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Topic Review
Park Supplementary Review System
When planning a travel or an adventure, sightseers increasingly rely on opinions posted on the Internet tourism related websites, such as TripAdvisor, Booking.com or Expedia. Unfortunately, beautiful, yet less-known places and rarely visited sightspots often do not accumulate sufficient number of valuable opinions on such websites. An approach is developed  consisting of a system (PSRS) for wildlife sightspots and propose a method for verifying collected geotagged tweets and using them as on-spot reviews. 
  • 472
  • 07 Mar 2022
Topic Review
Reinforcement Learning
Reinforcement Learning (RL) is an approach in Machine Learning that aims to solve dynamic and complex problems, in which autonomous entities, called agents, are trained to take actions that will lead them to an optimal solution
  • 472
  • 25 Jul 2023
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.
  • 472
  • 24 Aug 2023
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.
  • 472
  • 11 Mar 2024
Topic Review
Leak Detection Using Water Pipeline Vibration Sensor
Water leakage from aging water and wastewater pipes is a persistent problem, necessitating the improvement of existing leak detection and response methods. Artificial intelligence (AI)-based leak detection systems can quickly determine the source and location of a leak by analyzing data collected from various sensors and suggesting the best course of action to resolve it. IoT technology can be utilized to monitor leaks in real-time and respond automatically in conjunction with a centralized control system. 
  • 472
  • 13 Nov 2023
Topic Review
Regional-to-Local Point-Voxel Transformer
Semantic segmentation of large-scale indoor 3D point cloud scenes is crucial for scene understanding but faces challenges in effectively modeling long-range dependencies and multi-scale features. Researchers present RegionPVT, a novel Regional-to-Local Point-Voxel Transformer that synergistically integrates voxel-based regional self-attention and window-based point-voxel self-attention for concurrent coarse-grained and fine-grained feature learning. The voxel-based regional branch focuses on capturing regional context and facilitating inter-window communication. The window-based point-voxel branch concentrates on local feature learning while integrating voxel-level information within each window.
  • 470
  • 20 Oct 2023
Topic Review
Automation in Interior Space Planning
In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality.
  • 470
  • 08 Aug 2023
Topic Review
Federated Learning Algorithms in Healthcare
Federated Learning (FL), an emerging distributed collaborative artificial intelligence (AI) paradigm, is particularly suitable for smart healthcare by coordinating the training of numerous clients, that is, in healthcare institutes, without the exchange of private data.
  • 470
  • 26 Dec 2022
Topic Review
Markov Modeling of Acute Respiratory Distress Syndrome
This project focuses on utilizing mathematical Markov chain modeling as a stochastic process to analyze the stages of Acute Respiratory Distress Syndrome (ARDS). ARDS, characterized by a spectrum of severity ranging from floors to death, presents a complex clinical challenge. By employing Markov chain modeling, we aim to provide a structured framework for understanding the dynamic progression of ARDS. Our approach involves constructing a Markov chain that represents the transition of patients through various stages of ARDS, including floors, mild, moderate, severe, and ultimately death. Each stage is associated with specific clinical characteristics and outcomes, forming the basis of our modeling framework. In addition to describing the natural progression of ARDS, our project involves reviewing current clinical guidelines for managing the condition. We propose to examine the impact of each guideline on patient outcomes and the transition through different ARDS stages. By systematically analyzing the effects of various interventions and treatment strategies, we aim to provide insights into optimizing patient care and improving outcomes in ARDS management. Ultimately, this project serves as a comprehensive exploration of ARDS progression, providing healthcare professionals with a valuable framework for thinking about the condition. By integrating mathematical modeling with clinical guidelines, we seek to enhance our understanding of ARDS and contribute to more effective treatment approaches tailored to individual patient needs.
  • 469
  • 06 May 2024
Topic Review
Multi-Label Fundus Image Classification
Fundus images are used by ophthalmologists and computer-aided diagnostics to detect fundus disease such as diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, hypertension, and myopia.
