... the k -Nearest Neighbour (k -NN), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), AdaBoost, XGBoost, and Multi-Layer Perceptron (MLP) are Machine Learning (ML) models. DNN-single, DNN-CNN, DNN-convLSTM, and DNN...

... by adhering to the conventional ML paradigm. Subspace learning is a powerful framework for data representation, widely used in various fields such as computer vision, machine learning, and signal processing. The core idea of subspace learning is to represent high-dimensional data in a lower-dimensional...

... the feasibility of the proposed methodology, and its potential to realize real-time gesture recognition. FMCW radar hand gesture recognition machine learning lightweight (5) f I F = μ τ = μ × 2 ( R 0 + v r t ) c = R × 2 μ c According to (6...

... of SED. In this study, we delve into the methods proposed by various authors in the DCASE challenge series, providing a thorough discussion of feature extraction, machine learning techniques, and post-processing methods. We also study the results from top teams in each edition of the DCASE challenge...

...Lionel F. Gonzalez Casanovav; Po-Chiang Lin Machine learning is an effective analysis tool to tackle the challenges to detect any suspicious events in the network traffic flow. In this paper, our major contribution is to process and transform the CIRA-CIC-DoHBrw-2020-time series dataset to train...

...Yijing Yang; Wei Wang; Hongyu Fu; C.-C. Jay Kuo The application of machine learning to image and video data often yields a high dimensional feature space. Effective feature selection techniques identify a discriminant feature subspace that lowers computational and modeling costs with little...

...Zijian Cao; Hua Zhang; Le Liang; Geoffrey Ye Li Over the past decades, machine learning techniques have demonstrated excellent superiorities in a wide range of fields, such as computer vision, natural language processing, etc. Through efficient utilization of a huge amount of data, machine learning...

... to be related to depression [ 12 ] (see the appendix for details). Biomarkers major depression machine learning multimodal Due to a lack of profound knowledge of functional and physiological characteristics, the diagnosis of major depressive disorder (MDD) is currently based on lengthy...

... the performance levels of this set of participants on the types of questions tested. Moreover, the machine learning algorithms utilized to classify the participants in this study showed potential based on their performance and confidence levels. The study by Sharif and Maletic [ 24 ] found that high levels...

... of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited Machine learning Robustness Graph signal processing Laplacian Adversarial attacks...

... Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited Computer vision Image processing Machine learning In recent years, scene understanding has become...

... learning machine learning artificial intelligence data mining Research on graph representation learning has gained more and more attention in recent years since most real-world data can be represented by graphs conveniently. Examples include social networks [ 1 ], linguistic (word co-occurrence...

...Amir Said Machine learning (ML) has been producing major advances in several technological fields and can have a significant impact on media coding. However, fast progress can only happen if the ML techniques are adapted to match the true needs of compression. In this paper, we analyze why some...

... curatioon Machine learning Recent advances and adoption of e-commerce, streaming media, and social media have created a rapidly evolving content ecosystem that includes creation, curation, moderation, distribution, consumption, and redistribution. For example, Facebook has more than 2 billion...

.../licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited Artificial intelligence Deep learning Machine learning Computer vision Speech recognition Convolutional neural networks It is rare...

...Naonori Ueda; Futoshi Naya Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatiotemporal...

.... In this paper, we present a vision of the future where apps are no longer the dominant customer interaction in the mobile world. The alternative that we propose would “orchestrate” the mobile experience by using a “moment-first” model that would leverage machine learning and data mining to bridge a user’s needs...

... Limited must be obtained for commercial re-use or in order to create a derivative work. Deep neural networks Pattern recognition Machine learning Restrictive Boltzmann machine Feed-forward networks There is a recent surge in research activities around the idea of the so-called “deep...

... of ensemble methods and moving to the more-recently introduced field of meta-algorithmics, systems can be designed which are by nature to specifically incorporate new machine-learning technologies. These are more robust, more accurate, more adaptive, and ultimately less costly to build and maintain than...

...Shinji Watanabe; Atsushi Nakamura This paper focuses on applications of Bayesian approaches to acoustic modeling for speech recognition and related speech-processing applications. Bayesian approaches have been widely studied in the fields of statistics and machine learning, and one...