Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training *** address these issues,we propose a...
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Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training *** address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement *** algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light *** the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection *** the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image *** show the effectiveness and rationality of each module designed in this *** the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.
The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different dom...
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In the age of technology, many people have fallen victim to fake images. Photo editing has become easier as the process of making photos becomes more efficient. With the image processing tools at their disposal, peopl...
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Online shopping platforms are experiencing rapid growth, necessitating effective product recommendation systems to enhance customer satisfaction by recommending visually similar products. Traditional statistical techn...
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CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computationa...
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CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.
Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance an...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast *** disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age *** light of research investigations,it is vital to consider age as one of the key criteria when choosing the *** younger subjects are more susceptible to the perishable side than the older *** proposed investigation concentrated on the younger *** research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages *** proposed work is executed in three *** 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)*** Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of *** model was trained and tested to classify the five stages of *** ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of *** delve into...
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This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of *** delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited ***,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving *** such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private *** its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained *** to enhance efficiency have been made,but scalability remains a *** the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising ***,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)*** the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU *** experimental results demonstrate the effectiveness of *** an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/***,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade *** solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same *** summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded
Lymphoma is a type of malignant tumor that develops from lymphoid hematopoietic tissues. The precise diagnosis of lymphomas is one of the challenging tasks because of the similarity within the morphological features a...
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The study focuses on the argument component in argumentation mining, specifically examining claim and premise types. Various datasets exist for argumentation components, each with different classes. The study evaluate...
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