Junaid Rashid¹, Sumera Kanwal², Muhammad Wasif Nisar², Jungeun Kim^1,*, Amir Hussain³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1309-1324, 2023, DOI:10.32604/csse.2023.026018

Abstract In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many companies. In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN). An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use… More >

S. Muthukumaran^1,*, P. Geetha², E. Ramaraj¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 215-230, 2023, DOI:10.32604/iasc.2023.027449

Abstract Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most… More >

Smarajit Ghosh^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 181-197, 2023, DOI:10.32604/iasc.2023.026496

Abstract The primary purpose of the Energy Management Scheme (EMS) is to monitor the energy fluctuations present in the load profile. In this paper, the improved model predictive controller is adopted for the EMS in the power system. Emperor Penguin Optimization (EPO) algorithm optimized Artificial Neural Network (ANN) with Model Predictive Control (MPC) scheme for accurate prediction of load and power forecasting at the time of pre-optimizing EMS is presented. For the power generation, Renewable Energy Sources (RES) such as photo voltaic (PV) and wind turbine (WT) are utilized along with that the fuel cell is also presented in case of… More >

Polin Rahman¹, Ahmed Rifat¹, MD. IftehadAmjad Chy¹, Mohammad Monirujjaman Khan^1,*, Mehedi Masud², Sultan Aljahdali²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.021469

Abstract Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are… More >

Rania Jradi^1,*, Christophe Marvillet², Mohamed-Razak Jeday¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1511-1519, 2022, DOI:10.32604/fdmp.2022.021925

Abstract The accumulation of undesirable deposits on the heat exchange surface represents a critical issue in industrial heat exchangers. Taking experimental measurements of the fouling is relatively difficult and, often, this method does not lead to precise results. To overcome these problems, in the present study, a new approach based on an Artificial Neural Network (ANN) is used to predict the fouling resistance as a function of specific measurable variables in the phosphoric acid concentration process. These include: the phosphoric acid inlet and outlet temperatures, the steam temperature, the phosphoric acid density, the phosphoric acid volume flow rate circulating in the… More >

Thongchai Botmart¹, Wajaree Weera^1,*, Muhammad Asif Zahoor Raja², Zulqurnain Sabir³, Qusain Hiader⁴, Gilder Cieza Altamirano⁵, Plinio Junior Muro Solano⁶, Alfonso Tesen Arroyo⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 981-999, 2022, DOI:10.32604/cmc.2022.028921

Abstract In the present study, a design of a fractional order mathematical model is presented based on the schistosomiasis disease. To observe more accurate performances of the results, the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is executed. The preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows: uninfected with schistosomiasis, infected with schistosomiasis, recovered from infection, susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this disease. The solutions to the proposed system of the fractional order mathematical model will be presented… More >

Al-Batool Al-Ghamdi^1,*, Souad Kamel², Mashael Khayyat³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1365-1383, 2022, DOI:10.32604/cmc.2022.026246

Abstract Water is a vital resource. It supports a multitude of industries, civilizations, and agriculture. However, climatic conditions impact water availability, particularly in desert areas where the temperature is high, and rain is scarce. Therefore, it is crucial to forecast water demand to provide it to sectors either on regular or emergency days. The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions. This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future. Focusing on the… More >

Muhammad Zubair Rehman¹, Muhammad Aamir^2,*, Nazri Mohd. Nawi³, Abdullah Khan⁴, Saima Anwar Lashari⁵, Siyab Khan⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 493-511, 2022, DOI:10.32604/cmc.2022.021787

Abstract

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN.… More >

Muhammad Umar¹, Fazli Amin¹, Soheil Salahshour², Thongchai Botmart³, Wajaree Weera³, Prem Junswang^4,*, Zulqurnain Sabir¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6185-6202, 2022, DOI:10.32604/cmc.2022.027970

Abstract The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model (VP-HBM) using the feedforward artificial neural networks (ANNs) under the optimization of particle swarm optimization (PSO) hybridized with the active-set algorithm (ASA), i.e., ANNs-PSO-ASA. The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study. An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions. The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA. The designed performance of… More >

Prem Junswang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Waleed Adel^4,5, Thongchai Botmart^6,*, Wajaree Weera⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5015-5030, 2022, DOI:10.32604/cmc.2022.027523

Abstract The purpose of this paper is to present a numerical approach based on the artificial neural networks (ANNs) for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system. The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation (LMB), named the ANNs-LMB. This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution. Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model. The selection of the… More >