Pub Date : 2022-01-01DOI: 10.1504/ijbic.2022.123115
B. Lakshmanan, S. Anand
{"title":"Deep learning-based mitosis detection using genetic optimal feature set selection","authors":"B. Lakshmanan, S. Anand","doi":"10.1504/ijbic.2022.123115","DOIUrl":"https://doi.org/10.1504/ijbic.2022.123115","url":null,"abstract":"","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76911409","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 : 2021-09-23DOI: 10.1504/ijbic.2021.118091
María-Guadalupe Martínez-Peñaloza, E. Mezura-Montes, A. Morales-Reyes, H. Aguirre
This paper presents distance-based immune generalised differential evolution (DIGDE), an improved algorithmic approach to tackle dynamic multi-objective optimisation problems (DMOPs). Its novelty i...
{"title":"Distance-based immune generalised differential evolution algorithm for dynamic multi-objective optimisation","authors":"María-Guadalupe Martínez-Peñaloza, E. Mezura-Montes, A. Morales-Reyes, H. Aguirre","doi":"10.1504/ijbic.2021.118091","DOIUrl":"https://doi.org/10.1504/ijbic.2021.118091","url":null,"abstract":"This paper presents distance-based immune generalised differential evolution (DIGDE), an improved algorithmic approach to tackle dynamic multi-objective optimisation problems (DMOPs). Its novelty i...","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86372577","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 : 2021-09-22DOI: 10.1504/ijbic.2021.118101
M. Shanid, A. Anitha
Computed tomography (CT) for lung cancer detection is trending research in determining the lung cancer on its earlier stages. However, accurate lung cancer detection with severity levels is a major...
计算机断层扫描(CT)是肺癌早期诊断的研究热点。然而，准确的肺癌检测和严重程度是…
{"title":"Adaptive optimisation driven deep belief networks for lung cancer detection and severity level classification","authors":"M. Shanid, A. Anitha","doi":"10.1504/ijbic.2021.118101","DOIUrl":"https://doi.org/10.1504/ijbic.2021.118101","url":null,"abstract":"Computed tomography (CT) for lung cancer detection is trending research in determining the lung cancer on its earlier stages. However, accurate lung cancer detection with severity levels is a major...","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74303927","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 : 2021-09-22DOI: 10.1504/ijbic.2021.118084
L. Srinivas, B. Babu, S. Ram
This paper proposes a heuristic control of the series active power filter for power quality enhancement. In the context of power quality, the series active filter is better utilised as a voltage so...
{"title":"DVR-based power quality enhancement using adaptive particle swarm optimisation technique","authors":"L. Srinivas, B. Babu, S. Ram","doi":"10.1504/ijbic.2021.118084","DOIUrl":"https://doi.org/10.1504/ijbic.2021.118084","url":null,"abstract":"This paper proposes a heuristic control of the series active power filter for power quality enhancement. In the context of power quality, the series active filter is better utilised as a voltage so...","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84392916","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 : 2021-07-16DOI: 10.1504/IJBIC.2021.116548
Samira Ghorbanpour, T. Pamulapati, R. Mallipeddi
Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.
{"title":"Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects","authors":"Samira Ghorbanpour, T. Pamulapati, R. Mallipeddi","doi":"10.1504/IJBIC.2021.116548","DOIUrl":"https://doi.org/10.1504/IJBIC.2021.116548","url":null,"abstract":"Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91018892","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 : 2021-07-16DOI: 10.1504/IJBIC.2021.116549
B. Qu, Qian Zhou, Yongsheng Zhu, Jing J. Liang, C. Yue, Y. Jiao, Li Yan, P. N. Suganthan
This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.
{"title":"An improved brain storm optimisation algorithm for energy-efficient train operation problem","authors":"B. Qu, Qian Zhou, Yongsheng Zhu, Jing J. Liang, C. Yue, Y. Jiao, Li Yan, P. N. Suganthan","doi":"10.1504/IJBIC.2021.116549","DOIUrl":"https://doi.org/10.1504/IJBIC.2021.116549","url":null,"abstract":"This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74066460","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 : 2021-07-16DOI: 10.1504/IJBIC.2021.116615
Yingkang Hu, Xuesong Yan
Pollution source positioning is a complicated problem because urban water supply networks contain a huge number of nodes and it is also a computationally expensive problem. Surrogate model-based intelligent optimisation algorithms can effectively solve such problems. In this study, multiple offline neural network models were constructed using big data technology, which saves time otherwise needed for online model construction. Moreover, a variety of model management strategies are proposed and their validities are experimentally confirmed. Based on this, a neural network-assisted optimisation algorithm is proposed to rapid position of pollution source. The experimental results shown this novel algorithm can greatly reduce computing time while ensuring positioning accuracy.
