Pub Date : 2014-10-20DOI: 10.1109/UKCI.2014.6930184
Jian-Ping Li, F. Campean
This paper is to apply the species conserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A real-vector is used to represent the corresponding cross-sectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model has been developed to deal with more practical considerations in modeling, such as existences of members, kinematic stability analysis and the computation of stresses and displacements. Cross-sectional areas and node connections are taken as decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions and different topologies can be found by using the proposed algorithm in a single run and some trusses have smaller weight than the solutions in the literature.
{"title":"Truss topology optimization with species conserving genetic algorithm","authors":"Jian-Ping Li, F. Campean","doi":"10.1109/UKCI.2014.6930184","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930184","url":null,"abstract":"This paper is to apply the species conserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A real-vector is used to represent the corresponding cross-sectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model has been developed to deal with more practical considerations in modeling, such as existences of members, kinematic stability analysis and the computation of stresses and displacements. Cross-sectional areas and node connections are taken as decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions and different topologies can be found by using the proposed algorithm in a single run and some trusses have smaller weight than the solutions in the literature.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127490202","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930158
Haruna Isah, D. Neagu, P. Trundle
The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.
{"title":"Social media analysis for product safety using text mining and sentiment analysis","authors":"Haruna Isah, D. Neagu, P. Trundle","doi":"10.1109/UKCI.2014.6930158","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930158","url":null,"abstract":"The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131276733","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930186
Q. Al-Jubouri, W. Al-Nuaimy, Hamzah S. AlZu'bi, O. Zahran, Jonathan Buckley
Over the last two decades, zebrafish (Danio rerio) have emerged as an efficient model to aid in the research of a broad range of human diseases as well as such diverse applications as environmental modelling and drug discovery. Economically, the large number, low price and low maintenance requirements of this fish species encouraged its use for research. In addition to this, the study of zebrafish is being used to improve the understanding of fish physiology, with implications for fish welfare. In order to thoroughly model the behaviour, development and growth of these fish, it is important to be able to scrutinise the characteristics of individual fish as they respond to a range of stimuli, and to this end off-line fish recognition and on-line tracking using video data is employed. Tracking and identifying such small and fast-moving objects is a challenge, and this paper seeks to address this using a behavioural analysis approach. Utilising single high resolution camera and two low-cost synchronised video cameras, the proposed systems captures front (face) and side (profile) pictures of each isolated fish as they swim past a given marker. The acquired images are then subject to three separate processing routes in order to satisfy three complementary but distinct objectives. Initially, fish face and profile features are extracted to aid the identification of individual fish. Then, for each fish identified, behavioural features such as the frequency and intensity of the operculum beat rate or breathing cycle are quantified in order to assess aspects of the fish welfare. Additionally, the volume of each fish is estimated based on its profile dimensions, enabling the weight of the fish to be monitored throughout its lifetime. This paper presents preliminary experimental considerations and findings of this on-going research project. Results to date have been both encouraging and promising, validating the approach and the experimental configuration adopted.
