Brain Tumor Detection Based on Hybrid Convolutional Adaptive Neuro Fuzzy Inference System Using MRI Image.

The abnormal or irregular growth of cells in regions of the human body that affects surrounding tissues is termed a tumor. Brain tumors are among the most dangerous and life-threatening types of tumors, arising from the abnormal growth of cells within the brain. However, existing detection methods often suffer from limitations, such as poor noise handling in MRI images, inaccurate segmentation, and low generalization across varying datasets.

Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database "Image and Data Archive (IDA)" is given for preprocessing that is performed using Gaussian filter.

Hybrid Material of Salen Moieties Functionalized 2D-Iodinated GO: A Fluorescent Chemosensor for Cr Ion Detection.

This study presents the development of a hybrid material for the selective detection of chromium ions (Cr) in contaminated water. The material, synthesized by covalently intercalating a poly amino-alcohol into iodinated graphene oxide (GO), introduces multifunctional derivatives (-IO₄, -NH₂, -OH). These functional groups enhance the electron mobility of the material, making it highly effective for Cr ion binding.

Modified quantum dilated convolutional neural network for cancer prediction using gene expression data.

This paper proposes a modified Quantum Dilated Convolutional neural network (QDCNN) to detect cancer using gene expression data. Primarily, the input gene expression data is taken from a specified dataset. Then, data transformation is done using Adaptive Box-Cox transformation and feature fusion is done by a Deep Neural Network (DNN) with Kulczynski.

Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence.

Breast cancer is a disorder affecting women globally, and hence an early and precise classification is the best possible treatment to increase the survival rate. However, the breast cancer classification faced difficulties in scalability, fixed-size input images, and overfitting on limited datasets. To tackle these issues, this work proposes a Patho-Net model for breast cancer classification that overcomes the problems of scalability in color normalization, integrates the Gated Recurrent Unit (GRU) network with the U-Net architecture to process images without the need for resizing and computational efficiency, and addresses the overfitting problems.

Electrochemical Performance of Guanidinium Salt-Added PVP/PEO Solid Polymer Electrolyte with Superior Power Density.

Solid polymer electrolytes (SPEs) for symmetrical supercapacitors are proposed herein with activated carbon as electrodes and optimized solid polymer electrolyte membranes, which serve as the separators and electrolytes. We propose the design of a low-cost solid polymer electrolyte consisting of guanidinium nitrate (GuN) and poly(ethylene oxide) (PEO) with poly(vinylpyrrolidone) (PVP). Using the solution casting approach, blended polymer electrolytes with varying GuN weight percentage ratios of PVP and PEO are prepared.

Efficiency improvement in silicon and perovskite solar cells through nanofluid cooling using citrate and PVP stabilized silver nanoparticles.

This study investigates the enhancement of solar cell efficiency using nanofluid cooling systems, focusing on citrate-stabilized and PVP-stabilized silver nanoparticles. Traditional silicon-based and perovskite solar cells were examined to assess the impact of these nanofluids on efficiency improvement and thermal management. A Central Composite Design (CCD) was employed to vary nanoparticle concentration (0.

Investigation of electrochemical micromachining on magnesium alloy using hollow tool electrode.

This study investigates the application of Electrochemical Micromachining (ECMM) on magnesium alloy AZ31 using a hollow tool electrode. Magnesium alloys, particularly AZ31, are valued for their lightweight properties and strength-to-weight ratio but pose challenges in precision machining due to their high reactivity and susceptibility to corrosion. Utilizing a hollow tool electrode in ECMM offers potential advantages in precision and control, crucial for micro-scale manufacturing applications.

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.

The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI.

A robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data.

Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation.

An assessment of metal absorption competence of indigenous metal tolerant bacterial species- an in-vitro study.

Heavy metals pose a serious global threat to the environment. Hence, removing hazardous metals from soil samples has become complicated over the past few years. The current work looked into the remediation of heavy metals from aqueous solutions using a bacterial community and a unique bacterium obtained from metal-contaminated soil.

Performance evaluation and chemical oxygen demand removal of tannery wastewater through the aerobic-anaerobic route.

