Xia, H, Zhuge, R, Li, H, Song, S, Jiang, F & Xu, M 2018, 'Single image rain removal via a simplified residual dense network', IEEE Access, vol. 6, pp. 66522-66535.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. The single-image rain removal problem has attracted tremendous interests within the deep learning domains. Although deep learning based de-raining methods outperform many conventional methods, there are still unresolved issues in regards to improving the performance. In this paper, we propose a simplified residual dense network (SRDN) to improve the de-raining performance and cut down the computation time. Inspired by the image processing domain knowledge that a rainy image can be decomposed into a base (low-pass) layer and a detail (high-pass) layer, we train our network by directly learning the residual between the detail layer of rainy images and the detail layer of clean images. It can both significantly reduce the mapping range from input to output and easily employ the image enhancement operation to handle the heavy rain with hazy looks. Instead of designing a deeper network structure to increase the learning ability of network, we propose a simplified dense block to explore more effective information between layers and, hence, reduce the computation time of network. Experiments on both synthetic and real-world images demonstrate that our SRDN network can achieve competitive results in comparison with the benchmarked and conventional approaches for single-image rain removal.
Yu, D, Chen, N, Jiang, F, Fu, B & Qin, A 2017, 'Constrained NMF-based semi-supervised learning for social media spammer detection', Knowledge-Based Systems, vol. 125, pp. 64-73.View/Download from: UTS OPUS or Publisher's site
© 2017 Elsevier B.V.Within the past few years, social media platforms such as Facebook, Twitter, and Sina Weibo, have gradually become important channels for information dissemination and communication. However, in the meantime, these platforms are prone to be potentially attacked by spammers, who usually propagate disgusted information such as phishing URLs, false news, and even pornography to other users. Despite rapid increase of social media spammers, the traditional spammer detection methods become less effective. In this paper, we present a novel semi-supervised social media spammer detection approach, making full use of the message content and user behavior as well as the social relation information. First, we adapt the original constrained NMF-based semi-supervised learning (CNMF) algorithm, nonnegative matrix factorization (NMF) by imposing a label information constrain and sparseness constrain. Second, we present a novel CNMF-based integral framework for social media spammer detection by implementing the collaborative factorization on the message content matrix and the user behavior and social relation information matrix. Moreover, we explore the iterative update rule (IUR) and optimization algorithm for the spammer detection model. In addition, its corresponding convergence is also proven. Extensive experiments are conducted on the real-world dataset from Sina Weibo, the experiment results demonstrate that our proposed model performs significantly better than the conventionally applied supervised classifiers for the spammer detection.
Ling, SH, Chan, KY, Leung, FHF, Jiang, F & Nguyen, H 2016, 'Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization', Engineering Applications of Artificial Intelligence, vol. 47, pp. 68-80.View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier Ltd. This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.
Chaczko, ZC, Aslanzadeh, S, Jiang, F & Klempous, R 2013, 'The Implementation of 3TZ Model of Software Development', Kwartalnik Elektroniki i Telekomunikacji - Electronics and Telecommunications Quarterly, vol. 58, no. 4, pp. 433-439.View/Download from: UTS OPUS or Publisher's site
This paper presents the concepts and explores issues related to the 3 Time Zones (3TZ) model of software development in global workspace environment. The 3TZ model itself seeks to take advantages of differences in time zones between places around the world. By engaging software development teams in different regions separated by 8 hours each, it is possible for their combined working hours to cover the whole 24 hours period. Thus, while they each work their normal 8 hour days, together they are able to achieve in 1 day what a single team would achieve in 3 days. They are able to achieve this by passing on their work from one team to the next as one finishes their workday and the next team starts their workday. The 3TZ model of software development revolves around the employment of a software development team distributed in at least 3 different locations around the world in 3 different time zones. If work was passed on from one team to the next and adjacent teams were separated by 8 hours, then 24 hours continuous collaborative software development could be achieved.
Jiang, F, Dong, D, Cao, L & Frater, MR 2013, 'Agent-Based Self-Adaptable Context-Aware Network Vulnerability Assessment', IEEE Transactions on Network and Service Management, vol. 10, no. 3, pp. 255-270.View/Download from: UTS OPUS or Publisher's site
Immunology inspired computer security has attracted enormous attention as its potential impacts on the next generation service-oriented network operation system. In this paper, we propose a new agent-based threat awareness assessment strategy inspired by the human immune system to dynamically adapt against attacks. Specifically, this approach is based on the dynamic reconfiguration of the file access right for system calls or logs (e.g., file rewritability) with balanced adaptability and vulnerability. Based on an information-theoretic analysis on the coherently associations of adaptability, autonomy as well as vulnerability, a generic solution is suggested to break down their coherent links. The principle is to maximize context-situation awared systems' adaptability and reduce systems' vulnerability simultaneously. Experimental results show the efficiency of the proposed biological behaviour-inspired vulnerability awareness system.
Ling, SS, Jiang, F, Nguyen, HT & Chan, KY 2011, 'Hybrid Fuzzy Logic-Based Particle Swarm Optimization For Flow Shop Scheduling Problem', International Journal of Computational Intelligence and Applications, vol. 10, no. 3, pp. 335-356.View/Download from: UTS OPUS or Publisher's site
This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.
Hao, L, Ling, SH & Jiang, F 2018, 'Classification of Cardiovascular Disease via A New SoftMax Model.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Honolulu, USA, pp. 486-489.View/Download from: UTS OPUS or Publisher's site
Cardiovascular disease clinical diagnosis is an essentially problem of pattern recognition. In the traditional intelligent diagnosis, the evaluation of classification algorithm is based on the final accuracy of the disease diagnosis. In this paper, a new classification method called Softmax regression model is proposed and it uses the known state data of two-layer neural network structure of the Softmax regression model for training and learning, and then calculate the probability of reclassification data belonging to each category. These categories are corresponding to the maximum probability and the classification result of the data to be classified. It provides a new method for classification of disease with higher speed and higher accuracy. Experiment is designed to compare with the K-nearest neighbours and BP neural networks, and also verify the classification accuracy of Softmax regression model. ECG data from MIT-BIH open database is considered for the experiment. The correct classification rate of the diagnosis reaches 94.44% which outperforms than K- nearest neighbor method (77.78%) and BP neural network (72.27%) in regards to the detection of the Cardiovascular disease.
Xia, H, Zhao, W, Zhou, Z, Jiang, F, Li, H & He, XS 2017, 'Deformable Template Matching Using Proposal-Based Best-Buddies Similarity', 2017 IEEE Trustcom/BigDataSE/ICESS, IEEE International Conference On Trust, Security And Privacy In Computing And Communications, IEEE, Sydney, pp. 517-521.View/Download from: UTS OPUS or Publisher's site
We propose a new method for template matching based on the Best-Buddies Similarity (BBS) measure. Our method is able to match objects with large difference in size and hence achieves a deformable template matching. In addition, compared with the original method for template matching based on the BBS, our method significantly cuts down on the computation time. The fast and deformable template matching is implemented by measuring the BBS of only potential areas instead of all positions in an image. The potential areas, which can have different size from the given template, are found by a proposal generation based on edge priors and a selective search among the obtained proposals. The results from the experiments conducted on a challenging dataset demonstrate that our method out-performs the state-of-the-art methods in terms of accuracy.
Jiang, F, Tang, M & Tran, QA 2016, 'User preference-based spamming detection with coupled behavioral analysis', Security, Privacy, and Anonymity in Computation, Communication, and Storage (LNCS), International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, Springer, Zhangjiajie, China, pp. 466-477.View/Download from: Publisher's site
© Springer International Publishing AG 2016.Nowadays, the explosive growth of unsolicited emails on Internet has been challenging the spam filtering systems when at the presence of big data. Current spam filters suffer from the following problems: (1) Not personalised; (2) Comparatively static association rules defined in the firewalls, or gateways; (3) Cannot identify the extremely hidden information that mixed in the syntax or semantics. To overcome these problems, we develop and implement a new email spamming system leveraged by coupled text similarity analysis on user preference and a virtual meta-layer user-based email network, we take the social networks or campus LANs as the spam social network scenario. Fewer current practices exploit social networking initiatives to assist in spam filtering. Social network has essentially a large number of accounts features to be considered. We construct a new model called meta-layer email network which can reduce these features by only considering individual user's actions i.e., replying network, reading network and deleting network. For the first time, these common user actions are considered to construct a social behavior-based email network. Further, a coupled selection model is developed for this email network, we are able to consider all relevant factors/features in a whole and recommend the emails practically to the user individually. The experiment data comes from the Enron email dataset, which has been recognized as a representative dataset for testing and validation. The experimental results show the new approach can achieve higher precision and accuracy with better email ranking in favor of personalised preference.
