S. Rajagopal^1,*, T. Ramakrishnan², S. Vairaprakash³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1927-1941, 2022, DOI:10.32604/iasc.2022.024998

Abstract Video capsule endoscope (VCE) is a developing methodology, which permits analysis of the full gastrointestinal (GI) tract with minimum intrusion. Although VCE permits for profound analysis, evaluating and analyzing for long hours of images is tiresome and cost-inefficient. To achieve automatic VCE-dependent GI disease detection, identifying the anatomical region shall permit for a more concentrated examination and abnormality identification in each area of the GI tract. Hence we proposed a hybrid (Long-short term memory-Visual Geometry Group network) LSTM-VGGNET based classification for the identification of the anatomical area inside the gastrointestinal tract caught by VCE images. The video input data is… More >

Arpit Jain^1,*, Adesh Kumar², Anand Prakash Shukla³, Hammam Alshazly⁴, Hela Elmannai⁵, Abeer D. Algarni⁵, Roushan Kumar⁶, Jitendra Yadav⁶

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2007-2021, 2022, DOI:10.32604/iasc.2022.024770

Abstract Network on chip (NoC) is an integrated communication system on chip (SoC), efficiently connecting various intellectual property (IP) modules on a single die. NoC has been suggested as an enormously scalable solution to overcome the communication problems in SoC. The performance of NoC depends on several aspects in terms of area, latency, throughput, and power. In this paper, the 2D and 3D mesh NoC performance on Virtex-5 field-programmable gate array (FPGA) is studied. The design is carried in Xilinx ISE 14.7 and the behavior model is followed based on XY and XYZ routing for 2D and 3D mesh NoC respectively.… More >

A. Jameer Basha¹, N. Rajkumar², Mohammed A. AlZain³, Mehedi Masud⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1693-1706, 2022, DOI:10.32604/iasc.2022.024714

Abstract In the healthcare applications, Internet of Medical Things (IoMT) comforts the communication processes between the medical devices and the humans via wireless network. Moreover, this communication helps both the physicians and the patients to contact remotely for the diagnosis of the disease’s wearable devices sensor signals. However, IoMT system violates the privacy preserving of Patient’s Health Record (PHR) as well as self-sovereign identity of patient. In this regard, security action should be taken. Previous techniques used in IoMT are in lack of data consistency, confidentiality, and inaccessible of data. To overcome these issues, the fog computing-based technology is used in… More >

R. S. Latha¹, K. Venkatachalam², Jehad F. Al-Amri³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1803-1818, 2022, DOI:10.32604/iasc.2022.024539

Abstract Generation of massive data is increasing in big data industries due to the evolution of modern technologies. The big data industries include data source from sensors, Internet of Things, digital and social media. In particular, these big data systems consist of data extraction, preprocessing, integration, analysis, and visualization mechanism. The data encountered from the sources are redundant, incomplete and conflict. Moreover, in real time applications, it is a tedious process for the interpretation of all the data from different sources. In this paper, the gathered data are preprocessed to handle the issues such as redundant, incomplete and conflict. For that,… More >

D. Karthik Prabhu^1,*, R. V. Maheswari², B. Vigneshwaran²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1441-1454, 2022, DOI:10.32604/iasc.2022.024128

Abstract Measurement and recognition of Partial Discharge (PD) in power apparatus is considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. During operating conditions, PD may occur either in the form of single and multiple patterns in nature. Currently, for PD pattern recognition, deep learning approaches are used. To evaluate spatial order less features from the large-scale patterns, a pre-trained network is used. The major drawback of traditional approaches is that they generate high dimensional data or requires additional steps like dictionary learning and dimensionality reduction. However, in real-time applications, interference incorporated in the measured… More >

S. Hariprasad^1,*, T. Deepa¹, N. Bharathiraja²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856

Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that… More >

E. P. Kannan^1,*, S. S. Vinsley², T. V. Chithra³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1675-1691, 2022, DOI:10.32604/iasc.2022.022707

Abstract In this paper, Field-Programmable Gate Array (FPGA) implementation-based image demosaicing is carried out. The Newton Gregory interpolation algorithm is designed based on FPGA frame work. Interpolation is the method of assessing the value of a function for any in-between value of self-regulating variable, whereas the method of computing the value of the function outside the specified range is named extrapolation. The natural images are collected from Kodak image database and medical images are collected from UPOL (University of Phoenix Online) database. The proposed algorithm is executed on using Xilinx ISE (Integrated Synthesis Environment) Design Suite 14.2 and is confirmed on… More >

D. Yuvaraj¹, S. Shanmuga Priya^2,*, M. Braveen³, S. Navaneetha Krishnan⁴, S. Nachiyappan⁵, Abolfazl Mehbodniya⁶, A. Mohamed Uvaze Ahamed⁷, M. Sivaram⁸

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1505-1522, 2022, DOI:10.32604/iasc.2022.022151

Abstract Wireless sensor networks are extensively utilized as a communication mechanism in the field of the Internet of Things (IoT). Along with these services, numerous IoT based applications need stabilized transmission or delivery over unbalanced wireless connections. To ensure the stability of data packets delivery, prevailing works exploit diverse geographical routing with multi-hop forwarders in WSNs. Furthermore, critical Denial of Service (DoS) attacks frequently has an impact on these techniques, where an enormous amount of invalid data starts replicating and transmitted to receivers to prevent Wireless Sensor Networks (WSN) communication. In this investigation, a novel adaptive endorsement method is designed by… More >

Chunhua Pan^1,*, Yi Cao², Jinglong Ren³

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1081-1091, 2022, DOI:10.32604/iasc.2022.028763

Abstract Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary… More >

Yingnan Zhao^1,*, Peiyuan Ji¹, Fei Chen¹, Guanlan Ji¹, Sunil Kumar Jha²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >