Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

Doreswamy,; Kolla, Hemanth; Vastrad, Channabasayya M.; Nagaraju, Samudrala

doi:10.1007/978-3-642-17857-3_50

Cited by 7 publications

(9 citation statements)

References 22 publications

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“…Yan et al [72] developed a method for predictive maintenance using logistic regression. Naive Bayes clusters and decision trees are used by Doreswamy [73] to classify datasets of different manufacturing materials. The information gained from these data models enables decisions regarding the use of different materials for specific tasks or manufacturing pieces [73].…”

Section: Resultsmentioning

confidence: 99%

“…Naive Bayes clusters and decision trees are used by Doreswamy [73] to classify datasets of different manufacturing materials. The information gained from these data models enables decisions regarding the use of different materials for specific tasks or manufacturing pieces [73]. Adam et al [74] applied a Hybrid Artificial Neural Network-Naive Bayes classifier in the identification of reject products in the semiconductor manufacturing.…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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“…Given the class variable, NBCs assume that the value of a particular feature is independent of the value of any other feature. Despite their naïve design and oversimplified assumptions, these classifiers have worked quite well with complex multidimensional data sets (Doreswamy, 2011). An advantage of naïve Bayes is that it only requires a small number of training data for estimating the parameters necessary for classification.…”

Section: Machine Learning Techniques For Protein Toxicity Predictionmentioning

confidence: 99%

Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

et al. 2020

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Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide-based therapies for the procurement of numerous class of diseases. The main idea behind this is to anchor the properties of the receptor to design peptide-based therapeutics. As these macromolecules have distinct physicochemical properties over small molecules, it becomes an obligatory field for the treatment of diseases. For this, various in silico models have been developed to speculate the proteins by virtue of the application of machine learning and artificial intelligence. By analysing the properties and structural alert of toxic proteins, researchers aim to dissert some of the mechanisms of protein toxicity from which therapeutic insights may be drawn. Numerous models already exist worldwide emphasizing themselves as leading paramount for toxicity prediction in protein macromolecules. Few of them comparatively compete with the other predictive protein toxicity models and convincingly give a high-performance result in terms of accuracy. But their foundation is quite ambiguous, and varying approaches are found at the level of toxicoproteomic data utilization while building a machine learning model. In this review work, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data.

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“…Both parameters are used in a confusion matrix that shows results predicted by Table 1 shows the confusion matrix that relates the four parameters mentioned above (TP, TN, FP, and FN): It is worth mentioning that other variants of this formula exist such as F2-measure that gives higher weight to recall than precision or F0.5-measure that gives higher weight to precision than recall. However, in data mining context, F1-measure is the most commonly used formula (Doreswamy, 2012). When dealing with binary classification, an average F-measure can be used when more than two classes are available, each time setting the target class as the positive class, and all the rest combined as the negative class.…”

Section: Evaluating Classifiersmentioning

confidence: 99%

“…NB classifier is a probabilistic classifier that assumes independence of attributes (Doreswamy, 2012). Given a data set D, let the attributes x1 through xn represent attributes of each record in D.…”

Section: Nb Classifiersmentioning

confidence: 99%

Sentiment analysis and resources for informal Arabic text on social media

Itani¹

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Online content posted by Arab users on social networks does not generally abide by the grammatical and spelling rules. These posts, or comments, are valuable because they contain users' opinions towards different objects such as products, policies, institutions, and people. These opinions constitute important material for commercial and governmental institutions. Commercial institutions can use these opinions to steer marketing campaigns, optimize their products and know the weaknesses and/ or strengths of their products. Governmental institutions can benefit from the social networks posts to detect public opinion before or after legislating a new policy or law and to learn about the main issues that concern citizens. However, the huge size of online data and its noisy nature can hinder manual extraction and classification of opinions present in online comments. Given the irregularity of dialectal Arabic (or informal Arabic), tools developed for formally correct Arabic are of limited use. This is specifically the case when employed in sentiment analysis (SA) where the target of the analysis is social media content. This research implemented a system that addresses this challenge. This work can be roughly divided into three blocks: building a corpus for SA and manually tagging it to check the performance of the constructed lexicon-based (LB) classifier; building a sentiment lexicon that consists of three different sets of patterns (negative, positive, and spam); and finally implementing a classifier that employs the lexicon to classify Facebook comments. In addition to providing resources for dialectal Arabic SA and classifying Facebook comments, this work categorises reasons behind incorrect classification, provides preliminary solutions for some of them with focus on negation, and uses regular expressions to detect the presence of lexemes. This work also illustrates how the constructed classifier works along with its different levels of reporting. Moreover, it compares the performance of the LB classifier against Naïve Bayes classifier and addresses how NLP tools such as POS tagging and Named Entity Recognition can be employed in SA. In addition, the work studies the performance of the implemented LB classifier and the developed sentiment lexicon when used to classify other corpora used in the literature, and the performance of lexicons used in the literature to classify the corpora constructed in this research. With minor changes, the classifier can be used in domain classification of documents (sports, science, news, etc.). The work ends with a discussion of research questions arising from the research reported.

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Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

Cited by 7 publications

References 22 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

Sentiment analysis and resources for informal Arabic text on social media

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