Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communicatio...
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ISBN:
(数字)9798350361186
ISBN:
(纸本)9798350361193
Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.
Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coined as guidance. For example, in text-t...
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The enhanced representational power and broad applicability of deep learning models have attracted significant interest from the research community in recent years. However, these models often struggle to perform effe...
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Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, t...
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ISBN:
(数字)9798350391565
ISBN:
(纸本)9798350391572
Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques.
The video streams that are collected from CCTV surveillance camera can be used in many applications such as crowd analysis, forensic, self-profile analysis, and social network user’s analysis. Soft biometrics such as...
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Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerati...
Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerative condition affects regions of the brain integral to cognition, memory, and language. Due to its intricate nature, symptom experiences and progression can vary among individuals. This study proposes a predictive model to discern Alzheimer's stages. Leveraging “deep learning” techniques through convolutional neural networks (CNNs), the algorithm is trained on an extensive dataset of brain MRI scans. It learns to recognize pivotal features and correlate them with diverse disease phases. The Customized CNN model's accuracy of 94.37% surpasses alternative machine learning methods, including transfer learning. This precision empowers early prediction, significantly benefiting patient care and therapeutic strategies, thereby enhancing Alzheimer's disease management.
Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable lin...
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This survey comprehensively reviews the metrics for measuring the diversity of game scenarios, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player exp...
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Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set ...
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ISBN:
(数字)9798350384277
ISBN:
(纸本)9798350384284
Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection.
While federated learning (FL) has made significant strides in addressing data privacy concerns, the challenges of heterogeneous data and unfair performance among participants remain substantial. Existing solutions con...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
While federated learning (FL) has made significant strides in addressing data privacy concerns, the challenges of heterogeneous data and unfair performance among participants remain substantial. Existing solutions confront challenges such as high computational costs, difficulty in balancing performance with fairness, and poor convergence in partially data heterogeneous environments. The alternating direction method of multipliers (ADMM) is a highly promising approach that effectively addresses issues related to data heterogeneity by imposing constraints on local client updates through dual variables. In this paper, we propose a novel FL framework, named FAT (tilted FL with ADMM), designed to address the issue of data heterogeneity while reducing bias and unfair treatment towards different clients, and it provides a better trade-off between accuracy and fairness. We conducted experiments on two real-world datasets, and the results demonstrate that, compared to existing methods, FAT significantly improves fairness while maintaining accuracy. Our experiments demonstrate that FAT significantly outperforms existing state-of-the-art methods in both accuracy and fairness, offering a superior trade-off between these crucial aspects.
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