Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

 Abstract Objective: Comparison of femtosecond small incision lenticule extraction (FS-SMILE) versus Femtosecond laser Insitu keratomileusis (FS-LASIK) regarding dry eye disease (DED) and corneal sensitivity (CS) after those refractive surgeries. Methods: A comparative prospective study conducted for a period of 2 years; from March 2017 until February, 2019. Enrolled patients were diagnosed with myopia. Fifty patients (100 eyes) were scheduled for bilateral FS-SMILE and the other 50 patients (100 eyes) had been scheduled for bilateral FS-LASIK. Both groups were followed for six months after surgery. The age, gender, and preoperative refraction for both groups were matched. Complete evaluation of dry eye disease had been

 Abstract Objective: Comparison of femtosecond small incision lenticule extraction (FS-SMILE) versus Femtosecond laser Insitu keratomileusis (FS-LASIK) regarding dry eye disease (DED) and corneal sensitivity (CS) after those refractive surgeries. Methods: A comparative prospective study conducted for a period of 2 years; from March 2017 until February, 2019. Enrolled patients were diagnosed with myopia. Fifty patients (100 eyes) were scheduled for bilateral FS-SMILE and the other 50 patients (100 eyes) had been scheduled for bilateral FS-LASIK. Both groups were followed for six months after surgery. The age, gender, and preoperative refraction for both groups were matched. Complete evaluation of dry eye disease had been

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

The reason for conducting this study is to prolong release of miconazole in the ocular site of action by ocular-based gels (OBGs) formulations. The formulation factors affecting on the release from OBG should be studied using various gelling agents in various concentrations to achieve the improvement in retention and residence time in response to prolonged release. In this study, the formulations were prepared using carbopol 940, pectin, sodium alginate, poloxamer 407, and poly(methacrylic acid) at 0.5%, 1%, and 1.5% w/v, respectively. Hydroxypropyl methylcellulose E5 (HPMC E5) 1% was added as thickening agent/viscosity builder. The formulation containing carbopol 940, pectin and sodium alginate at 1.5% w/v, displayed a noticable im

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l