Meena N. Alnuaimi¹, Nourah S. Alqahtani¹, Mohammed Gollapalli², Atta Rahman^1,*, Alaa Alahmadi¹, Aghiad Bakry¹, Mustafa Youldash³, Dania Alkhulaifi¹, Rashad Ahmed⁴, Hesham Al-Musallam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2609-2623, 2024, DOI:10.32604/cmes.2024.055303 - 31 October 2024

Abstract Human beings are often affected by a wide range of skin diseases, which can be attributed to genetic factors and environmental influences, such as exposure to sunshine with ultraviolet (UV) rays. If left untreated, these diseases can have severe consequences and spread, especially among children. Early detection is crucial to prevent their spread and improve a patient’s chances of recovery. Dermatology, the branch of medicine dealing with skin diseases, faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance, type of skin, and… More >

Qian Liu^1,#, Hui Zhang^1,#, Juan Gao², Meiping Sha¹, Lijun Shen¹, Xianfeng Cheng^3,*, Hao Chen^4,*

Psycho-Oncologie, Vol.18, No.3, pp. 213-221, 2024, DOI:10.32604/po.2024.052607 - 12 September 2024

Abstract Background: Keratinocyte carcinoma (KC) is a common malignancy characterized by a high recurrence rate and considerable psychological distress. The incidence of KC is increasing in China, raising concerns about its psychological consequences and adverse effects on quality of life. Demographic and clinical factors are thought to influence mental health outcomes in these patients. Nonetheless, data on the prevalence of anxiety in Chinese patients with KC and the factors associated with this anxiety are notably lacking. Therefore, a comprehensive investigation into the anxiety of patients with KC is imperative. Objective: This study aimed to investigate the… More >

Cong Shen^1,*, Wei Zhang^1,2,*, Tanping Zhou^1,2, Yiming Zhang¹, Lingling Zhang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

Krisana Nilsuwan^1,*, Yolanda Victoria Rajagukguk², Umesh Patil¹, Thummanoon Prodpran^1,3, Soottawat Benjakul^1,4,*

Journal of Renewable Materials, Vol.12, No.6, pp. 1125-1143, 2024, DOI:10.32604/jrm.2024.051361 - 02 August 2024

Abstract Maillard reaction is a non-enzymatic browning reaction and its products (MRPs) have been proven to possess antioxidant properties. This research aimed to produce a fish gelatin-based packaging incorporated with MRPs to retard lipid oxidation in chicken skin oil (CSO) during storage at ambient temperature (28°C–30°C). MRPs produced from fish gelatin and fructose (1:1, 90°C, pH 11) showed the highest antioxidant properties compared to those prepared under other conditions. Different glycerol/MRPs ratios (30:0, 25:5, 20:10, 15:15, 10:20, 5:25, 0:30) were incorporated into the film and resulting films were characterized. Glycerol/MRPs at 10:20 ratio was chosen to… More > Graphic Abstract

Madiha Hameed^1,3, Aneela Zameer^1,*, Muhammad Asif Zahoor Raja²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124 - 08 July 2024

Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

Yuyuan Li^1,2, Lang Li^1,2,*, Yu Ou^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1245-1262, 2024, DOI:10.32604/cmc.2024.049882 - 25 April 2024

Abstract The Inner Product Masking (IPM) scheme has been shown to provide higher theoretical security guarantees than the Boolean Masking (BM). This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security. Some previous work unfolds when certain (adversarial and implementation) conditions are met, and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour. In this paper, we investigate the security characteristics of IPM under different conditions. In adversarial condition, the security properties of first-order IPMs obtained through parametric characterization More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418 - 25 April 2024

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model More >

Zihao Song, Yan Zhou^*, Wei Cheng, Futai Liang, Chenhao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034 - 26 March 2024

Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.… More >

PEI QIN^#, MIAO LIU^#, XIN WANG, JIANHUA MA^*

BIOCELL, Vol.48, No.3, pp. 513-523, 2024, DOI:10.32604/biocell.2023.045640 - 15 March 2024

Abstract Background: L-dopa (Levodopa) is well known for managing PD (Parkinson’s disease); however, its prolonged use caused dyskinesia (LID). Due to the varied presentation of LID, effective treatment options are scarce. Flavonoids reported their neuroprotective activity by ameliorating acetylcholinesterase, monoamine oxidase, and neuroinflammation. Kaempferol is another flavonoid bearing these potentials. Aim: To evaluate neuroprotective activity of kaempferol in dyskinetic rats. Methods: PD was developed in Sprague-Dawley rats by injecting combination of L-ascorbic acid (10 µL) + 6-OHDA (12 µg) in medial forebrain bundle to induce neuronal damage in substantial nigra (SNr). LID was induced by administrating combination… More > Graphic Abstract

Muhammad Suleman¹, Faizan Ullah¹, Ghadah Aldehim^2,*, Dilawar Shah¹, Mohammad Abrar^1,3, Asma Irshad⁴, Sarra Ayouni²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365 - 26 December 2023

Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from… More >