An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

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CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pre-trained encoder which is trained using only a limited number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier’s layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level

LPN: Language-guided Prototypical Network for few-shot classification

Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the accessible data, recent methods explore suitable measures for the similarity between the query and support images and better high-dimensional features with meta-training and pre-training strategies. However, the potential of multi-modality information has barely been explored, which may bring promising improvement for few-shot classification. In this paper, we propose a Language-guided Prototypical Network (LPN) for few-shot classification, which leverages the complementarity of vision and language modalities via two parallel branches. Concretely, to introduce language modality with limited samples in the visual task, we leverage a pre-trained text encoder to extract class-level text features directly from class names while processing images with a conventional image encoder. Then, a language-guided decoder is introduced to obtain text features corresponding to each image by aligning class-level features with visual features. In addition, to take advantage of class-level features and prototypes, we build a refined prototypical head that generates robust prototypes in the text branch for follow-up measurement. Finally, we aggregate the visual and text logits to calibrate the deviation of a single modality. Extensive experiments demonstrate the competitiveness of LPN against state-of-the-art methods on benchmark datasets

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented

MureTools. An Optical Music Recognition Supporting System

This project has the intention of providing supporting tools in the OMR (Optical Music Recognition) field by easing training and evaluation tasks. Through this whole work, the OMR field along with the relevant processes being carried out with the objective of digitizing and preserving musical pieces and tradition will be presented. Additionally, not only formats and representations will be introduced, but also neural networks models proven to be effective in this field, as well as its relevant concepts and metrics will be included throughout, unveiling also the procedures carried out in order to achieve the proposed objectives, as well as the solutions for issues that arise. Furthermore it will be important to showcase the possibilities of integrating all of these kind of tasks through web microservices and synchronous and asynchronous protocols, using task queuing so processes can take place over time steadily and effectively

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with..

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment