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  • Open Access

    ARTICLE

    Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

    Mahmoud Ragab1,2,3,*, Samah Alshehri4, Hani A. Alhadrami5,6,7, Faris Kateb1, Ehab Bahaudien Ashary8, S. Abdel-khalek9,10

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1323-1338, 2022, DOI:10.32604/cmc.2022.024775 - 24 February 2022

    Abstract Rapid advancements of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) pose serious security issues by revealing secret data. Therefore, security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time. Practically, AI techniques can be utilized to design image steganographic techniques in IIoT. In addition, encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access. In order to accomplish secure data transmission in IIoT environment, this study presents novel encryption with image steganography based… More >

  • Open Access

    ARTICLE

    An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

    Daniyal M. Alghazzawi1, Osama Bassam J. Rabie1, Surbhi Bhatia2, Syed Hamid Hasan1,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1173-1193, 2022, DOI:10.32604/cmc.2022.022748 - 24 February 2022

    Abstract The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved… More >

  • Open Access

    ARTICLE

    Transplanted choroidal plexus epithelial cells can integrate with organotypic spinal cord slices into a new system

    JINGJIE LIU1,#, XIAOYAN DING2,#, LI XIANG1, SHENGLI HUANG3,*

    BIOCELL, Vol.46, No.6, pp. 1537-1544, 2022, DOI:10.32604/biocell.2022.018441 - 07 February 2022

    Abstract This study aimed to evaluate the integration of transplanted choroidal plexus epithelial cells with organotypic spinal cord slices. Organotypic spinal cord slices, normally cultured for 6 days, were divided into control group (Ctrl) and transplanted group (T). The choroidal plexus epithelial cells were dissociated and primary cultured (C group). The choroidal plexus epithelial cells cultured for 6–7 days were labeled by 1,1’-dioctadecyl-3,3,3’,3’-tetramethyl-indocarbocyanineperchlorate (CM-Dil), and were identified by transthyretin (TTR) in immunocytochemistry. They were adjusted to the density of 0.5–1 × 107/ml, then 2 μl cells suspension were transplanted to the spinal cord slices in the T… More >

  • Open Access

    ARTICLE

    Improving Date Fruit Classification Using CycleGAN-Generated Dataset

    Dina M. Ibrahim1,2,*, Nada M. Elshennawy2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419 - 24 January 2022

    Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN)… More >

  • Open Access

    ARTICLE

    Effects of Manganese Toxicity on the Growth and Gene Expression at the Seedling Stage of Soybean

    Ying Liu1,3,*, Jingye Chen1,3, Xiaohao Li1,3, Shaoxia Yang1, Hanqiao Hu1,3, Yingbin Xue2,3,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.5, pp. 975-987, 2022, DOI:10.32604/phyton.2022.019057 - 24 January 2022

    Abstract In order to investigate the effects of Manganese (Mn) toxicity stress on the growth and gene expression at the seedling stage of soybean, soybean seedlings were treated with normal Mn concentration (5 μmol/L MnSO4) and excess Mn concentration (100 μmol/L MnSO4) by the method of hydroponic culture in this study. When soybean was subjected to Mn toxicity stress, excessive Mn could affect seedling growth, root development, the number of Mn oxide spots in leaves, and the Mn accumulation content in different parts of soybean. With the increase of exogenous Mn concentration and the prolongation of culture… More >

  • Open Access

    ARTICLE

    Impact of Doctoral Student Training Process Fit on Doctoral Students’ Mental Health

    Fulin Li1, Chuanyi Wang1,*, Xiaoguang Yue2

    International Journal of Mental Health Promotion, Vol.24, No.2, pp. 169-187, 2022, DOI:10.32604/ijmhp.2022.020034 - 18 January 2022

    Abstract Background: Doctoral students have much higher risk of anxiety or depression than general population. Doctoral students worldwide are facing varying degrees of mental health risks. Method: Based on the survey data of 6,812 doctoral students worldwide in Nature in 2019, Probit and Logit models are used to explore the correlation between the fit of doctoral education and training process and the mental health of doctoral students. Results: (1) The training environment fit of doctoral students has a significant positive impact on their mental health. (2) The academic profession fit of doctoral students has a significant positive impact… More >

  • Open Access

    ARTICLE

    Reinforced CNN Forensic Discriminator to Detect Document Forgery by DCGAN

    Seo-young Lim, Jeongho Cho*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6039-6051, 2022, DOI:10.32604/cmc.2022.024862 - 14 January 2022

    Abstract Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous.… More >

  • Open Access

    VIEWPOINT

    From organ-on-a-chip towards body-on-a-chip

    JONG HWAN SUNG*

    BIOCELL, Vol.46, No.5, pp. 1177-1180, 2022, DOI:10.32604/biocell.2022.019055 - 06 January 2022

    Abstract Organ-on-a-chip technology aims to reproduce the key physiological features of human organs and tissues, even complex actions of multi-organ interaction. While organ-on-a-chips at single-organ level has made notable achievement during the last decade, multi-organ-on-a-chips, which manifests unique advantages, has started gathering attention only recently. In this viewpoint, we discuss the current status of organ-on-a-chip technology, with a specific emphasis on multi-organ-on-a-chip. Key technological advances contributing to the maturation of the field, and challenges that need to be addressed before wider adoption in relevant fields are discussed. We will share our perspectives on how the multi-organ-on-a-chip More >

  • Open Access

    ARTICLE

    Inter-Purchase Time Prediction Based on Deep Learning

    Ling-Jing Kao1, Chih-Chou Chiu1,*, Yu-Fan Lin2, Heong Kam Weng1

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 493-508, 2022, DOI:10.32604/csse.2022.022166 - 04 January 2022

    Abstract Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of… More >

  • Open Access

    REVIEW

    Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

    Gulshan Kumar1,*, Hamed Alqahtani2

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1271-1307, 2022, DOI:10.32604/cmes.2022.018418 - 30 December 2021

    Abstract Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual… More >

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