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Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

  • Authors: Barhate, Rahul , Kulkarni, Pradnya
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
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  • Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
  • Description: E1

Apportioning allocations to users of multi-storage water supply systems : A case study of making a complex volume shared system more transparent

  • Authors: Barton, Andrew , Wilson, Kym
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2018 Hydrology and Water Resources Symposium: Water and Communities, HWRS 2018; Melbourne, Australia; 3rd-6th December 2018 p. 60-71
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  • Description: This paper describes principles for the apportionment of water allocations to users of a multi-reservoir water supply system utilising a volume shared entitlement and allocation framework. The challenge of this problem is that volume shared systems determine the available water for allocation based on a total system approach. The subsequent operational challenge is to then apportion this total volume of available water to specific reservoirs to meet individual user requirements. This is an important problem as entitlement and allocation frameworks usually have the water resource assessment process and high-level water sharing principles enshrined in a set of legally binding orders and instruments. However, some systems still have a subsequent apportionment of allocation problem, not codified in any binding document, where decisions need to be made around how much allocation should be made available from particular reservoirs for the various stakeholders or user groups. In shared systems where contests over water is common, or access to allocation may vary over time, it is desirable that the agency responsible for making the resource decisions uses an objective, fair and equitable method of allocating water. To work through this problem and present the set of principles for apportionment, the Wimmera-Glenelg System located in western Victoria, Australia, is used as a case study. The Wimmera-Glenelg System is a complex water resource system with multiple reservoirs and many different user groups and stakeholders. The region is also subject to a highly variable climate with frequent dry periods and water rationing, creating periods of time where the equitable apportionment of allocation becomes incredibly important. Concepts of capacity sharing have been used to help with the development of the apportionment principles to help maximise the transparency in decision making to stakeholders and because the system does have an emerging water market where commercial and economic certainty is becoming paramount. However, capacity sharing for systems with multiple reservoirs is not common, and so even this has limitations in use. The principles described can be universally applied to reservoir systems of varying complexity, where there are multiple users, and is compatible with both capacity shared systems and newer continuous sharing or continuous accounting systems. Results are shown for the Wimmera-Glenelg System. © CURRAN-CONFERENCE. All rights reserved.

Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble

Carbon negative platform chemicals from waste using enhanced geothermal systems

Characterisation of permanent deformation behaviour of unbound granular materials using repeated load triaxial testing

  • Authors: Zhalehjoo, Negin , Tolooiyan, Ali , Mackay, Rae , Bodin, Didier
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields, BCRRA 2017; Athens, Greece; 28th-30th June 2017; published in Bearing Capacity of Roads, Railways and Airfields p. 159-166
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  • Description: Unbound Granular Material (UGM) used in the base/subbase layers of a flexible pavement structure constitutes the vast majority of the material found in roads around the world. The permanent deformation of a compacted UGM layer due to cyclic deviatoric loading has a significant effect on the performance of the pavement structure. The accurate prediction of the magnitude of accumulated permanent strain at varying load cycles and stress levels plays an important role in improving the design and maintenance of flexible pavements. In this study, samples of two road base UGMs are tested to evaluate the characteristics of permanent deformation using the laboratory Repeated Load Triaxial (RLT) test. Three permanent deformation models are used to predict the magnitude of strain accumulation of the studied UGMs. The permanent strain results predicted by the models are compared against those measured by laboratory RLT tests to evaluate the prediction ability of each model.
  • Description: Bearing Capacity of Roads, Railways and Airfields - Proceedings of the 10th International Conference on the Bearing Capacity of Roads, Railways and Airfields, BCRRA 2017

Classifier-free extraction of power line wires from point cloud data

  • Authors: Awrangjeb, Mohammad , Gao, Yongsheng , Lu, Guojun
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
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  • Description: This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Comparison of pixel N-Grams with histogram, Haralick's features and bag-of-visual-words for texture image classification

  • Authors: Kulkarni, Pradnya , Stranieri, Andrew
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: IEEE 3rd International Conference on Convergence in Technology: Pune, India ; April 6th-8th, 2018 p. 1-4
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  • Description: Texture image classification is very useful in many domains. It has been tried using statistical, spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram, Haralick’s features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore, Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas, they were comparable with the BoVW approach.

Cuboid colour image segmentation using intuitive distance measure

Cyclically strained grade 800 and 1200 steel tube materials

  • Authors: Javidan, Fatemeh , Heidarpour, Amin , Zhao, Xiao-Ling , Fallahi, Hossein
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: Tubular Structures XVI : Proceedings of the 16th International Symposium for Tubular Structures; Melbourne; 4th-6th Dec, 2017 p. 349-355
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  • Description: Application of high tensile steel in various structural forms such as rolled or fabricated sections is currently increasing due its mechanical and economic advantages. Experimental investigations show the distinct plastic behavior of high tensile steel with grades greater than 700 MPa damaged under reversed tension-compression loading scenarios compared to the hardening performance of lower grades of steel. This paper investigates a combined nonlinear plastic model to predict the stress-strain equations of grades 800 and 1200 steel extracted from circular tubes under very low cycle structural damage. In the numerical modelling phase, relevant parameters are calibrated and model is verified against hysteretic experimental results. The numerical results provide a simulation tool for structures consisting of high tensile steel tubes under seismic loads.

