Sai Liu¹, Zhenjiang Zhang^1,*, Guangjie Han², Bo Shen¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2651-2668, 2023, DOI:10.32604/iasc.2023.038477

Abstract Integrated satellite-terrestrial network (ISTN) has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere. Being a promising paradigm, cloud computing and mobile edge computing (MEC) have been identified as key technology enablers for ISTN to further improve quality of service and business continuity. However, most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks, ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites. In addition, most computing tasks are transferred to cloud servers or offloaded to… More >

Jian Shang^1,2,*, Runmin Guan², Wei Wang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2609-2626, 2023, DOI:10.32604/iasc.2023.037694

Abstract The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years. In real-world scenarios, microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks. To meet the high hardware resource requirements, address the vulnerability to network attacks and poor reliability in the traditional centralized data storage schemes, this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph (DAG) consensus mechanism. Firstly, the microgrid data storage model is designed based on the edge computing technology. The blockchain, deployed on… More >

Ana María Peco Chacón, Isaac Segovia Ramírez, Fausto Pedro García Márquez^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2595-2608, 2023, DOI:10.32604/iasc.2023.037277

Abstract Maintenance operations have a critical influence on power generation by wind turbines (WT). Advanced algorithms must analyze large volume of data from condition monitoring systems (CMS) to determine the actual working conditions and avoid false alarms. This paper proposes different support vector machine (SVM) algorithms for the prediction and detection of false alarms. K-Fold cross-validation (CV) is applied to evaluate the classification reliability of these algorithms. Supervisory Control and Data Acquisition (SCADA) data from an operating WT are applied to test the proposed approach. The results from the quadratic SVM showed an accuracy rate of 98.6%. Misclassifications from the confusion… More >

Xu Yao^1,2, Chengjian Qiu¹, Yuqing Song¹, Zhe Liu^1,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2583-2594, 2023, DOI:10.32604/iasc.2023.037205

Abstract As the pancreas only occupies a small region in the whole abdominal computed tomography (CT) scans and has high variability in shape, location and size, deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background. To alleviate these issues, this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure, in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box, and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity… More >

Shoutao Li^1,2,*, Ruyi Qu¹, Yu Zhang¹, Dingli Yu³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and… More >

Huafei Yang¹, Wenqing Yang¹, Nan Zhang¹, Shanming Wei^2,*, Yingnan Shang¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2745-2765, 2023, DOI:10.32604/iasc.2023.036614

Abstract Equipment defect detection is essential to the security and stability of power grid networking operations. Besides the status of the power grid itself, environmental information is also necessary for equipment defect detection. At the same time, different types of intelligent sensors can monitor environmental information, such as temperature, humidity, dust, etc. Therefore, we apply the Internet of Things (IoT) technology to monitor the related environment and pervasive interconnections to diverse physical objects. However, the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve… More >

Seema Sabharwal^1,2,*, Priti Singla¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2567-2582, 2023, DOI:10.32604/iasc.2023.035497

Abstract Sign language is used as a communication medium in the field of trade, defence, and in deaf-mute communities worldwide. Over the last few decades, research in the domain of translation of sign language has grown and become more challenging. This necessitates the development of a Sign Language Translation System (SLTS) to provide effective communication in different research domains. In this paper, novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm (Hybrid-AO) for image segmentation is proposed for the translation of alphabet-level Indian Sign Language (ISLTS) with a 5-layer Convolution Neural Network (CNN). The focus of this paper is to analyze various… More >

Mesfer Al Duhayyim^1,*, Hala J. Alshahrani², Khaled Tarmissi³, Heyam H. Al-Baity⁴, Abdullah Mohamed⁵, Ishfaq Yaseen⁶, Amgad Atta Abdelmageed⁶, Mohamed I. Eldesouki⁷

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2549-2566, 2023, DOI:10.32604/iasc.2023.034827

Abstract Computational linguistics is an engineering-based scientific discipline. It deals with understanding written and spoken language from a computational viewpoint. Further, the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting. Named Entity Recognition (NER) is a fundamental task in the data extraction process. It concentrates on identifying and labelling the atomic components from several texts grouped under different entities, such as organizations, people, places, and times. Further, the NER mechanism identifies and removes more types of entities as per the requirements. The significance of the NER… More >

T. M. Nithya^1,*, R. Umanesan², T. Kalavathidevi³, C. Selvarathi⁴, A. Kavitha⁵

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2537-2547, 2023, DOI:10.32604/iasc.2022.028804

Abstract Big data analytics is a popular research topic due to its applicability in various real time applications. The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance. Since big data involves numerous features and necessitates high computational time, feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance. This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit (SBOA-OGRU) model for big data classification in Apache Spark. The SBOA-OGRU technique involves the… More >

Huaxiang Song^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315

Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it appears that training optimization strategies have received less attention in relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison. The overall accuracy (OA) trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline. Results show that there is a maximal OA… More >