Background: Differentiating flow gaps associated with hypoplastic transverse sinus from venous thrombosis is a diagnostic challenge in brain magnetic resonance imaging with venography.

Objectives: To assess the clinical benefit of anatomical signs to anticipate the side of the dominant transverse sinus.

Patients and Methods: A total of 100 patients underwent brain magnetic resonance imaging with venography at the radiology department /medical city and were retrospectively reviewed for the direction of superior sagittal sinus flow void, inclination of sulcus for the superior sagittal sinus, angulation of the posterior falx and direction of occipital lobe bending in axial non

Cracking of soils affects their geotechnical properties and behavior such as soil strength and stability. In this paper, 2D Electrical Resistivity Imaging Method, as a non-invasive technique, was adopted to investigate the effect of soil cracks of a centemetric scale on resistivity of sandy soil. The electrical resistivity measurements were carried out using ABEM SAS 300C Terrameter system at a laboratory scale using Wenner array. The measurements were interpreted using horizontal profiles, forward modeling and 2D inverse resistivity sections. The results showed that soil cracks cause significant changes in soil resistivity. These changes can be attributed to the high resistivity contrast between the highly resistive air-filled cracks an

The source and channel coding for wireless data transmission can reduce
distortion, complexity and delay in multimedia services. In this paper, a joint sourcechannel
coding is proposed for orthogonal frequency division multiplexing -
interleave division multiple access (OFDM-IDMA) systems to transmit the
compressed images over noisy channels. OFDM-IDMA combines advantages of
both OFDM and IDMA, where OFDM removes inter symbol interference (ISI)
problems and IDMA removes multiple access interference (MAI). Convolutional
coding is used as a channel coding, while the hybrid compression method is used as
a source coding scheme. The hybrid compression scheme is based on wavelet
transform, bit plane slicing, polynomi

The present work provides theoretical investigation of laser photoacoustic one dimensional imaging to detect a blood vessel or tumor embedded within normal tissue. The key task in photoacoustic imaging is to have acoustic signal that help to determine the size and location of the target object inside normal tissue. The analytical simulation used a spherical wave model representing target object (blood vessel or tumor) inside normal tissue. A computer program in MATLAB environment has been written to realize this simulation. This model generates time resolved acoustic wave signal that include both expansion and contraction parts of the wave. The photoacoustic signal from the target object is simulated for a range of laser pulse duration 1

Phishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency.  This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing websi

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

In this study, the dynamic modeling and step input tracking control of single flexible link is studied. The Lagrange-assumed modes approach is applied to get the dynamic model of a planner single link manipulator. A Step input tracking controller is suggested by utilizing the hybrid controller approach to overcome the problem of vibration of tip position through motion which is a characteristic of the flexible link system. The first controller is a modified version of the proportional-derivative (PD) rigid controller to track the hub position while sliding mode (SM) control is used for vibration damping. Also, a second controller (a fuzzy logic based proportional-integral plus derivative (PI+D) control scheme) is developed for both vibra

    Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedu

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS,