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Topic Review
Distributed Bayesian Inference for Large-Scale IoT Systems
The Internet of Things (IoT) has emerged as a transformative force in contemporary society, substantially impacting various facets of daily life. Nevertheless, the IoT ecosystem’s rapid expansion is accompanied by a significant increase in data generation, known as Big Data. This expansion presents a complex challenge, necessitating advanced, scalable, and efficient data processing techniques. Given the complex nature of large-scale data analysis in IoT systems, distributed Bayesian inference arises as a practical and efficient solution in this domain. Bayesian methods, which are influential in deriving informed conclusions and predictions from complex datasets, are widely recognized for their probabilistic underpinnings.
  • 183
  • 28 Dec 2023
Topic Review
3D Models of Oblique Photography Watermarking Algorithm
With the rapid development of oblique photogrammetry technology based on unmanned aerial vehicle (UAV), 3D models of oblique photography (3DMOP) are playing an increasingly significant role in the establishment of digital cities and twin watersheds, due to their huge advantages such as high accuracy, clear texture information, virtual simulation capability, etc. igital watermarking, an important branch of data hiding, has played an important role in security protection of digital products, such as digital images, CAD graphics, remote sensing images, vector maps, etc., and a huge amount of research results have been obtained.
  • 229
  • 28 Dec 2023
Topic Review
AI Advancements
Artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution.
  • 301
  • 28 Dec 2023
Topic Review
Advanced Persistent Threat Predictive Analytics
Advanced persistent threat (APT) audit logs information and uses a combination of causal graphs and deep learning techniques to perform predictive analysis of APT.
  • 147
  • 28 Dec 2023
Topic Review
Sound Source Localization and Detection Methods
Many acoustic detection and localization methods have been developed. However, all of the methods require capturing the audio signal. Therefore, any method’s essential element and requirement is using an acoustic sensor. In addition to converting sound waves into an electrical signal, they also perform other functions, such as: reducing ambient noise, or capturing sounds with frequencies beyond the hearing range of the human ear. Classic methods have stood the test of time and are still widely used due to their simplicity, reliability, and effectiveness. There are three main mathematical methods for determining the sound source. These include triangulation, trilateration, and multilateration
  • 309
  • 28 Dec 2023
Topic Review
Fake News Detection on Social Media
The spread of fake news on social media continues to be one of the main challenges facing internet users, prohibiting them from discerning authentic from fabricated pieces of information. Detecting fake news is a problem tackled through different approaches that can be categorized mainly into a content-based approach and a social-based approach. In the content-based approach, the textual features are the main features, whereas in the social-based approach other features, including users’ engagements, users’ profile features, and network propagation features, are considered.
  • 141
  • 27 Dec 2023
Topic Review
Efficient Detection of Forest Fire Smoke
Forest fires are a significant environmental threat, causing loss of biodiversity, alteration of ecosystems, and impacting human lives and properties. Early detection is critical for effective firefighting and minimizing damages. Smoke detection plays an indispensable role in the early monitoring of forest fires. Its rapid dispersion, visibility, and integration with contemporary sensor technologies render it not only an effective complement but also a potential substitute for flame monitoring. In this context, various forest fire smoke detection methods and systems have been developed. These methods include satellite-based smoke detection, ground-based sensors for smoke detection, and UAV-based detection, each with its unique approach, advantages, and limitations. Moreover, image processing technology occupies a crucial position in the detection of forest fire smoke.
  • 117
  • 27 Dec 2023
Topic Review
Peripheral Blood Smears Examination by Artificial Intelligence
Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses.
  • 212
  • 27 Dec 2023
Topic Review
Filmmaking Education and Artificial Intelligence
Artificial intelligence (AI) has witnessed remarkable advancements, revolutionizing various industries and domains, including education. From intelligent algorithms in the financial sector to diagnostic tools in healthcare and autonomous vehicles in transportation, artificial intelligence (AI) has demonstrated immense potential across various domains. However, despite its remarkable strides in many fields, the application of AI in the realm of education has lagged behind. Its full potential in the field of filmmaking education remains largely untapped. 
  • 241
  • 26 Dec 2023
Topic Review
Enhanced Genetic Method for Optimizing Multiple Sequence Alignment
In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences.
  • 261
  • 26 Dec 2023
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