<i>PP</i>and <i>P<span style='text-decoration:overline;'>P</span></i>Multi-Particles Production Investigation Based on CCNN Black-Box Approach

The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of an artificial neural network (ANN) black-box modeling approach based on the cascade correlation (CC) algorithm formulated to calculate and predict multiplicity distribution of proton-proton (antiproton) (PP and PP ) inelastic interactions full phase space at a wide range of center-mass of energy . In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution nch> as a function of . The CCNN model was designed based on available experimental data for = 30.4 GeV, 44.5 GeV, 52.6 GeV, 62.2 GeV, 200 GeV, 300 GeV, 540 GeV, 900 GeV, 1000 GeV, 1800 GeV, and 7 TeV. Our obtained empirical results for P(nch), as well as nch> for (PP and PP) collisions are compared with the corresponding theoretical ones which obtained from other models. This comparison shows a good agreement with the available experimental data (up to 7 TeV) and other theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV) we have predicted P(nch) and nch> which also, show a good agreement with different theoretical models.

Positron-Excited Lithium Atom Collisions

The inelastic scattering of positrons by excited lithium alkali atoms Li*(2p) have been investigated within the frame work of the coupled-static and frozen-core approximations with the assumption that the elastic and rearrangement channels are open. In the present work, a rather complicated computer code is developed based on the coupled-static, frozen-core and Green’s function partial wave expansion technique. The partial and total elastic and positronium (Ps) formation cross sections of e+-Li*(2p) are calculated through a wide range of incident energy of positrons ranging from 0.3 eV to 1000 eV. Also, we have calculated the partial and total elastic and rearrangement (reversal of the Ps formation) cross sections of Ps-Li+ collisions through the low, intermediate and high energy regions. The effect of polarization potential of the Ps atom is taken into our consideration. The total cross sections which corresponding to twelve partial cross sections (calculated at twelve values of the total angular momentum l = 0 to l = 11) are calculated for each channel. Our calculated total positronium formation cross sections are compared with experimental results and those calculated by other authors. The present calculations encourage the experimental physicists to carry out positron-lithium experiments by taking the excited lithium target into accounts in order to obtain more positronium especially in the low and intermediate energy regions.

Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis

This paper presents an intelligent algorithm for heart diseases diagnosis using phonocardiogram (PCG). The proposed technique consists of four stages: Data acquisition, pre-processing, feature extraction and classification. PASCAL heart sound database is used in this research. The second stage concerns with removing noise and artifacts from the PCG signals. Feature extraction stage is carried out using discrete wavelet transform (DWT). Finally, artificial neural network (ANN) has been used for classification stage with an overall accuracy 97%.

An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.