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Topic Review
Integrated GNN and DRL in E2E Networking Solutions
Graph neural networks (GNN) and deep reinforcement learning (DRL) are at the forefront of algorithms for advancing network automation with capabilities of extracting features and multi-aspect awareness in building controller policies. While GNN offers non-Euclidean topology awareness, feature learning on graphs, generalization, representation learning, permutation equivariance, and propagation analysis, it lacks capabilities in continuous optimization and long-term exploration/exploitation strategies. Therefore, DRL is an optimal complement to GNN, enhancing the applications towards achieving specific policies within the scope of end-to-end (E2E) network automation.
  • 76
  • 18 Mar 2024
Topic Review
OptiDJS+
The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, OptiDJS+ is a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. 
  • 184
  • 01 Mar 2024
Topic Review
Technological Breakthroughs in Sport
We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity.
  • 145
  • 29 Feb 2024
Topic Review
Data Lake, Spark and Hive
Big data are a large number of datasets that are difficult to store and process using existing database management tools. Big data have some characteristics, denoted by 5Vs: volume, velocity, veracity, variety, and value. Volume refers to the size of the data, velocity refers to the speed of the data from the sources to the destination (data flow), variety refers to different format types of the data, veracity refers to the quality of the data, and value refers to the importance of the data collected without analysis and insight. Lastly, the characteristics have become more than ten, like volatility and visualization value. 
  • 58
  • 29 Feb 2024
Topic Review
Dynamic Feedback-Driven Learning Optimization Framework
A novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology.
  • 93
  • 18 Feb 2024
Topic Review
High-Fidelity Synthetic Face Generation for Rosacea Skin Condition
Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. Generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images.
  • 80
  • 14 Feb 2024
Topic Review
Parallelization Strategies for Graph-Code-Based Similarity Search
The volume of multimedia assets in collections is growing exponentially, and the retrieval of information is becoming more complex. The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. Feature graphs contain semantic information on multimedia assets. Machine learning can produce detailed semantic information on multimedia assets, reflected in a high volume of nodes and edges in the feature graphs. Graph Codes provide fast and effective multimedia indexing and retrieval, even in billion-scale use cases.
  • 53
  • 19 Jan 2024
Topic Review
Cognitive Load Theory and eLearning of Crafts
Craft education and training are important for preserving cultural heritage and fostering artisanal skills. Craft education and training are challenging since they introduce learners to a multifaceted world, where they must acquire skills, knowledge, and appreciation for cultural heritage. Balancing these learning objectives is a cognitive challenge.
  • 99
  • 12 Jan 2024
Topic Review
Source Location Privacy Protection Techniques
Internet of Things (IoT) research has been considered as a paramount domain while enhancing current technologies such as wireless sensor networks (WSNs). Enhanced source location privacy and prolonged network lifetime are imperative for WSNs.
  • 60
  • 10 Jan 2024
Topic Review
Machine Learning and Deep Learning Techniques
The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field.
  • 99
  • 21 Dec 2023
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