Artificial intelligence has quickly invaded the realms of both creative and information-based writing, raising new questions about human originality, authorship and style. Despite its ability to produce writings that are coherent and stylistically varied, there are still concerns over the uniqueness and cultural neutrality of AI programs such as ChatGPT. This review covers significant recent advancements with artificial intelligence applications in both the literary and non-literary fields. It analyzes 35 recent studies contrasting authorship and creativity, or stylistic considerations and impressions, between human and AI texts. These studies range from poetic and fictional writing through essay, news article and academic publicati

Substance use disorders are a widely recognized problem among hepatitis C-infected patients; moreover, substance abuse by intravenous injection is a common mode of transmission of the hepatitis C virus worldwide. The frequency of substance use disorders and their relation to hepatitis C infection are still unknown in Iraq. This cross-sectional study, conducted among a sample of hepatitis C- infected patients attending the Gastrointestinal Tract Center in Baghdad Medical City, aimed to examine the prevalence of substance use disorders, the sociodemographic characteristics of the abusers, and the relation between intravenous

In the present paper, chitosan Schiff base has been synthesized from chitosan’s reaction with the salicyldehyde. The AuNPs was manufacture by extract of onion peels as a reducing agent. The Au NPs that have been prepared were characterized through the UV-vis spectroscopy, XRD analyses and SEM microscopy. The polymer blends of the chitosan Schiff base / PVP has been prepared through using the approach of solution casting. Chitosan Schiff base / PVP Au nano-composites was prepared. Nano composites and polymer blends have been characterized by FTIR which confirm the formation of Schiff base by revealing a new band of absorption at 1651cm-1 as a result of the (C=N) imine group. SEM, DSC and TGA confirms the thermal stability of the pr

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne