http://hdl.handle.net/10366/150202 2026-04-19T00:03:26Z 2026-04-19T00:03:26Z Alnasir, Jamie http://hdl.handle.net/10366/150222 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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.

2022-06-06T00:00:00Z Arya, Vishakha Mishra, Amit Kumar Mishra González Briones, Alfonso http://hdl.handle.net/10366/150221 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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./n

2022-06-06T00:00:00Z Mall, Pawan Singh, Pradeep Kumar http://hdl.handle.net/10366/150220 2025-06-05T12:36:20Z

In clinical treatment, deep learning plays a pivotal role in medical image classification. Deep learning techniques provide opportunities for radiologists and orthopedic to ease out their lives with faster and more accurate results. The traditional deep learning approach nevertheless reached its performance ceiling. Therefore, in this paper, we investigate different enhancement techniques to boost the deep neural networks performance and provide a solution as BoostNet. The experiment is categorized into four different phases. We have selected ChampNet from benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet18, VGG19). This phase helps to obtain the best model. In the second phase, The ChampNet evaluates with different resolutions dataset. This phase helps to finalize the dataset resolution to enhance the performance of ChampNet. In the third phase, ChampNet merges with image enhancement techniques Contrast Limited Adaptive Histogram Equalization (CLAHE), High-frequency filtering (HEF), and Unsharp masking (UM). This phase helps to obtain BoostNet with enriched performance. The last phase helps us to verify BoostNet results with Lightness Order Error (LOE). The presented research work fuses the image enhancement technique with ChampNet to generate BoostNet models. An assessment was performed on the Musculoskeletal Radiographs Bone Classification (MURA-BC) using classification schemes to demonstrate the proposed model's performance. The Classification accuracy of BoostNet was for the train, test dataset with and without enhancement techniques. The proposed model ChampNet+ CLAHE, ChampNet+ HEF, ChampNet+ UM approach achieved 95.88%, 94.99%, and 94.18% accuracy, respectively. This experiment leads to a more accurate and efficient classification model.

Foroozandeha, Zahra Ramos, Sérgio Soares, João Vale, Zita Gomes, António http://hdl.handle.net/10366/150218 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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.

2022-06-06T00:00:00Z Faia, Ricardo Pinto, Tiago Lezama, Fernando Vale, Zita Corchado Rodríguez, Juan Manuel González Briones, Alfonso http://hdl.handle.net/10366/150219 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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.

2022-06-06T00:00:00Z Agbenyo Folly, Komla Fa Mulumba, Tshina http://hdl.handle.net/10366/150217 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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.

2022-06-06T00:00:00Z Muñoz, Lilia Alonso-garcía, María Villarreal, Vladimir Hernández, Guillermo Nielsen, Mel Pinto Santos, Francisco Saavedra, Amilkar Areiza, Mariana Montenegro, Juan Sitton Candanedo, Inés Xiomara Caballero Gonzalez, Yen Air Trabelsi, Saber Corchado Rodríguez, Juan Manuel http://hdl.handle.net/10366/150216 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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./n; /n; /n; /n; /n; /n

2022-06-06T00:00:00Z Aljojo, Nahla http://hdl.handle.net/10366/150214 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z

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.

2022-06-06T00:00:00Z Editorial Team, Adcaij http://hdl.handle.net/10366/150215 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z 2022-06-06T00:00:00Z Editorial Team, Adcaij http://hdl.handle.net/10366/150213 2025-06-05T12:36:20Z 2022-06-06T00:00:00Z 2022-06-06T00:00:00Z