Gain In Accuracy Research Articles

Carleman approximants for non-linear differential systems

Abstract We examine a method introduced by Torsten Carleman a century ago to approximate the solution of non-linear differential systems. His idea consists in replacing the non-linear system with an infinite-dimensional linear one. Carleman approximants are obtained when solving a truncated version. We describe the linearization process and present two different approaches to apply the Carleman technique. In the first one, which is the common case, the Carleman approximants are centred at an equilibrium point in phase space. The resulting linear system is always homogeneous, and higher orders of approximation are usually analytically solvable. The closer the point of interest is to the equilibrium point, the more accurate the approximate solution. In a way, this approach may be viewed as an extension of the so-called local, or qualitative, analysis of non-linear differential systems. In the second one, the approximants are built up around the initial condition. In this novel approach, there is a gain in accuracy at the price of solving a non-homogeneous linear system that allows for an analytical solution too. We found a noteworthy pattern in the analyticity properties of the approximants that explains this improved behaviour. The connection with the Koopman operator and related methods is commented on. A number of systems are worked out intended to provide evidence of the practical utility of the Carleman approach. We also derive approximate solutions for the Susceptible-Infected-Removed epidemiological model, which leads to an extended classification of the evolutionary stage of an epidemic process.

An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE.

Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon's Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation.

Selective output smoothing regularization: Regularize neural networks by softening output distributions

Convolutional neural networks (CNNs) often exhibit overfitting due to overconfident predictions, which limits the effective utilization of training samples. Inspired by the diverse effects of training from different samples, we propose selective output smoothing regularization(SOSR) that improves model performance by encouraging the generation of equal logits on incorrect classes when handling samples that are correctly and overconfidently classified. This plug-and-play approach integrates seamlessly into diverse CNN architectures without altering their core design. SOSR demonstrates consistent improvements on various benchmarks, such as a 1.1% accuracy gain on ImageNet with ResNet-50 (77.30%). It synergizes effectively with several widely used techniques, such as CutMix and label smoothing, achieving incremental benefits, highlighting its potential as a foundational tool in advancing deep learning applications. Overall, SOSR effectively alleviates underutilization of high-confidence samples, enhances the generalizability of CNNs, and emerges as a robust tool for improving deep learning applications.

Elastography Enhances the Diagnostic Performance of Conventional Ultrasonography in Differentiating Benign from Malignant Superficial Lymphadenopathies.

Lymph node (LN) evaluation is critical in diagnosing, staging, and managing various diseases, particularly lymphoma and metastatic cancer. Although conventional ultrasound (US) is widely used for this purpose, its limitations in reliably differentiating between benign and malignant LNs persist. Ultrasound elastography (US-E), which evaluates tissue stiffness, has emerged as a promising adjunct to improve diagnostic accuracy. This study aims to evaluate the diagnostic performance of conventional US, power Doppler US, and strain elastography (SE) in distinguishing malignant from benign superficial lymph nodes. In this prospective study, 214 consecutive patients referred for US of enlarged LNs were enrolled. Conventional B-mode US, power Doppler, and SE were performed, and the strain ratio (SR) was calculated as a measure of LN stiffness. Histopathological examination was used as the reference standard. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, and multivariable logistic regression models were applied to determine the independent predictive role of SR. Among the 214 LNs (one for each patient), 74 (34.6%) were benign and 140 (65.4%) were malignant. The SR showed a significant association with malignancy (p < 0.001). For hematological malignancies, SR demonstrated high sensitivity (79-85%) and specificity (81-96%), with an overall area under the curve (AUC) of 0.91. Multivariable analysis confirmed that SR was an independent predictor of malignancy (continuous and dichotomous), with a 14% gain in predictive accuracy when treated as a continuous variable (p < 0.0001). US-E, particularly SR, is a valuable tool in the differentiation of benign and malignant superficial LNs. SR provides significant diagnostic value, especially in hematological neoplasms like Hodgkin lymphoma, and can serve as an independent predictor of malignancy. This technique, when used in combination with conventional US features, offers enhanced diagnostic performance for LN evaluation.

ExPERT: Modeling Human Behavior Under External Stimuli Aware Personalized MTPP

Marked Temporal Point Process (MTPP) -- the de-facto sequence model for continuous-time event sequences -- historically employed for modeling human-generated action sequences, lack awareness of external stimuli. In this study, we propose a novel framework developed over Transformer Hawkes Process (THP) to incorporate external stimuli in a domain-agnostic manner. Furthermore, we integrate personalization into our framework by employing language model-based representations of user and event descriptions, which is essential for modeling human-generated action sequences. Towards evaluating the efficacy, we put together a comprehensive benchmark comprising 5 datasets (2 novel additions, and 3 repurposed from existing open datasets) harvested from several domains, spanning education, e-commerce, online payment, and discussion forum. On average, we achieve 9.35% gain in type-prediction accuracy and 7.38% reduction in time-prediction RMSE across all datasets over SOTA MTPP baselines. We demonstrate the superior performance of our proposed model through extensive ablations and showcasing its ability to capture complex combinations of external stimuli in a synthetic set up.

