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Topic Review
Prediction of Water Quality Classification using Machine Learning
Machine Learning (ML) has been used for a long time and has gained wide attention over the last several years. It can handle a large amount of data and allow non-linear structures by using complex mathematical computations. However, traditional ML models do suffer some problems, such as high bias and overfitting. Therefore, this has resulted in the advancement and improvement of ML techniques, such as the bagging and boosting approach, to address these problems.
  • 2.6K
  • 21 Jun 2022
Topic Review
Videos Data Augmentation for Deep Learning Models
In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive to collect. Data augmentation can alleviate the problem, generating new data from a smaller initial dataset. Geometric and color space image augmentation methods can increase accuracy of Deep Learning models but are often not enough. More advanced solutions are Domain Randomization methods or the use of simulation to artificially generate the missing data. Data augmentation algorithms are usually specifically designed for single images. Most recently, Deep Learning models have been applied to the analysis of video sequences.
  • 2.5K
  • 25 Mar 2022
Topic Review
Academic and Administrative Role of AI in Education
Using AI in education can have a dramatic impact on the way academic and administrative staff use their time and the manner in which students are served individually. Artificial Intelligence Applications are assisting the education sector organizations at two main levels.1. Administrative level (admission, counseling, library services, etc.)2. Academic Level (assessment, feedback, tutoring, etc.)
  • 2.4K
  • 20 Apr 2022
Topic Review
Autonomous Vehicles
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving functions, without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors.  An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task.  Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030. 
  • 2.3K
  • 11 Feb 2021
Topic Review
Environmental Intelligence
We propose that coupled human and environmental information processing can be applied to concomitantly increase the scope and penetration of surveillance, leading to new opportunities to detect, track, quantify, predict and influence events and interactions across a wide range of operations. Academia and non-governmental organizations, state actors, and commercial interests are expected to utilize and benefit from environmental intelligence in differentiated manners.
  • 2.3K
  • 14 Jan 2022
Topic Review
DEFLATE
In computing, Deflate is a lossless data compression file format that uses a combination of LZSS and Huffman coding. It was designed by Phil Katz, for version 2 of his PKZIP archiving tool. Deflate was later specified in RFC 1951 (1996). Katz also designed the original algorithm used to construct Deflate streams. This algorithm was patented as U.S. Patent 5,051,745, and assigned to PKWARE, Inc. As stated in the RFC document, an algorithm producing Deflate files was widely thought to be implementable in a manner not covered by patents. This led to its widespread use, for example in gzip compressed files and PNG image files, in addition to the ZIP file format for which Katz originally designed it. The patent has since expired.
  • 2.3K
  • 11 Oct 2022
Topic Review
Artificial Intelligence and Cyber-Physical Systems
Modern society is living in an age of paradigm changes. In part, these changes have been driven by new technologies, which provide high performance computing capabilities that enable the creation of complex Artificial Intelligence systems. Those developments are allowing the emergence of new Cyber Systems where the continuously generated data is utilized to build Artificial Intelligence models used to perform specialized tasks within the system. While, on one hand, the isolated application of the cyber systems is becoming widespread, on the other hand, their synchronical integration with other cyber systems to build a concise and cognitive structure that can interact deeply and autonomously with a physical system is still a completely open question, only addressed in some works from a philosophical point of view. From this standpoint, the AI can play an enabling role to allow the existence of these cognitive CPSs.
  • 2.3K
  • 08 Oct 2021
Topic Review
Machine Learning and Student Performance Prediction
Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals.
  • 2.2K
  • 08 Dec 2021
Topic Review
Active and Assisted Living
Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today’s society from both an economic and societal standpoint. AAL can provide an array of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. 
  • 2.2K
  • 19 Nov 2021
Topic Review
AI Public Datasets for Railway Applications
The aim of this entry is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector.
  • 2.2K
  • 09 Oct 2021
Topic Review
Learning Paradigms
Learning paradigms are more like methodologies that guide problem solving. In addition to the most widely used supervised learning paradigm, other learning paradigms are also employed in the multimodal field, such as semi-supervised learning, self-supervised learning, and transfer learning. 
  • 2.2K
  • 06 Jul 2022
Topic Review
Deep Learning in Deconvolution Problem
In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution.
  • 2.2K
  • 24 Dec 2021
Topic Review
BCI Emotion Recognition
This entry gives an overview of available datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation for emotion recognition using EEG-based BCI systems.
  • 2.2K
  • 10 Oct 2020
Topic Review
Computer-Human Interaction and Collaboration
Through a series of projects carried out by the Computer–Human Interaction and COllaboration (CHICO) group of the University of Castilla-La Mancha, some proposals are presented to improve the current e-Learning systems by making use of different paradigms of human-computer interaction. Synchronous and asynchronous collaborative systems, ubiquitous computing, and augmented reality can improve the current learning environments. 
  • 2.2K
  • 18 Mar 2021
Topic Review
Attention Mechanism for Remote Sensing
Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods.
  • 2.2K
  • 24 Nov 2021
Topic Review
Formal Methods for Artificial Intelligence: Opportunities and Challenges
The use of formal approaches in machine learning is becoming increasingly crucial as ML systems are utilized in more critical applications such as autonomous driving and medical diagnosis. Formal methods give a rigorous approach to evaluating the accuracy and reliability of ML systems, which is critical for ensuring their safety and efficacy. Formal approaches, which use mathematical models and logic-based reasoning, can assist discover and eliminate flaws and vulnerabilities in ML systems, lowering the risk of unintended effects and boosting overall performance. As a result, using formal approaches is vital for developing trustworthy ML systems that can be depended on in safety-sensitive applications.
  • 2.2K
  • 22 May 2023
Topic Review
Damaged QR Code Reconstruction
QR codes often become difficult to recognize due to damage. Traditional restoration methods exhibit a limited effectiveness for severely damaged or densely encoded QR codes, are time-consuming, and have limitations in addressing extensive information loss.
  • 2.1K
  • 02 Nov 2023
Topic Review
Convolutional Neural Network + Recurrent Neural Network
A convolutional neural network and recurrent neural network (CNN + RNN) combination is an effective approach for many modern image recognition tasks that need to identify the behaviour of objects through a sequence of frames. For example, in a security CCTV camera footage, want to identify what abnormal actions a character is doing in the scene (e.g. fighting with someone, breaking into a store, etc.). A deep convolutional neural network (e.g. ResNet50) has many layers of abstraction and is good for extracting essential features in each frame of the input stream. These extracted features, which may represent low-level image features or even high-level objects, can be monitored over a sequence of frames by a recurrent neural network (e.g. ConvLSTM) so as to detect whether a certain action or event has happened.
  • 2.1K
  • 23 Feb 2022
Topic Review
Radar Object Detection
With the improvement of automotive radar resolution, radar target classification has become a hot research topic. Deep radar detection can be classified into point-cloud-based and pre-CFAR-based. Radar point cloud and pre-CFAR data are similar to the LiDAR point cloud and visual image, respectively. Accordingly, the architectures for LiDAR and vision tasks can be adapted for radar detection. 
  • 2.1K
  • 07 Jun 2022
Topic Review
Deep Residual Learning for Image Recognition
In 2015, a deep residual network (ResNet) was proposed for image recognition. It is a type of convolutional neural network (CNN) where the input from the previous layer is added to the output of the current layer. Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. 
  • 2.1K
  • 22 Sep 2022
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