SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.

Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets.

Addressing a decision problem through a bipolar Pythagorean fuzzy approach: A novel methodology applied to digital marketing.

Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing.

CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor.

Batch reactors are type of chemical reactors, where the reactants are loaded to process for a defined batch time and the products are removed after the polymerization reaction completion. Specialty chemicals and food processing industries widely use BRs due to their versatility and suitability for handling small- to medium-scale production, complex reactions, and varying reaction conditions. This article employs a CNN-LSTM-based nonlinear model predictive controller (NMPC) to effectively track the temperature profile of a BR.

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.

This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential to cause severe health complications including kidney disease, vision impairment and heart conditions.

Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran.

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging.

GAN-Augmented Naïve Bayes for identifying high-risk coronary artery disease patients using CT angiography data.

Coronary artery disease (CAD) is one of the most common cardiovascular disorders affecting millions of individuals globally. It is the leading cause of mortality in both the wealthy and impoverished nations. CAD patients exhibit a wide range of symptoms, some of which are not evident until a major incident occurs.

A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers.

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method.

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation.

Luminescent TiO nanoparticles synthesized from Mollugo Oppositifolia and Trianthema Portulacastrum for dye degradation, antimicrobial and antioxidant properties.

In modern times, nanoparticles have materialized as indispensable things in contemporary medicine, with a variety of uses in clinical, drug and gene conveyance. In the present study, TiO nanoparticles (NPs) prepared from the leaf extracts of Mollugooppositifolia and Trianthema portulacastrum were compared with the chemical TiO-NPs for antibacterial and antioxidant activities and environment-friendly nature through various tools like the UV-visible, X-ray diffraction with the aid of other analytical techniques like HR-TEM, Fourier transform infrared (FT-IR) and photoluminescence spectroscopic techniques. The morphology of green TiO-NPs is found to be spherical, which is supported by HR-TEM images.

HGATT_LR: transforming review text classification with hypergraphs attention layer and logistic regression.

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation.

Optimization of two-wheeler Bike engine among three different designs using design of experiments.

This research presents the results of a fin heat transfer study of three different models of the motorcycle engine. The objective of this study is to minimize the external body temperature because the optimum design will be found when the heat transfer rate is maximized. The study obtains the procurement of a balance between the outer boundary temperature due to convective heat transfer and air-cooled fin design.

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier.

Background: Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established.

Objective: We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used.

Incremental RBF-based cross-tier interference mitigation for resource-constrained dense IoT networks in 5G communication system.

The deployment of resource-constrained and densely distributed Internet of Things (IoT) devices poses significant challenges for 5G communication systems due to the increased likelihood of inter-tier interference. This interference can degrade network performance and hinder the transmission of data in a reliable and efficient manner. Using an incremental Radial Basis Function (RBF) technique, this paper proposes a novel approach for cross-tier interference mitigation in 5G communication among resource-constrained dense IoT networks.

Biosynthesis of TiO nano particles by using Rosemary (Rosmarinus officinalis) leaf extracts and its application for crystal dye degradation under sunlight.

Titanium dioxide (TiO) nanoparticles were prepared through Rosmarinus-officinalis leaf extracts at 90 and 200°C. In this research, the degradations of methylene blues by using TiO nanoparticles Sun light radiations were studied. The synthesized materials were characterized using XRDs, UV-Vis, PL, SEM, TEM, EDS and XPS.

Neural harmony: revolutionizing thyroid nodule diagnosis with hybrid networks and genetic algorithms.

In the contemporary world, thyroid disease poses a prevalent health issue, particularly affecting women's well-being. Recognizing the significance of maternal thyroid (MT) hormones in fetal neurodevelopment during the first half of pregnancy, this study introduces the HNN-GSO model. This groundbreaking hybrid approach, utilizing the MT dataset, integrates ResNet-50 and Artificial Neural Network (ANN) within a Glow-worm Swarm Optimization (GSO) framework for optimal parameter tuning.

Design of efficient binary multiplier architecture using hybrid compressor with FPGA implementation.

In signal processing applications, the multipliers are essential component of arithmetic functional units in many applications, like digital signal processors, image/video processing, Machine Learning, Cryptography and Arithmetic & Logical units (ALU). In recent years, Profuse multipliers are there. In that, Vedic multiplier is one of the high-performance multiplications and it is used to signal/image processing applications.

Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR).

Background: A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated.

Nicotine sensing behavior of nickel(II) complexes catalyzed oxidation and coupling reactions.

One of the main source of demise during the next ten years will be coronary heart disease and stroke, which are brought on by smoking (nicotine). To identify the percentage (%) of nicotine consumption by electrocatalytic sensor towards nicotine for target-specific prevent stroke, four uninuclear Ni complexes of substituted butanimidamide Schiff base ligands [HL] was prepared. All the complexes were thoroughly analyzed by using several spectroscopic techniques such as CHNS analysis, FT-IR, NMR (H & C) UV-Vis and NMR.

Performance optimization of energy-efficient solar absorbers for thermal energy harvesting in modern industrial environments using a solar deep learning model.

Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model.

Nanocomposite Marvels: Unveiling Breakthroughs in Photocatalytic Water Splitting for Enhanced Hydrogen Evolution.

An overview of the significant innovations in photocatalysts for H development, photocatalyst selection criteria, and photocatalytic modifications to improve the photocatalytic activity was examined in this Review, as well as mechanisms and thermodynamics. A variety of semiconductors have been examined in a structured fashion, such as TiO-, g-CN-, graphene-, sulfide-, oxide-, nitride-, oxysulfide-, oxynitrides, and cocatalyst-based photocatalysts. The techniques for enhancing the compatibility of metals and nonmetals is discussed in order to boost photoactivity within visible light irradiation.

Experimental Analysis for the Performance Assessment and Characteristics of Enhanced Magnesium Composites Reinforced with Nano-Sized Silicon Carbide Developed Using Powder Metallurgy.

Magnesium, which is lightweight and abundant by nature, was widely used in the 19th century to make parts for automobiles and airplanes. Due to their superior strength-to-weight ratios, magnesium alloys were favored for engineering applications over unadulterated magnesium. These alloys result from the combination of magnesium with various metals, including aluminum (Al), titanium (Ti), zinc (Zn), manganese (Mn), calcium (Ca), lithium (Li), and zirconium (Zr).