Recent advances in oracle bone inscriptions (OBI) classification have explored various strategies such as zero-shot learning, augmentation, and complex convolution architectures. These strategies ultimately represent ...
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Brain tumors are highly heterogeneous in both spatial distribution and scale, making their segmentation in medical images a challenging task that can lead to diagnostic and therapeutic errors. Automating tumor segment...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Brain tumors are highly heterogeneous in both spatial distribution and scale, making their segmentation in medical images a challenging task that can lead to diagnostic and therapeutic errors. Automating tumor segmentation has the potential to enhance objectivity and repeatability while significantly reducing turnaround time. Conventional convolutional neural networks (CNNs) often struggle to accurately capture the wide range of tumor sizes and shapes, leading to suboptimal performance. To address these limitations, UNet has emerged as a widely adopted solution for semantic segmentation, leveraging a downsampling-upsampling architecture to segment tumors. Building on this foundation, we propose a novel architecture that combines Attention-UNet with repeated Atrous Spatial Pyramid Pooling (ASPP). ASPP captures multi-scale contextual information effectively through parallel atrous convolutions with varying dilation rates. Our approach demonstrates substantial improvements over UNet, Attention UNet, and Attention UNet with Spatial Pyramid Pooling, establishing a new benchmark for tumor segmentation tasks.
In the age of rapid information spreading through social media platforms such as Twitter, Facebook, and Instagram have become serious arenas for information warfare. Coordinated misinformation campaigns and influence ...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
In the age of rapid information spreading through social media platforms such as Twitter, Facebook, and Instagram have become serious arenas for information warfare. Coordinated misinformation campaigns and influence operations pose significant threats to societal stability and public opinion. Addressing this issue, our project combines open-source intelligence (OSINT) techniques with advanced AI-driven sentiment analysis and summarization to detect, analyses, and provide actionable insights into politically sensitive and potentially harmful narratives. The proposed system integrates a Bi-LSTM model, which achieves a high accuracy of 98%, for sentiment analysis with an LLM (LLaMA 3) to classify and interpret polarized content. This system processes large volumes of Twitter data, filtered through keywords and contextual signals, to extract and analyses politically charged or conspiratorial content. It enables interactive querying and dynamic analysis to summarize critical topics and provide meaningful insights into the detected narratives. The results demonstrate the system's capability to accurately identify sentiments, summarize critical topics, and facilitate interactive exploration, making it an effective tool for combating misinformation and enhancing situational awareness in the realm of information warfare
Pre-trained diffusion models have demonstrated remarkable proficiency in synthesizing images across a wide range of scenarios with customizable prompts, indicating their effective capacity to capture universal feature...
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This study investigates the three-dimensional dynamics of droplet splitting in bifurcation microchannels using a two-phase flow simulation with the Level Set method implemented in COMSOL Multiphysics, capturing the be...
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Vision transformers (ViTs) have become essential backbones in advanced computer vision applications and multimodal foundation models. Despite their strengths, ViTs remain vulnerable to adversarial perturbations, compa...
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作者：
Lee, DonghyeokDas, Suprem R.Kwon, JiseokChang, JiwonYonsei University
Department of System Semiconductor Engineering Department of Materials Science and Engineering Seoul03722 Korea Republic of Kansas State University
Department of Industrial and Manufacturing Systems Engineering Department of Electrical and Computer Engineering ManhattanKS66506 United States The Catholic University of Korea
School of Information Communications and Electronic Engineering Gyeonggi-do14662 Korea Republic of
In this study, we propose ferroelectric-based reconfigurable field-effect transistors (FeRFETs) that utilizes the structure of a fully depleted silicon-on-insulator field-effect transistors (FDSOI FETs). In FeRFETs, t...
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The fusion of Artificial Intelligence (AI) and Machine Learning (ML) with distributed computing frameworks such as Fog Computing, Cloud Computing, and Edge Computing is vital for shaping the future of digital technolo...
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ISBN:
(数字)9798331527495
ISBN:
(纸本)9798331527501
The fusion of Artificial Intelligence (AI) and Machine Learning (ML) with distributed computing frameworks such as Fog Computing, Cloud Computing, and Edge Computing is vital for shaping the future of digital technology. “This review paper delves into the interplay between AI/ML and these distributed computing paradigms, exploring their fundamental concepts and structures. It examines how AI/ML technologies are integrated into these frameworks, resulting in improved resource management, enhanced data processing capabilities, and more intelligent decision-making at the network edges. The paper highlights real-world examples from various industries, including healthcare, finance, IoT, and Industry 4.0, showcasing the innovative breakthroughs driven by these technologies. However, it also acknowledges the challenges that need to be addressed, such as ethical concerns, security and privacy issues, and the need for standardized frameworks. The paper provides guidance for researchers, professionals, and policymakers to harness the synergies between these technologies, paving the way for a more efficient and intelligent computing landscape in the digital era.
The WannaCry ransomware attack of May 2017 marked a critical turning point in cybersecurity history, prompting profound ethical discussions about software vulnerability management. This comprehensive analysis examines...
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Artificial Intelligence (AI) systems frequently exhibit systematic blind spots, often referred to as hallucinations in Large Language Models (LLMs), posing risks in high-stakes applications such as autonomous systems,...
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