Pub Date : 2021-04-26DOI: 10.1504/IJSI.2021.114765
Soniya Lalwani
Multiple sequence alignment (MSA) is an NP-complete problem that is a challenging area from bioinformatics. Implementation of hidden Markov model (HMM) is one of the most effective approach for executing MSA, that performs training and testing of the sequence data so as to obtain alignment scores with accuracy. The training of HMM is again an NP-hard problem, hence it requires the implementation of metaheuristic methods. Proposed work presents a bi-level artificial bee colony (BL-ABC) algorithm to train hidden Markov models (HMMs) for MSA of proteins, i.e., BLABC-HMM. The trained stochastic model created by BL-ABC basically yields position-dependent probability matrices at higher prediction ratios. The performance of proposed algorithm is compared with the competitive state-of-the-art algorithms and different variants of particle swarm optimisation (PSO) algorithm on protein benchmark datasets from pfam and BAliBase database, and BLABC-HMM is found yielding better alignment scores and prediction accuracy.
{"title":"Design and implementation of bi-level artificial bee colony algorithm to train hidden Markov models for performing multiple sequence alignment of proteins","authors":"Soniya Lalwani","doi":"10.1504/IJSI.2021.114765","DOIUrl":"https://doi.org/10.1504/IJSI.2021.114765","url":null,"abstract":"Multiple sequence alignment (MSA) is an NP-complete problem that is a challenging area from bioinformatics. Implementation of hidden Markov model (HMM) is one of the most effective approach for executing MSA, that performs training and testing of the sequence data so as to obtain alignment scores with accuracy. The training of HMM is again an NP-hard problem, hence it requires the implementation of metaheuristic methods. Proposed work presents a bi-level artificial bee colony (BL-ABC) algorithm to train hidden Markov models (HMMs) for MSA of proteins, i.e., BLABC-HMM. The trained stochastic model created by BL-ABC basically yields position-dependent probability matrices at higher prediction ratios. The performance of proposed algorithm is compared with the competitive state-of-the-art algorithms and different variants of particle swarm optimisation (PSO) algorithm on protein benchmark datasets from pfam and BAliBase database, and BLABC-HMM is found yielding better alignment scores and prediction accuracy.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"29 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75207468","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 : 2021-04-26DOI: 10.1504/IJSI.2021.114757
F. Sharma, S. R. Kapoor
In recent years, soft computing strategies have played vital role to solve optimisation problems associated with real world. In this paper, an efficient soft computing strategy namely, artificial bee colony algorithm (ABCalgo) is modified with incorporating landmark operator. The proposed modified ABC algorithm is named as landmark inspired ABC (LMABC). The performance of LMABC is evaluated on benchmark functions. Further, the proposed LMABC is applied for vector control of induction motor (IM) and subsequently to improve its efficiency. The vector control of IM includes control of magnitude and phase of each phase current and voltage. In this research paper the field orientated control, a digital implementation which demonstrates the capability of performing direct torque control, of handling system limitations and of achieving higher power conversion efficiency is considered. The obtained outcomes are significantly better than other state-of-art algorithms available in literature.
{"title":"Landmark operator inspired artificial bee colony algorithm for optimal vector control of induction motor","authors":"F. Sharma, S. R. Kapoor","doi":"10.1504/IJSI.2021.114757","DOIUrl":"https://doi.org/10.1504/IJSI.2021.114757","url":null,"abstract":"In recent years, soft computing strategies have played vital role to solve optimisation problems associated with real world. In this paper, an efficient soft computing strategy namely, artificial bee colony algorithm (ABCalgo) is modified with incorporating landmark operator. The proposed modified ABC algorithm is named as landmark inspired ABC (LMABC). The performance of LMABC is evaluated on benchmark functions. Further, the proposed LMABC is applied for vector control of induction motor (IM) and subsequently to improve its efficiency. The vector control of IM includes control of magnitude and phase of each phase current and voltage. In this research paper the field orientated control, a digital implementation which demonstrates the capability of performing direct torque control, of handling system limitations and of achieving higher power conversion efficiency is considered. The obtained outcomes are significantly better than other state-of-art algorithms available in literature.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"171 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90231553","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10036482
Preeti Bhowmick, S. Gajjar, Shital Chaudhary
{"title":"Hyperparameter tuning and comparison of k nearest neighbour and decision tree algorithms for cardiovascular disease prediction","authors":"Preeti Bhowmick, S. Gajjar, Shital Chaudhary","doi":"10.1504/ijsi.2021.10036482","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10036482","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"15 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73891345","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 : 2021-01-01DOI: 10.1504/IJSI.2021.114766
Neha Rajawat, N. Gupta, Soniya Lalwani
{"title":"A comprehensive review of hidden Markov model applications in prediction of human mobility patterns","authors":"Neha Rajawat, N. Gupta, Soniya Lalwani","doi":"10.1504/IJSI.2021.114766","DOIUrl":"https://doi.org/10.1504/IJSI.2021.114766","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"266 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76383310","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10039928
S. Manroo, Suhail Ganiny
{"title":"A coupled multi-linear regression and genetic algorithm-based modelling and optimisation of surface roughness in machining of brass","authors":"S. Manroo, Suhail Ganiny","doi":"10.1504/ijsi.2021.10039928","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10039928","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"61 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79492173","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10040390
Mrityunjay Singh, A. Shukla, Amit Kumar Jakhar
{"title":"Dog breed classification using convolution neural network","authors":"Mrityunjay Singh, A. Shukla, Amit Kumar Jakhar","doi":"10.1504/ijsi.2021.10040390","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10040390","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"41 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81400115","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 : 2021-01-01DOI: 10.1504/IJSI.2021.10037658
Prakhar Srivastava, Pankaj Srivastava
{"title":"Predicting movie genre from plot summaries using Bi-LSTM network","authors":"Prakhar Srivastava, Pankaj Srivastava","doi":"10.1504/IJSI.2021.10037658","DOIUrl":"https://doi.org/10.1504/IJSI.2021.10037658","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"58 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86203399","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10041159
K. H. Bindu, N. Nihal, Burre Chandu, Kaza Phani Rohitha
{"title":"Abusive language detection using customised BERT","authors":"K. H. Bindu, N. Nihal, Burre Chandu, Kaza Phani Rohitha","doi":"10.1504/ijsi.2021.10041159","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10041159","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"66 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79614719","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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