornamental flowers can be damaged and lessen their life length. Moreover, it is time-consuming and costly<em><strong> </strong></em>and may lead to the production of massive waste and damage to the environment and the admittance of chemical materials into food products that can affect human health. Consequently, we aimed to design a classifier robot to recognize ornamental flowers based on the related product image at different times and surrounding conditions. Besides, it can increase the speed and accuracy of distinguishing and classifying the products, lower the pricing time, and increase the lifetime due to the absence of the need for movement and changing the position of the products. According to the datasheets provided by the robot that is stored in its database, we provide the possibility of identifying and introducing the product in different colors and shapes. Also, due to the preparation of a standard and small database tailored to the needs of the robot, the robot will be trained in a short time (less than five minutes) without the need for an Internet connection or a large hard drive for storage the data. On the other hand, by dividing each input photo into ten different sections, the system can, without the need for a detection system, simultaneously in several different images, decorative flowers in different conditions, angles and environments, even with other objects such as vases, detects very fast with a high accuracy of 97%.</p>

scholarly journals COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

2020 ◽  
Author(s):  
Erdi Acar ◽  
İhsan Yilmaz
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Ct Images ◽  
Nucleic Acid Amplification ◽  
World Health ◽  
Quantum Computers ◽  
Data Set ◽  
Small Quantum ◽  
Short Time

AbstractDiagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; Due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and Normal, in terms of machine learning, quantum computers seem to outperform traditional computers.

Wavelet-Based Online Evaluation and Classification of Wear State Using Acoustic Emission

2012 ◽  
Author(s):  
Dorra Baccar ◽  
Dirk Söffker
Keyword(s):  
Acoustic Emission ◽  
Low Cost ◽  
Time Frequency ◽  
Online Evaluation ◽  
Cost System ◽  
State Classification ◽  
Online Examination ◽  
Field Programmable ◽  
Short Time

Advanced signal processing approaches such time-frequency analysis are widely used for online evaluation, damage detection, and wear state classification. The idea of this paper is to introduce a new methodology for online examination of wear phenomena in metallic structure by means of acoustic emission (AE), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). The proposed novel low-cost system is developed for analyzing and monitoring specific signals indicating tribological effects with focus on field programmable gate array (FPGA) implementation of discrete WT (DWT). In addition, experimental results obtained from each approach are given showing the success of the introduced approach.

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