Single-Step Genomic BLUP With Unknown Parent Groups and Metafounders in Norwegian Red Evaluations.

The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and "Q-Q+" methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The "Q-Q+" approach corrects for the combined effects of UPG and "J factor" in non-genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (Grtn matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G05 constructed using AF = 0.5 and w = 0.0, and Gcal constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using Grtn), four MF models (MF14 and MF52 using Grtn or G05), and three Q-Q+ models (using Gcal, G05, or Grtn). The models were evaluated through cross-validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q-Q+ models using the Gcal or G05 matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G05 were second best for level bias and performed similarly or slightly better than Q-Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with Grtn had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G05 (1.18%), but pedigree-based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G05 showed the least bias (with a small standard error) and most stable predictions, while models using Grtn introduced biases and instability. Thus, the Q-Q+ and MF models combined with G05 and Q-Q+ with Gcal are recommended for their improved validation results and genetic trends.

BC-PMJRS: A Brain Computing-inspired Predefined Multimodal Joint Representation Spaces for enhanced cross-modal learning.

BC-PMJRS: A Brain Computing-inspired Predefined Multimodal Joint Representation Spaces for enhanced cross-modal learning.

Sensing accuracy gain, unified performance analysis and optimization in 6G cooperative ISAC systems

Sensing accuracy gain, unified performance analysis and optimization in 6G cooperative ISAC systems

Accuracy of Multi‐Environmental Trials in Predicting New Environments Using Different Approaches Based on Environmental Covariates: A Case in Barley (Hordeum vulgare L.) Breeding

ABSTRACTOne of the current innovations in predicting genotype performances in a target population of environments is integrating environmental covariates (ECs) into multi‐environment trial (MET) data analysis. In this study, a MET data set of a barley (Hordeum vulgare L.) breeding program in the years 2016 and 2017 was used. We evaluated and compared different approaches of using ECs in predicting genotype performances into new environments. The comparison was done using the mean squared error of predicted differences (MSEPD) under different linear mixed models. The MSEPD was computed for the new environments using a cross‐validation mechanism that drops out one environment at a time. Our results show that models with ECs resulted in smaller MSEPD compared with the model without ECs. Among the different approaches, the reduced rank regression approach with one component resulted in the smallest MSEPD followed by fitting both the first and the second synthetic covariates of the extended Finlay–Wilkinson regression. Overall, there is a potential gain in predictive accuracy in MET with integrating ECs in plant breeding programs.

Assessing the role of spatial aggregation schemes with varying campaign durations of mobile measurements on land use regression models for estimating nitrogen dioxide.

Assessing the role of spatial aggregation schemes with varying campaign durations of mobile measurements on land use regression models for estimating nitrogen dioxide.

SWARM INTELLIGENCE-BASED FEATURE SELECTION FOR SKIN CANCER DATA ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS

In this paper, a comparative analysis of three widely used deep learning models to perform data classification tasks with and without a Swarm Intelligence (SI) based Feature Selection is presented. The study explores the performance of each model in the context of types of data it can handle and the effect of SI-based feature selection on its performance. Traditionally used for image-based tasks, CNNs outperformed RNNs and LSTMs with a baseline accuracy of 90%, capitalizing on their strength to learn spatial hierarchies in image data. The feasibility of SI for feature selection was demonstrated on RNNs in retrieving data time series (reaching 85% accuracy, which was 5% higher than a 80% baseline accuracy achieved without feature selection). Additionally, we investigated the generation and the execution of data collection, tasks which we encoded in the limited capability decision making module and subsequently plan to contextualize with appropriate application knowledge. SI based feature selection through Particle Swarm Optimization (PSO) has enhanced the performance of all model by selecting the most paneled features and brought the dimensionality of the input data on much lesser side. For CNNs we demonstrated a 4% gain in accuracy from 90% to 94%. In terms of RNNs and LSTMs the improvement was a bit smaller (but not by much), so that the accuracies rose from 80% to 85% for RNNs, and from 85% to 88% for LSTMs. The results demonstrate the significance of feature selection in deep learning models with high dimensional datasets because it not only improves accuracy but also decreases model deviation and computational expense. The study concludes generally that CNN models are better for image-based tasks while RNN models and LSTMs are better for sequential data. The integration of Swarm Intelligence-based feature selection worked out to be effective for all the models, promising the potential to increase the deep learning performance in practical applications.

