820 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Maintaining the Integrity Over Wear Time of a Hydrocolloid-based Ostomy Adhesive Whilst Maintaining Skin Barrier Function

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    In this extensive body of work, a thorough exploration delves into hydrocolloid based adhesives, with a focus on addressing challenges faced by stoma patients, particularly the susceptibility of ostomy adhesives to breakdown upon exposure to liquids. Stoma patients, compelled to wear pouching systems continuously, encounter issues like the compromise of skin barrier integrity, leading to medical adhesive-related skin injuries. The primary objective of this thesis is to reinforce the structural integrity of ostomy adhesives while preserving the skin barrier during pouching system use, an aspect often overlooked in current literature due to the hydrophilic nature of hydrocolloid based adhesives. The study introduces novel aims, examining the potential link between handedness and the preferred direction of adhesive removal, and its impact on peristomal skin complications as well as a novel skin capacitive imagery stitching technique. Another goal involves developing hierarchical structures on adhesive surfaces to enhance integrity, initial tack, and minimize skin contact for optimal skin health. The introduction provides a detailed breakdown of hydrocolloid-based ostomy adhesives, stoma anatomy, and the purpose of pouching systems. A comprehensive literature review, utilizing the PICO approach, encompasses stoma anatomy, physiology, indications for stoma surgery, and methods for assessing skin health. The review explores various methodologies to improve the durability of hydrocolloid-based adhesives, incorporating hydrodynamics, crosslinking, and layering systems. The potential influence of handedness on adhesive removal techniques is examined, considering its impact on peristomal skin complications. Results reveal the consistent performance of Welland Medical Ltd.'s hydrocolloid based adhesive but highlight the need for improved integrity over wear time. Strategies include modifying sodium-carboxymethylcellulose degree of substitution and increasing pectin degree of esterification, resulting in enhanced fluid handling capabilities and reduced susceptibility to degradation. Residual testing indicates that residual particles on the skin can impair the barrier function, remedied by a silicone-based adhesive remover. Surveys show that a patient's dominant hand and following the skin's natural langer lines during adhesive removal may minimize skin trauma. The results also show that structured surface profiles on hydrocolloid-based adhesive surfaces impact the skin's functional barrier recovery time. The research goal of this project and its objectives have been reached, the approaches have been explained clearly and implementations have been assessed using experimental findings. This project's findings contribute to advancements in ostomy care by enhancing adhesive performance, understanding patient behaviour, and improving the overall user experience. It also facilitates the efficient detachment of the adhesive from the skin surface

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

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    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    Número 30 completo

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    Trends in Science and Technology for Sustainable Living

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    Dalam buku ini, dibahas mengenai perkembangan tren kajian dalam sains dan teknologi yang mendukung pembangunan berkelanjutan untuk mewujudkan kehidupan berkelanjutan. Pembangunan berkelanjutan mempunyai prinsip pembangunan yang bertujuan memenuhi kebutuhan generasi saat ini tetapi tidak mengurangi ataupun mengorbankan kemampuan generasi selanjutnya dalam memenuhi kebutuhan mereka; sehingga kehidupan yang baik akan terus berlanjut dalam waktu yang lama. Pembangunan berkelanjutan saat ini berfokus pada tiga hal, yaitu pembangunan keberlanjutan ekonomi dan sosial, serta perlindungan terhadap lingkungan untuk generasi mendatang. Ketiganya saling berhubungan dan mendukung dalam mencapai tujuan pembangunan serta stabilitas lingkungan dan sosial. Oleh karena itu, keseimbangan yang baik dalam aspek lingkungan,ekonomi, dan sosial harus dicapai untuk membentuk kehidupan berkelanjutan

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    A Survey of Feature Selection Strategies for DNA Microarray Classification

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    Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date:March 31st 202
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