BINGJIE LI^1,#, WENBO CAO^1,2,3,#, JINJING XIAO¹, YIXIAO CHEN¹, QIYING WEI¹, MINGJIN YUE⁴, SAIJUN MO^1,2,3,*

BIOCELL, Vol.48, No.6, pp. 923-934, 2024, DOI:10.32604/biocell.2024.049160 - 10 June 2024

Abstract Perineural invasion (PNI), a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread, has the capacity to extend well beyond the primary tumor site, infiltrating distant regions devoid of lymphatic or vascular structures. PNI often heralds a decrease in patient survival rates and is recognized as an indicator of an unfavorable prognosis across a variety of cancers. Despite its clinical significance, the underlying molecular mechanisms of PNI remain elusive, complicating the development of specific and efficacious diagnostic and therapeutic strategies. In the realm of cancer research, non-coding RNAs (ncRNAs) have attracted considerable… More >

D. Dorathy Prema Kavitha¹, L. Francis Raj¹, Sandeep Kautish^2,#, Abdulaziz S. Almazyad³, Karam M. Sallam⁴, Ali Wagdy Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902 - 30 December 2023

Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images… More >

Youping Lin¹, Huangbin Lin^2,*, Dezhi Wei¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1363-1379, 2023, DOI:10.32604/iasc.2023.038601 - 21 June 2023

Abstract The digital twin (DT) includes real-time data analytics based on the actual product or manufacturing processing parameters. Data from digital twins can predict asset maintenance requirements ahead of time. This saves money by decreasing operating expenses and asset downtime, which improves company efficiency. In this paper, a digital twin in braiding machinery based on IoT (DTBM-IoT) used to diagnose faults. When an imbalance fault occurs, the system gathers experimental data. After that, the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for More >

Rui Wen^1,*, Jie Zhou², Zhongliang Shen¹, Xiaorui Zhang^2,3,4, Sunil Kumar Jha⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889 - 26 May 2023

Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to… More >

Wencong Liu¹, Ahmed Mostafa Khalil^2,*, Rehab Basheer³, Yong Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2715-2730, 2023, DOI:10.32604/cmes.2023.024755 - 23 November 2022

Abstract In early December 2019, a new virus named “2019 novel coronavirus (2019-nCoV)” appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 More > Graphic Abstract

Anjani Kumar Singha¹, Nitish Pathak^2,*, Neelam Sharma³, Abhishek Gandhar⁴, Shabana Urooj⁵, Swaleha Zubair⁶, Jabeen Sultana⁷, Guthikonda Nagalaxmi⁸

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1065-1080, 2022, DOI:10.32604/iasc.2022.024172 - 03 May 2022

Abstract The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method… More >

Nouh Sabri Elmitwally^1,2, Maria Tariq^3,4, Muhammad Adnan Khan^5,*, Munir Ahmad³, Sagheer Abbas³, Fahad Mazaed Alotaibi⁶

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1001-1014, 2022, DOI:10.32604/csse.2022.022017 - 08 February 2022

Abstract In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country’s economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research More >

Abdullah Baz¹, Mohammed Baz^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1051-1069, 2022, DOI:10.32604/iasc.2022.020304 - 22 September 2021

Abstract COVID-19 constitutes one of the devastating pandemics plaguing humanity throughout the centuries; within about 18 months since its appearing, the cumulative confirmed cases hit 173 million, whereas the death toll approaches 3.72 million. Although several vaccines became available for the public worldwide, the speed with which COVID-19 is spread, and its different mutant strains hinder stopping its outbreak. This, in turn, prompting the desperate need for devising fast, cheap and accurate tools via which the disease can be diagnosed in its early stage. Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the mainstay tool used… More >

N. Jayanthi^1,*, D. Manohari², Mohamed Yacin Sikkandar³, Mohamed Abdelkader Aboamer³, Mohamed Ibrahim Waly³, C. Bharatiraja⁴

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1317-1329, 2022, DOI:10.32604/iasc.2022.018974 - 22 September 2021

Abstract Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard More >

Muhammad Sohaib Aslam¹, Muhammad Younas¹, Muhammad Umar Sarwar¹, Muhammad Arif Shah^2,*, Atif Khan³, M. Irfan Uddin⁴, Shafiq Ahmad⁵, Muhammad Firdausi⁵, Mazen Zaindin⁶

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151 - 05 February 2021

Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of… More >