Mohammad Sh. Daoud¹, Shabib Aftab^2,3, Munir Ahmad², Muhammad Adnan Khan^4,5,*, Ahmed Iqbal³, Sagheer Abbas², Muhammad Iqbal², Baha Ihnaini^6,7

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1287-1300, 2022, DOI:10.32604/iasc.2022.020362

Abstract Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of… More >

Mamdouh El Haj Assad¹, Ibrahim Mahariq², Raymond Ghandour², Mohammad Alhuyi Nazari³, Thabet Abdeljawad^4,5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 361-374, 2022, DOI:10.32604/cmc.2022.019048

Abstract Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their… More >

Chandrasekar Thaventhiran, K. R. Sekar^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 495-506, 2022, DOI:10.32604/iasc.2022.019546

Abstract Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given… More >

Sikander Hans¹, Smarajit Ghosh^2,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 29-41, 2022, DOI:10.32604/iasc.2022.019525

Abstract This paper offers an H-infinity (H∞) controller-based disturbance rejection along with the utilization of the water wave optimization (WWO) algorithm. H∞ controller is used to synthesize the guaranteed performance of certain applications as well as it provides maximum gain at any situation. The proposed work focuses on the conflicts of continuous stirred-tank reactor (CSTR) such as variation in temperature and product concentration. The elimination of these issues is performed with the help of the WWO algorithm along with the controller operation. In general, the algorithmic framework of WWO algorithm is simple, and easy to implement with a small-size population and… More >

Zulqurnain Sabir¹, Muhammad Umar¹, Muhammad Asif Zahoor Raja^2,*, Dumitru Baleanu^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 227-251, 2021, DOI:10.32604/cmes.2021.016611

Abstract The presented research aims to design a new prevention class (P) in the HIV nonlinear system, i.e., the HIPV model. Then numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks (ANNs) modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms (GAs) and active-set approach (ASA), i.e., GA-ASA. The optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding… More >

Mamdouh El Haj Assad¹, Ibrahim Mahariq^2,*, Zaher Al Barakeh², Mahmoud Khasawneh², Mohammad Ali Amooie³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3767-3781, 2021, DOI:10.32604/cmc.2021.018872

Abstract CO₂ emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities. Consequently, these factors must be considered for CO₂ emission prediction for seven middle eastern countries including Iran, Kuwait, United Arab Emirates, Turkey, Saudi Arabia, Iraq and Qatar. In order to propose a predictive model, a Multilayer Perceptron Artificial Neural Network (MLP ANN) is applied. Three transfer functions including logsig, tansig and radial basis functions are utilized in the hidden layer of the network. Moreover, various numbers of neurons are applied in the structure of the models. It… More >

Fatmah Abdulrahman Baothman^*, Osama Ahmed Abulnaja, Fatima Jafar Muhdher

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3337-3363, 2021, DOI:10.32604/cmc.2021.017637

Abstract Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated… More >

Tooba Batool¹, Sagheer Abbas¹, Yousef Alhwaiti², Muhammad Saleem¹, Munir Ahmad¹, Muhammad Asif^1,*, Nouh Sabri Elmitwally^2,3

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 513-525, 2021, DOI:10.32604/iasc.2021.018770

Abstract A Smart City understands the infrastructure, facilities, and schemes open to its citizens. According to the UN report, at the end of 2050, more than half of the rural population will be moved to urban areas. With such an increase, urban areas will face new health, education, Transport, and ecological issues. To overcome such kinds of issues, the world is moving towards smart cities. Cities cannot be smart without using Cloud computing platforms, the Internet of Things (IoT). The world has seen such incredible and brilliant ideas for rural areas and smart cities. While considering the Ecosystem in Smart Cities,… More >

Adel Hamdan Mohammad^*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 65-80, 2021, DOI:10.32604/iasc.2021.016140

Abstract Intrusion detection is an important topic that aims at protecting computer systems. Besides, feature selection is crucial for increasing the performance of intrusion detection. This paper employs a new hybrid feature selection model for intrusion detection. The implemented model uses Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms in a new manner. In addition, this study introduces two new models called (PSO-GWO-NB) and (PSO-GWO-ANN) for feature selection and intrusion detection. PSO and GWO show emergent results in feature selection for several purposes and applications. This paper uses PSO and GWO to select features for the intrusion detection system.… More >

Sundresan Perumal¹, Mujahid Tabassum¹, Ganthan Narayana², Suresh Ponnan^3,*, Chinmay Chakraborty⁴, Saju Mohanan⁵, Zeeshan Basit⁵, Mohammad Tabrez Quasim⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1447-1462, 2021, DOI:10.32604/cmc.2021.014854

Abstract A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of the main issues in the WSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The ad hoc on-demand distance vector (AODV)… More >