Jiang, F, Xia, H, Jin, K & Tran, QA 2016, 'A novel method of cervical cell image segmentation via region merging and SLIC', International Symposium on Information and Communication Technology, Ho Chi Min, 08 Dec 2016 - 09 Dec 2016. 31 Dec 2016, International Symposium on Information and Communication Technology (ISICT), ACM, Ho Chi Min.View/Download from: UTS OPUS or Publisher's site
Xia, H, Deng, S, Li, M & Jiang, F 2016, 'Robust retinal vessel segmentation via clustering-based patch mapping functions', Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, IEEE International Conference on Bioinformatics and Biomedicine, IEEE, China, pp. 520-523.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.Robust vessel segmentation of fundus images is of great interest for better diagnosis of many diseases like diabetic retinopathy, retinopathy of prematurity, vein occlusions and so on. In this paper, we propose a novel example-based vessel segmentation method, based on learning the mapping relationship between fundus images and their corresponding ground truths. Firstly, the training images and their corresponding ground truths are divided into patches and clustered. Secondly, the mapping functions for each cluster are computed in a simple and efficient way from the training patches to their manual segmentation patches. Finally, Vessel segmentation are reconstructed by the simple mapping functions. Experimental results show that our method is efficient and can achieve competitive performance for vessel segmentation problems.
Chinchore, A, Xu, G & Jiang, F 2016, 'Classifying sybil in MSNs using C4.5', Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, International Conference on Behavioral, Economic, Socio - Cultural Computing (BESC), IEEE, Durham, USA.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.Sybil detection is an important task in cyber security research. Over past years, many data mining algorithms have been adopted to fulfill such task. Using classification and regression for sybil detection is a very challenging task. Despite of existing research made toward modeling classification for sybil detection and prediction, this research has proposed new solution on how sybil activity could be tracked to address this challenging issue. Prediction of sybil behaviour has been demonstrated by analysing the graph-based classification and regression techniques, using decision trees and described dependencies across different methods. Calculated gain and maxGain helped to trace some sybil users in the datasets.
Jiang, F, Gan, J, Xu, Y & Xu, G 2016, 'Coupled behavioral analysis for user preference-based email spamming', Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, International Conference on Behavioral, Economic, Socio - Cultural Computing (BESC), IEEE, Durham, NC, pp. 1-5.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.In this paper, we develop and implement a new email spamming system leveraged by coupled text similarity analysis on user preference and a virtual meta-layer user-based email network, we take the social networks or campus LAN networks as the spam social network scenario. Fewer current practices exploit social networking initiatives to assist in spam filtering. Social network has essentially a large number of accounts features and attributes to be considered. Instead of considering large amount of users accounts features, we construct a new model called meta-layer email network which can reduce these features by only considering individual user's actions as an indicator of user preference, these common user actions are considered to construct a social behavior-based email network. With the further analytic results from text similarity measurements for each individual email contents, the behavior-based virtual email network can be improved with much higher accuracy on user preferences. Further, a coupled selection model is developed for this email network, we are able to consider all relevant factors/features in a whole and recommend the emails practically to the user individually. The experimental results show the new approach can achieve higher precision and accuracy with better email ranking in favor of personalised preference.
