Abdalla Alameen^*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 593-602, 2021, DOI:10.32604/iasc.2021.016950

Abstract It is forecasted that billions of Internet of Things (IoT) and sensor devices will be installed worldwide by 2020. These devices can provide infrastructure-based services for various applications such as in smart hospitals, smart industry, smart grids, and smart industrial towns. Among them, the hospital service system needs to authenticate devices, and medical data are recorded for diagnostic purposes. In general, digital signatures are employed, but the computational power and their huge numbers pose many challenges to the digital signature system. To solve such problems, we developed a ledger system for authenticating IoT medical devices. It is a centralized ledger… More >

Osama S. Faragallah^1,*, Walid El-Shafai², Ashraf Afifi¹, Ibrahim Elashry³, Mohammed A. AlZain¹, Jehad F. Al-Amri¹, Ben Soh⁴, Heba M. El-Hoseny⁵, Hala S. El-Sayed⁶, Fathi E.Abd El-Samie²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 353-367, 2021, DOI:10.32604/iasc.2021.016865

Abstract With the rapidly increasing rate of using online services and social media websites, cybercriminals have caused a great deterioration in the network security with enormous undesired consequences. Encryption techniques may be utilized to achieve data robustness and security in digital multimedia communication systems. From this perspective, this paper presents an optical ciphering framework using Double Random Phase Encoding (DRPE) for efficient and secure transmission of Three-Dimensional Videos (3DVs). Firstly, in the DRPE-based 3DV cybersecurity framework proposed in the paper, an optical emitter converts each frame of the transmitted 3DV into an optical signal. Then, the DRPE technique encrypts the obtained… More >

Muhammad Naeem¹, Aziz Khan², Saleem Abdullah^2,*, Shahzaib Ashraf³, Ahmad Ali Ahmad Khammash⁴

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 459-476, 2021, DOI:10.32604/iasc.2021.016822

Abstract Spherical fuzzy set (SFS) as one of several non-standard fuzzy sets, it introduces a number triplet (a,b,c) that satisfies the requirement a² + b² + c² ≤ 1 to express membership grades. Due to the expression, SFS has a more extensive description space when describing fuzzy information, which attracts more attention in scientific research and engineering practice. Just for this reason, how to describe the fuzzy information more reasonably and perfectly is the hot that scholars pay close attention to. In view of this hot, in this paper, the notion of spherical hesitant fuzzy set is introduced as a generalization… More >

Ying Cui¹, Shi Qiu^2,*, Tong Li³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 417-428, 2021, DOI:10.32604/iasc.2021.016802

Abstract It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty of fog distribution, a fog concentration estimation model is accordingly proposed. Establish the brightness and gradient model in the spatial domain, and establish the FFT model in the frequency domain. Also, a multiple branch network framework is established to realize the qualitative estimation of the fog concentration in images based on comprehensive analysis from spatial and frequency domain levels. In the aspect of foggy image… More >

Salisu Muhammed^*, Erbuğ Çelebi

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 397-416, 2021, DOI:10.32604/iasc.2021.016574

Abstract As the models with fewer operations help realize the performance of intelligent computing systems, we propose a novel deep network for DeepGait feature extraction with less operation for video sensor-based gait representation without dimension decomposition. The DeepGait has been known to have outperformed the hand-crafted representations, such as the frequency-domain feature (FDF), gait energy image (GEI), and gait flow image (GFI), etc. More explicitly, the channel-activated mapping network (CAMNet) is composed of three progressive triplets of convolution, batch normalization, max-pooling layers, and an external max pooling to capture the Spatio-temporal information of multiple frames in one gait period. We conducted… More >

Nagy I. Elkalashy^*, Sattam Al Otaibi, Salah K. Elsayed, Yasser Ahmed, Essam Hendawi, Ayman Hoballah

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 477-491, 2021, DOI:10.32604/iasc.2021.016558

Abstract In this study, the active traveling-wave fault location function is incorporated into the management of earth faults for smart unearthed and compensated distribution networks associated with distributed renewable generation. Unearthed and compensated networks are implemented mainly to attain service continuity, specifically during earth faults. This advantage is valued for service continuity of grid-interconnected renewable resources. However, overcurrent-based fault indicators are not efficient in indicating the fault path in these distribution networks. Accordingly, in this study, the active traveling-wave fault location is complemented using distributed Rogowski coil-based fault passage indicators. Active traveling waves are injected by switching the neutral point of… More >

Xinyue Lv¹, Xiaofeng Lian^2,*, Li Tan¹, Yanyan Song¹, Chenyu Wang³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 513-526, 2021, DOI:10.32604/iasc.2021.016450

Abstract With the rapid development of the Internet of Things and advanced sensors, vision-based monitoring and forecasting applications have been widely used. In the context of the Internet of Things, visual devices can be regarded as network perception nodes that perform complex tasks, such as real-time monitoring of road traffic flow, target detection, and multi-target tracking. We propose the High-Performance detection and Multi-Correlation measurement algorithm (HPMC) to address the problem of target occlusion and perform trajectory correlation matching for multi-target tracking. The algorithm consists of three modules: 1) For the detection module, we proposed the You Only Look Once(YOLO)v3_plus model, which… More >

Yang Wu¹, Lihong Xu^1,*, Erik D. Goodman²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 561-576, 2021, DOI:10.32604/iasc.2021.016415

Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then uses labeled data for supervised… More >

Alsehli Abrar¹, Wadood Abdul^1,*, Sanaa Ghouzali²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 577-591, 2021, DOI:10.32604/iasc.2021.016382

Abstract Image authentication is an important field that employs many different approaches and has several significant applications. In the proposed approach, we used a combination of two techniques to achieve authentication. Image watermarking is one of the techniques that has been used in many studies but the authentication field still needs to be studied. Blockchain technology is a relatively new technology that has significant research potential related to image authentication. The watermark is embedded into the third-level discrete wavelet transform (DWT) in the middle frequency regions to achieve security and imperceptibility goals. Peak signal-to-noise ratio PSNR, structural similarity matrix (SSIM), normalized… More >

Dianqing Liu^1,2, Lanqiu Zhang^1,2, Yanqiu Shao^1,2,*, Junzhao Sun³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 447-458, 2021, DOI:10.32604/iasc.2021.016320

Abstract Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then… More >