7 research outputs found

    An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

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    Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results

    Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

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    Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

    Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

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    Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

    Efficient Design, Training, and Deployment of Artificial Neural Networks

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    Over the last decade, artificial neural networks, especially deep neural networks, have emerged as the main modeling tool in Machine Learning, allowing us to tackle an increasing number of real-world problems in various fields, most notably, in computer vision, natural language processing, biomedical and financial analysis. The success of deep neural networks can be attributed to many factors, namely the increasing amount of data available, the developments of dedicated hardware, the advancements in optimization techniques, and especially the invention of novel neural network architectures. Nowadays, state-of-the-arts neural networks that achieve the best performance in any field are usually formed by several layers, comprising millions, or even billions of parameters. Despite spectacular performances, optimizing a single state-of- the-arts neural network often requires a tremendous amount of computation, which can take several days using high-end hardware. More importantly, it took several years of experimentation for the community to gradually discover effective neural network architectures, moving from AlexNet, VGGNet, to ResNet, and then DenseNet. In addition to the expensive and time-consuming experimentation process, deep neural networks, which require powerful processors to operate during the deployment phase, cannot be easily deployed to mobile or embedded devices. For these reasons, improving the design, training, and deployment of deep neural networks has become an important area of research in the Machine Learning field. This thesis makes several contributions in the aforementioned research area, which can be grouped into two main categories. The first category consists of research works that focus on designing efficient neural network architectures not only in terms of accuracy but also computational complexity. In the first contribution under this category, the computational efficiency is first addressed at the filter level through the incorporation of a handcrafted design for convolutional neural networks, which are the basis of most deep neural networks. More specifically, the multilinear convolution filter is proposed to replace the linear convolution filter, which is a fundamental element in a convolutional neural network. The new filter design not only better captures multidimensional structures inherent in CNNs but also requires far fewer parameters to be estimated. While using efficient algebraic transforms and approximation techniques to tackle the design problem can significantly reduce the memory and computational footprint of neural network models, this approach requires a lot of trial and error. In addition, the simple neuron model used in most neural networks nowadays, which only performs a linear transformation followed by a nonlinear activation, cannot effectively mimic the diverse activities of biological neurons. For this reason, the second and third contributions transition from a handcrafted, manual design approach to an algorithmic approach in which the type of transformations performed by each neuron as well as the topology of neural networks are optimized in a systematic and completely data-dependent manner. As a result, the algorithms proposed in the second and third contributions are capable of designing highly accurate and compact neural networks while requiring minimal human efforts or intervention in the design process. Despite significant progress has been made to reduce the runtime complexity of neural network models on embedded devices, the majority of them have been demonstrated on powerful embedded devices, which are costly in applications that require large-scale deployment such as surveillance systems. In these scenarios, complete on-device processing solutions can be infeasible. On the contrary, hybrid solutions, where some preprocessing steps are conducted on the client side while the heavy computation takes place on the server side, are more practical. The second category of contributions made in this thesis focuses on efficient learning methodologies for hybrid solutions that take into ac- count both the signal acquisition and inference steps. More concretely, the first contribution under this category is the formulation of the Multilinear Compressive Learning framework in which multidimensional signals are compressively acquired, and inference is made based on the compressed signals, bypassing the signal reconstruction step. In the second contribution, the relationships be- tween the input signal resolution, the compression rate, and the learning performance of Multilinear Compressive Learning systems are empirically analyzed systematically, leading to the discovery of a surrogate performance indicator that can be used to approximately rank the learning performances of different sensor configurations without conducting the entire optimization process. Nowadays, many communication protocols provide support for adaptive data transmission to maximize the data throughput and minimize energy consumption depending on the network’s strength. The last contribution of this thesis proposes an extension of the Multilinear Compressive Learning framework with an adaptive compression capability, which enables us to take advantage of the adaptive rate transmission feature in existing communication protocols to maximize the informational content throughput of the whole system. Finally, all methodological contributions of this thesis are accompanied by extensive empirical analyses demonstrating their performance and computational advantages over existing methods in different computer vision applications such as object recognition, face verification, human activity classification, and visual information retrieval

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

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    The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature
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