Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
AI Agent Model for Extrinsic Emotion Regulation
Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. To address this gap, a computational model for intelligent agents has been proposed for facilitating emotion regulation in individuals. This model is grounded in a multidimensional emotion representation and on J. Gross’s theoretical framework of emotion regulation. In this apporach, an intelligent agent selects the most appropriate regulation strategies to reach or maintain an individual’s emotional equilibrium considering the individual’s personality traits and specific characteristics.
  • 156
  • 11 Mar 2024
Topic Review
Anomaly Detection in Autonomous Robotic Missions
An anomaly in autonomous robotic missions (ARM) is a deviation from the expected behaviour, performance, or state of the robotic system and its environment, which may impact the mission’s objectives, safety, or efficiency; and this anomaly can be caused either by system faults or the change in the environmental dynamics of interaction. The nuanced understanding of anomaly categories facilitates a more strategic approach, ensuring that detection methods are more effective in addressing the specific nature of the anomaly.
  • 334
  • 11 Mar 2024
Topic Review
Applications of Blockchain-Based Federated Learning
Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. Blockchain-based federated learning (BFL) has gained the capability and prospects for applications in highly privacy-sensitive industries. 
  • 72
  • 08 Mar 2024
Topic Review
Transfer Learning Strategies
Discriminatively trained models perform well if labeled data are available in abundance, but they do not perform adequately for tasks with scarce datasets as this limits their learning abilities. To address this issue, Large language models (LLMs) were first pretrained on large unlabeled datasets using the self-supervised approach, where the learning was then transferred discriminatively on specific tasks. As a result, transfer learning helps to leverage the capabilities of pretrained models and is advantageous, especially in data-scare settings. For example, generative pretrained transformer (GPT) used the generative language model objective for pretraining, followed by discriminative finetuning. Compared to pretraining, the transfer learning process is inexpensive and converges faster than training the model from scratch. Additionally, pretraining uses an unlabeled dataset and follows a self-supervised approach, whereas transfer learning follows a supervised technique using a labeled dataset particular to the downstream task. The pretraining dataset comes from a generic domain, whereas, during transfer learning, data come from specific distributions (supervised datasets specific to the desired task).
  • 161
  • 08 Mar 2024
Topic Review
Machine Vision and Industry 4.0 to Industry 5.0
With the emergence of artificial intelligence (AI) and its integration into various intelligent robotics, the Fourth Industrial Revolution, also known as Industry 4.0, managed to trigger changes. Its need has been emphasized in multiple situations, such as that of the COVID-19 pandemic, entering every area of human life, with Industry 4.0 being more and more involved in production processes. Industry 4.0 is an emerging concept that is multidisciplinary and complex. Leveraging not just one, but a patchwork of technologies that can work individually as well as in combination, Industry 4.0 strives to achieve a more general digital transformation with high expectations both in the production of products and services in real-time. This effort is mainly based on advanced computers with fast processors able to store, manage, process, and analyze a large amount of data, spending less time and resources than ever before.
  • 103
  • 08 Mar 2024
Topic Review
Modelling and Measuring Trust in Human–Robot Collaboration
Human–Robot Collaboration (HRC) has emerged as a critical area in the engineering and social sciences domain. In any kind of collaboration, including HRC, trust has been identified as a significant factor that can either motivate or hinder cooperation, especially in scenarios characterized by incomplete or uncertain information.
  • 60
  • 08 Mar 2024
Topic Review
Advancements and Challenges in Handwritten Text Recognition
Handwritten Text Recognition (HTR) involves automatically transforming a source’s handwritten text within a digital image into its machine text representation.
  • 267
  • 07 Mar 2024
Topic Review
Challenges in Agricultural Image Datasets and Filter Algorithms
Smart farming is facilitated by remote sensing because it allows for the inexpensive monitoring of crops, crop classification, stress detection yield forecasting using lightweight sensors over a wide area in a relatively short amount of time. Deep learning (DL)-based computer vision is one of the important aspects of the automatic detection and monitoring of plant stress. Challenges for DL algorithms in the agricultural dataset include size variation in objects, image resolution, background clutter, precise annotation with the expert, high object density or the demand for different spectral images.
  • 107
  • 07 Mar 2024
Topic Review
Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
Inductive relationship prediction for knowledge graphs, as an important research topic, aims to predict missing relationships between unknown entities and many practical applications. Most of the existing approaches to this problem use closed subgraphs to extract features of target nodes for prediction; however, there is a tendency to ignore neighboring relationships outside the closed subgraphs, which leads to inaccurate predictions. In addition, they ignore the rich commonsense information that can help filter out less compelling results.
  • 82
  • 07 Mar 2024
Topic Review
NeRF-Based SLAM
Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. 
  • 127
  • 05 Mar 2024
  • Page
  • of
  • 115
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Feedback