Joint dysfunction disables are impacting millions of individuals worldwide. It significantly interferes with essential daily tasks like eating, drinking, and writing, often making self-care challenging for those affected. Exoskeleton robots are developed to enable individuals with impaired physical functions to perform daily activities and maintain independence. This study introduces a wearable exoskeleton control system for the elbow joint designed, providing an alternative assistive solution to traditional treatment methods. The elbow exoskeleton system used for therapy has nonlinearity and time-dependent parameters. To address these challenges, this work presents a sliding mode control (SMC) for tracking the path of an EES. To reduce the

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

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Theoretical spectroscopic study of  Beryllium Oxide has been carried out, Boltzmann distribution of P, Q and R branches in the range of (0<J<13) at temperature 4200K for (0-0) band for electronic transitions B¹Σ⁺-A¹Π and B¹Σ-X¹Σ. The Boltzmann distribution of these branches has a maximum values at equal J approximately while the values of relative population are different. For the B¹Σ⁺- X¹Σ⁺ transition the branch's lines extend towards lower wavenumber. This is because (B_v'-B_v") value is negative, i.e  B_v'< B_v" For B¹Σ⁺-A¹Π

Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various

This research includes structure interpretation of the Yamama Formation (Lower Cretaceous) and the Naokelekan Formation (Jurassic) using 2D seismic reflection data of the Tuba oil field region, Basrah, southern Iraq. The two reflectors (Yamama and Naokelekan) were defined and picked as peak and tough depending on the 2D seismic reflection interpretation process, based on the synthetic seismogram and well log data. In order to obtain structural settings, these horizons were followed over all the regions. Two-way travel-time maps, depth maps, and velocity maps have been produced for top Yamama and top Naokelekan formations. The study concluded that certain longitudinal enclosures reflect anticlines in the east and west of the study ar

In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.