ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal <p dir="ltr">The <a title="adcaij" href="" target="_blank" rel="noopener">Advances in Distributed Computing and Artificial Intelligence Journal</a> (ISSN: 2255-2863) is an open access journal that publishes articles which contribute new results associated with distributed computing and artificial intelligence, and their application in different areas, such as the Internet, electronic commerce, mobile communications, wireless devices, distributed computing and so on.&nbsp;These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.</p> <p dir="ltr">Adcaij focuses attention in the exchange of ideas between scientists and technicians. &nbsp;Both, academic and business areas, are essential to facilitate the development of systems that meet the demands of today's society. The journal is supported by the research group and start-up value <a title="bisite" href="" target="_blank" rel="noopener">BISITE</a>.</p> <p dir="ltr">The journal commenced publication in 2012; has quarterly periodicity and has published 192 articles with peer review. All the articles are written in scientific English language.</p> <p dir="ltr">It has indexed in DOAJ, ProQuest, Scholar, WorldCat, Dialnet, Sherpa ROMEO, Dulcinea, UlrichWeb, Emerging Sources Citation Index of Thomson Reuters, BASE y Academic Journals Database.</p> en-US <h3>Ethical standards for the ADCAIJ journal</h3><p>Upon submitting original contributions to the ADCAIJ joumal, the authors agree to accept the standard procedures for the scientific community: contributions will be original in nature, neither published nor under consideration by other journals. Likewise, any original material sent to ADCAIJ will not be sent to other publications until our joumal has completed its evaluation process.</p><p>The authors will adhere to international copyright standards for written, graphic and other materials included in their writings sent to ADCAIJ for publication. For their part, the editors, the editorial staff, and journal reviewers will ensure that the integrity of the research is upheld, an effort that corresponds first and foremost to the authors. Consequently, authors will be asked to continue avoiding the practice of plagiarism and self-­?plagiarism. The Corresponding Author signs for and accepts responsibility for releasing this material on behalf of any and all Co-Authors.</p> (Juan M. CORCHADO) (Ángel REDERO (Ediciones Universidad de Salamanca)) Mon, 06 Jun 2022 00:00:00 +0200 OJS 60 Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning <p>The financial risk of investing in Bitcoin is increasing, and everyone partic-ipating in the transaction is aware of it. The rise and fall of bitcoin’s value is difficult to predict, and the system is fraught with uncertainty. As a result, this study proposed to use the «Deep learning» technique for predicting fi-nancial risk associated with bitcoin investment, that is linked to its «weighted price» on the bitcoin market’s volatility. The dataset used included Bitcoin historical data, which was acquired «at one-minute intervals» from selected exchanges of January 2012 through December 2020. The deep learning lin-ear-SVM-based technique was used to obtain an advantage in handling the high-dimensional challenges related with bitcoin-based transaction transac-tions large data volume. Four variables («High», «Low», «Close», and «Volume (BTC)».) are conceptualized to predict weighted price, in order to indi-cate if there is a propensity of financial risk over the effect of their interaction. The results of the experimental investigation show that the fi-nancial risk associated with bitcoin investing is accurately predicted. This has helped to discover engagements and disengagements with doubts linked with bitcoin investment transactions, resulting in increased confidence and trust in the system as well as the elimination of financial risk. Our model had a significantly greater prediction accuracy, demonstrating the utility of deep learning systems in detecting financial problems related to digital currency.</p> Nahla Aljojo Copyright (c) 2021 Nahla Aljojo Mon, 06 Jun 2022 00:00:00 +0200 Distributed Computing in a Pandemic <p>The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.</p> Jamie Alnasir Copyright (c) 2021 Jamie Alnasir Mon, 06 Jun 2022 00:00:00 +0200 Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques <p>The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.