Data analytics to select markers and cut-off values for clinical scoring

  • Authors: Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi , Jelinek, Herbert
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference; Brisbane; 29th January -2nd February 2018 p. 1-6
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  • Description: Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.

Data exchange in delay tolerant networks using joint inter- and intra-flow network coding

  • Authors: Ostovari, Pouya , Wu, Jie , Jolfaei, Alireza
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 37th IEEE International Performance Computing and Communications Conference, IPCCC 2018; Orlando, United States; 17th-19th November 2018 p. 1-8
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  • Description: Data transmission in delay tolerant networks (DTNs) is a challenging problem due to the lack of continuous network connectivity and nondeterministic mobility of the nodes. Epidemic routing and spray-and-wait methods are two popular mechanisms that are proposed for DTNs. In order to reduce the transmission delay in DTNs, some previous works combine intra-flow network coding with the routing protocols. In this paper, we propose two routing mechanisms using systematic joint inter- and intra-flow network coding for the purpose of data exchange between the nodes. We discuss the reasons why inter-flow network coding helps to reduce the delivery delay of the packets, and we also analyze the delays related with only using intra-flow coding, and joint inter- and intra-flow coding methods. We empirically show the benefit of joint coding over just intra-flow coding. Based on our simulation, joint coding can reduce the delay up to 40%, compared to only intra-flow coding.
  • Description: 2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018

Decision support tools for preventive maintenance intervals and replacement decisions of engineering assets

Deep user modeling for content-based event recommendation in event-based social networks

  • Authors: Wang, Zhibo , Zhang, Yongquan , Chen, Honglong , Li, Zhetao , Xia, Feng
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: p. 1304-1312
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  • Description: Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, Meetup.com, and the experimental results show that DUMER outperforms the compared benchmarks.

Detecting intrusion in the traffic signals of an intelligent traffic system

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Saha, Tapash
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 20th International Conference on Information and Communications Security, ICICS 2018; Lille, France; 29th-31st October 2018; published in Lecure Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11149 LNCS, p. 696-707
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  • Description: Traffic systems and signals are used to improve traffic flow, reduce congestion, increase travel time consistency and ensure safety of road users. Malicious interruption or manipulation of traffic signals may cause disastrous instants including huge delays, financial loss and loss of lives. Intrusion into traffic signals by hackers can create such interruption whose consequences will only increase with the introduction of driverless vehicles. Recently, many traffic signals across the world are reported to have intruded, highlighting the importance of accurate detection. To reduce the impact of an intrusion, in this paper, we introduce an intrusion detection technique using the flow rate and phase time of a traffic signal as evidential information to detect the presence of an intrusion. The information received from flow rate and phase time are fused with the Dempster Shaffer (DS) theory. Historical data are used to create the probability mass functions for both flow rate and phase time. We also developed a simulation model using a traffic simulator, namely SUMO for many types of real traffic situations including intrusion. The performance of the proposed Intrusion Detection System (IDS) is appraised with normal traffic condition and induced intrusions. Simulated results show our proposed system can successfully detect intruded traffic signals from normal signals with significantly high accuracy (above 91%).
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Detecting splicing and copy-move attacks in color images

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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  • Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Detection of four-node motif in complex networks

  • Authors: Ning, Zhaolong , Liu, Lei , Yu, Shuo , Xia, Feng
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: Complex Networks & Their Applications VI; Lyon, France; November 29th-1st December, 2017 p. 453-462
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  • Description: Complex network analysis has gained research interests in a wide range of fields. Network motif, which is one of the most popular network properties, is a statistically significant network subgraph. In this paper, we propose a fast methodology, called Four-node Motif Detection Algorithm (FMDA), to extract four-node motifs in complex networks. Specifically, we employ a two-way spectral clustering method to cut big networks into small sub-graphs, and then identify motifs by recognition algorithm to reduce the computational complexity. After that, we use three isomorphic four-node motifs to analyze network structure by American Physical Society (APS) data set.

Enabling intelligent business processes with context awareness

  • Authors: Zhao, Xiaohui , Yongchareon, Sira , Cho, Namwook , Shen, Jun , Dewan, Saif
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 IEEE International Conference on Services Computing (SCC); San Francisco, CA, USA; 02-07 July 2018 p. 153-160
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  • Description: Sensing technologies provide system applications with the awareness of environmental conditions, customer behaviours, object movements, etc. Further, with such capability, system applications can be smart to intelligently adapt their responses to the changing conditions. With regard to business operations, these system applications ensure that business processes can run more intelligently and adaptively. These features will undoubtedly improve customer experience, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. To enable context awareness to business process management, this paper proposes a conceptual method of depicting the context of a business process and the related mechanism of perceiving the contextual dynamics. A running example demonstrates the applicability of the proposed method and the improvements to process performance are evaluated using process simulations.

Energy-efficient design for downlink cloud radio access networks

  • Authors: Vu, Tung , Ngo, Duy , Dao, Minh , Durrani, Salman , Nguyen, Duy , Middleton, Richard
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 IEEE International Conference on Communications (ICC); Kansas City, MO, USA; 20-24 May 2018 p. 1-6
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  • Description: This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.

Enhanced colour image retrieval with cuboid segmentation

  • Authors: Murshed, Manzur , Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
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  • Description: In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Enhancing the effectiveness of local descriptor based image matching

  • Authors: Hossain, Md Tahmid , Teng, Shyh , Zhang, Dengsheng , Lim, Suryani , Lu, Guojun
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-8
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  • Description: Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018