Browsing by Author "Ibrahim Khalaf, Osamah"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations(Hindawi Limited, 2022) Khan, Naveed Ahmad; Ibrahim Khalaf, Osamah; Tavera Romero, Carlos Andrés; Sulaiman, Muhammad; Bakar, Maharani A.In this study, the intelligent computational strength of neural networks (NNs) based on the backpropagated Levenberg-Marquardt (BLM) algorithm is utilized to investigate the numerical solution of nonlinear multiorder fractional differential equations (FDEs). The reference data set for the design of the BLM-NN algorithm for different examples of FDEs are generated by using the exact solutions. To obtain the numerical solutions, multiple operations based on training, validation, and testing on the reference data set are carried out by the design scheme for various orders of FDEs. The approximate solutions by the BLM-NN algorithm are compared with analytical solutions and performance based on mean square error (MSE), error histogram (EH), regression, and curve fitting. This further validates the accuracy, robustness, and efficiency of the proposed algorithm.Item Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-Based IoT Sensor Communication for Remote Healthcare Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Ibrahim Khalaf, Osamah; Natarajan, Rajesh; Mahadev, Natesh; Ranjith Christodoss, Prasanna; Nainan, Thangarasu; Andres Tavera Romero, Carlos; Muttashar Abdulsahib, GhaidaSeveral statistical methods have been playing a key role in data analytics, disease forecasting, and performing remote healthcare systems as far as medical sciences are concerned. In these fields, the research person and also practitioner’s main role depends on the efficient screening of remote healthcare data for significant forecasting. Specifically, remote healthcare data measurements involved in screening and forecasting are not precise and are found to be fuzzy or in interval forms. As a result, neutrosophic logic was instigated as one of the universal formations of fuzzy logic for estimating truthiness, falseness, and indeterminacy for remote healthcare data analysis. Neutrosophic Multiple-Criteria Decision-Making (Neutrosophic MCDM) was proposed by Hezam et al. [1] to develop an exploratory perception for classifying and ranking the most exemplary groups for instigating priority in gaining vaccines even at the initial stage. Initially, data analysis was performed using Analytic Hierarchy Processing under uncertainty to estimate and rank main and sub-criteria, owing to the reason that the inputs were obtained in the form of neutrophilic numbers. Second, neutrosophic TOPSIS was also applied for ranking vaccine alternatives. Finally, using Analytic Hierarchy Processing ranking efficiency and classification accuracy were found to be improved via measuring the weights of the sub-criteria. Despite improvement observed in terms of classification accuracy, the energy consumed in the process of decision-making was not focused. To address this aspect, a Blinder Oaxaca Linear Regression-based Preprocessing model is designed. The advantage of using this Linear Regression-based Preprocessing with Blinder Oaxaca function dynamically adjusts the sensing frequency of each corresponding device to fit with dynamic changes along with the monitored vital sign. This in turn reduces energy consumption.Item Noninvasive Prototype for Type 2 Diabetes Detection(2021) Castillo García, Javier Ferney; Ortiz, Jesús Hamilton; Ibrahim Khalaf, Osamah; Valencia Hernández, Adrián David; Rodríguez Timaná, Luis CarlosThe present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance (p < 0.05). The Kappa coefficient values were higher than 0.9, showing that the device has a good predictive capacity which would allow the screening process of type 2 diabetes. This development contributes to preventive medicine and makes it possible to determine at a low cost, comfortably, without medical preparation, and in less than 2 minutes whether a person has type 2 diabetes.