{"title":"Towards automated monitoring of adult zebrafish","authors":"Q. Al-Jubouri, W. Al-Nuaimy, Hamzah S. AlZu'bi, O. Zahran, Jonathan Buckley","doi":"10.1109/UKCI.2014.6930186","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930186","url":null,"abstract":"Over the last two decades, zebrafish (Danio rerio) have emerged as an efficient model to aid in the research of a broad range of human diseases as well as such diverse applications as environmental modelling and drug discovery. Economically, the large number, low price and low maintenance requirements of this fish species encouraged its use for research. In addition to this, the study of zebrafish is being used to improve the understanding of fish physiology, with implications for fish welfare. In order to thoroughly model the behaviour, development and growth of these fish, it is important to be able to scrutinise the characteristics of individual fish as they respond to a range of stimuli, and to this end off-line fish recognition and on-line tracking using video data is employed. Tracking and identifying such small and fast-moving objects is a challenge, and this paper seeks to address this using a behavioural analysis approach. Utilising single high resolution camera and two low-cost synchronised video cameras, the proposed systems captures front (face) and side (profile) pictures of each isolated fish as they swim past a given marker. The acquired images are then subject to three separate processing routes in order to satisfy three complementary but distinct objectives. Initially, fish face and profile features are extracted to aid the identification of individual fish. Then, for each fish identified, behavioural features such as the frequency and intensity of the operculum beat rate or breathing cycle are quantified in order to assess aspects of the fish welfare. Additionally, the volume of each fish is estimated based on its profile dimensions, enabling the weight of the fish to be monitored throughout its lifetime. This paper presents preliminary experimental considerations and findings of this on-going research project. Results to date have been both encouraging and promising, validating the approach and the experimental configuration adopted.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133516161","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930166
Fangyi Li, Q. Shen, Ying Li, Neil MacParthaláin
The recognition and classification of handwritten Chinese characters poses a significant challenge for automated methods. Indeed the sheer number of characters, intricate complexity of such characters, and variations in writing styles mean that the task can be difficult even for humans. Previous work in this area has focused upon methods which perform a certain form of feature extraction and segmentation as the basis for building systems to perform this task. This paper proposes two approaches for handwritten Chinese character recognition and classification using an image alignment technique based on a fuzzy-entropy metric. Rather than extracting features from the image, which can often result in subjective and poorly-fitting models, the proposed methods instead uses the mean image transformations of the training phase as a basis for building models. The use of a fuzzy-entropy based metric also means improved ability to model different types of uncertainty. The mean image transformations are then collated, and used as training data to classify the images of test characters. A nearest-neighbour classifier based on Euclidean distance is then used to classify each test character. The approaches are applied to a publicly available real-world database of handwritten Chinese characters and demonstrate that they can achieve high classification accuracy.
{"title":"A fuzzy image congealing-based handwritten Chinese character recognition and classification system","authors":"Fangyi Li, Q. Shen, Ying Li, Neil MacParthaláin","doi":"10.1109/UKCI.2014.6930166","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930166","url":null,"abstract":"The recognition and classification of handwritten Chinese characters poses a significant challenge for automated methods. Indeed the sheer number of characters, intricate complexity of such characters, and variations in writing styles mean that the task can be difficult even for humans. Previous work in this area has focused upon methods which perform a certain form of feature extraction and segmentation as the basis for building systems to perform this task. This paper proposes two approaches for handwritten Chinese character recognition and classification using an image alignment technique based on a fuzzy-entropy metric. Rather than extracting features from the image, which can often result in subjective and poorly-fitting models, the proposed methods instead uses the mean image transformations of the training phase as a basis for building models. The use of a fuzzy-entropy based metric also means improved ability to model different types of uncertainty. The mean image transformations are then collated, and used as training data to classify the images of test characters. A nearest-neighbour classifier based on Euclidean distance is then used to classify each test character. The approaches are applied to a publicly available real-world database of handwritten Chinese characters and demonstrate that they can achieve high classification accuracy.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738848","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930183
Sunday Iliya, E. Goodyer, M. Gongora, J. Shell, J. Gow
Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN's through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach.
{"title":"Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks","authors":"Sunday Iliya, E. Goodyer, M. Gongora, J. Shell, J. Gow","doi":"10.1109/UKCI.2014.6930183","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930183","url":null,"abstract":"Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN's through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831000","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930187
Dazhen Lin, Donglin Cao, Hualin Zeng
Motion state change object detection, such as stopped objects detection, is one of important topics in Video Surveillance Systems. Generally, backgrounds in the most Video Surveillance Systems have the property of pureness and self-similarity. In this paper, we propose a block background context based background model to solve the motion state change problem. Unlike the classical background model, our approach first models blocks of background, and then determines the learning rate of each block background model by using the block background context information. There are two main advantages. First, the model adaptively selects the learning rate for each block of background model, and that is more flexible than the adaptive learning rate for the whole background. Second, context information helps the determination of true foreground and brings in more reliable information in foreground detection. Our experiments results show that our model outperforms the higher and lower learning rate Gaussian mixture background model in motion state change object detection.
