Electrocardiogram (ECG) is a common non-invasive diagnostic technique used to detect cardiac disease. Several cardiac abnormalities can be uncovered by analysing the heart's electrical impulses or the combination ...
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As smart cities advance and increasingly use data from Internet of Things (IoT) sources to enhance urban environments, smart healthcare has become a key focus area. This paper investigates the integration of Deep Lear...
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ISBN:
(数字)9798331541064
ISBN:
(纸本)9798331541071
As smart cities advance and increasingly use data from Internet of Things (IoT) sources to enhance urban environments, smart healthcare has become a key focus area. This paper investigates the integration of Deep learning (DL) techniques in Smart Healthcare Recommendation Systems (SHRSs), presenting a foundational taxonomy, that categorizes these systems into four distinct types: Content-Based Filtering (CBF), Collaborative Filtering (CF), Context-Aware Filtering (CAF), and Hybrid Filtering (HF). These approaches, implemented through DL, enable advanced data processing, patternrecognition, and personalization. Additionally, the paper introduces a new classification based on the nature of intervention, dividing SHRSs into preventive, diagnostic, treatment-focused, and hybrid categories. This dual classification fills a critical gap in the comparative analysis of DL-driven healthcare, which demonstrates the efficacy of these systems in providing personalized health advice, leveraging real-time sensor data and optimizing the handling of complex health datasets. Furthermore, this research outlines future work and potential directions for advancing SHRSs, offering insights into the application and impact of sophisticated recommendation techniques within the context of urban health management.
The classification of any information as true or false has piqued the curiosity of researchers all around the world. Different types of studies are done to document the impact of misleading and fake news on the genera...
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Importance of food is self-evident and obvious. Healthy food provides us with the nourishment and energy we require to grow and develop, including carbohydrates, protein, vitamins, minerals, lipids, and other nutrient...
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ISBN:
(纸本)9781665476560
Importance of food is self-evident and obvious. Healthy food provides us with the nourishment and energy we require to grow and develop, including carbohydrates, protein, vitamins, minerals, lipids, and other nutrients. Rapid growth of technology has led to increase in ease of people's daily life. machinelearning has contributed a lot to food and dieting a lot in the past years. The notion of machinelearning and deep learning models can be applied to imageprocessing to consistently detect the type and quality of food. We developed a user friendly interface that displays the nutrition and calories content of the food that the user is consuming. The system is built on the YOLO algorithm, which uses convolutional neural networks (CNN) to detect the object at once. The accuracy of the system is improved by YOLO algorithm since it moves in only one direction only once to yield the output. The algorithm is proven to be faster that other CNN algorithm, so our application used this algorithm to get the output in a short amount of time.
In recent years, the amount of data collected by Remote Sensing (RS) devices has increased significantly. This has created a difficulty for practical administration and analysis of these datasets utilising desktop com...
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ISBN:
(数字)9798350355338
ISBN:
(纸本)9798350355345
In recent years, the amount of data collected by Remote Sensing (RS) devices has increased significantly. This has created a difficulty for practical administration and analysis of these datasets utilising desktop computer resources and traditional software packages. In response, Google unveiled the Earth Engine from Google (GEE), a platform for cloud computing designed to successfully handle the difficulties associated with huge data processing. Since its launch in the year 2010 GEE has shown its ability to handle large geospatial information across large regions and track changes in the environment over an extended period of time. It was created 10 years ago, but only lately has its full potential of RS applications come to light. This research aims to thoroughly examine a number of aspects of the GEE structure, including datasets, features, benefits, drawbacks, and a variety of applications. A comprehensive analysis of 450 scientific papers published in 150 publications between January 2010 and May 2020 demonstrated the extensive use of Sentinel and Landsat datasets in the GEE paradigm. Interestingly, supervised machinelearning algorithms—like Random Forest—have become more and more popular for image categorization applications. Numerous domains, such as hydrology, development, natural disaster observing, climate analysis, land cover/use categorization, and imageprocessing, have found use for GEE. This investigation reveals the immense potential of GEE with RS technologies and offers insightful information on its capabilities and range of uses throughout the studied time.
For modern manufacturing firms, automation has already become a norm but constantly needs to be improved as firms still face strong demand to increase their productivity. This can be achieved by reducing dependability...
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For modern manufacturing firms, automation has already become a norm but constantly needs to be improved as firms still face strong demand to increase their productivity. This can be achieved by reducing dependability on manpower, reaching lean and even unmanned production and this is where some of the standards of Industry 4.0 come in useful, not to mention: machine Vision, imagerecognition or machinelearning. In our paper, we present SODA - our approach to build a flexible ML and AI enabled framework for object detection, analysis, and simulation. The framework is designed to support a development process of solutions requiring real-time analysis of images of different types of moving objects on a conveyor belt. In our work we discuss architectural challenges of the developed framework as well as the basic components of the system. We do also provide information on how to use the framework and present a sample implementation of an actual system employing some of the machinelearning methods. (c) 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.
