Sanaa A. A. Ghaleb^1,3,4, Mumtazimah Mohamad¹, Syed Abdullah Fadzli¹, Waheed A.H.M. Ghanem^2,3,4,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4749-4766, 2022, DOI:10.32604/cmc.2022.020472

Abstract Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and… More >

Saima Hassan¹, Mojtaba Ahmadieh Khanesar², Nazar Kalaf Hussein³, Samir Brahim Belhaouari^4,*, Usman Amjad⁵, Wali Khan Mashwani⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3513-3531, 2022, DOI:10.32604/cmc.2022.022018

Abstract The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system (IT2-FLS) is a challenging task in the presence of uncertainty and imprecision. Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.… More >

V. R. Balaji^1,*, T. Kalavathi², J. Vellingiri³, N. Rajkumar⁴, Venkat Prasad Padhy⁵

Computer Systems Science and Engineering, Vol.42, No.1, pp. 289-301, 2022, DOI:10.32604/csse.2022.020971

Abstract Data offloading at the network with less time and reduced energy consumption are highly important for every technology. Smart applications process the data very quickly with less power consumption. As technology grows towards 5G communication architecture, identifying a solution for QoS in 5G through energy-efficient computing is important. In this proposed model, we perform data offloading at 5G using the fuzzification concept. Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption. Two base stations, small (SB) and macro (MB) stations, are initialized and the first tasks randomly… More >

S. Premkumar Deepak^*, M. B. Mukeshkrishnan

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 963-978, 2022, DOI:10.32604/iasc.2022.022126

Abstract Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm… More >

Xiaorui Zhang^1,2,*, Xun Sun¹, Wei Sun², Tong Xu¹, Pengpai Wang¹, Sunil Kumar Jha³

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1041-1053, 2022, DOI:10.32604/iasc.2022.016543

Abstract This paper proposes a soft tissue grasping deformation model, where BP neural network optimized by the genetic algorithm is used to realize the real-time and accurate interaction of soft tissue grasping during virtual surgery. In the model, the soft tissue epidermis is divided into meshes, and the meshes generate displacements under the action of tension. The relationship between the tension and displacement of the mesh is determined by the proposed cylindrical spiral spring model. The optimized BP neural network is trained based on the sample data of the mesh point and vertical tension, so as to obtain the force and… More >

Mustafa Abdul Salam^1,*, Ahmad Taher Azar², Rana Hussien²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6339-6363, 2022, DOI:10.32604/cmc.2022.020583

Abstract Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal. To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques. This paper displays five proposed hybrid Algorithms “Salp Swarm Algorithm (SSA-ELM), Grasshopper… More >

Yanfei Bi¹, Bin Wei¹, Ying Meng², Zhongzhao Li², Zhenghui Tang¹, Feng Yin³, Chuntao Qian^2,*

Phyton-International Journal of Experimental Botany, Vol.90, No.4, pp. 1161-1175, 2021, DOI:10.32604/phyton.2021.014396

Abstract Melon (Cucumis melo), belonging to the Cucurbitaceae family, is a globally important economic crop. GRAS (GAI, RGA, SCR) genes, which are a type of transcription factor, play a critical role in plant growth and development, including processes such as radial root patterning, light signalling, abiotic/biotic stress, axillary shoot meristem formation, and phytohormone (gibberellin) signal transduction. In this study, the GRAS family in melon was analysed comprehensively with respect to chromosomal location, motif prediction, gene structure, and expression pattern. A total of 37 GRAS genes were first identified in melon, after which a phylogenetic tree was built with the GRAS genes… More >

Chunxue Yang^*, Yajie Liu, Wenna Zhao, Na Wang

Phyton-International Journal of Experimental Botany, Vol.90, No.3, pp. 719-729, 2021, DOI:10.32604/phyton.2021.015024

Abstract To understand arbuscular mycorrhizal (AM) fungi resources and develop AM fungal species in ornamental plants with saline-alkaline tolerances, Iris lactea, which grows in the Songnen saline-alkaline grassland with a high ornamental value, was selected as the experimental material, and the colonization characteristics of its roots and the AM fungal diversity in its rhizosphere were explored. The results of the observations and calculations of mycorrhizae from ten different samples showed that AM fungi colonized the roots of I. lactea and formed Arum-type mycorrhizal structures. There was a significant correlation between soil spore density and pH value, while the colonization rate showed… More >

Shiyin Qiu^1,*, David Lodder², Feifan Du²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1517-1529, 2020, DOI:10.32604/iasc.2020.012144

Abstract Robotic grasping is an important issue in the field of robot control. In order to solve the problem of optimal grasping pose of the robotic arm, based on the Generative Grasping Convolutional Neural Network (GG-CNN), a new convolutional neural network called Hybrid Generative Grasping Convolutional Neural Network (HGG-CNN) is proposed by combining three small network structures called Inception Block, Dense Block and SELayer. This new type of convolutional neural network structure can improve the accuracy rate of grasping pose based on the GG-CNN network, thereby improving the success rate of grasping. In addition, the HGG-CNN convolutional neural network structure can… More >

Jalal Sadoon Hameed Al-bayati^1,*, Burak Berk Üstündağ²

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1479-1496, 2021, DOI:10.32604/cmc.2020.013329

Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay… More >