Πλοήγηση ανά Θέμα "Deep learning"
Αποτελέσματα 1-20 από 86
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Analytics and Blockchain for Data Integrity in the Pharmaceuticals Industry
(2022)The data quantity explosion that we witnessed during the last two decades has lead industrial organizations to exploit this sheer amount of data for tasks that previously would seem impossible. However, larger data volumes, ... -
Anomaly detection via blockchained deep learning smart contracts in industry 4.0
(2020)The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. ... -
Application of deep learning and chaos theory for load forecasting in Greece
(2021)In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies ... -
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 ... -
Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis
(2018)Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. ... -
Applying unsupervised and supervised machine learning methodologies in social media textual traffic data
(2019)Traffic increasingly shapes the trajectory of city growth and impacts on the climate change in modern cities. Traffic patterns’ monitoring can provide with innovative practices in understanding city traffic dynamics, ... -
Artificial intelligence implementations on the blockchain. Use cases and future applications
(2019)An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of ... -
Assessing image analysis filters as augmented input to convolutional neural networks for image classification
(2018)Convolutional Neural Networks (CNNs) have been proven very effective in image classification and object recognition tasks, often exceeding the performance of traditional image analysis techniques. However, training a CNN ... -
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 ... -
An Audiovisual Child Emotion Recognition System for Child-Robot Interaction Applications
(2021)We present an audiovisual emotion recognition system tailored to child-robot interaction scenarios. Our proposed system is based on deep learning and the Temporal Segment Networks framework, receives input from both the ... -
Automated Recognition of healthy Anterior Cruciate Ligament in Sagittal MR images using Lightweight Deep Learning
(2022)Anterior cruciate ligament (ACL) tears are very common among athletes. The success of enhanced ACL injury therapy hinges on accurate and cost-effective detection. Deep learning-based techniques have recently dominated ACL ... -
An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning
(2021)A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial ... -
A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing
(2022)Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals. ... -
Change detection and convolution neural networks for fall recognition
(2020)Accurate fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for motion tracking, ... -
Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting
(2022)The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting ... -
Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey
(2022)Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, ... -
A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology
(2022)This study targets on the development of an explainable Convolutional Neural Network (CNN) pipeline in the form of a handcrafted CNN to identify patients' coronary artery disease status (normal, ischemia or infarction). ... -
Convolutional neural networks for toxic comment classification
(2018)Flood of information is produced in a daily basis through the global internet usage arising from the online interactive communications among users. While this situation contributes significantly to the quality of human ... -
Convolutional Variational Autoencoders for Image Clustering
(2021)The problem of data clustering is one of the most fundamental and well studied problems of unsupervised learning. Image clustering, refers to one of the most challenging specifications of clustering, concerning image data. ... -
Deep affordance-grounded sensorimotor object recognition
(2017)It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object “affordances”, namely ...