The proceedings contain 46 papers. The special focus in this conference is on Mathematical Modelling, Computational Intelligence and Renewable energy. The topics include: Robotic Grasp Synthesis Using Deep learning Ap...
ISBN:
(纸本)9789811599521
The proceedings contain 46 papers. The special focus in this conference is on Mathematical Modelling, Computational Intelligence and Renewable energy. The topics include: Robotic Grasp Synthesis Using Deep learning Approaches: A Survey;a Masking-Based image Encryption Scheme Using Chaotic Map and Elliptic Curve Cryptography;automatic Speech recognition of Continuous Speech Signal of Gujarati Language Using machinelearning;Effectiveness of RSM Based Box Behnken DOE over Conventional Method for Process Optimization of Biodiesel Production;Dealing with COVID-19 Pandemic Using machinelearning Technique: A City Model Without Internal Lockdown;dynamic SentiPhraseNet to Support Sentiment Analysis in Telugu;numerical Solution of Counter-Current Imbibition Phenomenon in Homogeneous Porous Media Using Polynomial Base Differential Quadrature Method with Chebyshev-Gauss-Lobatto Grid Points;spray Behavior Analysis of Ethanol;3D Spherical—Thermal Model of Female Breast in stages of Its Development and Different Environmental Conditions;oscillating Central Force Field in Cylindrical-Polar Coordinates and Its Lagrange’s Equation of Motion;a Novel Approach for Sentiment Analysis of Hinglish Text;evolution of Sea Ice Thickness Over Various Seas of the Arctic Region for the Years 2012–13 and 2018–19;Einstein’s Cluster Demonstrating a stable Relativistic Model for strange star SAX J1808.4-3658;A Mathematical Model to study the Role of Buffer and ER Flux on Calcium Distribution in Nerve Cells;pattern Dynamics of Prey–Predator Model with Swarm Behavior via Turing Instability and Amplitude Equation;unsteady Magnetohydrodynamic Flow of Two Immiscible Fluids Through a Pipe in Presence of Heat Transfer;a Computational Model to study the Effect of Amyloid Beta on Calcium Dynamics.
Deep feedforward convolutional neural networks (CNNs) perform well in the saliency prediction of omnidirectional images (ODIs), and have become the leading class of candidate models of the visual processing mechanism ...
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ISBN:
(纸本)9781728188089
Deep feedforward convolutional neural networks (CNNs) perform well in the saliency prediction of omnidirectional images (ODIs), and have become the leading class of candidate models of the visual processing mechanism in the primate ventral stream. These CNNs have evolved from shallow network architecture to extremely deep and branching architecture to achieve superb performance in various vision tasks, yet it is unclear how brain-like they are. In particular, these deep feedforward CNNs are difficult to mapping to ventral stream structure of the brain visual system due to their vast number of layers and missing biologically-important connections, such as recurrence. To tackle this issue, some brain-like shallow neural networks are introduced. In this paper, we propose a novel brain-like network model for saliency prediction of head fixations on ODIs. Specifically, our proposed model consists of three modules: a CORnet-S module, a template feature extraction module and a ranking attention module (RAM). The CORnet-S module is a lightweight artificial neural network (ANN) with four anatomically mapped areas (V1, V2, V4 and IT) and it can simulate the visual processing mechanism of ventral visual stream in the human brain. The template features extraction module is introduced to extract attention maps of ODIs and provide guidance for the feature ranking in the following RAM module. The RAM module is used to rank and select features that are important for fine-grained saliency prediction. Extensive experiments have validated the effectiveness of the proposed model in predicting saliency maps of ODIs, and the proposed model outperforms other state-of-the-art methods with similar scale.
This article extends a classical marker-based image segmentation method proposed by Salembier and Garrido in 2000. In the original approach, the segmentation relies on two sets of pixels which play the role of object ...
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ISBN:
(数字)9783030766573
ISBN:
(纸本)9783030766566;9783030766573
This article extends a classical marker-based image segmentation method proposed by Salembier and Garrido in 2000. In the original approach, the segmentation relies on two sets of pixels which play the role of object and background markers. In the proposed extension, the markers are not represented by crisp sets, but by fuzzy ones, i.e., functions of the image domain into the real interval [0, 1] indicating the degree of membership of each pixel to the markers. We show that when the fuzzy markers are indicator functions of crisp sets, the proposed method produces the same result as the original one. We present a linear-time algorithm for computing the result of the proposed method given two fuzzy markers and we establish the correctness of this algorithm. Additionally, we discuss possible applications of the proposed approach, such as adjusting marker strength in interactive image segmentation procedures and optimizing marker locations with gradient descent methods.
Medical imageprocessing provides the information regarding the detection of brain tumor. imageprocessing techniques are used to find out the brain tumor with the help of various steps. The steps are image acquisitio...
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