IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks.

The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the ability to gather and collect information. Furthermore, IoT systems are designed to address the environment and the automation industry.

Chemical reactions with the Casson nanofluid flow by the bioconvective behavior of microorganisms over a spinning disc.

This study examines the behavior of the Casson nanofluid bioconvection flow around a spinning disc under various influences, including gyrotactic microorganisms, multiple slips, and thermal radiation. Notably, it accounts for the reversible nature of the flow and incorporates the esterification process. The aim of this study is to investigate the influence of reversible chemical reactions on the flow behavior of a Casson nanofluid in the presence of bioconvective microorganisms over a spinning disc.

Telemedicine and information technology in health care management: Perspectives and barriers among the nursing students.

Background: Telemedicine refers to using electronic information and telecommunication technologies to support numerous aspects of healthcare, including clinical care, patient and professional education and training, public health, and health administration. However, many challenges hinder the widespread adoption of telemedicine by healthcare providers. This study aimed to assess the perspectives and barriers in telemedicine and information technology in health care management among nursing students.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field.

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning.

In the air-to-ground transmissions, the lifespan of the network is based on the "unmanned aerial vehicle's (UAV)" life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate's minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage.

Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system.

The advancement in technology, with the "Internet of Things (IoT) is continuing a crucial task to accomplish distance medical care observation, where the effective and secure healthcare information retrieval is complex. However, the IoT systems have restricted resources hence it is complex to attain effective and secure healthcare information acquisition. The idea of smart healthcare has developed in diverse regions, where small-scale implementations of medical facilities are evaluated.

ERABiLNet: enhanced residual attention with bidirectional long short-term memory.

Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI).

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data.

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality.

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems.

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke.

Synthesis, Structural, and Optical Properties of 2-(2-methyl 8-hydroxyquinoline) Magnesium Nanorods for Optical Display Systems.

Organic semiconductors find widespread applications in the realm of organic light-emitting diodes (OLEDs) as well as organic photovoltaic cells. In the domain of OLED devices, it is plausible for nano-based Mg metal complexes to play a role as electron and hole-transport layers. In the present investigation, we synthesized 2(2-methyl 8-hydroxyquinoline) magnesium [Mg(mq)] nanorods through the employment of the precipitation method, using 2-methyl 8- hydroxyquinoline and magnesium acetate.

Investigating the thermo-physical properties of a new kind of graphitic carbon nitride included ternary hybrid nanofluids and the property correlations.

In this work, a simple and facile approach was employed for the preparation of the ternary hybrids comprising of titanium dioxide, zinc oxide and graphitic nitride (designated as TZG-TH) with varying compositions of the components. In the context of complex and multi-stages involved for preparation of many of the THs in the literature, the present work uses the much simpler mythology for the preparation of TH. Nanofluids (NF) were formulated in ethylene glycol: water base fluid using TZG TH as the solid particles.

Studies on the Functional Properties of Titanium Dioxide Nanoparticles Distributed in Silyl-Alkyl Bridged Polyaniline-Based Nanofluids.

In the present work, a new kind of nanocomposite (NC)-based solid component was prepared for formulating nanofluids (NFs). The NC comprised metal oxide (titanium dioxide, TiO) dispersed in a conducting polymer with polyaniline (PANI) and chemically linked silyl-alkyl units in it (PSA) that were designated as T-PSA NC. The NFs with ethylene glycol (EG) as a base fluid were prepared with T-PSA NCs with various compositions of TiO and PSA as well for various concentrations of T-PSA NCs.

fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network.

In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD.

A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu.

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic.

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue.

A Feasible Multimodal Photoacoustic Imaging Approach for Evaluating the Clinical Symptoms of Inflammatory Arthritis.

Numerous traditional medical imaging methods, including computed tomography with X-rays, positron emission tomography (PET), and magnetic resonance imaging (MRI), are utilized frequently in medical settings to screen for illnesses, diagnose patients, and track the effectiveness of treatments. When examining bone protrusions, CT is preferred over MRI for scanning connective tissue. Although the picture quality of PET is inferior to that of CT and MR, it is outstanding for detecting the molecular markers and metabolic functions of illnesses.

