This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

The fingerprints are the more utilized biometric feature for person identification and verification. The fingerprint is easy to understand compare to another existing biometric type such as voice, face. It is capable to create a very high recognition rate for human recognition. In this paper the geometric rotation transform is applied on fingerprint image to obtain a new level of features to represent the finger characteristics and to use for personal identification; the local features are used for their ability to reflect the statistical behavior of fingerprint variation at fingerprint image. The proposed fingerprint system contains three main stages, they are: (i) preprocessing, (ii) feature extraction, and (iii) matching. The preprocessi

Background: Obesity tends to appear in modern societies and constitutes a significant public health problem with an increased risk of cardiovascular diseases.

Objective: This study aims to determine the agreement between actual and perceived body image in the general population.

Methods: A descriptive cross-sectional study design was conducted with a sample size of 300. The data were collected from eight major populated areas of Northern district of Karachi Sindh with a period of six months (10^th January 2020 to 21^st June 2020). The Figure rating questionnaire scale (FRS) was applied to collect the demographic data and perception about body weight. Body mass index (BMI) used for ass

There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

This study employs wavelet transforms to address the issue of boundary effects. Additionally, it utilizes probit transform techniques, which are based on probit functions, to estimate the copula density function. This estimation is dependent on the empirical distribution function of the variables. The density is estimated within a transformed domain. Recent research indicates that the early implementations of this strategy may have been more efficient. Nevertheless, in this work, we implemented two novel methodologies utilizing probit transform and wavelet transform. We then proceeded to evaluate and contrast these methodologies using three specific criteria: root mean square error (RMSE), Akaike information criterion (AIC), and log

Money laundering is consider a crime in legal perspective and aphenomenon of negative effects in economic and Social perspective for thevarious countries of the world by the diversity of their economies to make thebadly damaged in the banking service and its reputation in some cases may leadto bankruptcy of banks, and the fact that banks is one of the most importantcircles that revolve the illicit funds, Due to its branched the banking operations,speed and overlap and increase it's complicated with the progress of bankingoperations and the use of electronic services modern here's challenges atvarious levels between the application of the legislation to combat moneylaundering and between actual fact it must find ways and means of legislat

The number of infections caused by microorganisms is increasing significantly over the last few years. A total of 140 patients admitted to the central teaching hospital of pediatrics from the 1st of Jun 2017 to 31 October 2017. The Clinical samples was processed from culture and sensitivity testing. Antibiotic discs used for gram negative isolates. The most prevalent gram negative isolates included Escherichia coli 63 (45.0 %), Pseudomonas spp. 21 (15.0 %), Klebsiella spp. 19 (13.6 %) predominantly. Escherichia coli were the most prevalent isolates from urine 45 (71.4 %), Klebsiella spp. 11 (57.9 %) and Enterobacter spp. 11 (68.8 %) followed by Escherichia coli 10 (15.9 %) predominant from blood. 68 (48.6 %) of specimens were urine, 47 (33.