Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Beyond the immediate content of speech, the voice can provide rich information about a speaker's demographics, including age and gender. Estimating a speaker's age and gender offers a wide range of applications, spanning from voice forensic analysis to personalized advertising, healthcare monitoring, and human-computer interaction. However, pinpointing precise age remains intricate due to age ambiguity. Specifically, utterances from individuals at adjacent ages are frequently indistinguishable. Addressing this, we propose a novel, end-to-end approach that deploys Mozilla's Common Voice dataset to transform raw audio into high-quality feature representations using Wav2Vec2.0 embeddings. These are then channeled into our self-attentio

The 17 α-ethinylestradiol (EE2) adsorption from aqueous solution was examined using a novel adsorbent made from rice husk powder coated with CuO nanoparticles (CRH). Advanced analyses of FTIR, XRD, SEM, and EDSwere used to identify the classification parameters of a CRH-like surface morphology, configuration, and functional groups. The rice husk was coated with CuO nanoparticles, allowing it to create large surface area materials with significantly improved textural qualities with regard to functional use and adsorption performance, according to a detailed characterization of the synthesized materials. The adsorption process was applied successfully with elimination effectiveness of 100% which can be kept up to 61.3%. The parameters of ads

Photocatalyst composed of core/shell magnetic zincoxysulfide nanocomposite coated with sulfonated polyindole ([email protected]/SPID) has been prepared and used for simultaneous photocatalytic H2 production and Bisphenol A (BPA) degradation. XRD, FE-SEM, EDX, BET surface area, UV-vis DRS and VSM were used to characterize the synthesized nanocomposites. The photocatalytic performance was evaluated using batch reactor under visible light irradiation. The photocatalytic activity of [email protected]/SPID nanocomposite was revealed to exceed that of [email protected] nanocomposite due to the heterojunctions between SPID and [email protected] species. The results exhibited that the effect of BPA initial concentration was found to be effectual on the improvement

Abstract

The use of modern scientific methods and techniques, is considered important topics to solve many of the problems which face some sector, including industrial, service and health. The researcher always intends to use modern methods characterized by accuracy, clarity and speed to reach the optimal solution and be easy at the same time in terms of understanding and application.

the research presented this comparison between the two methods of solution for linear fractional programming models which are linear transformation for Charnas & Cooper , and denominator function restriction method through applied on the oil heaters and gas cookers plant , where the show after reac

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be qua

Pseudomonas aeruginosa is a common and major opportunistic human pathogen, its causes many and dangersinfectious diseases due to death in some timesex: cystic fibrosis , wounds inflammation , burns inflammation , urinary tract infection , other many infections otitis external , Endocarditis , nosocomial infection and also causes other blood infections (Bacteremia). thereforebecomes founding fast and exact identification of P. aeruginosafrom samples culture very important.However, identification of this species may be problematic due to the marked phenotypic variabilitydemonstrated by samples isolates and the presence of other closely related species. To facilitate species identification, we used 16S ribosomal DNA(rRNA) sequence data

Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles in controlling prosthetic and upper limb orthotic devices, as well as in assessing electrical muscle activity for various biomedical engineering and rehabilitation applications. In this study, an advanced discrimination system is proposed for the identification of seven distinct shoulder girdle motions, aimed at improving prosthesis control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD) is employed to enhance motion recognition. Subsequently, the Spectral Regression (SR) method is utilized to reduce the dimensionality of the extracted features. A comparative analysis is conducted between the Linear Discriminant Analysis (LDA) class

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden