ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal最新文献_第6页

A Hybrid System For Pandemic Evolution Prediction 大流行演变预测的混合系统

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.28093

Lilia Muñoz, M. Alonso-García, Vladimir Villarreal, Guillermo Hernández, Mel Nielsen, Francisco Pinto-Santos, Amilkar Saavedra, Mariana Areiza, Juan Montenegro, Inés Sittón-Candanedo, Yen-Air Caballero-González, S. Trabelsi, J. Corchado

{"title":"A Hybrid System For Pandemic Evolution Prediction","authors":"Lilia Muñoz, M. Alonso-García, Vladimir Villarreal, Guillermo Hernández, Mel Nielsen, Francisco Pinto-Santos, Amilkar Saavedra, Mariana Areiza, Juan Montenegro, Inés Sittón-Candanedo, Yen-Air Caballero-González, S. Trabelsi, J. Corchado","doi":"10.14201/adcaij.28093","DOIUrl":"https://doi.org/10.14201/adcaij.28093","url":null,"abstract":"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. \u0000 \u0000 \u0000 \u0000 ","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"68 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84093410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning 利用深度学习预测比特币投资相关的金融风险

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.27269

N. Aljojo

{"title":"Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning","authors":"N. Aljojo","doi":"10.14201/adcaij.27269","DOIUrl":"https://doi.org/10.14201/adcaij.27269","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"93 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88933799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Study on the Impact of DE Population Size on the Performance Power System Stabilizers DE种群规模对电力系统稳定器性能影响的研究

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.27955

Komla Agbenyo Folly, Tshina Fa Mulumba

引用次数: 0

Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques 使用机器学习技术分析Covid-19发病的情绪

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.27348

Vishakha Arya, A. Mishra, Alfonso González-Briones

{"title":"Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques","authors":"Vishakha Arya, A. Mishra, Alfonso González-Briones","doi":"10.14201/adcaij.27348","DOIUrl":"https://doi.org/10.14201/adcaij.27348","url":null,"abstract":"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.\u0000 ","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"190 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79231399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model 智能建筑中电动汽车充放电调度与电池储能:一个混合二元线性规划模型

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.27904

Zahra Foroozandeha, S. Ramos, J. Soares, Vale Zita, António Gomes

{"title":"Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model","authors":"Zahra Foroozandeha, S. Ramos, J. Soares, Vale Zita, António Gomes","doi":"10.14201/adcaij.27904","DOIUrl":"https://doi.org/10.14201/adcaij.27904","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"91 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90971480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prosumers Flexibility as Support for Ancillary Services in Low Voltage Level 低电压辅助服务的产消灵活性支持

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI: 10.14201/adcaij.27896

Ricardo Faia, T. Pinto, F. Lezama, Z. Vale, J. Corchado, Alfonso González-Briones

引用次数: 1

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm 基于质心和层次聚类算法的k近邻查询并行处理

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-05-26 DOI: 10.30564/aia.v4i1.4668

Elaheh Gavagsaz

{"title":"Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm","authors":"Elaheh Gavagsaz","doi":"10.30564/aia.v4i1.4668","DOIUrl":"https://doi.org/10.30564/aia.v4i1.4668","url":null,"abstract":"The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"43 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82546097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Safety-critical Policy Iteration Algorithm for Control under Model Uncertainty 模型不确定性下控制的安全关键策略迭代算法

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-04-11 DOI: 10.30564/aia.v4i1.4361

Navid Moshtaghi Yazdani, R. Kardehi Moghaddam, Mohammad Hasan Olyaei

引用次数: 0

Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning 基于图像处理和机器学习的老年人跌倒敏感特征检测

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-04-11 DOI: 10.30564/aia.v4i1.4419

Mohammad Hasan Olyaei, A. Olyaei, Sumaya Hamidi

{"title":"Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning","authors":"Mohammad Hasan Olyaei, A. Olyaei, Sumaya Hamidi","doi":"10.30564/aia.v4i1.4419","DOIUrl":"https://doi.org/10.30564/aia.v4i1.4419","url":null,"abstract":"The world’s elderly population is growing every year. It is easy to say that the fall is one of the major dangers that threaten them. This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home. The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body. Several machine learning technologies have been proposed for automatic fall detection. The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera. The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities. These features are based on motion, changes in human shape, and oval diameters around the human and temporal head position. The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection. Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81% that have been tested with UR Fall Detection dataset.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"194 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77764001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metric-based Few-shot Classification in Remote Sensing Image 基于度量的遥感图像少拍分类

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-03-08 DOI: 10.30564/aia.v4i1.4124

Mengyue Zhang, Jinyong Chen, Gang Wang, Min Wang, Kang Sun

{"title":"Metric-based Few-shot Classification in Remote Sensing Image","authors":"Mengyue Zhang, Jinyong Chen, Gang Wang, Min Wang, Kang Sun","doi":"10.30564/aia.v4i1.4124","DOIUrl":"https://doi.org/10.30564/aia.v4i1.4124","url":null,"abstract":"Target recognition based on deep learning relies on a large quantity of samples, but in some specific remote sensing scenes, the samples are very rare. Currently, few-shot learning can obtain high-performance target classification models using only a few samples, but most researches are based on the natural scene. Therefore, this paper proposes a metric-based few-shot classification technology in remote sensing. First, we constructed a dataset (RSD-FSC) for few-shot classification in remote sensing, which contained 21 classes typical target sample slices of remote sensing images. Second, based on metric learning, a k-nearest neighbor classification network is proposed, to find multiple training samples similar to the testing target, and then the similarity between the testing target and multiple similar samples is calculated to classify the testing target. Finally, the 5-way 1-shot, 5-way 5-shot and 5-way 10-shot experiments are conducted to improve the generalization of the model on few-shot classification tasks. The experimental results show that for the newly emerged classes few-shot samples, when the number of training samples is 1, 5 and 10, the average accuracy of target recognition can reach 59.134%, 82.553% and 87.796%, respectively. It demonstrates that our proposed method can resolve fewshot classification in remote sensing image and perform better than other few-shot classification methods.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"166 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76177358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0