With characterized for complex and maximum substance (suspended solids, broke up oil, a mixture of inorganic and chromium sulfides), tannery wastewater was subjected to a treatment process on removal of chemical oxygen demand (COD) via upstream anaerobic sludge blanket reactor where we found reduced departure efficiencies and that process limits were affected by the assortments in regular stacking rates, closeness of chromium, and sulfides. Hence, a combination of the aerobic-anaerobic hybrid reactor was set up for sequential treatment to determine possible COD reduction. This study investigated the biological degradation of tannery wastewater in a laboratory-scale sequential up-flow aerobic-anaerobic reactor.

Silver-decorated SrTiO nanoparticles for high-performance supercapacitors and effective remediation of hazardous pollutants.

SrTiO/Ag nanocomposites were synthesized using a facile wet impregnation method, employing rigorous experimental techniques for comprehensive characterization. XRD, FTIR, UV, PL, FESEM, and HRTEM were meticulously utilized to elucidate their structural, functional, morphological, and optical properties. The electrochemical performance of the SrTiO/Ag nanocomposite was rigorously assessed, revealing an impressive specific capacitance of 850 F/g at a current density of 1 A.

Solar-activated and hydrothermally synthesized effective rGO/AgS composites for the destruction of naphthol green B dye and antibacterial applications.

Graphene-based nanocomposites are developing as a new class of materials with several uses. The varied weight percentages of rGO on AgS catalysts were synthesized using a simple hydrothermal process and employed for the decomposition of anionic dye naphthol green B (NGB) under solar light. The reduced graphene oxide-based silver sulfide (rGO/AgS) nanoparticles were then examined using XRD, SEM, EDS, HR-TEM, XPS, UV-DRS, and PL analysis.

Hydrothermal synthesis and photocatalytic application of ZnS-Ag composites based on biomass-derived carbon aerogel for the visible light degradation of methylene blue.

A facile and cost-effective hydrothermal followed by precipitation method is employed to synthesize visible light-driven ZnS-Ag ternary composites supported on carbon aerogel (CA). Extensive studies were conducted on the structural, morphological, and optical properties, confirming the successful formation of ternary nanocomposites. The obtained results evidently demonstrate the successful loading of ZnS and Ag onto the surface of the CA.

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches.

Background: Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

Objective: This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

Methods: The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication.

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue.

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction.

Validating Wound Severity Assessment via Region-Anchored Convolutional Neural Network Model for Mobile Image-Based Size and Tissue Classification.

Evaluating and tracking the size of a wound is a crucial step in wound assessment. The measurement of various indicators on wounds over time plays a vital role in treating and managing crucial wounds. This article introduces the concept of utilizing mobile device-captured photographs to address this challenge.

Environmentally benign, bright luminescent carbon dots from IV bag waste and chitosan for antimicrobial and bioimaging applications.

Luminescent carbon dots have gained significant attention in various fields due to their unique optical properties and potential applications. Here, the study was aimed to propose a novel and sustainable approach for the synthesis of luminescent carbon dots (ICDs) using IV (Intravenous) medical bag waste. The ICDs were synthesized through a facile and cost-effective method that involved the carbonization of IV bag waste followed by surface functionalization with chitosan.

Strategic rationalization for improved photocatalytic decomposition of toxic pollutants: Immobilizing BiTe nanorods and VO nanoparticles over MoS nanosheets.

Researchers have become increasingly interested in solar energy based on semiconductor photocatalysts to remove hazardous pollutants and clean the environment. In this work, an efficient MoS-BiTe-VO nanocomposite has been prepared through wet impregnation method. MoS-BiTe-VO photocatalyst was utilized to decompose the MB and Rh B dyes.

Functionalization of Fluorine on the Surface of SnO-Mg Nanocomposite as an Efficient Photocatalyst for Toxic Dye Degradation.

This work reports on the photocatalytic activity of tin oxide (SnO)-doped magnesium (Mg) and fluorine (F) nanoparticles for methyl orange and safranin dye degradation under sunlight irradiation. Nanocatalysis-induced dye degradation was examined using UV-visible spectroscopy and a pseudo-first-order kinetics model. The results indicate that the prepared nanoparticles exhibit superior photocatalytic activity, and the degradation of methyl orange (MO) dye is approximately 82%.

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

Introduction: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images.