Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision...
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Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a
Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semanti...
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Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semantically meaningful symbolic rules and representations, while deep learning (DL), or sometimes called subsymbolic AI, is based on the idea that intelligence emerges from the collective behavior of artificial neurons that are connected to each other. A major drawback of DL is that it acts as a 'black box,' meaning that predictions are difficult to explain, making the testing & evaluation (T&E) and validation & verification (V&V) processes of a system that uses subsymbolic AI a challenge. Since neurosymbolic AI combines the advantages of both symbolic and subsymbolic AI, this survey explores how neurosymbolic applications can ease the V&V process. This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and subsymbolic components in current applications. Additionally, an overview of current techniques for the T&E and V&V processes of these components is provided. Furthermore, it is investigated how the symbolic part is used for T&E and V&V purposes in current neurosymbolic applications. Our research shows that neurosymbolic AI has great potential to ease the T&E and V&V processes of subsymbolic AI by leveraging the possibilities of symbolic AI. Additionally, the applicability of current T&E and V&V methods to neurosymbolic AI is assessed, and how different neurosymbolic architectures can impact these methods is explored. It is found that current T&E and V&V techniques are partly sufficient to test, evaluate, verify, or validate the symbolic and subsymbolic part of neurosymbolic applications independently, while some of them use approaches where current T&E and V&V methods are not applicable by default, and adjustments or even new approaches are needed. Our research shows that th
Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning ...
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Magnetic nanoparticles have been used in various biomedical applications. Among these, magnetic particle imaging and hyperthermia are considered clinically useful diagnostic and therapeutic methods, respectively. An a...
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Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that ha...
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Network measurements are important for identifying congestion, DDoS attacks, and more. To support real-time analytics, stream ingestion is performed jointly by multiple nodes, each observing part of the traffic, perio...
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A newly designed small, almost square shaped Ultra Wideband antenna is presented in this paper. Free space simulations provided promising results with a wider bandwidth and high efficiency up to 97.68% within the UWB ...
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In the process of protecting power systems against different types of cyberattacks, the primary step is to precisely model such frameworks from attacker's perspective. This paper investigates a false data injectio...
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The substantial growth of Internet-of-Things technology and the ubiquity of smartphone devices has increased the public and industry focus on speech emotion recognition (SER) technologies. Yet, conceptual, technical, ...
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This study considers the problem of generation expansion planning and transmission expansion planning (GTEP) from the profit point of view of generation and transmission companies. A new framework is proposed, that co...
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