About 17.5% of adults worldwide are infertile, which emphasizes the urgent need for cutting-edge reproductive healthcare solutions. Based on statistics from June 2023. According to the World Health Organization (WHO),...
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In recent days, deep learning technologies have gained more and more interest in computer related task. In generative models, autoencoder (AE) has achieved a tremendous success in many fields, especially in image gene...
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Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play ...
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Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit machine learning methodology to dissect collected data, a...
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ISBN:
(纸本)9798350323481
The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit machine learning methodology to dissect collected data, and edge computing was introduced to enhance the efficiency and scalability in resource-constrained computing environments. Unfortunately, popular deep learning algorithms involve intensive computations that are overcomplicated for edge devices. Brain-inspired Hyperdimensional Computing (HDC) has been considered a promising approach to address this issue. However, existing HDC methods use static encoders, and thus require extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge loss of efficiency and severely impedes the application of HDC algorithms in power-limited machines. In this paper, we propose DistHD, a novel HDC framework with a unique dynamic encoding technique consisting of two parts: top-2 classification and dimension regeneration. Our top-2 classification provides top-2 labels for each data sample based on cosine similarity, and dimension regeneration identifies and regenerates dimensions that mislead the classification and reduce the learning quality. The highly parallel algorithm of DistHD effectively accelerates the learning process and achieves the desired accuracy with considerably lower dimensionality. Our evaluation on a wide range of practical classification tasks shows that DistHD is capable of achieving on average 2.12% higher accuracy than state-of-the-art (SOTA) HDC approaches while reducing dimensionality by 8.0×. It delivers 5.97× faster training and 8.09× faster inference than SOTA learning algorithms. Additionally, the holographic distribution of patterns in high dimensional space provides DistHD with 12.90× higher robustness against hardware errors than SOTA DNNs. DistHD has been open-sourced to enable future research in this field.1
Pneumonia disease is a lung infection caused by the bacteria Streptococcus Pneumonia that affects the lungs air sacs to fill with fluid or puss. Detecting pneumonia through lung auscultation is a challenging task. Thi...
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Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country i...
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Segmentation of choroidal vessels is crucial for diagnosing various fundus diseases but poses a significant challenge. The complex and diverse morphology of choroidal vessels makes obtaining pixel-wise annotations ext...
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Remote photoplethysmography (rPPG) has the potential to significantly enhance driver safety systems by enabling the detection of critical conditions, such as driver drowsiness and sudden illness, through non-invasive ...
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The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is...
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The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.
We are in the process of developing a hybrid-integrated compact frequency-comb Fourier-domain mode-locked (FDML) laser source for swept-source optical coherence tomography (SS-OCT) applications. Our approach incorpora...
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