Πλοήγηση ανά Θέμα "Learning algorithms"
Αποτελέσματα 1-20 από 48
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Application of Fuzzy Cognitive Maps to water demand prediction
(2015)This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with ... -
Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective
(2019)Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms’ intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can ... -
Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis
(2020)Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential ... -
Approximate kNN Classification for Biomedical Data
(2020)We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution ... -
Assessing the effect of human factors in healthcare cyber security practice: An empirical study
(2021)The main goal of this research paper is to address the problem of SPECT myocardial perfusion imaging (MPI) diagnosis, exploring the capabilities of convolutional neural networks (CNN). Up to date, very few research studies ... -
Bagged nonlinear Hebbian learning algorithm for fuzzy cognitive maps working on classification tasks
(2012)Learning of fuzzy cognitive maps (FCMs) is one of the most useful characteristics which have a high impact on modeling and inference capabilities of them. The learning approaches for FCMs are concentrated on learning the ... -
Classifying mammography images by using fuzzy cognitive maps and a new segmentation algorithm
(2018)Mammography is one of the best techniques for the early detection of breast cancer. In this chapter, a method based on fuzzy cognitive map (FCM) and its evolutionary-based learning capabilities is presented for classifying ... -
A comparative assessment of machine learning algorithms for events detection
(2019)Nowadays, one can observe massive amount of data production by numerous devices interacting with their environment and end users. [1] Such data can be the subject of advanced processing usually through machine learning ... -
D2D-assisted federated learning in mobile edge computing networks
(2021)With the proliferation of edge intelligence and the breakthroughs in machine learning, Federated Learning (FL) is capable of learning a shared model across several edge devices by preserving their private data from being ... -
Deep Learning Models for Yoga Pose Monitoring
(2022)Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately ... -
A Deep Reinforcement Learning Motion Control Strategy of a Multi-rotor UAV for Payload Transportation with Minimum Swing
(2022)This paper addresses the problem of controlling a multirotor UAV with a cable-suspended load. In order to ensure the safe transportation of the load, the swinging motion, induced by the strongly coupled dynamics, has to ... -
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification
(2018)This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only ... -
Effective products categorization with importance scores and morphological analysis of the titles
(2018)During the past few years, the e-commerce platforms and marketplaces have enriched their services with new features to improve their user experience and increase their profitability. Such features include relevant products ... -
Efficient Learning Rate Adaptation for Convolutional Neural Network Training
(2019)Convolutional Neural Networks (CNNs) have been established as substantial supervised methods for classification problems in many research fields. However, a large number of parameters have to be tuned to achieve high ... -
Enhancing Sedona (formerly GeoSpark) with Efficient k Nearest Neighbor Join Processing
(2021)Sedona (formerly GeoSpark) is an in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, indexes, and ... -
ETH analysis and predictions utilizing deep learning
(2020)This paper attempts to provide a data analysis of cryptocurrency markets. Such markets have been developed rapidly and their volatility poses significant research challenges and justifies intensive behavior analysis. For ... -
Evaluating the Effects of Modern Storage Devices on the Efficiency of Parallel Machine Learning Algorithms
(2020)Big Data analytics is presently one of the most emerging areas of research for both organizations and enterprises. The requirement for deployment of efficient machine learning algorithms over huge amounts of data led to ... -
Exploring an ensemble of methods that combines fuzzy cognitive maps and neural networks in solving the time series prediction problem of gas consumption in Greece
(2019)This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim ... -
Fuzzy cognitive maps and multi-step gradient methods for prediction: Applications to electricity consumption and stock exchange returns
(2015)The paper focuses on the application of fuzzy cognitive map (FCM) with multi-step learning algorithms based on gradient method and Markov model of gradient for prediction tasks. Two datasets were selected for the implementation ... -
A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling
(2022)Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches ...