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Topic Review
Generative AI
Generative AI models harness the capabilities of neural networks to discern patterns and structures within existing datasets and create original content. These AI models draw inspiration from human neuronal processes, learning from data inputs to create new output that matches learned patterns.
  • 286
  • 22 Feb 2024
Topic Review
Machine-Learning Methods for Speech and Handwriting Detection
Brain–Computer Interfaces (BCIs) have become increasingly popular due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people.
  • 285
  • 30 Jun 2023
Topic Review
Explainable Artificial Intelligence in Medicine
Due to the success of artificial intelligence (AI) applications in the medical field over the past decade, concerns about the explainability of these systems have increased. The reliability requirements of black-box algorithms for making decisions affecting patients pose a challenge even beyond their accuracy. Recent advances in AI increasingly emphasize the necessity of integrating explainability into these systems. While most traditional AI methods and expert systems are inherently interpretable, the recent literature has focused primarily on explainability techniques for more complex models such as deep learning.
  • 285
  • 16 Oct 2023
Topic Review
Brain Tumor  Segmentation
Brain tumor segmentation plays a crucial role in the diagnosis, treatment planning, and monitoring of brain tumors. Accurate segmentation of brain tumor regions from multi-sequence magnetic resonance imaging (MRI) data is of paramount importance for precise tumor analysis and subsequent clinical decision making. The ability to delineate tumor boundaries in MRI scans enables radiologists and clinicians to assess tumor size, location, and heterogeneity, facilitating treatment planning and evaluating treatment response. Traditional manual segmentation methods are time-consuming, subjective, and prone to inter-observer variability. Therefore, the automatic segmentation algorithm has received widespread attention as an alternative solution. For instance, the self-organizing map (SOM) is an unsupervised exploratory data analysis tool that leverages principles of vector quantization and similarity measurement to automatically partition images into self-similar regions or clusters. Segmentation methods based on SOM have demonstrated the ability to distinguish high-level and low-level features of tumors, edema, necrosis, cerebrospinal fluid, and healthy tissue.
  • 285
  • 04 Mar 2024
Topic Review
Abnormal Activity Recognition for Visual Surveillance
Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. Automatic abnormal event detection such as theft, burglary, or accidents may be helpful in many situations. However, there are significant difficulties in processing video data acquired by several cameras at a central location, such as bandwidth, latency, large computing resource needs, and so on. 
  • 284
  • 11 Jan 2024
Topic Review
Intelligent Source Code Completion Assistants
As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. 
  • 284
  • 17 Jan 2024
Topic Review
Deep Learning for IDSs in Time Series Data
Classification-based intrusion detection systems (IDSs) use machine learning algorithms to classify incoming data into different categories based on a set of features. Even though classification-based IDSs are effective in detecting known attacks, they can be less effective in identifying new and unknown attacks that have a small correlation with the training dataset. On the other hand, anomaly detection-based approaches use statistical models and machine learning algorithms to establish a baseline of normal behavior and identify deviations from that baseline.
  • 284
  • 29 Feb 2024
Topic Review
Requirements of Edge Machine Learning
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. 
  • 283
  • 27 Sep 2023
Topic Review
Multi-Access Edge Computing
Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network.
  • 282
  • 22 Sep 2023
Topic Review
Assessment of Parent–Child Interaction Quality from Dyadic Dialogue
The quality of parent–child interaction is critical for child cognitive development. The Dyadic Parent–Child Interaction Coding System (DPICS) is commonly used to assess parent and child behaviors. However, manual annotation of DPICS codes by parent–child interaction therapists is a time-consuming task. To assist therapists in the coding task, researchers have begun to explore the use of artificial intelligence in natural language processing to classify DPICS codes automatically.
  • 282
  • 20 Nov 2023
Topic Review
A Lightweight Object Detection Network with Attention Modules
Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices.
  • 282
  • 22 Nov 2023
Topic Review
Multi-Scene Mask Detection
Deep learning for mask detection has important demand in medical and industrial production, which reflects the application of neural networks and image sensors in daily life. During an epidemic of respiratory viruses, mask detection can effectively supervise the wearing of masks, thereby reducing the risk of virus transmission.
  • 282
  • 04 Dec 2023
Topic Review
LiDAR Local Domain Adaptation for Autonomous Vehicles
Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model’s performance. Domain adaptation is a form of transfer learning that aims to minimize the domain shift between datasets.
  • 282
  • 05 Jan 2024
Topic Review
Cross-Lingual Document Retrieval
Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information.
  • 281
  • 24 Feb 2023
Topic Review
Home-Based Rehabilitation (Shoulder) Using Auxiliary Systems and AI
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state-of-the-art and elucidate the key features of the hardware and software employed in these rehabilitation systems.
  • 281
  • 15 Aug 2023
Topic Review
Metaverse and AI for Internet of City Things
The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. 
  • 281
  • 22 Sep 2023
Topic Review
HVAC Data-Driven Maintenance
Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment. The adoption of Internet of Things (IoT) sensors integrated into HVAC systems has paved the way for data-driven predictive maintenance (PdM) grounded in real-time operational metrics.
  • 281
  • 27 Oct 2023
Topic Review
Hybrid Multi-Label Classification Model for Medical Applications
Multi-label classification is typically used in different data mining applications, like labeling videos, images, music, and texts. Multi-label classification classifies documents into various classes simultaneously based on their properties.
  • 280
  • 16 Aug 2023
Topic Review
Strawberry Ripeness Classification
Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment.
  • 280
  • 07 Feb 2024
Topic Review
Brain Tumor Segmentation, Deep Learning and GAN Network
Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. 
  • 279
  • 10 Nov 2023
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