Applications of Intelligent Model to Analyze the Green Finance for Environmental Development in the Context of Artificial Intelligence.

Green finance can be referred to as financial investments made on sustainable projects and policies that focus on a sustainable economy. The procedures include promoting renewable energy sources, energy efficiency, water sanitation, industrial pollution control, transportation pollution control, reduction of deforestation, and carbon emissions, etc. Mainly, these green finance initiatives are carried out by private and public agents like business organizations, banks, international organizations, government organizations, etc.

Identification of operational parameter levels that optimize the production in solar stills with plain, corrugated, and compartmental basin.

Researchers integrate various operational parameters in the conventional type solar still to improve the distillate production. But maximum production can be obtained only when the optimum parameter levels which suit the solar still design are identified and incorporated. In this study, an attempt has been taken to identify the optimum level of four operational parameters-Mass of Heat Storage Material, Basin Water Depth, Basin Cover Thickness, and External Mirror Position, which suit well in plain basin, corrugated basin, and compartmental basin solar still.

A Review Article on Wireless Sensor Networks in View of E-epidemic Models.

Epidemic simulations have recently been used to model the dynamics of malicious codes in the network of wireless sensors. This is because of its open existence, which offers a simple target for malware attacks aimed at disrupting network operations or, worse, causing complete network failure. The Susceptible-Exposed-Infectious-Quarantined-Recovered-Susceptible with Vaccination compartments models like SIR-M, SEIRV, SEIQRV, SEIRS, SITR, SIR with delay are studied by various authors and some of such models that characterize worm dynamics in WSN.

Performance Studies of Proton Exchange Membrane Fuel Cells with Different Flow Field Designs - Review.

Proton Exchange Membrane Fuel Cell (PEMFC) is majorly used for power generation without producing any emission. In PEMFC, the water generated in the cathode heavily affects the performance of fuel cell which needs better water management. The flow channel designs, dimensions, shape and size of the rib/channel, effective area of the flow channel and material properties are considered for better water management and performance enhancement of the PEMFC in addition to the inlet reactant's mass flow rate, flow directions, relative humidity, pressure and temperature.

Quantum chemical studies and spectroscopic investigations on 22-amino-3-methyl-5-nitropyridine by density functional theory.

Quantum chemical calculations on energy and molecular structure of 2-amino-3-methyl-5-nitropyridine (2A3M5NP) have been attempted by implementing DFT/B3LYP method using 6-311G (d,p), 6-311G++ (d,p) and cc-pVTZ basis sets. The optimized geometry and the vibrational analysis for energetically most stable configuration, are carried out theoretically by using B3LYP/cc-pVTZ basis set. The computed vibrational frequencies were scaled by using scaling factors and compared with the experimental Fourier Transform Infra-Red (FTIR) solid phase spectrum in the region 4000-400 cm and FT-Raman spectrum in the region 4000-100 cm.

Investigations on the enhanced dye degradation activity of heterogeneous BiFeO-GdFeO nanocomposite photocatalyst.

Perovskite types of nanocomposites of BiFeO-GdFeO (BFO-GFO) has been synthesized using sol-gel route for the first time. The nanocomposite powders were characterized by powder X-Ray diffraction (PXRD) to confirm the existence of mixed crystallographic phases. EDX analysis on nanocomposites estimates the composition of individual element present in BFO-GFO matrix.

Efficient Segmentation of Brain Tumor Using FL-SNM with a Metaheuristic Approach to Optimization.

Nowadays, automatic tumor detection from brain images is extremely significant for many diagnostic as well as therapeutic purposes, due to the unpredictable shape and appearance of tumors. In medical image analysis, the automatic segmentation of tumors from brain using magnetic resonance imaging (MRI) data is the most critical issue. Existing research has some limitations, such as high processing time and lower accuracy, because of the time required for the training process.

Artificial Intelligence Based Skin Classification Using GMM.

This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illnesses have similarities in sample of infection and symptoms along with redness and rash. Various skin problems have similar symptoms.