Chinchore, A, Jiang, F & Xu, G 2015, 'Intelligent Sybil attack detection on abnormal connectivity behavior in mobile social networks', Knowledge Management in Organizations - Lecture Notes in Business Information Processing, Knowledge Management in Organizations, Springer, Maribor, Slovenia, pp. 602-617.View/Download from: UTS OPUS or Publisher's site
© Springer International Publishing Switzerland 2015. There have been a large number of researches on mobile networks in the literature, focusing on a variety of secured applications over the network, including the use of their connections, fake identification and attacks on social group. These applications are created for the intention to collect confidential information, money laundering, blackmailing and to perform other crime activity. The purpose of this research is to identify the behavior of the honest node (network account) and fake node (network account) on mobile social network. In this research, the behavior survey of these nodes is carried out and further analysed with the help of graph-based Sybil detection system. This paper particularly studies Sybil attacks and its defense system for IoT (Internet-of-Things) environment. To be implied, the identification of each forged Sybil node is to be tracked on the basis of nodes connectivity and their timing of connectivity as well as frequency among each other. Sybil node has a forged identity in different locations and also reports its virtual location information to servers.
Liu, W, Deng, Z, Gong, X, Jiang, F & Tsang, W 2015, 'Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network', Online proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAI, Austin, Texas, pp. 210-216.View/Download from: UTS OPUS
Effective forecasting of future prevalent topics plays animportant role in social network business development.It involves two challenging aspects: predicting whethera topic will become prevalent, and when. This cannotbe directly handled by the existing algorithms in topicmodeling, item recommendation and action forecasting.The classic forecasting framework based on time seriesmodels may be able to predict a hot topic when a seriesof periodical changes to user-addressed frequency in asystematic way. However, the frequency of topics discussedby users often changes irregularly in social networks.In this paper, a generic probabilistic frameworkis proposed for hot topic prediction, and machine learningmethods are explored to predict hot topic patterns.Two effective models, PreWHether and PreWHen, areintroduced to predict whether and when a topic will becomeprevalent. In the PreWHether model, we simulatethe constructed features of previously observed frequencychanges for better prediction. In the PreWHen model,distributions of time intervals associated with the emergenceto prevalence of a topic are modeled. Extensiveexperiments on real datasets demonstrate that ourmethod outperforms the baselines and generates moreeffective predictions.
Jiang, F & Luo, D 2014, 'A new coupled metric learning for real-time anomalies detection with high-frequency field programmable gate arrays', Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, IEEE International Conference on Data Mining, IEEE, Shenzhen; China, pp. 1254-1261.View/Download from: UTS OPUS or Publisher's site
Billions of internet end-users and device to device connections contribute to the significant data growth in recent years, large scale, unstructured, heterogeneous data and the corresponding complexity present challenges to the conventional real-time online fraud detection system security. With the advent of big data era, it is expected the data analytic techniques to be much faster and more efficient than ever before. Moreover, one of the challenges with many modern algorithms is that they run too slowly in software to have any practical value. This paper proposes a Field Programmable Gate Array (FPGA) -based intrusion detection system (IDS), driven by a new coupled metric learning to discover the inter- and intra-coupling relationships against the growth of data volumes and item relationship to provide a new approach for efficient anomaly detections. This work is experimented on our previously published NetFlow-based IDS dataset, which is further processed into the categorical data for coupled metric learning purpose. The overall performance of the new hardware system has been further compared with the presence of conventional Bayesian classifier and Support Vector Machines classifier. The experimental results show the very promising performance by considering the coupled metric learning scheme in the FPGA implementation. The false alarm rate is successfully reduced down to 5% while the high detection rate (=99.9%) is maintained.
Ling, SS & Jiang, F 2014, 'Application on Self-Provisioning of Communication Network Service using Fuzzy Particle Swarm Optimization', Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on, International Conference on Control Automation Robotics & Vision, IEEE, Singapore, pp. 1245-1250.View/Download from: UTS OPUS or Publisher's site
In this paper, a self-provisioning of communication network service based on a fuzzy particle swarm optimization is proposed to minimize the configuration cost of four layer
communication network. An swarm optimization called fuzzy particle swarm optimization (FPSO) is introduced. In this FPSO, the inertia weight of PSO is adaptively determined by a set of fuzzy rule. Also, a cross-mutated operation is presented to drive the solution to escape from local optima where the control parameter of this operation is also governed by a set of fuzzy rule.
A performance comparison is given to show the performance of the proposed FPSO on the self-provisioning of communication network service and found that the performance of FPSO is significantly better than that of the existing hybrid PSO methods in a statistical sense.
Yu, PS, Cao, L, Ras, Z, Wong, L, Jiang, F & Li, J 2013, 'Preface to the 2013 international workshop on domain driven data mining', Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013.View/Download from: Publisher's site
Chan, KY, Ling, SS, Nguyen, HT & Jiang, F 2012, 'A hypoglycemic episodes diagnosis system based on neural networks for Type 1 diabetes mellitus', IEEE Congress on Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Brisbane, QLD, Australia, pp. 2046-2051.View/Download from: UTS OPUS or Publisher's site
Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients blood glucose levels based on these patients physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.
Ling, SS, Nguyen, HT, Leung, FH, Chan, KY & Jiang, F 2012, 'Intelligent fuzzy particle swarm optimization with cross-mutated operation', IEEE Congress on Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Australia, pp. 3009-3016.View/Download from: UTS OPUS or Publisher's site
This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCM method performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods
Jiang, F, Ling, SS, Chan, KY, Chaczko, ZC, Leung, FH & Frater, MR 2012, 'An immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations', 2012 IEEE International on Fuzzy Systems (FUZZ-IEEE),, IEEE International Conference on Fuzzy Systems, IEEE WCCI, Brisbane, Australia, pp. 1279-1286.View/Download from: UTS OPUS or Publisher's site
In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.
Jiang, F, Ling, SS & Agbinya, JI 2011, 'A nature inspired anomaly detection system using multiple detection engines', 2011 6th International Conference on Broadband and Biomedical Communications (IB2Com), International Conference on Broadband and Biomedical Communications, IEEE, Melbourne, Australia, pp. 200-205.View/Download from: UTS OPUS or Publisher's site
The rapid growth of computer networks presents challenges to the single detection engine based system, which has been insufficient in meeting end-users' requirements in the large-scale distributed complex network. In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed. The principle is to coordinate the results from each single-engine intrusion alert system, by seamlessly integrating with the multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviour of system calls are further examined by an advanced fuzzy-based inference process called HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and reduce the false positive rate significantly. The proposed HPSOWM especially works for the efficient classification of unknown behaviors and malicious attacks.
Jiang, F, Ling, SS & Frater, M 2011, 'A Distributed Smart Routing Scheme for Terrestrial Sensor Networks with Hybrid Neural Rough Sets', Proceedings of IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems, IEEE, Taipei, Taiwan, pp. 2238-2244.View/Download from: UTS OPUS or Publisher's site
Abstract The limited power consumption, as a major constraint, presents challenges in improving the network throughput for Wireless Sensor Networks (WSNs). Due to the limited computational power, the applications of WSNs in Terrestrial Networks require the capability to pre-process the observation data so as to remove irrelevant features or factors from multi-dimensional dataset. This paper proposes a intelligent distributed energy efficient routing algorithm inspired from natural learning and adaptation process with the aid of hybrid Neural Rough Sets theory, which is used to efficiently reduce the dimensionality of input dataset. The algorithmic implementation and experimental validation are described in this paper. Details of the algorithm and its testing procedures are presented in comparison with the other power-aware protocols, e.g., mini-hop. The validation of the proposed model is carried out via a wireless sensor network test-bed implemented in Castalia Simulator. The experimental results show the network performance measurements such as delay, throughput and packet loss that have been greatly improved as the outcome of applying this integration with Neural Rough Sets.
Ling, SS, Jiang, F, Chan, KY & Nguyen, HT 2011, 'Permutation flow shop scheduling: fuzzy particle swarm optimization approach', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference On Fuzzy Systems, IEEE, Taipei, Taiwam, pp. 572-578.View/Download from: UTS OPUS or Publisher's site
AbstractA fuzzy particle swarm optimization (PSO) for the minimization of makespan in permutation flow shop scheduling problem is presented in this paper. In the proposed fuzzy PSO, the inertia weight of PSO and the control parameter of the crossmutated operation are determined by a set of fuzzy rules. To escape the local optimum, cross-mutated operation is introduced. In order to make PSO suitable for solving permutation flow shop scheduling problem, a roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a swap-based local search for scheduling problem is designed for the local exploration on a discrete job permutation space. Flow shop benchmark functions are employed to evaluate the performance of the fuzzy PSO for flow shop scheduling problems and the results indicate that the algorithm performs better compared with existing hybrid PSO algorithms.