K. K. Thyagharajan, I. Kiruba Raji^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591 - 21 July 2021

Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew,… More >

Juan Huang¹, Bifang Zhou¹, Huajun Huang^2,*, Jianjiang Liu¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2111-2121, 2021, DOI:10.32604/cmc.2021.016770 - 21 July 2021

Abstract To overcome the deficiency of traditional mathematical statistics methods, an adaptive Lasso grey model algorithm for regional FDI (foreign direct investment) prediction is proposed in this paper, and its validity is analyzed. Firstly, the characteristics of the FDI data in six provinces of Central China are generalized, and the mixture model's constituent variables of the Lasso grey problem as well as the grey model are defined. Next, based on the influencing factors of regional FDI statistics (mean values of regional FDI and median values of regional FDI), an adaptive Lasso grey model algorithm for regional… More >

V. Nivedita^1,*, N. Nandhagopal²

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 771-786, 2021, DOI:10.32604/iasc.2021.017547 - 01 July 2021

Abstract The mobile ad-hoc network (MANET) output is critically impaired by the versatility and resource constraint of nodes. Node mobility affects connection reliability, and node resource constraints can lead to congestion, which makes the design of a routing MANET protocol with quality of service (QoS) very difficult. An adaptive clustering reputation model (ACRM) method is proposed to improve energy efficiency with a cluster-based framework. The proposed framework is employed to overcome the problems of data protection, privacy, and policy. The proposed ACRM-MRT approach that includes direct and indirect node trust computation is introduced along with the… More >

Yunxing Zhang, Shan Ma, Kangping Liao, Wenyang Duan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 43-64, 2021, DOI:10.32604/cmes.2021.014847 - 28 June 2021

Abstract In this study, a numerical model for simulating two-phase flow is developed. The Cartesian grid with Adaptive Mesh Refinement (AMR) is adopted to reduce the computational cost. An explicit projection method is used for the time integration and the Finite Difference Method (FDM) is applied on a staggered grid for the discretization of spatial derivatives. The Volume of Fluid (VOF) method with Piecewise-Linear Interface Calculation (PLIC) is extended to the AMR grid to capture the gas-water interface accurately. A coarse-fine interface treatment method is developed to preserve the flux conservation at the interfaces. Several two-dimensional More >

Yu-Jin Na¹, Ji-Sung Jung¹, Young-Hwan You², Hyoung-Kyu Song^1,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1361-1373, 2021, DOI:10.32604/cmc.2021.018481 - 04 June 2021

Abstract As the installation of small cells increases, the use of relay also increases. The relay operates as a base station as well as just an amplifier. As the roles and types of relays become more diverse, appropriate relay selection technology is an effective way to improve communication performance. Many researches for relay selection have been studied to secure the reliability of relay communication. In this paper, the relay selection scheme is proposed for a cooperative system using decode-and-forward (DF) relaying scheme in the mobile communication system. To maintain the transmission rate, the proposed scheme classifies… More >

Abu Jahid¹, Mohammed H. Alsharif², Raju Kannadasan³, Mahmoud A. Albreem⁴, Peerapong Uthansakul^5,*, Jamel Nebhen⁶, Ayman A. Aly⁷

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 81-98, 2021, DOI:10.32604/cmc.2021.017711 - 04 June 2021

Abstract The Internet subscribers are expected to increase up to 69.7% (6 billion) from 45.3% and 25 billion Internet-of-things connections by 2025. Thus, the ubiquitous availability of data-hungry smart multimedia devices urges research attention to reduce the energy consumption in the fifth-generation cloud radio access network to meet the future traffic demand of high data rates. We propose a new cell zooming paradigm based on joint transmission (JT) coordinated multipoint to optimize user connection by controlling the cell coverage in the downlink communications with a hybrid power supply. The endeavoring cell zooming technique adjusts the coverage… More >

Ghufran Ahmed¹, Saiful Islam², Ihsan Ali³, Isra Adil Hayder⁴, Abdelmuttlib Ibrahim Abdalla Ahmed³, Muhammad Talha⁵, Sultan S. Alshamrani⁶, Ag Asri Ag Ibrahim^7,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1301-1322, 2021, DOI:10.32604/cmc.2021.017062 - 04 June 2021

Abstract Underwater acoustic sensor network (UASN) refers to a procedure that promotes a broad spectrum of aquatic applications. UASNs can be practically applied in seismic checking, ocean mine identification, resource exploration, pollution checking, and disaster avoidance. UASN confronts many difficulties and issues, such as low bandwidth, node movements, propagation delay, 3D arrangement, energy limitation, and high-cost production and arrangement costs caused by antagonistic underwater situations. Underwater wireless sensor networks (UWSNs) are considered a major issue being encountered in energy management because of the limited battery power of their nodes. Moreover, the harsh underwater environment requires vendors… More >

Memoona Malik^*, Iftikhar Azim, Amir Hanif Dar, Sohail Asghar

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 165-182, 2021, DOI:10.32604/iasc.2021.017437 - 12 May 2021

Abstract Despeckling of SAR imagery is a crucial step prior to their automated interpretation as information extraction from noisy images is a challenging task. Though a huge despeckling literature exists in this regard, there is still a room for improvement in existing techniques. The contemporary despeckling techniques adversely affect image edges during the noise reduction process and are thus responsible for losing the significant image features. Therefore, to preserve important features during the speckle reduction process, a two phase hybrid despeckling filter is proposed in this study. The first phase of the hybrid filter focuses on More >

Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981 - 12 May 2021

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate More >

Diantao Hu, Cong Zhang^*, Wenqi Cao, Xintao Lv, Songwu Xie

Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317 - 13 April 2021

Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising… More >