{"title":"Cooperation through Communication in a Distributed Problem-Solving Network","authors":"Anisha Singh, Akarshita Jain, Bipin Kumar Rai","doi":"10.1201/9781003038467-8","DOIUrl":"https://doi.org/10.1201/9781003038467-8","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85143905","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}
Pub Date : 2020-12-07DOI: 10.1201/9781003038467-18
V. Radhika
{"title":"Data Science and Distributed AI","authors":"V. Radhika","doi":"10.1201/9781003038467-18","DOIUrl":"https://doi.org/10.1201/9781003038467-18","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76571301","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}
Pub Date : 2020-12-07DOI: 10.1201/9781003038467-15
Anuj Kumar, S. Yadav, S. Mittal
{"title":"DAI for Document Retrieval","authors":"Anuj Kumar, S. Yadav, S. Mittal","doi":"10.1201/9781003038467-15","DOIUrl":"https://doi.org/10.1201/9781003038467-15","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91329059","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}
Niharika Dhingra, Mahima Gupta, N. Bhati, P. Kumari, Rijwan Khan
{"title":"Instantiating Descriptions of Organizational Structures","authors":"Niharika Dhingra, Mahima Gupta, N. Bhati, P. Kumari, Rijwan Khan","doi":"10.1201/9781003038467-9","DOIUrl":"https://doi.org/10.1201/9781003038467-9","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82874370","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}
Distributed artificial intelligence (DAI) has emerged as a powerful paradigm for representing and solving complex problems. The growth of this field has been spurred by the advances in distributed computing environments and wide spread information connectivity. Although DAI started as a branch of artificial intelligence over twenty-five years ago, it has emerged as an independent research discipline in its own right, representing a confluence of ideas from several disciplines.
{"title":"Distributed Artificial Intelligence","authors":"Annu Mishra","doi":"10.1201/9781003038467-1","DOIUrl":"https://doi.org/10.1201/9781003038467-1","url":null,"abstract":"Distributed artificial intelligence (DAI) has emerged as a powerful paradigm for representing and solving complex problems. The growth of this field has been spurred by the advances in distributed computing environments and wide spread information connectivity. Although DAI started as a branch of artificial intelligence over twenty-five years ago, it has emerged as an independent research discipline in its own right, representing a confluence of ideas from several disciplines.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83551825","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}
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.
{"title":"Distributed Computing in a Pandemic","authors":"J. Alnasir","doi":"10.14201/adcaij.27337","DOIUrl":"https://doi.org/10.14201/adcaij.27337","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85736462","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}
Pub Date : 2020-09-13DOI: 10.14201/ADCAIJ2020934968
Hanaa Al-Lohibi, Tahani Alkhamisi, Maha Assagran, Amal H. Aljohani, A. Aljahdali
The abundance of photos on the internet, along with smartphones that could implement computer vision technologies allow for a unique way to browse the web. These technologies have potential used in many widely accessible and globally available reverse-image search applications. One of these applications is the use of reverse-image search to help people finding items which they're interested in, but they can’t name it. This is where Awjedni was born. Awjedni is a reverse-image search application compatible with iOS and Android smartphones built to provide an efficient way to search millions of products on the internet using images only. Awjedni utilizes a computer vision technology through implementing multiple libraries and frameworks to process images, recognize objects, and crawl the web. Users simply upload/take a photo of a desired item and the application returns visually similar items and a direct link to the websites that sell them.
{"title":"Awjedni: A Reverse-Image-Search Application","authors":"Hanaa Al-Lohibi, Tahani Alkhamisi, Maha Assagran, Amal H. Aljohani, A. Aljahdali","doi":"10.14201/ADCAIJ2020934968","DOIUrl":"https://doi.org/10.14201/ADCAIJ2020934968","url":null,"abstract":"The abundance of photos on the internet, along with smartphones that could implement computer vision technologies allow for a unique way to browse the web. These technologies have potential used in many widely accessible and globally available reverse-image search applications. One of these applications is the use of reverse-image search to help people finding items which they're interested in, but they can’t name it. This is where Awjedni was born. Awjedni is a reverse-image search application compatible with iOS and Android smartphones built to provide an efficient way to search millions of products on the internet using images only. Awjedni utilizes a computer vision technology through implementing multiple libraries and frameworks to process images, recognize objects, and crawl the web. Users simply upload/take a photo of a desired item and the application returns visually similar items and a direct link to the websites that sell them.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74184909","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}
Pub Date : 2020-06-20DOI: 10.14201/adcaij2020927990
Noor Fatima
Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.
{"title":"Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms","authors":"Noor Fatima","doi":"10.14201/adcaij2020927990","DOIUrl":"https://doi.org/10.14201/adcaij2020927990","url":null,"abstract":"Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82980854","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}
Pub Date : 2020-06-18DOI: 10.14201/adcaij2020924968
Amit Pimpalkar, R. Raj
Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. For polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification. The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers.
数据分析及其相关应用近年来已成为重要的研究领域。如今，研究人员关注的主题是每分每秒产生的大量数据，因为人们不断地分享对与他们相关的事物的想法和观点。然而，社交媒体信息仍然是非结构化的、分散的、难以处理的，需要建立一个坚实的基础，这样它们才能被用作特定主题的有价值的信息。在噪声、关联、表情符号、大众分类法和俚语等方面处理这一领域的非结构化数据确实非常具有挑战性，因此需要在获得正确的情感之前进行适当的数据预处理。从Kaggle和Twitter中提取数据集，使用NLTK和Scikit-learn进行预处理，并对Words Bag (BOW)、Term Frequency (TF)和Inverse Document Frequency (IDF)方案进行特征选择和提取。对于极性识别，我们评估了五种不同的机器学习(ML)算法，即多项式朴素贝叶斯(MNB)、逻辑回归(LR)、决策树(DT)、XGBoost (XGB)和支持向量机(SVM)。我们对这些算法的成功进行了比较分析，以确定哪种算法在召回率、准确性、f1分数和精度方面最适合给定的数据集。我们评估了各种预处理技术对两个数据集的影响;一个有定义域，另一个没有。结果表明，SVM分类器的准确率和精密度分别达到73.12%和94.91%，优于其他分类器。本研究还强调了特征的选择和表示以及各种预处理技术对分类性能的积极影响。最终结果表明情绪分类有所改善，我们注意到预处理方法明显表明分类器的效率有所提高。
{"title":"Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features","authors":"Amit Pimpalkar, R. Raj","doi":"10.14201/adcaij2020924968","DOIUrl":"https://doi.org/10.14201/adcaij2020924968","url":null,"abstract":"Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. \u0000For polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification. The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2020-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91335602","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}
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