Jae Dong Lee^1,2, Hyo Soung Cha¹, Shailendra Rathore², Jong Hyuk Park^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774

Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

Abdul Hannan Khan^1,2, Muhammad Adnan Khan^3,*, Sagheer Abbas², Shahan Yamin Siddiqui^1,2, Muhammad Aanwar Saeed⁴, Majed Alfayad⁵, Nouh Sabri Elmitwally^6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1399-1412, 2021, DOI:10.32604/cmc.2021.012737

Abstract Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization… More >

Jinping Zhang^1,2, Youlai Jin¹, Bin Sun^1,*, Yuping Han³, Yang Hong⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 755-770, 2021, DOI:10.32604/cmes.2021.012686

Abstract The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult. Currently, some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, a new time-frequency analysis method based on the empirical mode decomposition (EMD) algorithm, to decompose non-stationary raw data in order to obtain relatively stationary components for further study. However, the endpoint effect in CEEMDAN is often neglected, which can lead to decomposition errors that reduce the accuracy of the research results. In this study, we processed an original runoff sequence using the radial basis function neural network (RBFNN) technique… More >

Adel Saad Assiri^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 435-450, 2021, DOI:10.32604/csse.2021.014894

Abstract Artificial neural networks (ANNs) are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines, including but not limited to physics, biology, chemistry, and engineering. However, ANNs lack several key characteristics of biological neural networks, such as sparsity, scale-freeness, and small-worldness. The concept of sparse and scale-free neural networks has been introduced to fill this gap. Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights. When the network is initialized, the neural network is fully… More >

Rajesh Kaluri¹, Dharmendra Singh Rajput¹, Qin Xin^2,*, Kuruva Lakshmanna¹, Sweta Bhattacharya¹, Thippa Reddy Gadekallu¹, Praveen Kumar Reddy Maddikunta¹

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 453-469, 2021, DOI:10.32604/iasc.2021.014369

Abstract Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water.… More >

Arif Iqbal^*, Abdul Basit, Imran Ali, Junaid Babar, Ihsan Ullah

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 299-309, 2021, DOI:10.32604/iasc.2021.014250

Abstract Automated meter reading has recently been adopted by utility service providers for improving the reading and billing process. Images captured during meter reading are incorporated in consumer bills to prevent reporting false reading and ensure transparency. The availability of images captured during the meter reading process presents the potential of completely automating the meter reading process. This paper proposes a convolutional network-based multi-box model for the automatic meter reading. The proposed research leverages the inception model with a single shot detector to achieve high accuracy and efficiency compared to the existing state-of-the-art machine learning methods. We tested the multi-box detector… More >

Huaning Song¹, Jiansheng Peng^1,2,*, Nianyang Tuo¹, Haiying Xia², Yiyun Peng³

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 581-592, 2021, DOI:10.32604/iasc.2021.014152

Abstract Accurate identification of sugarcane buds, as one of the key technologies in sugarcane plantation, becomes very necessary for its mechanized and intelligent advancements. In the traditional methods of sugarcane bud recognition, a significant amount of algorithms work goes into the location and recognition of sugarcane bud images. A Convolutional Neural Network (CNN) for classifying the bud conditions is proposed in this paper. Firstly, we convert the colorful sugarcane images into gray ones, unify the size of them, and make a TFRecord format data set, which contains 1100 positive samples and 1100 negative samples. Then, a CNN mode is developed to… More >

Aolin Sun, Xiang Cao^*, Xu Xiao, Liwen Xu

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 551-564, 2021, DOI:10.32604/iasc.2021.01008

Abstract An essential issue in a target search is safe navigation while quickly finding targets. In order to improve the efficiency of a target search and the smoothness of AUV’s (Autonomous Underwater Vehicle) trajectory, a fuzzy-based bio-inspired neural network approach is proposed in this paper. A bio-inspired neural network is applied to a multi-AUV target search, which can effectively plan search paths. In the meantime, a fuzzy algorithm is introduced into the bio-inspired neural network to make the trajectory of AUV obstacle avoidance smoother. Unlike other algorithms that need repeated training in the parameters selection, the proposed approach obtains all the… More >

Xiang Cao^1,2,*, Ling Chen¹, Liqiang Guo³, Wei Han⁴

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 391-407, 2021, DOI:10.32604/iasc.2021.01002

Abstract As one of the classical problems in autonomous underwater vehicle (AUV) research, path planning has obtained a lot of research results. Many studies have focused on planning an optimal path for AUVs. These optimal paths are sometimes too close to obstacles. In the real environment, it is difficult for AUVs to avoid obstacles according to such an optimal path. To solve the safety problem of AUV path planning in a dynamic uncertain environment, an algorithm combining a bio-inspired neural network and potential field is proposed. Based on the environmental information, the bio-inspired neural network plans the optimal path for the… More >