Πλοήγηση ανά Θέμα "Machine learning"
Αποτελέσματα 21-40 από 71
-
Exploring deep learning capabilities in knee osteoarthritis case study for classification
(2019)This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the ... -
Extreme Interval Electricity Price Forecasting of Wholesale Markets Integrating ELM and Fuzzy Inference
(2019)The electricity wholesale market is inherently volatile in a deregulated market structure where market participants like power generators and retailors drive the price of electricity. Timely forecasting of the wholesale ... -
A federated machine learning protocol for fog networks
(2021)In this paper, we present a federated learning (FL) protocol for fog networking applications. The fog networking architecture is compatible with the Internet of Things (IoT) edge computing concept of the Internet Engineering ... -
Fuzzy Cognitive Maps for Interpretable Image-based Classification
(2022)Image classification is a fundamental component of intelligent vision systems. Developing classifiers capable of explaining how or why a classification result occurs, in a way compatible with human perception, remains a ... -
Hardware Trojan Classification at Gate-level Netlists based on Area and Power Machine Learning Analysis
(2021)The 21st century has been characterized by incredible technological advancements. A key factor of this revolution is the ever-growing circuits complexity that are the core components of all electronic devices. This revolution ... -
A hybrid metaheuristics-based algorithm for electricity load curves profiling
(2021)Clustering-based load profiling utilizes unsupervised machine learning algorithms to form homogenous clusters composed by electricity load curves with similar characteristics. Due to the importance of load profiling in ... -
Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms
(2022)Current advances in the Internet of Things (IoT) and Cloud involve the presence of an additional layer between them acting as mediator for data transfer and processing in close distance to end users. This mediator is the ... -
Investigating cross-dataset abnormality detection in endoscopy with a weakly-supervised multiscale convolutional neural network
(2018)The detection of abnormalities in endoscopic video frames can contribute in the early and more accurate detection of pathologic conditions. In this paper we present a novel Convolutional Neural Network (CNN) architecture ... -
Investigating the efficiency of machine learning algorithms on mapreduce clusters with SSDs
(2018)In the big data era, the efficient processing of large volumes of data has became a standard requirement for both organizations and enterprises. Since single workstations cannot sustain such tremendous workloads, MapReduce ... -
IPLS: A Framework for Decentralized Federated Learning
(2021)The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate federated learning, a paradigm that enables mobile devices to produce a machine-learning model ... -
Large scale “speedtest” experimentation in Mobile Broadband Networks
(2021)Characterizing and evaluating the performance of Mobile Broadband (MBB) networks is a vital need for today's societies. Testbed-based measurements are of great significance in this context, since they allow for controlled ... -
Leveraging Machine Learning for Gate-level Timing Estimation Using Current Source Models and Effective Capacitance
(2022)With process technology scaling, accurate gate-level timing analysis becomes even more challenging. Highly resistive on-chip interconnects have an ever-increasing impact on timing, signals no longer resemble smooth saturated ... -
A Lipschitz - Shapley Explainable Defense Methodology Against Adversarial Attacks
(2021)Every learning algorithm, has a specific bias. This may be due to the choice of its hyperparameters, to the characteristics of its classification methodology, or even to the representation approach of the considered ... -
A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors
(2019)This paper examines the application of machine learning techniques in NILM methodologies based on the first three odd harmonic order current vectors as the only attributes of the appliances. Proper formulation of the ... -
A Machine Learning approach to the EFT re-interpretation of the WZjj fully leptonic electroweak production
(2021)In this paper we study the use of Machine Learning techniques to set constraints on indirect signatures of physics beyond the Standard Model in Vector Boson Scattering (VBS), in the electroweak (EWK) production of ... -
Machine learning for all: A more robust federated learning framework
(2019)Machine learning and especially deep learning are appropriate for solving multiple problems in various domains. Training such models though, demands significant processing power and requires large data-sets. Federated ... -
Machine Learning for Hardware Trojan Detection: A Review
(2019)Every year, the rate at which technology is applied on areas of our everyday life is increasing at a steady pace. This rapid development drives the technology companies to design and fabricate their integrated circuits ... -
Machine learning product key performance indicators and alignment to model evaluation
(2021)Machine Learning has seen amazing progress the past years with increasing commercial use from industries across the business spectrum. Businesses strive for alignment of vision and mission statement to the actual products ... -
Machine learning symbolic equations for diffusion with physics-based descriptions
(2022)This work incorporates symbolic regression to propose simple and accurate expressions that fit to material datasets. The incorporation of symbolic regression in physical sciences opens the way to replace "black-box"machine ... -
A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability
(2020)Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute ...