Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different *** is therefore crucial in ensuring the safety of lives and essentia...
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Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different *** is therefore crucial in ensuring the safety of lives and essential equipment in areas such as nuclear power plants,radiotherapy facilities,space exploration,and *** Intelligent technologies have become desirable in modeling shielding materials’attenuation behavior due to their unique *** The overview aims to present the recent application of AI technologies in modeling the radiation attenuation behavior of *** A total of 41 relevant articles were obtained using Scopus and web of science *** search was restricted to articles and conference papers published within the last two *** From the overview,it was realized that AI techniques can predict the attenuation properties of shielding materials and optimize the shield *** methods can be grouped into predictive models which are:fuzzy logic,Support Vector Regression,Neural Networks,and optimization models which include Genetic algorithms,Ant Colony,and Particle Swarm *** networks are the most robust and widely used *** predictive models are used in predicting parameters such as attenuation coefficient,buildup factor,shield thickness,and radiation dose rates,whiles the optimization techniques are employed in single and multi-objective attenuator *** In the overview,the accuracies and complexities of the various AI techniques have been discussed giving insight into their *** AI techniques are easy to model compared to conventional methods and can save computational time when coupled with conventional statistical and deterministic models or employed as a standalone technique.
Surface reconstruction is the process of representing surfaces using the data obtained from scanning devices. When the data obtained is unstructured during data collection, it can cause problems in presenting the surf...
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In this work, we investigate a newly integrated radar and communication (IRAC) scheme that allows a UAV to transmit data to a ground user equipment (UE) under ultra-reliable and low-latency communications (URLLC) whil...
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Skin cancer is among the deadliest diseases engulfing the world. The early detection of this disease may provide a great relief to the patients and healthcare systems worldwide. Recently, Machine Learning (ML)-based d...
Skin cancer is among the deadliest diseases engulfing the world. The early detection of this disease may provide a great relief to the patients and healthcare systems worldwide. Recently, Machine Learning (ML)-based detection techniques have become popular in early skin cancer prediction. This paper comprehensively presents an overview of the latest research in the field of skin cancer prediction using ML techniques, especially the use of Convolutional Neural Networks (CNN) for the said purpose. Various methodologies employed in the past have been discussed along with the outcomes achieved, limitations, and advantages of these predictive models. A detailed review of the latest key references in the area is also discussed. At the end, a way of achieving ensemble using CNN methods for skin care has also been discussed, which we will implement in the next phase of our research.
Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological respons...
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As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart...
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As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart sensors, IoT systems etc., are gaining more and more interest and new communication paradigms are needed. This thesis addresses these challenges by combining semantic communication with generative models to optimize image compression and resource allocation in edge networks. Unlike traditional bit-centric communication systems, semantic communication prioritizes the transmission of meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit of the focus solely on the relevant parts of the data due to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression algorithms, utilizing advanced generative models such as Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These algorithms compress images by encoding only semantically relevant features and exploiting the generative power at the receiver side. This allows for the accurate reconstruction of high-quality images with minimal data transmission. The thesis also introduces a Goal-Oriented edge network optimization framework based on the Information Bottleneck problem and stochastic optimization, ensuring that communication resources are dynamically allocated to maximize efficiency and task performance. By integrating semantic communication into edge networks, the proposed system achieves a balance between computational efficiency and communication effectiveness, making it particularly suited for real-time applications. The thesis compares the performance of these semantic communication models with conventional image compression techniques, using both classical and semantic-awar
Understanding spatial distribution of infertility risk is essential for identifying environmental and infrastructural factors affecting reproductive health. Despite its relevance, infertility has rarely been examined ...
Understanding spatial distribution of infertility risk is essential for identifying environmental and infrastructural factors affecting reproductive health. Despite its relevance, infertility has rarely been examined through a geographic lens. This study highlights the need for integrating spatial analysis into infertility research to uncover hidden patterns linked to environmental exposures. This study aims to review the effect of environmental and genetic risk factors on infertility in individuals and provide a continuous model of infertility probability. This study analyzes infertility factors in Tehran province using Geographic Information system (GIS) and fuzzy logic. Initially, we estimate infertility probabilities with non-spatial (genetic) factors using fuzzy logic rules. Subsequently, we assess the influence of environmental factors on infertility through three fuzzy rule scenarios. The results of this study suggest that spatial factors, such as air pollution, high-voltage towers, power lines, and telecommunication towers, are associated with infertility, accounting for approximately 63% of the explanatory power in our model, while the genetic and behavioral (non-spatial) factors contribute about 37%. These percentages reflect the relative performance of spatial vs. non-spatial fuzzy inference systems and do not imply causal proportions. Given the stronger influence of spatial factors, we produced a probability distribution map of infertility across Tehran Province.
Data science is a combination of several disciplines that aims to get accurate insights from a bunch of data, develop the technology, and algorithm to solve the complicated problems analytically. Today, data science p...
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This work investigates the recognition of multiple dental treatment and diagnosis conditions in a full scan dental panoramic image. In this study, we proposed a single-stage oriented deep learning model for five denta...
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The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient n...
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