Dan Zhao^1,*, Qiong Wu¹, Minyao Gan², Ke Li¹, Wenhong Ma¹, Qun Wu¹, Liqiang Dong¹, Shaogang Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2041-2055, 2023, DOI:10.32604/cmes.2022.021026

Abstract To reduce the vibration and sound radiation of underwater cylindrical shells, a skin composed of micro floating raft arrays and a compliant wall is proposed in this paper. A vibroacoustic coupling model of a finite cylindrical shell covered with this skin for the case of turbulence excitation is established based on the shell theories of Donnell. The model is solved with the modal superposition method to investigate the effects of the structural parameters of micro floating raft elements on the performance of reducing vibration and sound radiation of the cylindrical shell of this skin. The results indicate that increasing the… More >

S. Ayshwarya Lakshmi^1,*, K. Anandavelu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3403-3413, 2023, DOI:10.32604/iasc.2023.030970

Abstract Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection. The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process. Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties. One of the most significant requirements is to design optimal solutions to optimize their use. There is no particular technique that can answer all optimization issues. The proposed enhanced cuckoo search optimization method indicates… More >

S. Gopikha^*, M. Balamurugan

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2727-2742, 2023, DOI:10.32604/csse.2023.028609

Abstract Melanoma is the most lethal malignant tumour, and its prevalence is increasing. Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people. Recently, deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction. A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data. As a result, overfitting and poor performance occur. To deal with this issue, we proposed a novel Consecutive Layerwise weight Constraint MaxNorm model (CLCM-net)… More >

Vinay Gautam¹, Naresh Kumar Trivedi¹, Abhineet Anand¹, Rajeev Tiwari^2,*, Atef Zaguia³, Deepika Koundal⁴, Sachin Jain⁵

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2259-2275, 2023, DOI:10.32604/csse.2023.026358

Abstract Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have… More >

Tahir Alyas^1,*, Khalid Alissa², Abdul Salam Mohammad³, Shazia Asif⁴, Tauqeer Faiz⁵, Gulzar Ahmed⁶

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4869-4883, 2022, DOI:10.32604/cmc.2022.031376

Abstract Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every… More >

Nithya Rekha Sivakumar^1,*, Sara Abdelwahab Ghorashi¹, Faten Khalid Karim¹, Eatedal Alabdulkreem¹, Amal Al-Rasheed²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6253-6267, 2022, DOI:10.32604/cmc.2022.029266

Abstract Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these… More >

Saleem Mustafa¹, Arfan Jaffar¹, Muhammad Waseem Iqbal^2,*, Asma Abubakar², Abdullah S. Alshahrani³, Ahmed Alghamdi⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549

Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

Xuechuan Wang^1,2, Wenying Zhao^1,2, Xugang Dang^1,2,*, Yiqing Wang^1,2, Huijie Zhang^1,2,*

Journal of Renewable Materials, Vol.10, No.11, pp. 2747-2763, 2022, DOI:10.32604/jrm.2022.021555

Abstract As a non-toxic, highly reactive biomass material, gelatin is wildly used as the component of biomass-based adhesive. However, like most aqueous adhesives, gelatin-based adhesives suffer from long solidifying time or high solidifying temperature due to the low volatility of water, which highly limits the application potential of gelatinbased adhesives. Inspired by the fast adhesion of marine organisms through the formation of chemical crosslinks, herein, a kind of low temperature curable eco-friendly gelatin-based adhesive with good adhesive properties and fast curing at low temperature is developed by introducing clicking chemical Diels-Alder (DA) reaction between blocked waterborne polyurethane (MWPU) and gelatin. The… More > Graphic Abstract

Yuma Nonomiya^*, Takashi Yokokawa^†, Kazuyoshi Kawakami^†, Kazuo Kobayashi^†, Takeshi Aoyama^†, Tomomi Takiguchi^†, Takahito Sugisaki^†, Kenichi Suzuki^†, Mitsukuni Suenaga^‡, Takeru Wakatsuki^‡, Kensei Yamaguchi^‡, Yoshikazu Sugimoto^*, Toshihiro Hama^†

Oncology Research, Vol.27, No.5, pp. 551-556, 2019, DOI:10.3727/096504018X15291727589740

Abstract Regorafenib is a multikinase inhibitor for the treatment of metastatic colorectal cancer. Regorafenib-induced hand–foot skin reaction (HFSR) is a common side effect during treatment. The reported frequency of HFSR was 80% (grade 3: 28%) in the Japanese subpopulation in the CORRECT trial; however, more detailed data regarding HFSR in terms of onset and sites of susceptibility are unclear. Additionally, the risk factors for regorafenib-induced severe HFSR are unknown. The aim of this study was to compare HFSR between the hands and feet and identify preexisting risk factors for severe HFSR in Japanese patients receiving regorafenib. We retrospectively examined the onset… More >

Yi Sheng¹, Wu Zeng^1,*, Qiuyu Hu¹, Weihua Ou², Yuxuan Xie³, Jie Li¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2773-2783, 2022, DOI:10.32604/cmc.2022.027985

Abstract Heart rate is an important metric for determining physical and mental health. In recent years, remote photoplethysmography (rPPG) has been widely used in characterizing physiological signals in human subjects. Currently, research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery. However, this method is very sensitive to the movement of the test subject and light intensity variation, and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate. In this paper, an improved method for rPPG signal preprocessing is proposed. Based on the… More >