IoT-based automated system for water-related disease prediction.

Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.

Ultra-wideband microstrip folded antenna for wireless LAN, 5G and Internet of Things applications.

This paper presents an ultra-wideband (UWB) microstrip antenna with a simple structure operating at 2.2-6 GHz bandwidth. The proposed antenna has been demonstrated for its suitability in UWB and multi-band applications, such as wireless local area network (WLAN), Wi-Fi, 5G, Internet of Things (IoT), etc.

Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.

Clinical biomedical applications of genomic technologies are extensive and provide possibilities to enhance healthcare covering the span of medical talents. Genome disorder prediction is an important issue in biomedical research. Genome disorders cause multivariate diseases such as cancer, dementia, diabetes, Leigh syndrome, etc.

Improving crop production using an agro-deep learning framework in precision agriculture.

Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets.

Design & development of adulteration detection system by fumigation method & machine learning techniques.

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil.

Blockchain-enabled verification of medical records using soul-bound tokens and cloud computing.

The Crucial and costly process of verifying medical documents frequently depends on centralized databases. Nevertheless, manual validation of document verification wastes a great deal of time and energy. The application of Blockchain technology could potentially alleviate the problem by reducing fraud and increasing efficiency.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).

Ribosomal computing: implementation of the computational method.

Background: Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis.

Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis.

Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach.

Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer.

Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers.

Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique.

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network.

Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain.

Multimodal hybrid convolutional neural network based brain tumor grade classification.

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death.

Development of a mobile-based intelligent module for identification and tracking of household appliances.

Every year manufacturers of household appliances improve their devices, trying to make everyday life easier for users. New smart devices have many useful features, but not all users can easily cope with the complexity of the devices. One of the main tasks of household appliance manufacturers is to ensure the convenience of using appliances, taking into account the increasing complexity.

Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.

Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights.

Enhanced brain tumor classification using graph convolutional neural network architecture.

The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma).

Ride comfort and segmental vibration transmissibility analysis of an automobile passenger model under whole body vibration.

The examination of seated occupants' ride comfort under whole-body vibration is a complex topic that involves multiple factors. Whole-body vibration refers to the mechanical vibration that is transmitted to the entire body through a supporting surface, such as a vehicle seat, when traveling on rough or uneven surfaces. There are several methods to assess ride comfort under whole-body vibration, such as subjective assessments, objective measurements, and mathematical models.

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets.

Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors.

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images.

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus.

Trust-Based Smart Contract for Automated Agent to Agent Communication.

Blockchain technology is now regarded as one of the most interesting and possibly innovative technologies. It enables information to be stored and exchanged securely and transparently without the need for a centralized authority to regulate it. Some of the primary benefits of this technology are the atomicity of the stored data.

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis.

Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone.

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims' chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters.

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS.

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments.