Time series forecasting is a challenging problem, that has a wide variety of application domains such as in engineering, environment, finance and others. When confronted with a time series forecasting application, typically a number of different forecasting models are tested and the best one is considered. Alternatively, instead of choosing the single best method, a wiser action could be to choose a group of the best models and then to combine their forecasts. In this study we propose a combined model consisting of Multi-layer perceptron (MLP), Gaussian Processes Regression (GPR) and a

The human β-coronavirus SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China. It causes Covid-19 disease which has become pandemic. Each of the five-known human β-coronaviruses has four major structural proteins (E, M, N and S) and 16 non-structural proteins encoded by ORF1a and ORF1b together (ORF1ab) that are involved in virus pathogenicity and infectivity. Here, we performed detailed positive selection analyses for those six genes among the four previously known human β-coronaviruses and within 38 SARS-CoV-2 genomes to assess signatures of adaptive evolution using maximum

Optic Disc (OD) localization is a fundamental step in developing computer-assisted diagnostics. In this work, an ultrafast method to locate the OD in retinal fundus images is presented. The proposed method is based on transforming the localization problem into two 1D problems by projecting the image features onto two perpendicular directions. Image features such as the directionality of the retinal vessels, the brightness and the size of the OD have been used in the current method. Two publicly available databases were used to evaluate the accuracy and the computation time of the proposed

This paper presents a model that was developed to address SemEval Task 7: "Determining Sentiment Intensity of English and Arabic Phrases", with focus on 'Arabic Phrases'. The goal of this task is to determine the degree to which some given term is associated with positive sentiment. The underlying premise behind the model that we have adopted is that determining the context (positive or negative) in which a term usually occurs can determine its strength. Since the focus is on Twitter terms, Twitter was used to collect tweets for each term for which a strength value was to be derived. An Arabic

This paper describes our participation in the SemEval-2016 task 5, Aspect Based Sentiment Analysis (ABSA). We participated in two slots in the sentence level ABSA (Subtask 1) namely: aspect category extraction (Slot 1) and sentiment polarity extraction (Slot 3) in English Restaurants and Laptops reviews. For Slot 1, we applied different models for each domain. In the restaurants domain, we used an ensemble classifier for each aspect which is a combination of a Convolutional Neural Network (CNN) classifier initialized with pretrained word vectors, and a Support Vector Machine (SVM) classifier

In this paper we introduce a new method for detecting a type of English Multiword Expressions (MWEs), which is phrasal verbs, into an English-Arabic phrase-based statistical machine translation (PBSMT) system. The detection starts with parsing the English side of the parallel corpus, detecting various linguistic patterns for phrasal verbs and finally integrate them into the En-Ar PBSMT system. In addition, the paper explores the effect of cliticizing specific words in English that have no Arabic equivalent. The results, which reported with the BLEU scores, showed that some patterns achieved

artificial neural network provides a cybernetic model that is similar to human intelligence in terms of parallel processing, generalization and memory stacking on the same neural network. From the era of neurogenesis, research models expect the rules that govern new neuron to depend on old mature circuitry. Other research models show the existence of catastrophic interference associated with new neurons if species is exposed to variable information content environment. In this work, the model developed provides a theoretical framework for a novel attention sensitive neural network as well as

Pandemics raise huge challenges yet brought several opportunities. The sudden attack of COVID-19 revealed the importance of the information technology (IT) applications. The Reliance on the IT sector has become imperative to ensure sustainability and to raise most sectors' performance efficiency, especially the services' ones. This study applied PESTEL analysis to evaluate the current status of IT in Egypt. SWOT analysis was performed to explore points of strength, weakness, opportunities, and threats that face the IT sector in Egypt as a result of the COVID19 attack. The process of foreseeing

Artificial Intelligence has been strongly evolving disrupting almost every research and application domain. One of the key attributes - and at the same time an enabler - of today's innovations is the massive connectivity resulted in the opportunity to exploit Artificial Intelligence across distributed network of self-contained smart agents those could range from software bots to complex devices like autonomous vehicles, IoT collations, UAVs and Robot Swarms. Such heterogenous networks of Autonomous Agents could differ in size, networking topology, protocols, computing profiles, algorithms

The huge amount of data being generated and transferred each day on the Internet leads to an increase of the need to automate knowledge-extraction tasks. Sentiment analysis is an ongoing research field in knowledge extraction that faces many challenges. In this paper, different machine learning, neural networks, deep learning models were evaluated over the IMDB benchmark dataset for movies reviews. Moreover, various word-embedding techniques were tested. Among all the presented models, the results of this work showed that the Long Short-Term Memory (LSTM) model with Bidirectional Encoder