Intelligent System For Brain Disease Diagnosis Using Rotation Invariant Features And Fuzzy Neural Network

The characteristic features of the magnetic resonant image (MRI) for Alzheimer’s patient’s brain image and normal image can be distinguished in terms of dimensional features with the help of wavelet decomposition. From the literature review, it is observed that when datasets used are a combination of the MR images having a very mild cognitive impairment and mild cognitive impairment, the performance of the classifier reduces. Because the features of this kind of MR image are difficult to distinguish from normal brain images. To solve this problem, the lossless feature extraction method along with the feature reduction method having a selection approach is suggested as a solution here. In this paper, the 12 directional, rotation invariant two-dimensional discrete-time continuous wavelet transform (R-DTCWT) and a genetic algorithm (GA) are used for feature selection and feature vector size reduction. The fuzzy neural network (FNN) which is suitable for pattern recognition is used here. The FNN with and without feature reduction is evaluated for identification of combinational dataset, shows satisfactory performance over an artificial neural network (ANN), probabilistic neural network (PNN) classifiers. This method is compared with other state of algorithm to prove the enhanced performanc

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data&mdash;a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented<br /

Towards the Control and Prevention of Waste in IT Service Operation Using Fuzzy Logic: Focus in Incident Management Process

All production lines are continuously confronted with the phenomenon of waste, especially in IT operations. A waste is assessed in terms of the required resources and the cost employed to solve the problem behind it. Eliminating the waste in daily operations is essential to improve IT service management. This article aims to provide an estimation of the level of potential waste, where waste generation trends are provoked by the activities of IT service management processes. We are going to focus particularly on the possibility of applying a Lean improvement process to IT services processes when using fuzzy logic method. We specifically demonstrate our contribution through the application of fuzzy analysis to the incident management process. This approach also aims at developing a theoretical and pragmatic model and promoting the knowledge of IT experts. In order to make our framework as generic as possible, concepts of IT operations, including the incident management, are inspired by the Information Technology Infrastructure Library (ITIL), the most prominent framework for IT service governance according to the current literature

Modeling and Verification of Naturalistic Lane Keeping System

In order to lower human drivers’ driving load and to enhance their systematic performance during driving, driver assistant systems have been introduced during the past few decades. Unfortunately, a large proportion of existing lane keeping techniques only focus on how to hold the car in the center of the lane, which may be contrary to the driver's natural motion sense. This research focuses on developing a rational and precise driver model with fully human driver operating behavior, which is crucial for the study of active safety technology and can provide drivers with a comfortable motion by imitating driving habits and trajectory. Modeling a naturalistic lane keeping control requires understanding of how a driver operates the vehicle, analysis from vehicle lateral dynamics perspective, and knowledge of the combination of driver’s physical limitation. Another requirement to build an adaptive steering control model is to regard driver’s steering behavior as a reciprocal process between anticipation and compensation. Based on two angles (near and far angles) mechanism and experimental data recorded by the SIMULINK and dSpace co-platform, a close-loop system is designed. The whole system is a combination of a PI (proportional–integral) controller driver model and a vehicle model, which integrates vehicle lateral dynamic characteristics and upcoming road information. Moreover, a nonlinear steering driver model is designed. This open loop driver model can effectively correct steering wheel angle by minimizing the error between recorded driving data and that of the simulated model. The simulation outcome shows that the proposed model captures human drivers’ behavior well and has an excellent adaptability towards the change of vehicle dynamic parameters and external disturbances

Guidance on the integrated assessment of complex health technologies: the INTEGRATE-HTA model

