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.

Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques.

Skin cancer is the uncontrolled growth of irregular cancer cells in the human-skin's outer layer. Skin cells commonly grow in an uneven pattern on exposed skin surfaces. The majority of melanomas, aside from this variety, form in areas that are rarely exposed to sunlight.

Artificial Intelligence to Combat the Sting of the Pandemic on the Psychological Realms of Human Brain.

We are inhabitants of the universe in this anomalous time due to the novel corona virus. COVID-19, which WHO has annotated as a pandemic, is an infectious and contagious disease hastened by the most freshly perceived coronavirus. COVID-19 has gravely hit different people in different ways, some with physical symptoms and some patients will likely be more susceptible to insignificant and extreme symptoms of mental illness.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset.

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages.

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications.

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks.

Effect of image transformation on EfficientNet model for COVID-19 CT image classification.

The Novel Corona Virus 2019 has drastically affected millions of people all around the world and was a huge threat to the human race since its evolution in 2019. Chest CT images are considered to be one of the indicative sources for diagnosis of COVID-19 by most of the researchers in the research community. Several researchers have proposed various models for the prediction of COVID-19 using CT images using Artificial Intelligence based algorithms (Alimadadi e al.

Preparedness for managing pandemic using distributed mobile brokers - Using COVID 19 use case.

The human community has experienced the worst disaster during this pandemic spread of COVID-19. Getting essential services viz., medical, food, water, and oxygen support have been challenging during the pandemic.