{"title":"Improving motion state change object detection by using block background context","authors":"Dazhen Lin, Donglin Cao, Hualin Zeng","doi":"10.1109/UKCI.2014.6930187","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930187","url":null,"abstract":"Motion state change object detection, such as stopped objects detection, is one of important topics in Video Surveillance Systems. Generally, backgrounds in the most Video Surveillance Systems have the property of pureness and self-similarity. In this paper, we propose a block background context based background model to solve the motion state change problem. Unlike the classical background model, our approach first models blocks of background, and then determines the learning rate of each block background model by using the block background context information. There are two main advantages. First, the model adaptively selects the learning rate for each block of background model, and that is more flexible than the adaptive learning rate for the whole background. Second, context information helps the determination of true foreground and brings in more reliable information in foreground detection. Our experiments results show that our model outperforms the higher and lower learning rate Gaussian mixture background model in motion state change object detection.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127461882","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 : 2014-09-10DOI: 10.1109/UKCI.2014.6930190
Pamela A. Hardaker, Benjamin N. Passow, D. Elizondo
For as long as people have been able to survive limb threatening injuries prostheses have been created. Modern lower limb prostheses are primarily controlled by adjusting the amount of damping in the knee to bend in a suitable manner for walking and running. Often the choice of walking state or running state has to be controlled manually by pressing a button. This paper examines how this control could be improved using sensors attached tofa the limbs of two volunteers. The signals from the sensors had features extracted which were passed through a computational intelligence system. The system was used to determine whether the volunteer was walking or running and their movement speed. Two new features are presented which identify the movement states of standing, walking and running and the movement speed of the volunteer. The results suggest that the control of the prosthetic limb could be improved.
{"title":"Multiple sensor outputs and computational intelligence towards estimating state and speed for control of lower limb prostheses","authors":"Pamela A. Hardaker, Benjamin N. Passow, D. Elizondo","doi":"10.1109/UKCI.2014.6930190","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930190","url":null,"abstract":"For as long as people have been able to survive limb threatening injuries prostheses have been created. Modern lower limb prostheses are primarily controlled by adjusting the amount of damping in the knee to bend in a suitable manner for walking and running. Often the choice of walking state or running state has to be controlled manually by pressing a button. This paper examines how this control could be improved using sensors attached tofa the limbs of two volunteers. The signals from the sensors had features extracted which were passed through a computational intelligence system. The system was used to determine whether the volunteer was walking or running and their movement speed. Two new features are presented which identify the movement states of standing, walking and running and the movement speed of the volunteer. The results suggest that the control of the prosthetic limb could be improved.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133122616","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 : 2014-09-01DOI: 10.1109/UKCI.2014.6930192
M. Zaka, Yonghong Peng, C. Sutton
Triple-negative breast cancers (TNBC) are clinically heterogeneous, an aggressive form of breast cancer with poor diagnosis and highly therapeutic resistant. It is urgently needed for identifying novel biomarkers with increased sensitivity and specificity for early detection and personalised therapeutic intervention. Microarray profiling offered significant advances in molecular classification but sample scarcity and cohort heterogeneity remains challenging areas. Here, we investigated diagnostics signatures derived from human triple-negative tissue. We applied REMARK criteria for the selection of relevant studies and compared the signatures gene lists directly as well as assessed their classification performance in predicting diagnosis using leave-one-out cross-validation. The cross-validation results shows excellent classification accuracy ratios using all data sets. A subset signature (17-gene) extracted from the convergence of eligible signatures have also achieved excellent classification accuracy of 89.37% across all data sets. We also applied gene ontology functional enrichment analysis to extract potentially biological process, pathways and network involved in TNBC disease progression. Through functional analysis, we recognized that these independent signatures have displayed commonalities in functional pathways of cell signaling, which play important role in the development and progression of TNBC. We have also identified five unique TNBC pathways genes (SYNCRIP, NFIB, RGS4, UGCG, LOX and NNMT), which could be important for therapeutic interventions as indicated by their close association with known drivers of TNBC and previously published experimental studies.
