School of Computer Science and Informatics
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Item Metadata only 2PROM: A two-phase image retrieval optimization on dataspace using predictive modeling(IEEE, 2012) Fanzou Tchuissang, G. N.; Wang, N.; Kuicheu, N. C.; Siewe, Francois; Xu, D.Item Embargo 3D Object Reconstruction with Deep Learning(Springer, 2024-05-06) Aremu, Stephen S.; Taherkhani, Aboozar; Liu, Chang; Yang, ShengxiangRecent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh re-construction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score.Item Open Access A dynamic preference-driven evolutionary algorithm for solving dynamic multi-objective problems(ACM, 2024-07-01) Wang, Xueqing; Zheng, Jinhua; Zou, Juan; Hou, Zhanglu; Liu, Yuan; Yang, ShengxiangConsidering the decision-maker's preference information in static multi-objective optimization problems (MOPs) has been extensively studied. However, incorporating dynamic preference information into dynamic MOPs is a relatively less explored area. This paper introduces a preference information-driven DMOEA and proposes a preference-based prediction method. Specifically, a preference-based inverse model is designed to respond to the time-varying preference information, and the model is used to predict an initial population for tracking the changing ROI. Furthermore, a hybrid prediction strategy, that combines a linear prediction model and estimation of population manifolds in the ROI, is proposed to ensure convergence and distribution of population when the preference remain constant. The experimental results show that the proposed algorithm has significant advantages over existing representative DMOEAs through experimental tests on 19 common test problems.Item Metadata only A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops(Springer Nature, 2023-04-09) Pena, Alejandro; Puerta, Alejandro; Bonet, Isis; Caraffini, Fabio; Ochoa, Ivan; Gongora, Mario AugustoOperational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are related to the agricultural business process and to external risk events. An operational risk assessment allows one to identify the limits of environmental and financial sustainability. Specifically, in oil palm cultivation, the characterisation of the associated risk remains a challenge from a technological perspective. To advance in this direction, researchers have used different technologies, including spectral aerial images, unmanned aerial vehicles to construct a vegetation index, intelligent augmented platforms for real-time monitoring, and adaptive fuzzy models to estimate operational risk. In line with these technological developments, in this article we propose a framework for the estimation of the risk assessment associated with the disease of Lethal Wilt (LW) in oil palm plantations. Although our purpose is not to predict lethal wilt, since the framework starts from the result of a prediction model, a model to detect LW in an early stage is used for the demonstration. For the implementation of the prediction model, we use a novel deep learning system based on two neural networks. This refers to a case study conducted at UNIPALMAS. We show that the suitability of our system aims to evaluate operational risks of LW with a confidence level of 99.9% and for a period of 6 months.Item Embargo A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification(IEEE, 2021-11-01) Rassem, Taha H.; Alkareem, Fatimah A.; Mohammed, Mohammed Falah; Makbol, Nasrin M.; Sallam, AmerTo overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC’s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.Item Metadata only A preliminary study on crop classification with unsupervised algorithms for time series on images with olive trees and cereal crops(Springer, 2020-08-29) Rivera, Antonio Jesus; Perez-Godoy, Maria Dolores; Elizondo, David; Deka, Lipika; del Jesus, Maria JoseItem Open Access A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling(IEEE, 2023-09) Qiu, Junxiang; Li, Changhe; Yang, ShengxiangAiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.Item Metadata only Abstraction Based Domain Ontology Extraction for Idea Creation(IEEE, 2013) Jing, D. L.; Yang, Hongji; Tian, Y. C.Item Metadata only Access control mechanism for mobile ad hoc network of networks.(IEEE Computer Society, 2010-03) Al-Bayatti, Ali Hilal; Zedan, Hussein; Siewe, FrancoisItem Metadata only Access network selection using combined fuzzy control and MCDM in heterogeneous networks.