Optimized node-level capsule graph neural network for subject-independent emotion recognition from EEG signals.

Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications.

Chitosan-coated silver nanoparticles synthesized using Moringa oleifera flower extract: A potential therapeutic approach against triple-negative breast cancer.

Optimizing nanoparticle (NP) synthesis from biological sources for effective anticancer activity remains a challenge in treating triple-negative breast cancer (TNBC) because of the lack of receptors. This study investigates the use of Moringa oleifera (M. oleifera) flower extract to synthesize chitosan (Ch)-coated silver (Ag) nanoparticles (F-Ch-AgNPs) and assesses their efficacy against TNBC cells (MDA-MB-231) in vitro.

Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging.

Brain tumor segmentation plays a crucial role in clinical diagnostics and treatment planning, yet accurate and efficient segmentation remains a significant challenge due to complex tumor structures and variations in imaging modalities. Multi-feature selection and region classification depend on continuous homogeneous features to improve the precision of tumor detection. This classification is required to suppress the discreteness across various extraction rates to consent to the smallest segmentation region that is infected.

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.

Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient.

Sporting footpaths and toxic dust: a risk-based assessment of urban, suburban, and rural exposure.

This study aims to assess the concentrations of potentially toxic elements (PTEs) in deposited dust (DD) from urban, suburban, and residential sporting footpaths (UFPs, SFPs, and RFPs) in Coimbatore, India, and evaluate the associated ecological and health risks. Dust samples were collected from sporting footpaths located in urban, suburban, and residential areas. The total concentrations of PTEs were found to be in USFs (1431 mg/kg), SFs (1073 mg/kg), and RSFs (892 mg/kg).

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Purpose: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

Procedures: The study utilizes histopathological slide images from the NCT-CRC-HE-100 K and PAIP 2020 databases. Key procedures include self-attentive adversarial stain normalization for data standardization, tumor delineation via a Slimmable Transformer, and radiomics feature extraction using a hybrid quantum-classical neural network.

An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

Background: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction issues and limited training models.

Methods: This research introduced a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) to classify mammogram images effectively. It used wiener filtering, unsharp masking, and adaptive histogram equalisation to enhance images and remove noise, followed by Grey-Level Co-occurrence Matrix (GLCM) for feature extraction.

NAVT-net neuron attention visual taylor network for lung cancer detection using CT images.

Lung Cancer is regarded as a common fatal disease affecting humans throughout the entire world. Early diagnosis is vital to save the patient's life and Computed Tomography (CT) scans are referred to as the major imaging modes but, the manual examination of a CT scan is time-consuming and results in errors. Hence, a novel system of Neuron Attention Visual Taylor Network (NAVT-Net) is developed to detect lung cancer.

Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor on early stage can improve the patient's survival rate.

Improved DeTraC Binary Coyote Net-Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole-Slide Pathological Images.

Background: Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis.

Methods: This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net-based Multiple Instance Learning (ImDeTraC-BCNet-MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double-dimensional clustering techniques.

Optimized memory augmented graph neural network-based DoS attacks detection in wireless sensor network.

Wireless Sensor Networks (WSNs) are mainly used for data monitoring and collection purposes. Usually, they are made up of numerous sensor nodes that are utilized to gather data remotely. Each sensor node is small and inexpensive.

Physicochemical and photocatalytic properties of AgCrO/FeO/CeO ternary nanocomposite.

The study presents AgCrO/FeO/CeO ternary nanocomposite, based on FeO/CeO binary composites, which demonstrated excellent photocatalytic performance in the photodegradation of methylene blue under solar irradiation. The AgCrO/FeO/CeO nanocomposites was orthorhombic, ilmenite, and cubic-fluorite phases of AgCrO, FeO, and CeO, respectively, according to the XRD examination. A strong bond between AgCrO, FeO, and CeO within the nanocomposite was demonstrated by the SEM and TEM investigations.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing.

Intelligent Alzheimer's Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks.

Alzheimer's disease (AD) is an illness that affects the nervous system, leading to a loss in cognitive and logical abilities. Gene regulatory expressions, which are the complex language exhibited by DNA, serve several functionalities, including the physical and biological life cycle processes in the human body. The gene expression sequence affects the pathology experienced by an individual, its longevity, and potential for a cure.

A Detailed Study on using Novel LM 25 Aluminium Alloy Hybrid Metal Matrix Nanocomposite for Nuclear Applications.

Aim: This article describes the use of graphite (Gr) and boron carbide (B4C) as multiple nanoparticle reinforcements in LM25 aluminum alloy. Because boron carbide naturally absorbs neutron radiation, aluminium alloy reinforced with boron carbide metal matrix composite has gained interest in nuclear shielding applications. The primary goal of the endeavor is to create composite materials with high wear resistance, high microhardness, and high ultimate tensile strength for use in nuclear applications.

Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer.

Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage.

Analysis of barriers affecting Industry 4.0 implementation: An interpretive analysis using total interpretive structural modeling (TISM) and Fuzzy MICMAC.

The purpose of this study is to build a structural relationship model based on total interpretive structural modeling (TISM) and fuzzy input-based cross-impact matrix multiplication applied to classification (MICMAC) for analysis and prioritization of the barriers influencing the implementation of Industry 4.0 technologies. 10 crucial barriers that affect the deployment of Industry 4.

Exploring Modified Rice Straw Biochar as a Sustainable Solution for Simultaneous Cr(VI) and Pb(II) Removal from Wastewater: Characterization, Mechanism Insights, and Application Feasibility.

This study aimed to investigate the efficacy of a rice straw biosorbent in batch adsorption for the removal of chromium (Cr(VI)) and lead (Pb(II)) heavy-metal ions from wastewater. The biosorbent was chemically synthesized and activated by using concentrated sulfuric acid. The produced biosorbent was then characterized by using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analyses, which provided insights into surface morphology and functional groups.

Electric vehicle parameter identification and state of charge estimation of Li-ion​ batteries: Hybrid WSO-HDLNN method.

This manuscript proposes a hybrid method for measuring the battery's dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error.

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes.

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation.

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution.

Ensemble classifier fostered detection of arrhythmia using ECG data.

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories.

Polyaromatic hydrocarbons (PAHs) in the water environment: A review on toxicity, microbial biodegradation, systematic biological advancements, and environmental fate.

Polycyclic aromatic hydrocarbons (PAHs) are considered a major class of organic contaminants or pollutants, which are poisonous, mutagenic, genotoxic, and/or carcinogenic. Due to their ubiquitous occurrence and recalcitrance, PAHs-related pollution possesses significant public health and environmental concerns. Increasing the understanding of PAHs' negative impacts on ecosystems and human health has encouraged more researchers to focus on eliminating these pollutants from the environment.

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification.

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.