Challenges in assessments of health technologies In recent years there have been major advances in the development of health technology assessment (HTA). However, HTA still has certain limitations when assessing technologies which are complex, i.e. consist of several interacting components, target different groups or organizational levels, have multiple and variable outcomes, and/or permit a certain degree of flexibility or tailoring (Craig et al., 2008), fi are context-dependent - current HTA usually focusses on the technology, not on the system within which it is used, fi perform differently depending on the way they are implemented, fi have different effects on different individuals. Furthermore, HTA usually assesses and appraises aspects side-by-side, while decision-making needs an integrated perspective on the value of a technology. In the EU-funded INTEGRATE-HTA project, we developed concepts and methods to deal with these challenges, which are described in six guidances. Because of the interactions, an integrated assessment needs to start from the beginning of the assessment. This guidance provides a systematic five-step-process for an integrated assessment of complex technologies (the INTEGRATE-HTA Model). Purpose and scope of the guidance The aim of the INTEGRATE-HTA project is to provide concepts and methods that enable a patient-centred, comprehensive, and integrated assessment of complex health technologies. The purpose of this guidance is to structure the overall HTA-process. The INTEGRATE-HTA Model outlines an integrated scoping process, a coordinated application of assessment methods for different aspects and an integrated and structured decision-making process. It is intended for HTA agencies, HTA researchers and those engaged in the evaluation of complex health technologies. As it links the assessment to the decision-making process, it also addresses HTA commissioners and other stakeholders using or planning HTAs. While all technologies are arguably complex, some are more complex than others. Applying this guidance might lead to a more thorough and therefore more time-consuming process. Depending on the degree of complexity, one might choose to follow the whole process as described in this guidance, or only focus on certain steps. The guidance provides an operational definition to assess the complexity of technologies which can be used to identify specific aspects that will need more attention than others. What the guidance does not provide is a post-hoc solution for assessments that have already been completed. | 6 Development of the guidance The INTEGRATE-HTA Model presented in this guidance was developed based on a systematic literature search on approaches for integration, on the experiences of traditional HTAs, as well as on the other methodological guidances developed in the INTEGRATE-HTA project. It was tested in a case study on palliative care and iteratively revised during the practical application. The guidance was again revised after internal and external peer-review. Application of this guidance For a comprehensive integrated assessment of a complex technology, we developed a five-step process, the INTEGRATE-HTA model. In Step 1, the HTA objective and the technology are defined with the support from a panel of stakeholders. An initial logic model is developed in Step 2. The initial logic model provides a structured overview of the technology, the context, implementation issues, and relevant patient groups. It then frames the assessment of the effectiveness, as well as economic, ethical, legal, and socio-cultural aspects in Step 3. In Step 4, a graphical overview of the assessment results, structured by the logic model, is provided. Step 5 is a structured decision-making process informed by the HTA (and is thus not formally part of the HTA, but follows it). fi Step 1: In step 1, the technology under assessment and the objective of the HTA are defined. Especially for complex technologies, such as palliative care, the definition of the technology alone is a challenge that must not be underestimated. It is recommended to do this based on a tentative literature review and with the support of stakeholder advisory panels (SAPs) which should comprise clinical experts, academics, patients, possibly their relatives and/or other caretakers, and the public. The setting of an objective considering all relevant aspects of complexity and structured by assessment criteria is important. The assessment criteria will usually reflect values of the stakeholders as well as the input from the theoretical, methodological and empirical literature. fi Step 2: In step 2, an initial logic model is developed (see Guidance on the use of logic models in health technology assessments of complex interventions). The model provides a structured overview on participants, interventions, comparators, and outcomes. Parallel to this, groups of patients that are distinguished by different preferences and treatment moderators (see Guidance for the assessment of treatment moderation and patients’ preferences) are identified. Specific context and implementation issues are also identified as part of the initial logic model (see Guidance for the Assessment of Context and Implementation in Health Technology Assessments (HTA) and Systematic Reviews of Complex Interventions). The product of this step is the logic model as a graphical representation of all aspects and their interactions that are relevant for the assessment of the complex technology. fi Step 3: In step 3, the logic model serves as a conceptual framework that guides the evidence assessment. Depending on the specific aspect (e.g. effectiveness, economic, ethical, socio-cultural, or legal aspects) different methods are available for the assessment (see Guidance for assessing effectiveness, economic aspects, ethical aspects, socio-cultural aspects and legal aspects in complex technologies). The outputs of step 3 are evidence reports and standardized evidence summaries for each assessment aspect (e.g. report on economics, report on ethical aspects, etc.). fi Step 4: In step 4, the assessment results of step 3 are structured using the logic model developed in step 2. Whereas the initial logic model in step 2 specifies what evidence is relevant, the extended logic model to assist decision-making in step 4 visualizes the assessment results as well as the interaction with respect to the HTA objectives. It also allows for the consideration of different scenarios depending on the variation in context, implementation and patient characteristics. 7 | fi Step 5: Step 5 involves a structured decision-making process and is not an integral part of the HTA in the narrow sense. Decision-making can be supported by applying quantitative e.g. MCDA- (Multi-criteria decision analysis) or qualitative decision support tools. Flexibility in the application of these tools by the decision committee is crucial, taking different decision settings and evidence needs into consideration. Conclusions In current HTA, different aspects are usually assessed and presented independent of each other. Context, implementation issues and patient characteristics are rarely considered. The INTEGRATE-HTA Model enables a coordinated assessment of all these aspects and addresses their interdependencies. The perspective of stakeholders such as patients and professionals with their values and preferences is integrated in the INTEGRATE-HTA Model to obtain HTA results that are meaningful for all relevant stakeholders. Finally, health policy makers obtain an integrated perspective of the assessment results to achieve fair and legitimate conclusions at the end of the HTA process. The application of the model will usually require more time and resources than traditional HTA. An initial assessment of the degree and the character of complexity of a technology might be helpful to decide whether or not the whole process or only specific elements will be applied

