One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.
no code implementations • 5 Jul 2021 • Javad Hassannataj Joloudari, Sanaz Mojrian, Issa Nodehi, Amir Mashmool, Zeynab Kiani Zadegan, Sahar Khanjani Shirkharkolaie, Tahereh Tamadon, Samiyeh Khosravi, Mitra Akbari, Edris Hassannataj, Roohallah Alizadehsani, Danial Sharifrazi, Amir Mosavi
Based on our best knowledge, Deep Convolutional Neural Network (DCNN) methods are highly required methods developed for the early diagnosis of MID on the ECG signals.
First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models.
Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk.
no code implementations • 28 Apr 2021 • Nooshin Ayoobi, Danial Sharifrazi, Roohallah Alizadehsani, Afshin Shoeibi, Juan M. Gorriz, Hossein Moosaei, Abbas Khosravi, Saeid Nahavandi, Abdoulmohammad Gholamzadeh Chofreh, Feybi Ariani Goni, Jiri Jaromir Klemes, Amir Mosavi
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019.
Accordingly, two hypotheses were raised illustrating that the travel time (i. e., the time it takes for a customer to reach the retail center) and the variety of shops (in a retail center) increase the percentage of people who spend their leisure time and recreational activities retail centers.
The results of the sensitivity analysis show that the most influential parameter for the SSD in a smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one.
Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology.
The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models.
In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance.
One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack.
The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research.
The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively.
The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants.
The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage.
Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.
Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique.
The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.
The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals.
Prediction of crop yield is essential for food security policymaking, planning, and trade.
In recent decades, attention has been directed at anemia classification for various medical purposes, such as thalassemia screening and predicting iron deficiency anemia (IDA).
In this regard, EC-EARTH near surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy.
Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models.
Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace.
Industry uses various solvents in the processes of refrigeration and ventilation.
In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014).
no code implementations • 11 Feb 2020 • Mohammad Hossein Ahmadi, Alireza Baghban, Milad Sadeghzadeh, Mohammad Zamen, Amir Mosavi, Shahaboddin Shamshirband, Ravinder Kumar, Mohammad Mohammadi-Khanaposhtani
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies.
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods.
no code implementations • 16 Jan 2020 • Javad Hassannataj Joloudari, Edris Hassannataj Joloudari, Hamid Saadatfar, Mohammad GhasemiGol, Seyyed Mohammad Razavi, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Laszlo Nadai
Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate.
This paper proposes a model based on gene expression programming for predicting the discharge coefficient of triangular labyrinth weirs.
We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels.
The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea.
This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain.
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering.
So, drag, lift, and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters.
Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.
In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide.
The performance of the proposed model is further compared with a linear-SVM model.
Accurate prediction of mercury content emitted from fossil fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation.
Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive.
Emotion detection from the text is an important and challenging problem in text analytics.
The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions.
Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies.
This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task.