Mehmood Ahmed^1,3,*, Noraini B. Ibrahim¹, Wasif Nisar², Adeel Ahmed³, Muhammad Junaid^3,*, Emmanuel Soriano Flores⁴, Divya Anand⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1399-1422, 2024, DOI:10.32604/cmc.2023.046648

Abstract Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This study introduces a novel hybrid… More >

Mohammed Zakariah¹, Salman A. AlQahtani^2,*, Abdulaziz M. Alawwad¹, Abdullilah A. Alotaibi³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752

Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are slowing computation. In addition, networked… More >

Shoaib Khan, Bilal Khan, Saifullah Jan^*, Subhan Ullah, Aiman

Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579

Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the potential of ML techniques in… More >

Bin Li^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649

Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R² of 0.847 on the testing. The partial dependence plots (PDP) indicate that… More >

Muhammad Hammad Waseem¹, Malik Sajjad Ahmed Nadeem^1,*, Ishtiaq Rasool Khan², Seong-O-Shim³, Wajid Aziz¹, Usman Habib⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710

Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the prediction ability and the convergence… More >

Suthep Suantai^1,2, Zulqurnain Sabir^3,4, Muhammad Asif Zahoor Raja⁵, Watcharaporn Cholamjiak^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2155-2170, 2023, DOI:10.32604/cmc.2023.034699

Abstract The purpose of this study is to present the numerical performances and interpretations of the SEIR nonlinear system based on the Zika virus spreading by using the stochastic neural networks based intelligent computing solver. The epidemic form of the nonlinear system represents the four dynamics of the patients, susceptible patients S(y), exposed patients hospitalized in hospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIR model. The computing numerical outcomes and performances of the system are examined by using the artificial neural networks (ANNs) and the scaled conjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG. The… More >

Li Yan¹, Zulqurnain Sabir², Esin Ilhan³, Muhammad Asif Zahoor Raja⁴, Wei Gao⁵, Haci Mehmet Baskonus^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 201-221, 2023, DOI:10.32604/cmes.2023.025094

Abstract In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff… More >

Zulqurnain Sabir¹, Sánchez-Chero Manuel², Muhammad Asif Zahoor Raja³, Gilder-Cieza–Altamirano⁴, María-Verónica Seminario-Morales², Fernández Vásquez José Arquímedes⁵, Purihuamán Leonardo Celso Nazario⁶, Thongchai Botmart^7,*, Wajaree Weera⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3455-3470, 2023, DOI:10.32604/cmc.2023.033352

Abstract The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient, called ANNs-SCG. The fractional derivatives have been applied to get more reliable performances of the system. The mathematical form of the biological Leptospirosis system is divided into five categories, and the numerical performances of each model class will be provided by using the ANNs-SCG. The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results. The reference solutions have been obtained… More >

Wajaree Weera¹, Thongchai Botmart^1,*, Charuwat Chantawat¹, Zulqurnain Sabir^2,3, Waleed Adel^4,5, Muhammad Asif Zahoor Raja⁶, Muhammad Kristiawan⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2711-2724, 2023, DOI:10.32604/cmc.2023.033233

Abstract The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data… More >

Narongsak Yotha¹, Qusain Hiader², Zulqurnain Sabir³, Muhammad Asif Zahoor Raja⁴, Salem Ben Said⁵, Qasem Al-Mdallal⁵, Thongchai Botmart⁶, Wajaree Weera^6,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2415-2430, 2023, DOI:10.32604/cmc.2023.031352

Abstract This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD.… More >