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Topic Review
Machine Learning-Based Forecasting of Renewable Energy
With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus machine learning (ML) and deep learning (DL) algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions.
  • 2.8K
  • 27 Apr 2023
Topic Review
3D Point Cloud Classification
Three-dimensional (3D) point cloud classification methods based on deep learning have good classification performance. The 3D point cloud is mainly collected by light detection and ranging (LiDAR) scanner, red, green, blue, and depth (RGB-D) camera, and other sensor equipment or obtained by model conversion using computer software.
  • 2.8K
  • 10 Jun 2022
Topic Review
DNA Circuits
Deoxyribonucleic acid (DNA), a genetic material, encodes all living information and living characteristics, e.g., in cell, DNA signaling circuits control the transcription activities of specific genes. In recent years, various DNA circuits have been developed to implement a wide range of signaling and for regulating gene network functions. In particular, a synthetic DNA circuit, with a programmable design and easy construction, has become a crucial method through which to simulate and regulate DNA signaling networks. Importantly, the construction of a hierarchical DNA circuit provides a useful tool for regulating gene networks and for processing molecular information. Moreover, via their robust and modular properties, DNA circuits can amplify weak signals and establish programmable cascade systems, which are particularly suitable for the applications of biosensing and detecting. Furthermore, a biological enzyme can also be used to provide diverse circuit regulation elements. 
  • 2.8K
  • 13 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.
  • 2.8K
  • 10 Oct 2020
Topic Review
Fault Detection for Belt Conveyor Idlers
Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations.
  • 2.8K
  • 22 Feb 2023
Topic Review
Deep Learning Algorithms and Their Applications
Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data.
  • 2.8K
  • 17 Mar 2022
Topic Review
Federated Learning and Blockchain
The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node.
  • 2.8K
  • 30 Aug 2023
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.
  • 2.7K
  • 31 Aug 2023
Topic Review
Smart Sensing Chair for Sitting Posture Monitoring
Smart sensing chairs, equipped with advanced sensor technologies such as Force Sensing Resistors (FSRs), show significant potential in mitigating the negative health impacts of incorrect sitting postures, which can lead to spinal misalignment and musculoskeletal disorders. Research emphasizes the use of sophisticated machine learning techniques, including Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), for classifying sitting postures. Notably, these advanced models do not always outperform traditional models due to the limitations of the training datasets, which often lack sufficient diversity in representing different human body types and health conditions. This finding underscores the critical need for datasets that more accurately reflect the demographic and physiological diversity of users. Additionally, this research highlights a significant opportunity for innovation in user feedback mechanisms within smart sensing chairs, suggesting that enhanced interactive features could improve posture correction efforts and overall user health outcomes.
  • 2.7K
  • 13 May 2024
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.
  • 2.7K
  • 08 Oct 2021
Topic Review
Machine Learning in Healthcare Industry
Machine learning is a mechanism that enables machines to learn automatically without explicit programming. The main area of machine learning is to use advanced algorithms and statistical techniques to access the data and predict accuracy instead of a rule-based system. The dataset is a primary component of machine learning accuracy prediction. As a result, the data are more relevant and the prediction is more accurate. Machine learning has been used in different fields, such as finance, retail, and the healthcare industry. The rising use of machine learning in healthcare provides more opportunities for disease diagnosis and treatment. Machine learning has a great feature of continuous improvement for data accurate prediction and classification purposes for disease analysis.
  • 2.7K
  • 05 May 2023
Topic Review
Intelligent Connected Vehicle Cooperative Driving Development
Intelligent connected vehicle formation is mainly for more intelligent snatched vehicles in a complex traffic environment. By adjusting their driving speed and steering, it makes itself and nearby intelligent connected vehicles keep relatively stable geometric posture and the same movement, and meets the task requirements and constraints (such as obstacle avoidance), so as to realize more intelligent connected vehicles between wireless communication collaborative driving behavior. The main technologies involved in the autonomous vehicle formation include: vehicle combination positioning and multi-sensor and multi-source information fusion technology, collaborative formation control technology, and cooperative perception and communication technology.
  • 2.7K
  • 23 Nov 2022
Topic Review
Expert System and Decision Support System
Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis.
  • 2.7K
  • 13 Dec 2022
Topic Review
From Word Embeddings to Pre-Trained Language Models
With the advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. Prior embedding models as well as breakthroughs in the field of PLMs are provided in this entry.
  • 2.7K
  • 18 Nov 2022
Topic Review
A Systematic Approach to Healthcare Knowledge Management Systems
Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and technologists. A healthcare knowledge management system that ensures the systematic knowledge development process on various data in hospitals was proposed. It leverages big data technologies to capture, organize, transfer, and manage large volumes of medical knowledge, which cannot be handled with traditional data-processing technologies. In addition, machine-learning algorithms are used to derive knowledge at a higher level in supporting diagnosis and treatment.
  • 2.7K
  • 13 May 2022
Topic Review
Convolutional Neural Network in Histopathology
Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research.
  • 2.7K
  • 04 May 2023
Topic Review
Cryptographically-Secure Pseudorandom Number Generator
A cryptographically secure pseudorandom number generator (CSPRNG) or cryptographic pseudorandom number generator (CPRNG) is a pseudorandom number generator (PRNG) with properties that make it suitable for use in cryptography. It is also loosely known as a cryptographic random number generator (CRNG) (see Random number generation § "True" vs. pseudo-random numbers). Most cryptographic applications require random numbers, for example: key generation nonces salts in certain signature schemes, including ECDSA, RSASSA-PSS The "quality" of the randomness required for these applications varies. For example, creating a nonce in some protocols needs only uniqueness. On the other hand, the generation of a master key requires a higher quality, such as more entropy. And in the case of one-time pads, the information-theoretic guarantee of perfect secrecy only holds if the key material comes from a true random source with high entropy, and thus any kind of pseudorandom number generator is insufficient. Ideally, the generation of random numbers in CSPRNGs uses entropy obtained from a high-quality source, generally the operating system's randomness API. However, unexpected correlations have been found in several such ostensibly independent processes. From an information-theoretic point of view, the amount of randomness, the entropy that can be generated, is equal to the entropy provided by the system. But sometimes, in practical situations, more random numbers are needed than there is entropy available. Also, the processes to extract randomness from a running system are slow in actual practice. In such instances, a CSPRNG can sometimes be used. A CSPRNG can "stretch" the available entropy over more bits.
  • 2.6K
  • 20 Oct 2022
Topic Review
Human Detection in Heavy Smoke Scenarios
The most dangerous factor in a fire scene is smoke and heat, especially smoke. How to locate people and guide them out of a heavy smoke environment will be the key to surviving an evacuation process. A variety of instruments have been studied that can be used in fire and smoky situations, including visible camera, kinetic depth sensor, LIDAR, night vision, IR camera, radar, and sonar.
  • 2.6K
  • 04 Aug 2022
Topic Review
Methods for Crowd Counting
The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, then the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. 
  • 2.6K
  • 21 Jul 2022
Topic Review
Handwritten Character Recognition in Handwritten Character Recognition
Neural networks have made big strides in image classification. Convolutional neural networks (CNN) work successfully to run neural networks on direct images. Handwritten character recognition (HCR) is now a very powerful tool to detect traffic signals, translate language, and extract information from documents, etc. Although handwritten character recognition technology is in use in the industry, present accuracy is not outstanding, which compromises both performance and usability.
  • 2.6K
  • 13 May 2022
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