Elio Caruso^1,*, Silvia Farruggio¹, Alfredo Di Pino¹, Paolo Guccione¹, Mohammadrafie Khorgami²

Congenital Heart Disease, Vol.17, No.5, pp. 551-556, 2022, DOI:10.32604/chd.2022.023711

Abstract We present the case of an infant admitted to our department for a rapid broad complex tachycardia and cardiovascular collapse. The patient was submitted to genetic testing because of a conduction defect at baseline ECG and family history of gene mutation. A new SCN5A gene mutation variant was found leading to diagnosis of sodium-channel dysfunction arrhythmia. More > Graphic Abstract

U. S. Mahabaleshwar¹, T. Anusha1 and M. Hatami^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.2, pp. 541-567, 2023, DOI:10.32604/fdmp.2022.022002

Abstract The unsteady stagnation-point flow of a hybrid nanofluid over a stretching/shrinking sheet embedded in a porous medium with mass transpiration and chemical reactions is considered. The momentum and mass transfer problems are combined to form a system of partial differential equations, which is converted into a set of ordinary differential equations via similarity transformation. These ordinary differential equations are solved analytically to obtain the solution for velocity and concentration profiles in exponential and hypergeometric forms, respectively. The concentration profile is obtained for four different cases namely constant wall concentration, uniform mass flux, general power law wall con-centration and general power… More >

Chaithra^1,*, G. M. Lingaraju², S. Jagannatha³

Computer Systems Science and Engineering, Vol.45, No.1, pp. 803-817, 2023, DOI:10.32604/csse.2023.028770

Abstract In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the… More >

Federica Caldaroni¹, Massimo Chessa², Alessandro Varrica¹, Alessandro Giamberti^1,*

Congenital Heart Disease, Vol.17, No.4, pp. 491-494, 2022, DOI:10.32604/chd.2022.019119

Abstract Multimodal imaging, including augmented or mixed reality, transforms the physicians’ interaction with clinical imaging, allowing more accurate data interpretation, better spatial resolution, and depth perception of the patient’s anatomy. We successfully overlay 3D holographic visualization to magnetic resonance imaging images for preoperative decision making of a complex case of cardiac tumour in a 7-year-old girl. More >

Fuad A. M. Al-Yarimi^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 129-142, 2023, DOI:10.32604/csse.2023.024297

Abstract A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training… More >

Jian Liu^1,*, Xiaodong Xia¹, Chunyang Han², Jiao Hui³, Jim Feng⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227

Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the… More >

Ajina Mohamed Ameer^*, M. Victor Jose

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924

Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is… More >

S. Rooban¹, Moru Leela¹, Md. Zia Ur Rahman^1,*, N. Subbulakshmi², R. Manimegalai³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1309-1321, 2022, DOI:10.32604/cmc.2022.023934

Abstract Considerable research has considered the design of low-power and high-speed devices. Designing integrated circuits with low-power consumption is an important issue due to the rapid growth of high-speed devices. Embedded static random-access memory (SRAM) units are necessary components in fast mobile computing. Traditional SRAM cells are more energy-consuming and with lower performances. The major constraints in SRAM cells are their reliability and low power. The objectives of the proposed method are to provide a high read stability, low energy consumption, and better writing abilities. A transmission gate-based multi-threshold single-ended Schmitt trigger (ST) 9T SRAM cell in a bit-interleaving structure without… More >

Hadee Madadum^*, Yasar Becerikli

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 681-695, 2022, DOI:10.32604/iasc.2022.023831

Abstract Convolutional Neural Network (ConNN) implementations on Field Programmable Gate Array (FPGA) are being studied since the computational capabilities of FPGA have been improved recently. Model compression is required to enable ConNN deployment on resource-constrained FPGA devices. Logarithmic quantization is one of the efficient compression methods that can compress a model to very low bit-width without significant deterioration in performance. It is also hardware-friendly by using bitwise operations for multiplication. However, the logarithmic suffers from low resolution at high inputs due to exponential properties. Therefore, we propose a modified logarithmic quantization method with a fine resolution to compress a neural network… More >

Fuad Ali Mohammed Al-Yarimi^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >