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Topic Review
Artificial Intelligence in Pathology
Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. 
  • 903
  • 05 Jul 2021
Topic Review
Multimodal Segmentation Techniques in Autonomous Driving
Semantic Segmentation has become one of the key steps toward scene understanding, especially in autonomous driving scenarios. In the standard formulation, Semantic Segmentation uses only data from color cameras, which suffer significantly in dim lighting or adverse weather conditions. A solution to this problem is the use of multiple heterogeneous sensors (e.g., depth and thermal cameras or LiDARs) as the input to machine learning approaches tackling this task, allowing to cover for the shortcomings of color cameras and to extract a more resilient representation of the scene.
  • 901
  • 15 Aug 2022
Topic Review
Facial Information for Healthcare Applications
The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g. the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.
  • 896
  • 28 Oct 2020
Topic Review
Fuel Consumption and CO2 of Light-Duty Vehicles
Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.
  • 895
  • 20 Jan 2022
Topic Review
Transfer Learning
Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks/domains to improve generalization in the tasks/domains of interest.
  • 895
  • 09 Jan 2022
Topic Review
Financial Technology Influence on the Banking Industry
The synthesis of technology and finance is known as financial technology (Fintech), which brings together two of the biggest industries in harmony. Fintech disruption is a deviation from the norm, resulting in a significant shift in banking services and, as a result, risk. 
  • 894
  • 17 Nov 2022
Topic Review
Robotic Platform for Horticulture
The modern level of development of infocommunication and computer technologies, microprocessor technology and equipment, communication and positioning makes possible the development and practical application of automated and robotic technologies and technical means to improve the efficiency of agricultural production. Currently, intensive horticulture is becoming increasingly widespread due to rapid fruiting and high yield rates. At the same time, the process of harvesting apples in intensive horticulture is the most time-consuming, and harvesting is carried out mainly by a team of pickers. In the production process of cultivating fruit crops, this is an important final stage which requires the development of automated devices and robotic platforms with a control system capable of offline harvesting.
  • 891
  • 02 Dec 2022
Topic Review
Image Segmentation Techniques
Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. 
  • 889
  • 06 May 2023
Topic Review
Fall Detection and Prevention
A fall can be described as an unpredicted event leading the participants to rest on the lower level (ground or floor). As a result, it causes injuries that can often be fatal. Psychological grievances are also considered as the consequence of falls. People may suffer from anxiety, depression, activity restriction, and fear of falling. The primary physiological issue in older adults is fear of falling, restricting their Activities of Daily Life (ADL). This fear leads to activity restriction, which may lead to inadequate gait balance and weakened muscle that affects the mobility and independence of older adults. Therefore, remote/wearable technologies are required to track, detect, and prevent falls for improving the overall quality of life (QoL). For this purpose, understanding of falls can be classified as fall prevention and fall detection. Fall detection refers to the detection of a fall using sensors/cameras to summon help. In contrast, fall prevention aims to avert falls by observing human locomotion. Numerous systems have been developed using different sensors and algorithms to detect and prevent the fall.
  • 885
  • 09 Aug 2021
Topic Review
Physical Layer Authentication in Wireless Networks
The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity.
  • 881
  • 15 Mar 2023
Topic Review
Interval Scheduling
Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be executed. For instance, task A might run from 2:00 to 5:00, task B might run from 4:00 to 10:00 and task C might run from 9:00 to 11:00. A subset of intervals is compatible if no two intervals overlap. For example, the subset {A,C} is compatible, as is the subset {B}; but neither {A,B} nor {B,C} are compatible subsets, because the corresponding intervals within each subset overlap. The interval scheduling maximization problem (ISMP) is to find a largest compatible set - a set of non-overlapping intervals of maximum size. The goal here is to execute as many tasks as possible. In an upgraded version of the problem, the intervals are partitioned into groups. A subset of intervals is compatible if no two intervals overlap, and moreover, no two intervals belong to the same group (i.e. the subset contains at most a single representative interval of each group). The group interval scheduling decision problem (GISDP) is to decide whether there exists a compatible set in which all groups are represented. The goal here is to execute a single representative task from each group. GISDPk is a restricted version of GISDP in which the number of intervals in each group is at most k. The group interval scheduling maximization problem (GISMP) is to find a largest compatible set - a set of non-overlapping representatives of maximum size. The goal here is to execute a representative task from as many groups as possible. GISMPk is a restricted version of GISMP in which the number of intervals in each group is at most k. This problem is often called JISPk, where J stands for Job. GISMP is the most general problem; the other two problems can be seen as special cases of it:
  • 878
  • 07 Nov 2022
Topic Review
Communication Costs in Federated Learning
Federated learning (FL) is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients.
  • 872
  • 31 Aug 2023
Topic Review
Machine Learning Methods in Weather and Climate Applications
With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. 
  • 872
  • 15 Dec 2023
Topic Review
Technologies for Improving Storage Efficiency in Blockchain-Based IIoT
The Internet of Things (IoT) and blockchain have contributed to massive advancements in the fields to which they have been applied. The benefits of the blockchain, which include enhanced security, transparency, and greater traceability, make it a promising technology for integration with IIoT, which has long had issues with security. However, there are several issues that limit the integration of blockchain into Industrial Internet of Things (IIoT) systems. One of these issues is the huge storage requirement of the blockchain. There are several solutions to address these concerns. These solutions, which include summarization-based, compression-based, and storage scheme optimization methods, are necessary to enable the further development of blockchain–IIoT integration. However, these solutions have shortcomings that reduce their effectiveness. Compression-based schemes produce compressed blocks or data that accumulate over time and may not ensure enough storage savings on peers. This can be alleviated by designing compression techniques that provide an efficient representation of data for IIoT systems to yield better compression ratios. Summarization-based schemes reduce redundancy in block data by using the net change in transferring entities between parties and, thus, are better suited for financial systems than for IIoT systems. 
  • 869
  • 30 Oct 2022
Topic Review
LiFi Network
Light Fidelity (LiFi), a new technology that uses light to transmit data as a high-speed wireless connection system from a wide spectrum of domains.
  • 869
  • 01 Dec 2021
Topic Review
Industrial Control Systems Technologies
Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. 
  • 862
  • 21 Nov 2023
Topic Review
Bilinear Filtering
Bilinear filtering is a texture filtering method used to smooth textures when displayed larger or smaller than they actually are. Most of the time, when drawing a textured shape on the screen, the texture is not displayed exactly as it is stored, without any distortion. Because of this, most pixels will end up needing to use a point on the texture that is "between" texels – assuming the texels are points (as opposed to, say, squares) – in the middle (or on the upper left corner, or anywhere else; it does not matter, as long as it is consistent) of their respective "cells". Bilinear filtering uses these points to perform bilinear interpolation between the four texels nearest to the point that the pixel represents (in the middle or upper left of the pixel, usually).
  • 861
  • 02 Nov 2022
Topic Review
Augmented Reality Mobile App to Learn Writing
Augmented reality (AR) has been widely used in education, particularly for child education. This entry presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing.
  • 860
  • 10 Jan 2022
Topic Review
HAR in Smart Homes
Human Activity Recognition (HAR) consists in monitoring and analyzing the behavior of one or more persons in order to deduce their activity. In a smart home context, the HAR consists in monitoring daily activities of the residents, based on a network of IoT devices. Owing to this monitoring, a smart home can offer personalized home assistance services to improve quality of life, autonomy and health of their residents, especially for elderly and dependent people.
  • 858
  • 19 Nov 2021
Topic Review
Machine Learning Approaches in SCRM
Machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. The applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM.
  • 856
  • 28 Sep 2021
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