Existing deep clustering approaches often struggle with redundant feature learning, which limits their effectiveness. The primary goal of this study is to address these issues by developing a more robust deep clusteri...
详细信息
Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow...
详细信息
Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow uncontrollably, forming a tumor on the skin. To prevent skin cancer from spreading and potentially leading to serious complications, it's critical to identify and treat it as early as possible. An innovative two-fold deep learning based skin cancer detection model is presented in this research work. Five main stages make up the proposed model: Preprocessing, segmentation, feature extraction, feature selection, and skin cancer detection. Initially, the Min–max contrast stretching and median filtering used to pre-process the collected raw image. From the pre-processed image, the Region of Intertest (ROI) is identified via optimized mask Region-based Convolutional Neural Network (R-CNN). Then, from the identified ROI areas, the texture features like Illumination-invariant Binary Gabor Pattern (II-BGP), Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Color feature such as Color Correlogram and Histogram Intersection, and Shape feature including Moments, Area, Perimeter, Eccentricity, Average bending energy are extracted. To choose the optimal features from the extracted ones, the Golden Eagle Mutated Leader Optimization (GEMLO) is used. The proposed Golden Eagle Mutated Leader Optimization (GEMLO) is the conceptual amalgamation of the standard Mutated Leader Algorithm (MLA) and Golden Eagle Optimizer are used to select best features (GEO). The skin cancer detection is accomplished via two-fold-deep-learning-classifiers, that includes the Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The final outcome is the combination of the outcomes acquired from Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The PYTHON platform is being used to implement the suggested model. Using the curre
Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and gener...
详细信息
Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain *** this paper,we propose a deep residual channel attention network(DeRCAN)for *** channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image *** addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed *** qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.
Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
详细信息
1 Introduction In recent years,foundation Vision-Language Models(VLMs),such as CLIP[1],which empower zero-shot transfer to a wide variety of domains without fine-tuning,have led to a significant shift in machine learn...
详细信息
1 Introduction In recent years,foundation Vision-Language Models(VLMs),such as CLIP[1],which empower zero-shot transfer to a wide variety of domains without fine-tuning,have led to a significant shift in machine learning *** the impressive capabilities,it is concerning that the VLMs are prone to inheriting biases from the uncurated datasets scraped from the Internet[2–5].We examine these biases from three perspectives.(1)Label bias,certain classes(words)appear more frequently in the pre-training data.(2)Spurious correlation,non-target features,e.g.,image background,that are correlated with labels,resulting in poor group robustness.(3)Social bias,which is a special form of spurious correlation,focuses on societal *** image-text pairs might contain human prejudice,e.g.,gender,ethnicity,and age,that are correlated with *** biases are subsequently propagated to downstream tasks,leading to biased predictions.
Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation ***,existing knowledge-aware recommendation methods face challenges such as weak user-it...
详细信息
Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation ***,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge *** tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge ***,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and ***,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view *** paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge ***,this paper introduces multi-task learning to mitigate the problem of weak supervisory *** validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM *** results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.
Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these proce...
详细信息
Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.
Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
详细信息
Removing noise in the real-world scenario has been a daunting task in the field of natural language processing. Research has shown that Deep Neural Networks (DNN) have proven to be very useful in terms of noise genera...
详细信息
In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless ...
详细信息
In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized *** nested structure of polar codes ensures that the MMI quantization can be implemented stage by *** results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization ***,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
暂无评论