741 research outputs found

    Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data

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    The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure

    20th SC@RUG 2023 proceedings 2022-2023

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    20th SC@RUG 2023 proceedings 2022-2023

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    Developing green interventions for the implementation of the Water Framework Directive through systems-based economic approaches

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    Across the globe, society faces significant environmental challenges. Deforestation, intensive agriculture, pollution, and overexploitation of natural resources among other pressures create the conditions for natural capital depletion, ultimately jeopardizing economic development. To reverse such trends in the water sector and avoid the collapse of inland water ecosystems, the European Commission introduced the Water Framework Directive. A distinct example of integrated water resources management, the Directive aims to provide a holistic approach of managing inland waters efficiently by substituting previous fragmented legislation that focused on managing different aspects of water resources. Still, despite progress in the Directive’s implementation, approximately half of the EU’s surface waters are not in good condition. Analysing the efforts of Member States to assess the socio-economic dimensions of water ecosystems and develop programmes of measures to achieve the objectives of the Directive, indicates a lack of understanding of what and how should be assessed. The information included in the River Basin Management Plans denotes insufficient connections between pressures and interventions, as well as poor economic analysis that often disregards a great portion of benefits and costs associated with transitioning towards a desired state. To support the implementation of the Directive and to provide a systems thinking perspective on environmental management decisions, the presented work blends ecosystem services with economic methodologies, develops integrated tools and approaches and tests their applicability in real cases. Findings show that such tools improve the robustness of the socioeconomic assessments of proposed measures and have the potential to engage stakeholders in the process. Additionally, they enable the use of natural capital accounting methodologies to improve the understanding of the connection between management interventions and the overall system status. Furthermore, the included research assesses the effectiveness of nature-based approaches as interventions on the system to influence the interactions of components within socioecological systems and drive them towards the desired state. Additionally, given the way economic dimensions permeate decision-making processes and current debates on environmental management, the current thesis proposes an alternative vision for the sustainable use of natural resources. Overall, the undertaken research demonstrates the need for using economic tools through a systems prism for the implementation of the Water Framework Directive and environmental policies in general to deliver socioecological improvements.Open Acces

    Multimodal Assessment of Cognitive Decline: Applications in Alzheimer’s Disease and Depression

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    The initial diagnosis and assessment of cognitive decline are generally based around the judgement of clinicians, and commonly used semi-structured interviews, guided by pre-determined sets of topics, in a clinical set-up. Publicly available multimodal datasets have provided an opportunity to explore a range of experiments in the automatic detecting of cognitive decline. Drawing on the latest developments in representation learning, machine learning, and natural language processing, we seek to develop models capable of identifying cognitive decline with an eye to discovering the differences and commonalities that should be considered in computational treatment of mental health disorders. We present models that learn the indicators of cognitive decline from audio and visual modalities as well as lexical, syntactic, disfluency and pause information. Our study is carried out in two parts: moderation analysis and predictive modelling. We do some experiments with different fusion techniques. Our approaches are motivated by some of the recent efforts in multimodal fusion for classifying cognitive states to capture the interaction between modalities and maximise the use and combination of each modality. We create tools for detecting cognitive decline and use them to analyze three major datasets containing speech produced by people with and without cognitive decline. These findings are being used to develop multimodal models for the detection of depression and Alzheimer’s dementia

    Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

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    Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure

    Successful Public Policy in the Nordic Countries

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    This book presents 23 in-depth case studies of successful public policies and programmes in Sweden, Denmark, Finland, Norway and Iceland. Each chapter tells the story of the policy’s origins, aims, design, decision-making and implementation processes, and assesses in which respects—programmatically, process-wise, politically and over time—and to what extent it can be considered a policy success. It also points towards the driving forces of success, and the challenges that have had to be overcome to achieve it. Combined, the chapters provide a resource for policy evaluation researchers, educators and students of public policy and public administration, both within and beyond the Nordic region

    Deep language models for software testing and optimisation

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    Developing software is difficult. A challenging part of production development is ensuring programs are correct and fast, two properties satisfied with software testing and optimisation. While both tasks still rely on manual effort and expertise, the recent surge in software applications has led them to become tedious and time-consuming. Under this fast-pace environment, manual testing and optimisation hinders productivity significantly and leads to error-prone or sub-optimal programs that waste energy and lead users to frustration. In this thesis, we propose three novel approaches to automate software testing and optimisation with modern language models based on deep learning. In contrast to our methods, existing few techniques in these two domains have limited scalability and struggle when they face real-world applications. Our first contribution lies in the field of software testing and aims to automate the test oracle problem, which is the procedure of determining the correctness of test executions. The test oracle is still largely manual, relying on human experts. Automating the oracle is a non-trivial task that requires software specifications or derived information that are often too difficult to extract. We present the first application of deep language models over program execution traces to predict runtime correctness. Our technique classifies test executions of large-scale codebases used in production as “pass” or “fail”. Our proposed approach reduces by 86% the amount of test inputs an expert has to label by training only on 14% and classifying the rest automatically. Our next two contributions improve the effectiveness of compiler optimisation. Compilers optimise programs by applying heuristic-based transformations constructed by compiler engineers. Selecting the right transformations requires extensive knowledge of the compiler, the subject program and the target architecture. Predictive models have been successfully used to automate heuristics construction but their performance is hindered by a shortage of training benchmarks in quantity and feature diversity. Our next contributions address the scarcity of compiler benchmarks by generating human-likely synthetic programs to improve the performance of predictive models. Our second contribution is BENCHPRESS, the first steerable deep learning synthesizer for executable compiler benchmarks. BENCHPRESS produces human-like programs that compile at a rate of 87%. It targets parts of the feature space previously unreachable by other synthesizers, addressing the scarcity of high-quality training data for compilers. BENCHPRESS improves the performance of a device mapping predictive model by 50% when it introduces synthetic benchmarks into its training data. BENCHPRESS is restricted by a feature-agnostic synthesizer that requires thou sands of random inferences to select a few that target the desired features. Our third contribution addresses this inefficiency. We develop BENCHDIRECT, a directed language model for compiler benchmark generation. BENCHDIRECT synthesizes programs by jointly observing the source code context and the compiler features that are targeted. This enables efficient steerable generation on large scale tasks. Compared to BENCHPRESS, BENCHDIRECT matches successfully 1.8× more Rodinia target benchmarks, while it is up to 36% more accurate and up to 72% faster in targeting three different feature spaces for compilers. All three contributions demonstrate the exciting potential of deep learning and language models to simplify the testing of programs and the construction of better optimi sation heuristics for compilers. The outcomes of this thesis provides developers with tools to keep up with the rapidly evolving landscape of software engineering

    Power Quality Management and Classification for Smart Grid Application using Machine Learning

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    The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development
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