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Topic Review
Sensor Data Fusion Algorithms
Sensor Data Fusion (SDT) algorithms and methods have been utilised in many applications ranging from automobiles to healthcare systems. They can be used to design a redundant, reliable, and complementary system with the intent of enhancing the system’s performance. SDT can be multifaceted, involving many representations such as pixels, features, signals, and symbols.
  • 379
  • 12 Dec 2023
Topic Review
Semantically Interoperable Social Media Platforms
Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability.
  • 184
  • 19 Sep 2023
Topic Review
Semantic Web and Web GIS
The field of geographic information science and its associated technologies have undergone rapid technological advancement and geographic information systems (GIS) now have wide-ranging functional capabilities. The field is characterised by specific expertise, one with a longstanding history of forward thinking and a track record for ongoing innovation and with this, the field has adopted many disruptive technologies from the fields of computer and information sciences through this transition towards web GIS. Most interestingly in this regard is the (often limited) uptake of semantic web technologies by the field and its associated technologies, the lack of which has resulted in a technological disjoint between these fields. As the field seeks to make geospatial information more accessible to more users and in more contexts through ‘self-service’ applications and web GIS applications, the use of these technologies is imperative to support the interoperability between distributed data sources and services. 
  • 997
  • 21 Feb 2021
Topic Review
Semantic Video Segmentation
Recent approaches for fast semantic video segmentation have reduced redundancy by warping feature maps across adjacent frames, greatly speeding up the inference phase. Researchers build a non-key-frame CNN, fusing warped context features with current spatial details. Based on the feature fusion, the context feature rectification (CFR) module learns the model’s difference from a per-frame model to correct the warped features. 
  • 133
  • 22 Nov 2023
Topic Review
Semantic Trajectory and Recommender Systems in Cultural Spaces
Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. 
  • 909
  • 22 Dec 2021
Topic Review
Semantic Textual Similarity
Semantic textual similarity (STS) refers to the degree of similarity between two pieces of text based on their meaning or semantics and assesses how closely related or similar two pieces of text are based on the information they convey, regardless of variations in wording or structure. STS has been explored from both linguistic and computational perspectives. It holds significance in various NLP applications, and in recent years, Transformer-based neural language models have emerged as the state-of-the-art solutions for many of these applications.
  • 215
  • 08 Dec 2023
Topic Review
Semantic Segmentation of Medical Images
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. Lesion detection is one of the primary objectives of medical imaging, as the size and location of lesions are often directly associated with a patient’s diagnosis, treatment, and prognosis. Since the development of computer vision algorithms, however, researchers have begun to utilize these algorithms in the field of medical imaging.
  • 349
  • 02 Dec 2022
Topic Review
Semantic Search and SemSime
This paper presents SemSime, a semantic similarity method for searching over a setof digital resources previously annotated by means of concepts from a weighted reference ontology.It is based on a frequency approach for weighting the ontology, and refines both the user request and the annotations of the digital resources with rating scores. Such scores are High, Medium, and Low and, in the user request, indicate the preferences assigned by the user to each of the concepts representing the searching criteria whereas, in the annotations of the digital resources, they represent the levels of quality associated with each concept in describing the resources. The experiment we have performed shows that SemSime outperforms the previous semantic search method SemSim.
  • 566
  • 29 Oct 2020
Topic Review
Semantic Modeling, Simulation and Cybersecurity in the IoUT
As maritime and military missions become more and more complex and multi-factorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in swarm formations, towards obtaining deeper insights related to the critical issues of cybersecurity and interoperability. The research topics, which are constantly emerging in this domain, are closely related to the communication, interoperability, and secure operation of UUVs, as well as to the volume, velocity, variety, and veracity of data transmitted in low bit-rate due to the medium, i.e., the water. 
  • 540
  • 09 Jan 2023
Topic Review
Semantic Image Segmentation with Scantly Annotated Data
Semantic image segmentation is the task of assigning to each pixel the class of its enclosing object or region as its label, thereby creating a segmentation mask. The success of deep networks for the semantic segmentation of images is limited by the availability of annotated training data. The manual annotation of images for segmentation is a tedious and time-consuming task that often requires sophisticated users with significant domain expertise to create high-quality annotations over hundreds of images.
  • 442
  • 18 Jul 2022
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