Initially, sparse anchors are utilized for accelerating graph construction, resulting in a parameter-free anchor similarity matrix. Inspired by the intra-class similarity maximization in self-organizing maps (SOM), we subsequently designed an intra-class similarity maximization model applied to the anchor and sample layers to mitigate the anchor graph cut problem while exploiting explicit data structures. Simultaneously, a rapid coordinate rising (CR) algorithm is implemented to iteratively refine the discrete labels of samples and anchors within the designed model. The experimental findings highlight EDCAG's exceptional speed and competitive clustering ability.
High-dimensional data benefits from the competitive performance of sparse additive machines (SAMs) in variable selection and classification, stemming from their adaptable representations and interpretable nature. Nonetheless, the prevalent methods frequently adopt unbounded or non-differentiable functions as proxies for 0-1 classification loss, which might lead to impaired effectiveness for data containing unusual values. In order to mitigate this problem, we present a robust classification method, termed SAM with correntropy-induced loss (CSAM), integrating correntropy-induced loss (C-loss), data-dependent hypothesis space, and weighted lq,1-norm regularizer (q1) into additive machines. Using a novel error decomposition technique alongside concentration estimation, the theoretical generalization error bound is estimated, exhibiting a potential convergence rate of O(n-1/4) given appropriate parameter conditions. The theoretical basis for the consistency of variable selection is further examined. Consistently, experimental results across synthetic and real-world datasets confirm the effectiveness and resilience of the suggested approach.
Privacy-preserving distributed machine learning, in the form of federated learning, holds promise for the Internet of Medical Things (IoMT). It enables training of a regression model without requiring the collection of raw data from individuals. Traditional interactive federated regression training (IFRT) methodologies, however, necessitate repeated communication exchanges to forge a unified model, but still confront numerous privacy and security challenges. In order to surmount these predicaments, a range of non-interactive federated regression training (NFRT) strategies have been proposed and deployed in various settings. Furthermore, significant hurdles to success exist: 1) protecting the confidentiality of local datasets owned by individual contributors; 2) creating regression models that scale independently of data size; 3) ensuring consistent data owner participation; and 4) allowing data owners to validate the accuracy of the aggregated results from the cloud provider. For IoMT, we introduce two practical non-interactive federated learning strategies: HE-NFRT (homomorphic encryption) and Mask-NFRT (double-masking). These strategies address NFRT, privacy, performance, robustness, and verifiability considerations in a comprehensive and detailed way. The security analysis confirms that our proposed schemes protect the local training data privacy of each data owner, withstand collusion attacks, and provide strong verification for every data owner. In performance evaluations, the HE-NFRT scheme proved desirable for IoMT applications with high dimensionality and high security requirements, whereas the Mask-NFRT scheme was found to be more suitable for applications with high dimensionality and large scale.
The electrowinning process, a key operation in nonferrous hydrometallurgy, incurs a substantial power cost. Closely linked to power consumption, current efficiency is a significant process parameter; thus, maintaining the electrolyte temperature near the optimal point is crucial. rapid immunochromatographic tests Nonetheless, achieving optimal electrolyte temperature control presents the following obstacles. Estimating current efficiency accurately and establishing the ideal electrolyte temperature is made difficult by the temporal influence of process variables on current efficiency. Another factor contributing to difficulty is the considerable fluctuation in the variables influencing electrolyte temperature, thereby making it challenging to maintain the desired temperature close to the optimum. Third, the complicated electrowinning mechanism makes the creation of a dynamic process model virtually unachievable. In summary, the issue revolves around optimizing the index in a multivariable fluctuating environment, leaving process modeling unutilized. In order to address this issue, an integrated optimal control approach is devised, utilizing temporal causal networks and reinforcement learning (RL). Under diverse working conditions, a temporal causal network assesses current efficiency, allowing for the accurate determination of the optimal electrolyte temperature, through an analytical approach based on a divided working condition model. Subsequently, a reinforcement learning controller is implemented for each operational condition, incorporating the optimal electrolyte temperature into the controller's reward function to aid in the learning process of the control strategy. A zinc electrowinning process experiment, presented as a case study, is utilized to ascertain the proposed method's effectiveness. This study directly shows the method's ability to maintain the electrolyte temperature within the target range without relying on modeling.