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well

A Historical Account of Types of Fuzzy Sets and Their Relationships

In this paper, we review the definition and basic properties of the different types of fuzzy sets that have appeared up to now in the literature. We also analyze the relationships between them and enumerate some of the applications in which they have been used

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Deep Symbolic Learning Architecture for Variant Calling in NGS

[EN]The Variant Detection process (Variant Calling) is fundamental in bioinformatics, demanding maximum precision and reliability. This study examines an innovative integration strategy between a traditional pipeline developed in-house and an advanced Intelligent System (IS). Although the original pipeline already had tools based on traditional algorithms, it had limitations, particularly in the detection of rare or unknown variants. Therefore, SI was introduced with the aim of providing an additional layer of analysis, capitalizing on deep and symbolic learning techniques to improve and enhance previous detections. The main technical challenge lay in interoperability. To overcome this, NextFlow, a scripting language designed to manage complex bioinformatics workflows, was employed. Through NextFlow, communication and efficient data transfer between the original pipeline and the SI were facilitated, thus guaranteeing compatibility and reproducibility. After the Variant Calling process of the original system, the results were transmitted to the SI, where a meticulous sequence of analysis was implemented, from preprocessing to data fusion. As a result, an optimized set of variants was generated that was integrated with previous results. Variants corroborated by both tools were considered to be of high reliability, while discrepancies indicated areas for detailed investigations. The product of this integration advanced to subsequent stages of the pipeline, usually annotation or interpretation, contextualizing the variants from biological and clinical perspectives. This adaptation not only maintained the original functionalities of the pipeline, but was also enhanced with the SI, establishing a new standard in the Variant Calling process. This research offers a robust and efficient model for the detection and analysis of genomic variants, highlighting the promise and applicability of blended learning in bioinformaticsThis study has been funded by the AIR Genomics project (with file number CCTT3/20/SA/0003), through the call 2020 R&D PROJECTS ORIENTED TO THE EXCELLENCE AND COMPETITIVE IMPROVEMENT OF THE CCTT by the Institute of Business Competitiveness of Castilla y León and FEDER fund

Fuzzy Systems

This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students