Water covers more than 75% of the earth's surface in the form of the ocean. The ocean investigation is far-fetched because the underwater environment has distinct phenomenal activities. The expansion of human activities inside underwater environments includes environmental monitoring, offshore field exploration, tactical surveillance, scientific data collection, and port security. This led to increased demand for underwater application communication systems. Therefore, the researcher develops many methods for underwater VLC Visible Light Communications. The new technology of blue laser is a type of VLC that has benefits in the application of underwater communications. This research article investigated the benefits of underwater blu

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

Humanity's relationship with the environment is a delicate balance. Since the industrial revolution, the world's population has grown at an exponential rate, and this has a major environmental effect. Deforestation, pollution, and global climate change are just a few of the negative consequences of population and technological growth. Particulates, Sulphur dioxide (SO2), and nitrogen oxides (NOx) are the primary pollutants that harm our health. These contaminants may be directly emitted into the atmosphere (primary pollutants) or formed in the atmosphere from primary pollutants reacting (secondary pollutants. Tropospheric ozone is created When water reacts with volatile organic compounds (VOC) and nitrogen oxides (NOx) in the presen