{"title":"Neural network-assisted expensive optimisation algorithm for pollution source rapid positioning of drinking water","authors":"Yingkang Hu, Xuesong Yan","doi":"10.1504/IJBIC.2021.116615","DOIUrl":"https://doi.org/10.1504/IJBIC.2021.116615","url":null,"abstract":"Pollution source positioning is a complicated problem because urban water supply networks contain a huge number of nodes and it is also a computationally expensive problem. Surrogate model-based intelligent optimisation algorithms can effectively solve such problems. In this study, multiple offline neural network models were constructed using big data technology, which saves time otherwise needed for online model construction. Moreover, a variety of model management strategies are proposed and their validities are experimentally confirmed. Based on this, a neural network-assisted optimisation algorithm is proposed to rapid position of pollution source. The experimental results shown this novel algorithm can greatly reduce computing time while ensuring positioning accuracy.","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86894252","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 : 2021-07-16DOI: 10.1504/IJBIC.2021.116608
Beshiba Wilson, J. Dhas, R. Sreedharan, Ram P. Krish
Iron deposition in the brain has been observed with normal aging and is associated with neurodegenerative diseases. The automated classification of brain magnetic resonance images (MRI) based on iron deposition in basal ganglia region of the brain has not been performed, to our knowledge. It is very difficult to analyse iron regions in brain using simple MRI techniques. The MRI sequence namely susceptibility weighted imaging (SWI) helps to distinguish brain iron regions. The objective of our work is to investigate the iron regions in selected areas of basal ganglia region of brain and classify MR images. The study included a total of 60 MRI images which consists of 40 subjects with iron region and 20 subjects of healthy controls. We performed Gaussian smoothing followed by construction of 40 localised patches of each MR image based on iron and normal regions. Grey level co-occurrence matrix (GLCM) features are extracted from the patches and fed to random forest (RF) classifier for patch-based classification of iron region. Training of data patch features was done by random forest classifier and the performance of classifier in terms of accuracy was measured. The experimental results show that the proposed localised patch-based approach for classification of brain iron images using random forest classifier achieved 96.25% classification accuracy in identifying normal and iron regions from brain MR sequences.
{"title":"Ensemble learning-based classification on local patches from magnetic resonance images to detect iron depositions in the brain","authors":"Beshiba Wilson, J. Dhas, R. Sreedharan, Ram P. Krish","doi":"10.1504/IJBIC.2021.116608","DOIUrl":"https://doi.org/10.1504/IJBIC.2021.116608","url":null,"abstract":"Iron deposition in the brain has been observed with normal aging and is associated with neurodegenerative diseases. The automated classification of brain magnetic resonance images (MRI) based on iron deposition in basal ganglia region of the brain has not been performed, to our knowledge. It is very difficult to analyse iron regions in brain using simple MRI techniques. The MRI sequence namely susceptibility weighted imaging (SWI) helps to distinguish brain iron regions. The objective of our work is to investigate the iron regions in selected areas of basal ganglia region of brain and classify MR images. The study included a total of 60 MRI images which consists of 40 subjects with iron region and 20 subjects of healthy controls. We performed Gaussian smoothing followed by construction of 40 localised patches of each MR image based on iron and normal regions. Grey level co-occurrence matrix (GLCM) features are extracted from the patches and fed to random forest (RF) classifier for patch-based classification of iron region. Training of data patch features was done by random forest classifier and the performance of classifier in terms of accuracy was measured. The experimental results show that the proposed localised patch-based approach for classification of brain iron images using random forest classifier achieved 96.25% classification accuracy in identifying normal and iron regions from brain MR sequences.","PeriodicalId":13636,"journal":{"name":"Int. J. Bio Inspired Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76240674","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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