Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable

The protozoan parasite Entamoeba histolytica is a causative agent of amoebiasis, where it causes millions of cases of dysentery and liver abscess each year. Metronidazole is a drug of choice against amoebiasis. The drug is a choice because of its efficacy and low cost, but at the same time it causes several adverse side effects; therefore, it is important to find effective medications to treat amoebiasis without any complications or any side effects. The aim of this study is to evaluate the effectiveness of different concentrations (50, 75 and 100 µg/ml) of silver nanoparticle (AgNPs) against trophozoites stages of E. histolytica in vitro. The results showed a significant decrease (p ? 0.05) in numbers of trophozoites stages after treated

Reflective cracking is one of the primary forms of deterioration in pavements. It is widespread when Asphalt concrete (AC) overlays are built over a rigid pavement with discontinuities on its surface. Thus, this research work aims to reduce reflection cracks in asphalt concrete overlay on the rigid pavement. Asphalt Concrete (AC) slab specimens were prepared in three thicknesses (4, 5, and 6 cm). All these specimens were by testing machine designed and manufactured at the Engineering Consulting Office of the University of Baghdad to examine for the number of cycles and loads needed to propagate the reflection cracking in the asphalt concert mixture at three temperatures (20, 30, and 30°C). It was noticed that the higher thickness A