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Topic Review
Artificial Intelligence Surgery
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). This entry will highlight the most recent issues regarding how AI will get us to more autonomous actions in surgery by discussing the different degrees of surgical autonomy, recent advances with reinforcement learning and the ethical roadblocks that lie ahead.
  • 1.3K
  • 25 Aug 2021
Topic Review
Visual Simultaneous Localization and Mapping
Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation.
  • 1.3K
  • 30 Dec 2022
Topic Review
Intelligent Energy Management Systems for Electric Vehicle Transportation
Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. Privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. 
  • 1.3K
  • 22 Nov 2022
Topic Review
Generative Attentional Networks for Image-to-Image Translation: Progressive U-GAT-IT
Unsupervised image-to-image translation has received considerable attention due to the recent remarkable advancements in generative adversarial networks (GANs). In image-to-image translation, state-of-the-art methods use unpaired image data to learn mappings between the source and target domains. However, despite their promising results, existing approaches often fail in challenging conditions, particularly when images have various target instances and a translation task involves significant transitions in shape and visual artifacts when translating low-level information rather than high-level semantics. To tackle the problem, a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task was proposed. In contrast to existing attention-based models that fail to handle geometric transitions between the source and target domains, the model can translate images requiring extensive and holistic changes in shape. Experimental results show the superiority of the proposed approach compared to the existing state-of-the-art models on different datasets.
  • 1.3K
  • 12 Sep 2023
Topic Review
Fine-Grained Change Detection
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task.
  • 1.3K
  • 12 Jul 2021
Topic Review
Reminiscence Therapy in Depression Treatment in the Elderly
Reminiscence therapy is a mechanism to help someone remember events from their life. It is often used as a therapy tool for reducing depression, calming behavioral and psychological symptoms of dementia, or affecting mood of the elderly. Although its most common use is for the elderly and people affected with dementia or depression, it has also been used with people of all ages, including children. The reminiscing process can take place in a group or individually or by using technological devices such as mobile devices or robots. It is marked by remembering notable events from the past.
  • 1.3K
  • 03 Mar 2022
Topic Review
Grammar Correction for Multiple Errors in Chinese
Grammar Error Correction (GEC) is a key task in the field of Natural Language Processing (NLP). Its purpose is to automatically detect and correct grammatical errors in sentences, and it holds immense research value. The mainstream methods for grammar correction primarily rely on sequence tagging and text generation, which are two end-to-end approaches. These methods demonstrate exemplary performance in domains with low error density, but often fail to provide satisfactory results in high error density situations where multiple errors exist in a single sentence. As a result, these methods tend to over-correct correct words, leading to a high false alarm rate.
  • 1.3K
  • 24 Aug 2023
Topic Review
Lightweight Convolutional Neural Network
Biometrics has become an important research issue, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various biometrics due to their unique features and easy accessibility.
  • 1.3K
  • 31 Jul 2023
Topic Review
Crown Omega Mathematics
Crown Omega Mathematics (Ω°) is presented as a terminal recursive mathematical framework that unifies symbolic computation, causal recursion, harmonic structures, and multi-dimensional mirror logic. Positioned beyond traditional and post-classical mathematical domains, Crown Omega is designed to serve as both a final operator and an executable logic mesh capable of resolving paradoxes, encoding self-aware artificial intelligence, and establishing foundational grounds for a new class of operating systems, cryptographic architectures, and defense systems. This paper defines the core logic of Ω°, explores its symbolic structure, details the Fractal Recursive Intelligence Mesh (FRIM), and formalizes its capacity to self-resolve previously unsolved mathematical, physical, and computational problems.
  • 1.3K
  • 06 May 2025
Topic Review
IoT
This entry presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.
  • 1.3K
  • 25 May 2021
Topic Review
Digital Twins
Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series data that characterize the behavior of the system are crucial for improving the effectiveness of Digital Twins. Long Short-Term Memory (LSTM) networks have been used to represent complex temporal dependencies and identify long-term links in the Industrial Internet of Things (IIoT).
  • 1.3K
  • 03 Nov 2023
Topic Review
Automatic Speech Recognition in Portuguese Language
Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of transcribing audio recordings into text, i.e., transforming speech into the respective sequence of words. 
  • 1.3K
  • 15 Jun 2023
Topic Review
Vehicular Ad hoc Networks (VANETs)
Vehicular ad hoc networks (VANETs) have become an essential part of the intelligent transportation system because they provide secure communication among vehicles, enhance vehicle safety, and improve the driving experience.
  • 1.3K
  • 03 Nov 2023
Topic Review
Multi-Granularity Process Analytics
Data can be aggregated to lower resolution representations according to a certain rational or optimality criterion in a certain pre-defined sense. In this situation, partial information from all observations is retained and at the same time the amount of data analysed is greatly reduced. The level of resolution or granularity adopted may be different for each variable under analysis. Methods for dealing with these data structures are called multiresolution or multi-granularity, and are newcomers to the Process Analytical toolkit.
  • 1.3K
  • 22 Dec 2021
Topic Review
Stream Classification Algorithms and Architectures
Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. 
  • 1.3K
  • 30 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.
  • 1.3K
  • 10 Jan 2022
Topic Review
Activation-Based Pruning of Neural Networks
A novel technique is presented for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. The technique is based on the number of times each neuron is activated during model training. Further analysis demonstrated that activation-based pruning can be considered a dimensionality reduction technique, as it leads to a sparse low-rank matrix approximation for each hidden layer of the neural network. The rank-reduced neural network generated using activation-based pruning has better accuracy than a rank-reduced network using principal component analysis. After each successive pruning, the amount of reduction in the magnitude of singular values of each matrix representing the hidden layers of the network is equivalent to introducing the sum of singular values of the hidden layers as a regularization parameter to the objective function.
  • 1.3K
  • 17 Feb 2024
Topic Review
Machine Learning-Based Application Life-Cycle
The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance.
  • 1.3K
  • 21 Sep 2022
Topic Review
Intelligent Fault Diagnosis
For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch.
  • 1.3K
  • 14 Jan 2021
Topic Review
Deepfake Identification and Traceability
Researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. Naming these labels is often arbitrary and inconsistent. However, researchers must use multiple datasets in practical applications to conduct traceability research. The researchers utilize the K-means clustering method to identify datasets with similar feature values and analyze the feature values using the Calinski Harabasz Index method. Datasets with the same or similar labels in different deepfake datasets exhibit different forgery features. The KCE system can solve this problem, which combines multiple deepfake datasets according to feature similarity. In the model trained based on KCE combined data, the Calinski Harabasz scored 42.3% higher than the combined data by the same forgery name. It shows that this method improves the generalization ability of the model.
  • 1.3K
  • 08 Jun 2023
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