Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization.

The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction.

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification.

Microscopy image analysis gives quantitative support for enhancing the characterizations of various diseases, including breast cancer, lung cancer, and brain tumors. As a result, it is crucial in computer-assisted diagnosis and prognosis. Understanding the biological principles underlying these dynamic image sequences often necessitates precise analysis and statistical quantification, a major discipline issue.

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment.

The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data.

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.

Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures.

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks.

Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network.

Photocatalytic decolourization of Rhodamine-B dye by visible light active ZIF-8/BiFeO composite.

In this work, preparation of ZIF-8 supported BiFeO photocatalyst by ultrasound cavitation technique was reported. The synthesized materials were characterized using solid UV absorption spectroscopy, Raman Spectroscopy, and SEM. The catalytic function of synthesized photocatalyst under ultraviolet and visible light was examined for the decolourization of Rhodamine-B (Rh-B) dye.

Optimization of multiple parameters for adsorption of arsenic (III) from aqueous solution using Psidium guajava leaf powder.

The conventional method of water treatment using activated carbon from several sources has been focused on extensively in the last two decades. However, rare attention has been noticed on natural adsorbents such as plant leaves. Therefore, the Psidium guajava (guava) leaf has been investigated to understand its adsorption efficacy for Arsenic (III) [As(III)] in this study.

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and "Internet of Things" (IoT) solutions have become necessary for diagnosis.

Heat Management in Supersonic/Hypersonic Vehicles Using Endothermic Fuel: Perspective and Challenges.

In this study, an in-depth analysis has been performed on various technoengineering aspects of hydrocarbon cracking under supercritical conditions toward developing an endothermic fuel. The paper is segregated into several sections with specific emphasis. The major areas covered in this work include physicochemical characteristics of different endothermic fuels, supercritical pyrolysis of hydrocarbon fuels, phenomena of coking, and its suppression from an application viewpoint.

Machine Learning in Drug Discovery: A Review.

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review.

Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19.

The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases.

Sonochemical approach for synthesis of zinc oxide-poly methyl methacrylate hybrid nanoparticles and its application in corrosion inhibition.

Hybrid nanoparticles (HNPs) with zinc oxide and polymethyl metha acrylate (inorganic/ polymer) were synthesized through the exploitation of ultrasound approach. The synthesized HNPs were further characterized employing transmission electron microscopy and x-ray diffraction. ZnO-PMMA based HNPs exhibit excellent protection properties to mild steel from corrosion when gets exposed to acidic condition.

Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm.

The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application.

Lipase catalyzed transesterification of ethyl butyrate synthesis in -hexane- a kinetic study.

Kinetics of lipase catalyzed transesterification of ethyl caprate and butyric acid was investigated. The objective of this work was to propose a reaction mechanism and develop a rate equation for the synthesis of ethyl butyrate by transesterification using surfactant coated lipase from . The reaction rate could be described in terms of Michaelis-Menten equation with a Ping-Pong Bi-Bi mechanism and competitive inhibition by both the substrates.

Big and small numbers: Empirical support for a single, flexible mechanism for numerosity perception.

The existence of perceptually distinct numerosity ranges has been proposed for small (i.e., subitizing range) and larger numbers based on differences in precision, Weber fractions, and reaction times.