Speech is the essential way to interact between humans or between human and machine. However, it is always contaminated with different types of environment noise. Therefore, speech enhancement algorithms (SEA) have appeared as a significant approach in speech processing filed to suppress background noise and return back the original speech signal. In this paper, a new efficient two-stage SEA with low distortion is proposed based on minimum mean square error sense. The estimation of clean signal is performed by taking the advantages of Laplacian speech and noise modeling based on orthogonal transform (Discrete Krawtchouk-Tchebichef transform) coefficients distribution. The Discrete Kra
In this article, the high accuracy and effectiveness of forecasting global gold prices are verified using a hybrid machine learning algorithm incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The hybrid approach had successes that enabled it to be a good strategy for practical use. The ARIMA-ANFIS hybrid methodology was used to forecast global gold prices. The ARIMA model is implemented on real data, and then its nonlinear residuals are predicted by ANFIS, ANFIS-PSO, and ANFIS-GWO. The results indicate that hybrid models improve the accuracy of single ARIMA and ANFIS models in forecasting. Finally, a comparison was made between the hybrid foreca
... Show MoreCOVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in
Left ventricular hypertrophy (LVH) caused by high blood pressure is linked to increased mortality and arrhythmia risk. This study aimed to evaluate arrhythmia in hypertensive patients due to left ventricular hypertrophy (LVH). A cross-sectional study was performed, assessing participants' blood pressure, echocardiography and electrocardiography, and Holter monitoring in certain cases. There were 300 hypertensive patients >18 years attending the cardiology unit of Baghdad medical city. The study was conducted between January–June 2022. The electrocardiograms at rest for 300 adults with hypertension were investigated. 130 (43.5%) were females, and 170 (56.5%) were males. The mean age of participants was 58 years. Forty-nine (16.3
... Show MoreAbstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition
... Show MoreHeart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac
... Show MoreWith the wide developments of computer science and applications of networks, the security of information must be increased and make it more complex. The most important issues is how to control and prevent unauthorized access to secure information, therefore this paper presents a combination of two efficient encryption algorithms to satisfy the purpose of information security by adding a new level of encryption in Rijndael-AES algorithm. This paper presents a proposed Rijndael encryption and decryption process with NTRU algorithm, Rijndael algorithm is important because of its strong encryption. The proposed updates are represented by encryption and decryption Rijndael S-Box using NTRU algorithm. These modifications enhance the degree of
... Show MoreWith the wide developments of computer applications and networks, the security of information has high attention in our common fields of life. The most important issues is how to control and prevent unauthorized access to secure information, therefore this paper presents a combination of two efficient encryption algorithms to satisfy the purpose of information security by adding a new level of encryption in Rijndael-AES algorithm. This paper presents a proposed Rijndael encryption and decryption process with NTRU algorithm, Rijndael algorithm is widely accepted due to its strong encryption, and complex processing as well as its resistance to brute force attack. The proposed modifications are implemented by encryption and decryption Rijndael
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