Most below-knee prostheses are manufactured in Iraq without considering the fast progress in smart prostheses, which can offer movements in the desired directions according to the type of control system designed for this purpose. The proposed design appears to have the advantages of simplicity, affordability, better load distribution, suitability for subjects with transtibial amputation, and viability in countries with people having low socio-economic status. The designed prosthetics consisted of foot, ball, and socket joints, two stepper motors, a linkage system, and an EMG shield. All these materials were available in the local markets in Iraq. The experimental results showed t

As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detec

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

The aim of this research is to diagnose the attention deficit hyperactivity disorder among primary school pupils in Baquba city of Diyala province. The sample of the study consisted of (25) male and female pupils. The American Guide of Attention Deficit Hyperactivity Scale (DSM-IV, 1994) was used in this study in addition to Conner’s (1996) scale to measure the attention deficit hyperactivity disorder for teachers and parents. The result revealed that (19) male and female pupils diagnosed with attention deficit hyperactivity to various degrees.

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

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