Since gastric cancer is growing fast, accurate and prompt diagnosis is essential, utilizing computer-aided diagnosis (CAD) systems is an efficient way to achieve this goal. Using methods related to computer vision ena...
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Since gastric cancer is growing fast, accurate and prompt diagnosis is essential, utilizing computer-aided diagnosis (CAD) systems is an efficient way to achieve this goal. Using methods related to computer vision enables more accurate predictions and faster diagnosis, leading to timely treatment. CAD systems can categorize photos effectively using deep learning techniques based on image analysis and classification. Accurate and timely classification of histopathology images is critical for enabling immediate treatment strategies, but remains challenging. We propose a hybrid deep learning and gradient-boosting approach that achieves high accuracy in classifying gastric histopathology images. This approach examines two classifiers for six networks known as pre-trained models to extract features. Extracted features will be fed to the classifiers separately. The inputs are gastric histopathological images. The GasHisSDB dataset provides these inputs containing histopathology gastric images in three 80px, 120px, and 160px cropping sizes. According to these achievements and experiments, we proposed the final method, which combines the EfficientNetV2B0 model to extract features from the images and then classify them using the CatBoost classifier. The results based on the accuracy score are 89.7%, 93.1%, and 93.9% in 80px, 120px, and 160px cropping sizes, respectively. Additional metrics including precision, recall, and F1-scores were above 0.9, demonstrating strong performance across various evaluation criteria. In another way, to approve and see the model efficiency, the GradCAM algorithm was implemented. Visualization via Grad-CAM illustrated discriminative regions identified by the model, confirming focused learning on histologically relevant features. The consistent accuracy and reliable detections across diverse evaluation metrics substantiate the robustness of the proposed deep learning and gradient-boosting approach for gastric cancer screening from histopathology
Purpose: Potassium imbalance, often symptomless but potentially fatal, is prevalent in patients with kidney or heart conditions. Traditional laboratory tests for potassium measurement are costly and require skilled te...
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Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) with a wide ...
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This paper presents a chopper-stabilized three-stage operational amplifier (OpAmp) with a unity gain bandwidth of 69 MHz and an input referred noise density of 3 nV√Hz. The proposed design achieves a stable unity gai...
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The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid ***,grid operators have incorporated ramp-rate ...
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The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid ***,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid *** the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power ***,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently *** paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid *** addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is ***,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is *** BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined *** BESS sizing is directly related to the constant RR achieved using the proposed ***,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.
Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing met...
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Medical experts are utilizing neuroimaging and clinical assessments to enhance the early identification of Parkinson's disease. The current research initiative offers ways to identify Parkinson's disease using...
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Medical experts are utilizing neuroimaging and clinical assessments to enhance the early identification of Parkinson's disease. The current research initiative offers ways to identify Parkinson's disease using machine learning and transfer learning. To carry out this, we extracted 7500 MRI images from 2022 and 2023 and 12 clinical assessment records from 2010 to 2023 from the well-known Parkinson's Progression Marker Initiative (PPMI) database. Then, we applied machine and transfer learning approaches using clinical assessment records and MRI images, respectively. To identify Parkinson's Disease (PD) using samples from clinical assessments, four distinct resampling techniques were employed. Subsequently, three machine learning models were applied to train on these resample records, and the recall score was analyzed. A hybrid of SMOTE and ENN proved to be the most effective approach for handling all of the imbalanced data, according to the recall study. Later, four different feature selection methods were used to find the top 10 features using these new samples. Lastly, we trained and validated the model using nine machine-learning algorithms. We also used explainable AI techniques like LIME and SHAP to interpret clinical assessment records. The extra tree classifier outperformed the others in terms of accuracy, reaching 98.44% using the tree-based feature selection technique. In addition to examining clinical assessment samples, this study investigated Parkinson's disease using neuroimaging data. In pursuit of this objective, four pre-trained architectures were employed to analyze MRI images through two distinct approaches. The first approach involved utilizing the convolutional layer while replacing the remaining two layers with a customized Artificial Neural Network (ANN). Subsequently, training and evaluation are performed using our MRI samples, followed by analyzing significant weights using a LIME interpretable explainer. The second approach employs an improvis
作者：
Byun, HyungjoASRI
Department of Electrical and Computer Engineering Seoul National University Korea Republic of
Controlling nonlinear systems with linear feedback controller after linearization is a widely used method. This paper proposes a new method to efficiently train a reinforcement learning agent to select the control gai...
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作者：
Shim, HyungboASRI
Electrical and Computer Engineering Department Seoul National University Korea Republic of
A swarm of individuals often exhibits behaviors that are not possible for each individual. This phenomenon is called emergence, and this paper mathematically demonstrates that new dynamics can arise in swarm behavior ...
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This paper introduces Deep Convolutional Generative Adversarial Networks (DCGAN) as a potential solution for wireless systems aiming to enhance the Block Error Rate (BLER). The DCGAN under consideration consists of a ...
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