Blind or low vision (BLV) people were recently reported to be live streamers on the online platforms that employed content curation algorithms. Recent research uncovered algorithm biases suppressing the content create...
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The detection of ovarian cancer is limited by factors such high tumor heterogeneity, ineffective screening techniques, and late-stage diagnosis brought on by nebulous symptoms. Accuracy is affected by imaging problems...
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ISBN:
(数字)9798331507244
ISBN:
(纸本)9798331507251
The detection of ovarian cancer is limited by factors such high tumor heterogeneity, ineffective screening techniques, and late-stage diagnosis brought on by nebulous symptoms. Accuracy is affected by imaging problems such as noise and poor contrast. To increase early detection rates and classification performance, computational models need better feature extraction, segmentation, and optimization methods. Utilizing the TCIA (The Cancer Imaging Archive) dataset, the efficacy of the Resampling Uncertainty Asynchronous Propagation Penalized Neural Network with Secretary Bird Optimization Algorithm (RU-APPNNet-SBOA) for ovarian cancer detection is assessed. First, image quality is improved by the Resampling Cubature Kalman Filter (RCKF). Regions were impacted by the Uncertainty-Aware Decision Transformer (UADT) segments according to variances. Ovarian cancer types are classified via the Asynchronous Propagation Penalized Neural Network (APPNNet), which extracts characteristics. The Secretary Bird Optimization Algorithm (SBOA) examines many impacted areas in the medical imaging to improve categorization accuracy. The TCIA dataset is used in the Python test script, and the results demonstrate that RU-APPNNet-SBOA outperforms current techniques with 99.9% efficiency and 99.8% sensitivity, demonstrating the potential of computer systems to replace manual diagnosis.
Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a...
Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.
The use of the MathML language made possible to improve the accessibility of mathematics for blind or low-vision persons in digital media. Synthetic speech technologies have advanced significantly using MathML, howeve...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitati...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative
The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores ...
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A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational che...
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This paper examines how decentralized energy systems can be enhanced usingcollaborative Edge Artificial Intelligence. Decentralized grids use localrenewable sources to reduce transmission losses and improve energy ***...
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This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning ex...
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