Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Deliverable D1.1 State of the art and requirements analysis for hypervideo

This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary

Recent Advances in Deep Learning Techniques for Face Recognition

In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp. 99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613

Target and Non-Target Approaches for Food Authenticity and Traceability

Over the last few years, the subject of food authenticity and food fraud has received increasing attention from consumers and other stakeholders, such as government agencies and policymakers, control labs, producers, industry, and the research community. Among the different approaches aiming to identify, tackle, and/or deter fraudulent practices in the agri-food sector, the development of new, fast, and accurate methodologies to evaluate food authenticity is of major importance. This book, entitled “Target and Non-Target Approaches for Food Authenticity and Traceability”, gathers original research and review papers focusing on the development and application of both targeted and non-targeted methodologies applied to verify food authenticity and traceability. The contributions regard different foods, among which some are frequently considered as the most prone to adulteration, such as olive oil, honey, meat, and fish. This book is intended for readers aiming to enrich their knowledge through reading contemporary and multidisciplinary papers on the topic of food authentication

Design and Operation of a Microwave Flow Cytometer for Single Cell Detection and Identification

Microwave dielectric sensing has become a popular technique in biological cell sensing for its potential in online, label-free, and real-time sensing. At microwave frequencies probing signals are sensitive to intracellular properties since they are able to penetrate cell membranes, making microwave flow cytometry a promising technology for label-free biosensing. In this dissertation a microwave flow cytometer is designed and used to measure single biological cells and micro particles. A radio frequency (RF)/microwave interferometer serves as the measurement system for its high sensitivity and tunability and we show that a two-stage interferometer can achieve up to 20 times higher sensitivity than a single interferometer. A microstrip sensor with an etched microfluidic channel is used as the sensing structure for measuring single cells and particles in flow. The microwave flow cytometer was used to measure changes in complex permittivity, , of viable and nonviable Saccharomyces cerevisiae and Saccharomyces pastorianus yeast cells and changes in complex permittivity and impedance of two lifecycle stages of Trypanosoma brucei, a unicellular eukaryotic parasite found in sub-Saharan Africa, at multiple frequencies from 265 MHz to 7.65 GHz. Yeast cell measurements showed that there are frequency dependent permittivity differences between yeast species as well as viability states. Quadratic discriminate analysis (QDA) and k-nearest neighbors (KNN) were employed to validate the ability to classify yeast species and viability, with minimum cross-validation error of with cross validation errors of 19% and 15% at 2.38 GHz and 265 MHz, respectively. Measurements of changes in permittivity and impedance of single procyclic form (PCF) and bloodstream form (BSF) T. brucei parasites also showed frequency dependence. The two cell forms had a strong dependence on the imaginary part of permittivity at 2.38 GHz and below and a strong dependence on the real part of permittivity at 5.55 GHz and above. Three PCF cell lines were tested to verify that the differences between the two cell forms were independent of cell strain. QDA gave maximum cross-validation errors of 15.4% and 10% when using one and three PCF strains, respectively. Impedance measurements were used to improve cell classification in cases where the permittivity of a cell cannot be detected. Lastly, a microwave resistance temperature detector (RTD) is designed, and a model is developed to extract the temperature and complex permittivity of liquids in a microfluidic channel. The microwave RTD is capable of measuring temperature to within 0.1°C. The design can easily be modified to increase sensitivity be lengthening the sensing electrode or modified for smaller volumes of solute by shortening the electrode

Multimodal radar sensing for ambient assisted living

Data acquired from health and behavioural monitoring of daily life activities can be exploited to provide real-time medical and nursing service with affordable cost and higher efficiency. A variety of sensing technologies for this purpose have been developed and presented in the literature, for instance, wearable IMU (Inertial Measurement Unit) to measure acceleration and angular speed of the person, cameras to record the images or video sequence, PIR (Pyroelectric infrared) sensor to detect the presence of the person based on Pyroelectric Effect, and radar to estimate distance and radial velocity of the person. Each sensing technology has pros and cons, and may not be optimal for the tasks. It is possible to leverage the strength of all these sensors through information fusion in a multimodal fashion. The fusion can take place at three different levels, namely, i) signal level where commensurate data are combined, ii) feature level where feature vectors of different sensors are concatenated and iii) decision level where confidence level or prediction label of classifiers are used to generate a new output. For each level, there are different fusion algorithms, the key challenge here is mainly on choosing the best existing fusion algorithm and developing novel fusion algorithms that more suitable for the current application. The fundamental contribution of this thesis is therefore exploring possible information fusion between radar, primarily FMCW (Frequency Modulated Continuous Wave) radar, and wearable IMU, between distributed radar sensors, and between UWB impulse radar and pressure sensor array. The objective is to sense and classify daily activities patterns, gait styles and micro-gestures as well as producing early warnings of high-risk events such as falls. Initially, only “snapshot” activities (single activity within a short X-s measurement) have been collected and analysed for verifying the accuracy improvement due to information fusion. Then continuous activities (activities that are performed one after another with random duration and transitions) have been collected to simulate the real-world case scenario. To overcome the drawbacks of conventional sliding-window approach on continuous data, a Bi-LSTM (Bidirectional Long Short-Term Memory) network is proposed to identify the transitions of daily activities. Meanwhile, a hybrid fusion framework is presented to exploit the power of soft and hard fusion. Moreover, a trilateration-based signal level fusion method has been successfully applied on the range information of three UWB (Ultra-wideband) impulse radar and the results show comparable performance as using micro-Doppler signature, at the price of much less computation loads. For classifying ‘snapshot’ activities, fusion between radar and wearable shows approximately 12% accuracy improvement compared to using radar only, whereas for classifying continuous activities and gaits, our proposed hybrid fusion and trilateration-based signal level improves roughly 6.8% (before 89%, after 95.8%) and 7.3% (before 85.4%, after 92.7%), respectively