{"title":"Integrated microarray analytics for the discovery of gene signatures for triple-negative breast cancer","authors":"M. Zaka, Yonghong Peng, C. Sutton","doi":"10.1109/UKCI.2014.6930192","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930192","url":null,"abstract":"Triple-negative breast cancers (TNBC) are clinically heterogeneous, an aggressive form of breast cancer with poor diagnosis and highly therapeutic resistant. It is urgently needed for identifying novel biomarkers with increased sensitivity and specificity for early detection and personalised therapeutic intervention. Microarray profiling offered significant advances in molecular classification but sample scarcity and cohort heterogeneity remains challenging areas. Here, we investigated diagnostics signatures derived from human triple-negative tissue. We applied REMARK criteria for the selection of relevant studies and compared the signatures gene lists directly as well as assessed their classification performance in predicting diagnosis using leave-one-out cross-validation. The cross-validation results shows excellent classification accuracy ratios using all data sets. A subset signature (17-gene) extracted from the convergence of eligible signatures have also achieved excellent classification accuracy of 89.37% across all data sets. We also applied gene ontology functional enrichment analysis to extract potentially biological process, pathways and network involved in TNBC disease progression. Through functional analysis, we recognized that these independent signatures have displayed commonalities in functional pathways of cell signaling, which play important role in the development and progression of TNBC. We have also identified five unique TNBC pathways genes (SYNCRIP, NFIB, RGS4, UGCG, LOX and NNMT), which could be important for therapeutic interventions as indicated by their close association with known drivers of TNBC and previously published experimental studies.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124900192","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 : 2014-09-01DOI: 10.1109/UKCI.2014.6930162
Tomasz D. Sikora, G. D. Magoulas
The increased interest in autonomous control in Application Service Management-ASM environments has driven the demand for analysis of multivariate datasets in this area. Gathered metrics form time-series that can be considered as signals, which should be decomposed in order to find relations between system utilization and effective activity. This paper introduces a metrics signal deconvolution method that can be used to support human administrators or can be incorporated into feature extraction schemes that feed decision blocks of autonomous controllers. The method considers ASM environments signals decomposition as a search problem that is solved using heuristics and metaheuristic strategies. Quantitative and qualitative relations between activity and system resources signals are searched with use of a model that is based on similarity and variability of the changes, under minimal assumptions about the ASM system architecture and design. Experimental results show that the model can be successfully integrated with optimization techniques and the results produced when tested using data produced through queue modeling meet human perception of the signal unmixing problem.
{"title":"Search-guided activity signals extraction in application service management control","authors":"Tomasz D. Sikora, G. D. Magoulas","doi":"10.1109/UKCI.2014.6930162","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930162","url":null,"abstract":"The increased interest in autonomous control in Application Service Management-ASM environments has driven the demand for analysis of multivariate datasets in this area. Gathered metrics form time-series that can be considered as signals, which should be decomposed in order to find relations between system utilization and effective activity. This paper introduces a metrics signal deconvolution method that can be used to support human administrators or can be incorporated into feature extraction schemes that feed decision blocks of autonomous controllers. The method considers ASM environments signals decomposition as a search problem that is solved using heuristics and metaheuristic strategies. Quantitative and qualitative relations between activity and system resources signals are searched with use of a model that is based on similarity and variability of the changes, under minimal assumptions about the ASM system architecture and design. Experimental results show that the model can be successfully integrated with optimization techniques and the results produced when tested using data produced through queue modeling meet human perception of the signal unmixing problem.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114782820","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 : 2014-09-01DOI: 10.1109/UKCI.2014.6930185
Michael Milliken, Y. Bi, L. Galway
As Network Intrusions have become larger and more pervasive the methods of detection have changed, a number of systems use ensemble methods to improve upon results from single classifiers or algorithms. The solutions proposed in the literature achieve good results, which primarily focus on classification of Network Intrusions by tailoring classification algorithms and feature selection. However fewer studies focus on investigation of relation between pairs of attributes, such as IP address and Port, as a single attribute. This paper proposes an effect analysis of pairs of attributes in order to improve intrusion detection using an ensemble-based classification approach.
{"title":"The effect of attribute pairings in intrusion detection","authors":"Michael Milliken, Y. Bi, L. Galway","doi":"10.1109/UKCI.2014.6930185","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930185","url":null,"abstract":"As Network Intrusions have become larger and more pervasive the methods of detection have changed, a number of systems use ensemble methods to improve upon results from single classifiers or algorithms. The solutions proposed in the literature achieve good results, which primarily focus on classification of Network Intrusions by tailoring classification algorithms and feature selection. However fewer studies focus on investigation of relation between pairs of attributes, such as IP address and Port, as a single attribute. This paper proposes an effect analysis of pairs of attributes in order to improve intrusion detection using an ensemble-based classification approach.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115906888","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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