Int. J. Bio Inspired Comput.最新文献_第7页

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification 一种集成代理辅助的协同粒子群算法用于水源识别

Int. J. Bio Inspired Comput. Pub Date : 2022-01-01 DOI: 10.1504/ijbic.2022.10047735

Jinyu Gong, Xuesong Yan, Chengyu Hu

引用次数: 2

Deep learning-based mitosis detection using genetic optimal feature set selection 基于遗传最优特征集选择的深度学习有丝分裂检测

Int. J. Bio Inspired Comput. Pub Date : 2022-01-01 DOI: 10.1504/ijbic.2022.123115

B. Lakshmanan, S. Anand

引用次数: 0

RVEA-based multi-objective workflow scheduling in cloud environments 云环境下基于rvea的多目标工作流调度

Int. J. Bio Inspired Comput. Pub Date : 2022-01-01 DOI: 10.1504/ijbic.2022.10049616

Hengliang Tang, Yang Cao, Siqing You, Yuelu Gong, Fei Xue, Qiuru Hai

引用次数: 7

Distance-based immune generalised differential evolution algorithm for dynamic multi-objective optimisation 基于距离的免疫广义差分进化动态多目标优化算法

Int. J. Bio Inspired Comput. Pub Date : 2021-09-23 DOI: 10.1504/ijbic.2021.118091

María-Guadalupe Martínez-Peñaloza, E. Mezura-Montes, A. Morales-Reyes, H. Aguirre

引用次数: 4

Adaptive optimisation driven deep belief networks for lung cancer detection and severity level classification 基于自适应优化的深度信念网络用于肺癌检测和严重程度分类

Int. J. Bio Inspired Comput. Pub Date : 2021-09-22 DOI: 10.1504/ijbic.2021.118101

M. Shanid, A. Anitha

引用次数: 2

DVR-based power quality enhancement using adaptive particle swarm optimisation technique 基于自适应粒子群优化技术的dvr电能质量增强

Int. J. Bio Inspired Comput. Pub Date : 2021-09-22 DOI: 10.1504/ijbic.2021.118084

L. Srinivas, B. Babu, S. Ram

引用次数: 0

Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects 能量分解的群算法和进化算法:挑战与展望

Int. J. Bio Inspired Comput. Pub Date : 2021-07-16 DOI: 10.1504/IJBIC.2021.116548

Samira Ghorbanpour, T. Pamulapati, R. Mallipeddi

{"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}

引用次数: 6

An improved brain storm optimisation algorithm for energy-efficient train operation problem 列车节能运行问题的改进头脑风暴优化算法

Int. J. Bio Inspired Comput. Pub Date : 2021-07-16 DOI: 10.1504/IJBIC.2021.116549

B. Qu, Qian Zhou, Yongsheng Zhu, Jing J. Liang, C. Yue, Y. Jiao, Li Yan, P. N. Suganthan

引用次数: 2

Neural network-assisted expensive optimisation algorithm for pollution source rapid positioning of drinking water 饮用水污染源快速定位的神经网络辅助昂贵优化算法

Int. J. Bio Inspired Comput. Pub Date : 2021-07-16 DOI: 10.1504/IJBIC.2021.116615

Yingkang Hu, Xuesong Yan

引用次数: 3

Ensemble learning-based classification on local patches from magnetic resonance images to detect iron depositions in the brain 基于集成学习的磁共振图像局部斑块分类检测脑内铁沉积

Int. J. Bio Inspired Comput. Pub Date : 2021-07-16 DOI: 10.1504/IJBIC.2021.116608

Beshiba Wilson, J. Dhas, R. Sreedharan, Ram P. Krish

{"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}

引用次数: 4