</p> <p> </p> Vishakha Arya, Amit Kumar Mishra Mishra, Alfonso González-Briones Copyright (c) 2021 Dr. Amit Kumar Mishra Mishra Mon, 06 Jun 2022 00:00:00 +0200 Prosumers Flexibility as Support for Ancillary Services in Low Voltage Level <p>The prosumers flexibility procurement has increased due to the current penetration of distributed and variable renewable energy sources. The prosumers flexibility is often able to quickly adjust the power consumption, making it well suited as a primary and secondary reserve for ancillary services. In the era of smart grids, the role of the aggregator has been increasingly exploited and considered as a player that can facilitate small prosumers' participation in electricity markets. This paper proposes an approach based on the use of prosumers flexibility by an aggregator to support ancillary services at a low voltage level. An asymmetric pool market approach is considered for flexibility negotiation between prosumers and the local marker operator (aggregator). From the achieved results it is possible to conclude that the use of flexibility can bring technical and economic benefits for network operators.</p> Ricardo Faia, Tiago Pinto, Fernando Lezama, Zita Vale, Juan Manuel Corchado, Alfonso González-Briones Copyright (c) 2021 Ricardo Faia, Tiago Pinto, Fernando Lezama, Zita Vale, Juan Manuel Corchado, Alfonso González-Briones Mon, 06 Jun 2022 00:00:00 +0200 Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model <p>Nowadays, the buildings have an important role on high demand of electricity energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage devices in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over long time period to minimize the electricity cost of the building. The proposed model is design by a Mixed Binary Linear rogramming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over a one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported.</p> Zahra Foroozandeha, Sérgio Ramos, João Soares, Vale Zita, António Gomes Copyright (c) 2021 Sérgio Ramos Mon, 06 Jun 2022 00:00:00 +0200 A Study on the Impact of DE Population Size on the Performance Power System Stabilizers <p>The population size of DE plays a significant role in the way the algorithm performs as it influences whether good solutions can be found. Generally, the population size of DE algorithm is a user-defined input that remains fixed during the optimization process. Therefore, inadequate selection of DE population size may seriously hinder the performance of the algorithm. This paper investigates the impact of DE population size on (i) the performance of DE when applied to the optimal tuning of power system stabilizers (PSSs); and (ii) the ability of the tuned PSSs to perform efficiently to damp low-frequency oscillations. The effectiveness of these controllers is evaluated based on frequency domain analysis and validated using time-domain simulations. Simulation results show that a small population size may lead the algorithm to converge prematurely, and thus resulting in a poor controller performance. On the other hand, a large population size requires more computational effort, whilst no noticeable improvement in the performance of the controller is observed.</p> Komla Agbenyo Folly, Tshina Fa Mulumba Copyright (c) 2021 Komla Folly Mon, 06 Jun 2022 00:00:00 +0200 A Hybrid System For Pandemic Evolution Prediction <p>The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures.</p> <p></p> <div id="EkleguLightBox">&nbsp;</div> <p></p> <div id="EkleguLightBox">&nbsp;</div> Lilia Muñoz, María Alonso-García, Vladimir Villarreal, Guillermo Hernández, Mel Nielsen, Francisco Pinto-Santos, Amilkar Saavedra, Mariana Areiza, Juan Montenegro, Inés Sittón-Candanedo, Yen Caballero-González, Saber Trabelsi, Juan M. Corchado Copyright (c) 2022 Lilia Muñoz, María Alonso-García, Vladimir Villarreal, Guillermo Hernández, Mel Nielsen, Francisco Pinto-Santos, Amilkar Saavedra, Mariana Areiza, Juan Montenegro, Inés Sittón-Candanedo, Yen Caballero-González, Saber Trabelsi, Juan M. Corchado Mon, 06 Jun 2022 00:00:00 +0200 Index ADCAIJ editorial Team Copyright (c) 2022 ADCAIJ editorial Team Mon, 06 Jun 2022 00:00:00 +0200 Staff ADCAIJ editorial Team Copyright (c) 2022 ADCAIJ editorial Team Mon, 06 Jun 2022 00:00:00 +0200