Izzat Al-Darraji^1,2, Morched Derbali³, Houssem Jerbi⁴, Fazal Qudus Khan³, Sadeeq Jan^5,*, Dimitris Piromalis⁶, Georgios Tsaramirsis⁷

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2771-2790, 2021, DOI:10.32604/cmc.2021.017236

Abstract The autonomous navigation of an Unmanned Aerial Vehicle (UAV) relies heavily on the navigation sensors. The UAV’s level of autonomy depends upon the various navigation systems, such as state measurement, mapping, and obstacle avoidance. Selecting the correct components is a critical part of the design process. However, this can be a particularly difficult task, especially for novices as there are several technologies and components available on the market, each with their own individual advantages and disadvantages. For example, satellite-based navigation components should be avoided when designing indoor UAVs. Incorporating them in the design brings no added value to the final… More >

Farah Akif¹, Kiran Sultan^2,*, Aqdas N. Malik¹, Ijaz M. Qureshi³, Saba Mahmood⁴

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2075-2093, 2021, DOI:10.32604/cmc.2021.016721

Abstract Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of (Access Point) AP which will eventually result in a larger carbon footprint. In this paper, we propose a base station off-loading and cell range extension (CRE) scheme based on multi-hop device-to-device (MHD2D) path selection between transmitter and receiver node. The paper also provides derivations of upper and lower bounds for energy efficiency, capacity, and transmit power. The proposed path selection scheme is inspired by the foraging behavior of honey bees. We present the algorithm as a modified variant of the artificial bee… More >

Hegazy Rezk^1,2, Irik Z. Mukhametzyanov³, Mujahed Al-Dhaifallah^4,*, Hamdy A. Ziedan⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2001-2027, 2021, DOI:10.32604/cmc.2021.015895

Abstract Several models of multi-criteria decision-making (MCDM) have identified the optimal alternative electrical energy sources to supply certain load in an isolated region in Al-Minya City, Egypt. The load demand consists of water pumping system with a water desalination unit. Various options containing three different power sources: only DG, PV-B system, and hybrid PV-DG-B, two different sizes of reverse osmosis (RO) units; RO-250 and RO-500, two strategies of energy management; load following (LF) and cycle charging (CC), and two sizes of DG; 5 and 10 kW were taken into account. Eight attributes, including operating cost, renewable fraction, initial cost, the cost… More >

Ornela Bardhi^1,2,*, Begonya Garcia Zapirain¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1595-1613, 2021, DOI:10.32604/cmc.2021.015326

Abstract Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178 features for BCa and 143… More >

Muhammad Naeem¹, Aziz Khan², Saleem Abdullah^2,*, Shahzaib Ashraf³, Ahmad Ali Ahmad Khammash⁴

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 459-476, 2021, DOI:10.32604/iasc.2021.016822

Abstract Spherical fuzzy set (SFS) as one of several non-standard fuzzy sets, it introduces a number triplet (a,b,c) that satisfies the requirement a² + b² + c² ≤ 1 to express membership grades. Due to the expression, SFS has a more extensive description space when describing fuzzy information, which attracts more attention in scientific research and engineering practice. Just for this reason, how to describe the fuzzy information more reasonably and perfectly is the hot that scholars pay close attention to. In view of this hot, in this paper, the notion of spherical hesitant fuzzy set is introduced as a generalization… More >

Caili Li¹, Dalin Tang^2,*,3, Jing Yao^4,*, Christopher Baird⁵, Haoliang Sun⁶, Chanjuan Gong⁷, Luyao Ma⁶, Yanjuan Zhang⁴, Liang Wang², Han Yu², Chun Yang⁸, Yongfeng Shao⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 159-174, 2021, DOI:10.32604/cmes.2021.014580

Abstract Aortic valve replacement (AVR) remains a major treatment option for patients with severe aortic valve disease. Clinical outcome of AVR is strongly dependent on implanted prosthetic valve size. Fluid-structure interaction (FSI) aortic root models were constructed to investigate the effect of valve size on hemodynamics of the implanted bioprosthetic valve and optimize the outcome of AVR surgery. FSI models with 4 sizes of bioprosthetic valves (19 (No. 19), 21 (No. 21), 23 (No. 23) and 25 mm (No. 25)) were constructed. Left ventricle outflow track flow data from one patient was collected and used as model flow conditions. Anisotropic Mooney–Rivlin… More >

Ahmed Silik^1,2, Mohammad Noori^3,*, Wael A. Altabey^1,4, Ramin Ghiasi¹, Zhishen Wu¹

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751

Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this… More >

Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

Fatimah O. Albalawi, Mashael S. Maashi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657

Abstract In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers’ expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem… More >

Mohammed Farsi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 83-92, 2021, DOI:10.32604/iasc.2021.015460

Abstract Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to models using Support Vector Machine… More >