(2007) Alkhawlani, Mohammed; Ayesh, Aladdin, 1972-Item Open Access Acoustic scene classification: from a hybrid classifier to deep learning(2017-11-16) Vafeiadis, Anastasios; Kalatzis, Dimitrios; Votis, Konstantinos; Giakoumis, Dimitrios; Tzovaras, Dimitrios; Chen, Liming; Hamzaoui, RaoufThis report describes our contribution to the 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. We investigated two approaches for the acoustic scene classification task. Firstly, we used a combination of features in the time and frequency domain and a hybrid Support Vector Machines - Hidden Markov Model (SVM-HMM) classifier to achieve an average accuracy over 4-folds of 80.9% on the development dataset and 61.0% on the evaluation dataset. Secondly, by exploiting dataaugmentation techniques and using the whole segment (as opposed to splitting into sub-sequences) as an input, the accuracy of our CNN system was boosted to 95.9%. However, due to the small number of kernels used for the CNN and a failure of capturing the global information of the audio signals, it achieved an accuracy of 49.5% on the evaluation dataset. Our two approaches outperformed the DCASE baseline method, which uses log-mel band energies for feature extraction and a Multi-Layer Perceptron (MLP) to achieve an average accuracy over 4-folds of 74.8%.Item Metadata only Adapting the pheromone evaporation rate in dynamic routing problems(Springer, 2013) Mavrovouniotis, Michalis; Yang, ShengxiangItem Open Access Adapting Traffic Simulation for Traffic Management: A Neural Network Approach(2013-10) Passow, Benjamin N.; Elizondo, David; Chiclana, Francisco; Witheridge, S.; Goodyer, E. N.Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts.Item Metadata only Adaptive computational tools for elastohydrodynamic lubrication.(2010) Tan, Xincai Cato; Goodyer, C. E.; Jimack, P. K.; Walkley, M. A.; Taylor, R. I.Item Embargo Adaptive control for smart water distribution systems(IEEE, 2021-07-10) Zaman, Mostafa; Al Islam, Maher; Tantawy, Ashraf; Fung, Carol; Abdelwahed, SherifThe rationalization of energy and water consumption is becoming increasingly important. Water distribution systems require energy to operate, and a consumption trade-off became a necessity. Existing water distribution systems still rely on traditional feedback control that is reactive and sub-optimal. The proliferation of the Internet of Things (loT) provides a myriad of opportunities to achieve new levels of optimization for water distribution systems. This paper presents the design and simulation of a water distribution testbed currently under construction at Virginia Commonwealth University (VCU). The simulation results show the superiority of loT-based adaptive control schemes over existing control approaches, with no additional cost. The paper provides a roadmap for loT-based system design and advanced control to minimize the consumption under user convenience constraints.Item Open Access Adaptive crossover in genetic algorithms using statistics mechanism(MIT Press, 2002) Yang, ShengxiangGenetic Algorithms (GAs) emulate the natural evolution process and maintain a population of potential solutions to a given problem. Through the population, GAs implicitly maintain the statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. Inspired by this idea, a statistics-based adaptive non-uniform crossover (SANUX) has been proposed. SANUX uses the statistics information of the alleles in each locus to adaptively calculate the swapping probability of that locus for crossover operation. A simple triangular function has been used to calculate the swapping probability. In this paper new functions, the trapezoid and exponential functions, are proposed for SANUX instead of the triangular function. Experiment results show that both functions further improve the performance of SANUX.Item Metadata only Adaptive Differential Evolution Applied to Point Matching 2D GIS Data(IEEE, 2015-12-07) Khan, N.; Neri, Ferrante; Ahmadi, SamadThe impetus behind data analytics and integration is the need for greater insight and data visibility, but since a growing share of our data is multimedia, there is a parallel need for methods that can align multimedia data. This paper explores georeferencing, which is used to combine spatial datasets and used here to align map images to 2D GIS models. This paper surveys various approaches for building the key components of a georeferencing solution, notes their strengths and weaknesses, and comments on their trajectory to help orient future work. The implementation presented here uses Hough transforms for feature detection, nearest neighbor correspondences with simplistic similarity measures, and a population based optimizer. The comparison among metaheuristics has shown that Differential Evolution (DE) frameworks appear especially suited for this problem. In particular, the controlled randomization of DE parameters appears to display the best performance in terms of execution time and competitive performance in terms of function evaluations even with respect to more complex memetic implementations.Item Open Access An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions(IEEE Press, 2021-12-05) Chen, Baojian; Li, Changhe; Zeng, Sanyou; Yang, Shengxiang; Mavrovouniotis, MichalisThe research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multi-objective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms.Item Metadata only An adaptive FPGA implementation of multi-core K-nearest neighbour ensemble classifier using dynamic partial reconfiguration.(IEEE, 2012) Hussain, H. M.; Benkrid, K.; Hong, C.; Seker, H.Item Metadata only An adaptive implementation of a dynamically reconfigurable K-nearest neighbour classifier on FPGA(IEEE, 2012) Hussain, H. M.; Benkrid, K.; Seker, H.