Measuring sleep quality and diagnosing sleep disorders hinges on the accuracy of automatic sleep stage classification. Despite the range of methods developed, the majority are limited to using single-channel electroencephalogram signals for the task of classification. Multiple signal channels are recorded during polysomnography (PSG), allowing for the selection of the most suitable method for extracting and combining data from various channels, thereby enhancing sleep staging accuracy. MultiChannelSleepNet, a transformer-encoder-based model for automatic sleep stage classification using multichannel PSG data, is presented. Its architecture employs a transformer encoder for individual-channel feature extraction and subsequent multichannel feature amalgamation. In a single-channel feature extraction block, the features are extracted independently from the time-frequency images of each channel by transformer encoders. Our integration strategy dictates that feature maps extracted from individual channels are fused within the multichannel feature fusion block. This block employs a residual connection to maintain the initial information from each channel, and further uses another set of transformer encoders to capture interwoven features. Three publicly accessible datasets showcase the superior classification performance of our method compared to the leading techniques currently in use. Clinical applications benefit from the precision sleep staging enabled by MultiChannelSleepNet's method of extracting and integrating information from multichannel PSG data. The source code of MultiChannelSleepNet is publicly available at the URL https://github.com/yangdai97/MultiChannelSleepNet.
Bone age (BA) and teenage growth and development are closely correlated, with the accuracy of the assessment relying on the careful extraction of the reference carpal bone. The fluctuating dimensions and irregular contours of the reference bone, combined with the potential for imprecise estimations, will undoubtedly impact the precision of Bone Age Assessment (BAA). Precision sleep medicine Recent smart healthcare systems have extensively incorporated machine learning and data mining strategies. Through the utilization of these two instruments, this study addresses the stated problems by proposing a Region of Interest (ROI) extraction method for wrist X-ray images, employing an optimized YOLO model. YOLO-DCFE comprises Deformable convolution-focus (Dc-focus), Coordinate attention (Ca) module, and Feature level expansion, along with Efficient Intersection over Union (EIoU) loss. Refinement of the model allows for the enhanced recognition of irregular reference bone features, reducing the potential for misidentification amongst similar-shaped structures, which consequently improves the detection accuracy. A dataset comprising 10041 images captured by professional medical cameras was selected to evaluate the performance of YOLO-DCFE. learn more Statistics unequivocally support the notion that YOLO-DCFE excels in both detection speed and accuracy. 99.8% accuracy in detecting all ROIs stands as a superior performance compared to alternative models. Amongst the comparative models, YOLO-DCFE is notably the fastest, reaching a frame rate of 16 frames per second.
The acceleration of disease comprehension hinges on the essential sharing of pandemic data at the individual level. A wealth of COVID-19 data has been amassed to aid in public health surveillance and research initiatives. In order to respect the privacy of individual data subjects, the process of publication in the United States usually involves removing identifying information from these data. The current dissemination methods for this category of data, including those used by the U.S. Centers for Disease Control and Prevention (CDC), have failed to respond effectively to the shifting patterns of infection rates. Finally, the policies stemming from these strategies are prone to either increasing privacy vulnerabilities or overprotecting the data, thus impairing its practical value (or usability). Our novel game-theoretic model dynamically adjusts policies for sharing individual COVID-19 data, focusing on the interplay between privacy and the value of the data, guided by infection patterns. We employ a two-player Stackelberg game to model the data publishing process, featuring roles for both a data publisher and a data recipient, and we then seek the publisher's most effective strategic approach. Our game's evaluation framework incorporates two key metrics: firstly, the average performance of forecasting future case counts; secondly, the mutual information characterizing the relationship between the original data and the released data. Vanderbilt University Medical Center's COVID-19 case data spanning from March 2020 to December 2021 will be utilized to demonstrate the effectiveness of the newly developed model.