  • 468
  • 30 Jun 2022
Topic Review
Deep Reinforcement Learning and Games
Deep learning (DL) algorithms were established in 2006 and have been extensively utilized by many researchers and industries in subsequent years. Ever since the impressive breakthrough on the ImageNet classification challenge in 2012, the successes of supervised deep learning have continued to pile up. Many researchers have started utilizing this new and capable family of algorithms to solve a wide range of new tasks, including ways to learn intelligent behaviors in reward–driven complex dynamic problems successfully. The agent––environment interaction expressed through observation, action, and reward channels is the necessary and capable condition of characterizing a problem as an object of reinforcement learning (RL). Learning environments can be characterized as Markov decision problems, as they satisfy the Markov property, allowing RL algorithms to be applied. From this family of environments, games could not be absent. In a game–based environment, inputs (the game world), actions (game controls), and the evaluation criteria (game score) are usually known and simulated. With the rise of DL and extended computational capability, classic RL algorithms from the 1990s could now solve exponentially more complex tasks such as games over time, traversing through huge decision spaces.
  • 468
  • 22 Feb 2023
Topic Review
Contextual Information Enhancement Network for Crack Segmentation Methods
Convolutional neural-network-based crack segmentation methods have performed excellently. However, existing crack segmentation methods still suffer from background noise interference, such as dirt patches and pitting, as well as the imprecise segmentation of fine-grained spatial structures. 
  • 467
  • 24 Nov 2022
Topic Review
Malicious Social Network Messages
The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages.
  • 467
  • 19 Sep 2023
Topic Review
Deep Learning for Robotic Vision Methods
Robotic vision algorithms serve three primary functions in visual perception. Pattern recognition in machine vision is the process of identifying and classifying objects or patterns in images or videos using machine learning algorithms. Deep learning in robotic vision reveals a plethora of promising approaches, each with its own unique strengths and characteristics. Robotic vision systems can leverage the strengths of each architecture to improve object detection, tracking, and the understanding of complex visual scenes in dynamic environments. Big data and federated learning play significant roles in advancing the field of computer vision. Big data provides a wealth of diverse visual information, which is essential for training deep learning models that power computer vision applications. These datasets enable more accurate object recognition, image segmentation, and scene understanding.
  • 467
  • 07 Feb 2024
Topic Review
Unmixing-Guided Convolutional Transformer for Spectral Reconstruction
Specifically, transformer and ResBlock components are embedded in Paralleled-Residual Multi-Head Self-Attention (PMSA) to facilitate fine feature extraction guided by the excellent priors of local and non-local information from CNNs and transformers. Furthermore, the Spectral–Spatial Aggregation Module (S2AM) combines the advantages of geometric invariance and global receptive fields to enhance the reconstruction performance.
  • 466
  • 19 Jun 2023
Topic Review
Hill Climb Assembler Encoding
Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks. HCAE is a light variant of HCMAE and it originates from both AE and AEEO. All the algorithms are based on three key components, i.e., a network definition matrix (NDM), which represents the neural networks, assembler encoding program (AEP), which operates on NDM, and evolutionary algorithm, whose task is to produce optimal AEPs, NDMs, and, consequently, the networks.
  • 465
  • 15 Aug 2022
Topic Review
Deep Learning Approaches for Distance Estimation
Visual impairment (VI) is a significant public health concern that affects people of all ages and is caused by a range of factors, including age-related eye diseases, genetic disorders, injuries, and infections. Therefore, governments of different countries are attempting to design various assistive living facilities for individuals with visual impairments. Machine learning techniques have greatly improved object recognition accuracy in computer vision [8]. This has led to the development of sophisticated models that can recognize objects in complex environments. 
  • 465
  • 16 Oct 2023
Topic Review
Automated Stuttering Classification
Speech disfluency, particularly stuttering, can have a significant impact on effective communication. Stuttering is a speech disorder characterized by repetitions, prolongations, and blocks in the flow of speech, which can result in communication difficulties, social isolation, and low self-esteem. Stuttering can also lead to negative reactions from listeners, such as impatience or frustration, which can further exacerbate communication difficulties.
  • 463
  • 07 Oct 2023
Topic Review
Raft (Computer Science)
Raft is a consensus algorithm designed as an alternative to the Paxos family of algorithms. It was meant to be more understandable than Paxos by means of separation of logic, but it is also formally proven safe and offers some additional features. Raft offers a generic way to distribute a state machine across a cluster of computing systems, ensuring that each node in the cluster agrees upon the same series of state transitions. It has a number of open-source reference implementations, with full-specification implementations in Go, C++, Java, and Scala. It is named after Reliable, Replicated, Redundant, And Fault-Tolerant. Raft is not a Byzantine fault tolerant algorithm: the nodes trust the elected leader.
  • 462
  • 31 Oct 2022
Topic Review
AI-Enabled Models for Parkinson’s Disease Diagnosis
Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. 
  • 462
  • 15 May 2023
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