Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images.

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.

Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things.

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.

Preliminary probiotic and technological characterization of Pediococcus pentosaceus strain KID7 and in vivo assessment of its cholesterol-lowering activity.

The study was aimed to characterize the probiotic properties of a Pediococcus pentosaceus strain, KID7, by in vitro and in vivo studies. The strain possessed tolerance to oro-gastrointestinal transit, adherence to the Caco-2 cell line, and antimicrobial activity. KID7 exhibited bile salt hydrolase activity and cholesterol-lowering activity, in vitro. In vivo cholesterol-lowering activity of KID7 was studied using atherogenic diet-fed hypercholesterolemic mice. The experimental animals (C57BL/6J mice) were divided into 4 groups viz., normal diet-fed group (NCD), atherogenic diet-fed group (HCD), atherogenic diet- and KID7-fed group (HCD-KID7), and atherogenic diet- and Lactobacillus acidophilus ATCC 43121-fed group (HCD-L.ac) as positive control. Serum total cholesterol (T-CHO) level was significantly decreased by 19.8% in the HCD-KID7 group (P < 0.05), but not in the HCD-L.ac group compared with the HCD group. LDL cholesterol levels in both HCD-KID7 and HCD-L.ac groups were decreased by 35.5 and 38.7%, respectively, compared with HCD group (both, P < 0.05). Glutamyl pyruvic transaminase (GPT) level was significantly lower in the HCD-KID7 and HCD-L.ac groups compared to HCD group and was equivalent to that of the NCD group. Liver T-CHO levels in the HCD-KID7 group were reduced significantly compared with the HCD group (P < 0.05) but not in the HCD-L.ac group. Analysis of expression of genes associated with lipid metabolism in liver showed that low-density lipoprotein receptor (LDLR), cholesterol-7α-hydroxylase (CYP7A1) and apolipoprotein E (APOE) mRNA expression was significantly increase in the HCD-KID7 group compared to the HCD group. Furthermore, KID7 exhibited desired viability under freeze-drying and subsequent storage conditions with a combination of skim milk and galactomannan. P. pentosaceus KID7 could be a potential probiotic strain, which can be used to develop cholesterol-lowering functional food after appropriate human clinical trials.