Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600502
Khaled A. Ismail, M. A. E. Ghany
Fast and accurate Machine Learning (ML) models for predicting input stimulus in verification testbenches are proposed in this paper. Multiple (ML) models: Artificial Neural Network (ANN), Deep Neural Network (DNN), Support Vector Regression (SVR) and Decision Trees (DT) are examined to constrain randomization of input values to hit the planned coverage metrics. Accuracy of the models is measured using (ML) evaluation metrics such as: Mean Squared Error (MSE) and (R2 score). Training time required for each (ML) model is calculated and compared. Investigated (ML) models show an average improvement of 63.5% in the number of simulation cycles needed to reach full coverage closure compared to existing work. Comparative analysis between the models shows that (DT) is the most suitable (ML) model for a functional verification environment, due to its high accuracy and low training time required.
{"title":"High Performance Machine Learning Models for Functional Verification of Hardware Designs","authors":"Khaled A. Ismail, M. A. E. Ghany","doi":"10.1109/NILES53778.2021.9600502","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600502","url":null,"abstract":"Fast and accurate Machine Learning (ML) models for predicting input stimulus in verification testbenches are proposed in this paper. Multiple (ML) models: Artificial Neural Network (ANN), Deep Neural Network (DNN), Support Vector Regression (SVR) and Decision Trees (DT) are examined to constrain randomization of input values to hit the planned coverage metrics. Accuracy of the models is measured using (ML) evaluation metrics such as: Mean Squared Error (MSE) and (R2 score). Training time required for each (ML) model is calculated and compared. Investigated (ML) models show an average improvement of 63.5% in the number of simulation cycles needed to reach full coverage closure compared to existing work. Comparative analysis between the models shows that (DT) is the most suitable (ML) model for a functional verification environment, due to its high accuracy and low training time required.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126724165","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 : 2021-10-23DOI: 10.1109/NILES53778.2021.9600515
E. Abdelrazik, Mariam Oweda, M. El-Hadidi
Antimicrobial resistance (AMR) is one of the ten dangers threatening our world, according to the world health organization (WHO). Nowadays, there are plenty of electronic microbial genomics and metagenomics data records that represent host-associated microbiomes. These data introduce new insights and a comprehensive understanding of the current antibiotic resistance threats and the upcoming resistance outbreak. Many bioinformatics tools have been developed to detect the AMR genes based on different annotated databases of bacterial whole genome sequences (WGS). The number and structure of databases used may affect prediction quality. Herein, we aim to check the performance of four AMR gene detection tools and characterize the detection quality by comparing predicted results to reference antibiotic susceptibility test (AST) data. This may enhance the precise in-silico prediction of resistance phenotype and reduce false-positive predictions, which lead to more biologically relevant results. Four AMR gene detection tools; AMRFinder, ABRicate, ResFinder, and SraX are used for the benchmarking using Salmonella enterica isolates (n=104) retrieved from National Center for Biotechnology Information (NCBI) Assembly Database in July 2021. Performance is checked in terms of accuracy, precision, and specificity for each tool. Pearson x2 test is used to compare predicted results with antibiotic susceptibility testing (true results). All performance measures are assessed via scikit-learn package 0.21.3 and R software V 4.0.3. The highest accuracy was achieved by AMRFinder (0.89), while ResFinder had the highest precision score (0.93) and ABRicate has the lowest time and memory consumption. On the other hand, ResFinder's results confirmed the null hypothesis of the Pearson x2 test. We conclude that ResFinder is the best tool where its results have the tiniest difference compared to the phenotypic antibiotic susceptibility (true results).
{"title":"Benchmarking of Antimicrobial Resistance Gene Detection Tools in Assembled Bacterial Whole Genomes","authors":"E. Abdelrazik, Mariam Oweda, M. El-Hadidi","doi":"10.1109/NILES53778.2021.9600515","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600515","url":null,"abstract":"Antimicrobial resistance (AMR) is one of the ten dangers threatening our world, according to the world health organization (WHO). Nowadays, there are plenty of electronic microbial genomics and metagenomics data records that represent host-associated microbiomes. These data introduce new insights and a comprehensive understanding of the current antibiotic resistance threats and the upcoming resistance outbreak. Many bioinformatics tools have been developed to detect the AMR genes based on different annotated databases of bacterial whole genome sequences (WGS). The number and structure of databases used may affect prediction quality. Herein, we aim to check the performance of four AMR gene detection tools and characterize the detection quality by comparing predicted results to reference antibiotic susceptibility test (AST) data. This may enhance the precise in-silico prediction of resistance phenotype and reduce false-positive predictions, which lead to more biologically relevant results. Four AMR gene detection tools; AMRFinder, ABRicate, ResFinder, and SraX are used for the benchmarking using Salmonella enterica isolates (n=104) retrieved from National Center for Biotechnology Information (NCBI) Assembly Database in July 2021. Performance is checked in terms of accuracy, precision, and specificity for each tool. Pearson x2 test is used to compare predicted results with antibiotic susceptibility testing (true results). All performance measures are assessed via scikit-learn package 0.21.3 and R software V 4.0.3. The highest accuracy was achieved by AMRFinder (0.89), while ResFinder had the highest precision score (0.93) and ABRicate has the lowest time and memory consumption. On the other hand, ResFinder's results confirmed the null hypothesis of the Pearson x2 test. We conclude that ResFinder is the best tool where its results have the tiniest difference compared to the phenotypic antibiotic susceptibility (true results).","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128479411","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 : 2021-10-23DOI: 10.1109/NILES53778.2021.9600539
Ravinesh Chand, Sandeep Kumar, R. P. Chand
This paper presents a dynamic n-link revolute robotic arm that can perform a sequence of tasks and navigate via hierarchal landmarks to its target. The stability condition with multiple Lyapunov functions for switched systems is considered. The multiple Lyapunov functions are formulated from the Lyapunov-based Control Scheme (LbCS) as a tool for analyzing Lyapunov stability. A new set of switched nonlinear, time-invariant, continuous, and stabilizing velocity controllers of the proposed Rn robotic arm are developed.
{"title":"Navigation of an n-link Revolute Robotic Arm via Hierarchal Landmarks","authors":"Ravinesh Chand, Sandeep Kumar, R. P. Chand","doi":"10.1109/NILES53778.2021.9600539","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600539","url":null,"abstract":"This paper presents a dynamic n-link revolute robotic arm that can perform a sequence of tasks and navigate via hierarchal landmarks to its target. The stability condition with multiple Lyapunov functions for switched systems is considered. The multiple Lyapunov functions are formulated from the Lyapunov-based Control Scheme (LbCS) as a tool for analyzing Lyapunov stability. A new set of switched nonlinear, time-invariant, continuous, and stabilizing velocity controllers of the proposed Rn robotic arm are developed.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114959323","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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