Mohammad B. Khan¹, Abdul R. Khan^2,*, Hasan Alkahtani²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2333-2346, 2022, DOI:10.32604/cmc.2022.020623 - 07 December 2021

Abstract Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widely used for general purpose computing. Processors based on the data flow architecture employ fine-grain data-driven parallelism. These architectures have the potential to exploit the inherent parallelism in compute intensive applications like signal processing, image and video processing and so on and can thus achieve faster throughputs and higher power efficiency. In this paper, several data flow computing architectures are explored, and their main architectural features are studied. Furthermore, a classification of the processors is presented based… More >

Ming Zhang, Hongbo Li, Xing Deng^*

Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916 - 22 November 2021

Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To More >

Jinho Bang¹, SongEe Park², Haemin Jeon^2,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1503-1519, 2022, DOI:10.32604/cmc.2022.020485 - 03 November 2021

Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied More >

Meenakshi Malik¹, Rainu Nandal^1,*, Surjeet Dalal², Vivek Jalglan³, Dac-Nhuong Le^4,5

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 87-99, 2022, DOI:10.32604/iasc.2022.020249 - 26 October 2021

Abstract It has been observed that driver behavior has a direct and considerable impact upon factors like fuel consumption, environmentally harmful emissions, and public safety, making it a key consideration of further research in order to monitor and control such related hazards. This has fueled our decision to conduct a study in order to arrive at an efficient way of analyzing the various parameters of driver behavior and find ways and means of positively impacting such behavior. It has been ascertained that such behavioral patterns can significantly impact the analysis of traffic-related conditions and outcomes. In… More >

Raniyah Wazirali¹, Rami Ahmad^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4922-4946, 2022, DOI:10.32604/cmc.2022.020044 - 11 October 2021

Abstract Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.… More >

Jhonn Pablo Rodríguez^1,*, David Camilo Corrales^1,2, David Griol³, Zoraida Callejas³, Juan Carlos Corrales¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135 - 11 October 2021

Abstract Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In… More >

JIANBO LU^1,2,#,*, XIAOHAN SUN^3,#, XU MA^1,2,*

BIOCELL, Vol.46, No.1, pp. 185-193, 2022, DOI:10.32604/biocell.2021.015301 - 29 September 2021

Abstract The concentration of cell-free fetal DNA fragments should be detected before noninvasive prenatal testing (NIPT). The fetal DNA molecules have significant clinical potential in determining the overall performance of NIPT and clinical interpretation. It is important to measure fetal DNA fraction before NIPT. However, there is still little research on how to calculate the concentration of female fetuses. Two estimation approaches were proposed to calculate fetal DNA fraction, including the fragments size-based approach, aneuploid-based approach, which are all approaches based on chromosome segments. Based on high-throughput sequencing data, two approaches to calculate the DNA fraction More >

Taher M. Ghazal^1,2, Sajida Noreen³, Raed A. Said⁴, Muhammad Adnan Khan^5,*, Shahan Yamin Siddiqui^3,6, Sagheer Abbas³, Shabib Aftab³, Munir Ahmad³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658 - 03 September 2021

Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme More >

FAISAL HASSAN TOBEIGEI¹, REEM M. GAHTANI², AHMAD SHAIKH², AMER AL ALI³, NADER KAMELI^4,5, HOSSAM KAMLI², PRASANNA RAJAGOPALAN^2,6,*

Oncology Research, Vol.29, No.5, pp. 305-318, 2021, DOI:10.32604/or.2022.025187 - 10 October 2022

Abstract Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase (PI3K), and RAS/BRAF pathways. In this work, through diversity-based high-throughput virtual screening we identified a lead molecule that selectively targets PI3K and BRAF^V600E kinases. Computational screening, Molecular dynamics simulation and MMPBSA calculations were performed. PI3K and BRAF^V600E kinase inhibition was done. A375 and G-361 cells were used for in vitro cellular analysis to determine antiproliferative effects, annexin V binding, nuclear fragmentation and cell cycle analysis. Computational screening of small molecules indicates compound CB-006-3 selectively targets PI3KCG (gamma subunit), PI3KCD (delta subunit) and BRAF^V600E. Molecular… More >

Atiqe Ur Rahman^1,*, Muhammad Saeed¹, Sultan S. Alodhaibi², Hamiden Abd El-Wahed Khalifa^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 743-777, 2021, DOI:10.32604/cmes.2021.016736 - 22 July 2021

Abstract Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set. This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes. The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function. Firstly, we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set, intuitionistic fuzzy set and neutrosophic set along with More >