Small-Dose Behavioral Treatment Effects: Learning Following 2 Hours of Computer-Based Conversational Script Training in Individuals With Poststroke Aphasia.

Optimal dosage parameters are underspecified for aphasia therapy. This study evaluated effects of small doses of conversational script training in individuals with chronic poststroke aphasia. Ten adults with chronic poststroke aphasia completed 2 hr of computerized conversational script training on two consecutive days via AphasiaScripts. Accuracy and rate of production of a trained and an untrained conversational script were probed at three baseline timepoints and various timepoints after the first and second treatment sessions up to 2 weeks posttreatment. Generalization in accuracy of trained script production was evaluated through a live conversational interaction. Mixed-effects linear regression models evaluated changes in accuracy and rate of script production across timepoints. Participants showed significantly improved accuracy and rate of trained script production immediately following 1 and 2 hr of treatment. Gains in script production accuracy and rate were maintained up to 1 week posttreatment. Generalization probe production accuracy improved significantly from baseline to immediately posttreatment and 2 weeks posttreatment. Improvement in production of trained conversational scripts following 1 and 2 hr of treatment can be documented in individuals with poststroke aphasia. These results provide estimates for the effects of 1 and 2 hr of conversational script training that can be used in future dosage manipulations. https://doi.org/10.23641/asha.28339925.

Automated reading of handheld echocardiograms is feasible and shows strong agreement with high-resolution echocardiography - Results from the PAVE project

Abstract Background We recently showed that population data-based machine-learning can improve the automated echocardiographic quantification of cardiac structure and function. The respective gain in accuracy and precision strengthens the confidence into automated echocardiographic readings and carries potential for applications in various settings. Purpose We applied the automated detector to high-resolution standard echocardiograms and to echocardiograms acquired with a handheld device. Methods and Results PAVE (Pathology-oriented reading of echocardiography) is a cooperation project between University Hospital Wuerzburg (UKW) and Tomtec Imaging Systems using an established federated machine-learning environment. We recruited 2043 patients (mean age 64±16, 44% women) presenting at the UKW for transthoracic echocardiography (TTE). After high-resolution standard TTE (Vivid E95, GE) trained sonographers acquired images with a handheld ultrasound device (Lumify 2.0, Philips) according to a pre-specified protocol. Images of both modalities were loaded into the analysis platform of the Academic CoreLab Ultrasound-based Cardiovascular Imaging (TomtecArena®, Tomtec, Germany). For the current analysis, we selected n=51 random patients (mean age 65±16, 39% women) from the PAVE cohort. Reading of high-resolution and handheld TTE, respectively, was performed by a trained sonographer (&amp;gt;14 days apart and blinded to the reading results) as well as by the automated detector. We here present first results regarding 2D (parasternal long axis) measurements of interventricular septum (IVS), left ventricular diameter (LVD) and posterior wall thickness (PW) at end-diastole from manual CoreLab reading (CL) and automated detector reading (AD) of high-resolution TTE (11.3±4.1mm, 10.9±2.5mm; 48.3±8.1mm, 49.5±7.4mm; PW 9.3±3.2mm, 10.0±1.7mm) as well as of the respective handheld echocardiograms (IVS 10.7±3.4mm, 11.0±2.2mm; LVD 47.7±7.4mm, 48.3±6.6mm; PW 9.7±2.7mm, 10.2±1.6mm). The agreement between measurements performed in high-resolution and handheld TTE was assessed using Bland-Altman analysis (Table 1; Figure 1). Conclusions The application of an automated detector to handheld TTE was feasible and led to measurement values in the same range but with higher agreement of measurements when compared to human reading. Our results await extension to further echocardiographic parameters and confirmation in larger cohorts but suggest that automated echocardiographic reading might become a valuable tool in patient care. Accurate automated reading of handheld TTE might extend applicability of echocardiography to non-expert settings.

RA-MOSAIC : Resource Adaptive Edge AI Optimization over Spatially Multiplexed Video Streams

Sustaining real-time, high fidelity AI-based vision perception on edge devices is challenging due to both the high computational overhead of increasingly “deeper” Deep Neural Networks (DNNs) and the increasing resolution/quality of camera sensors. Such high-throughput vision perception is even more challenging in multi-tenancy systems, where video streams from multiple such high-quality cameras need to share the same GPU resource on a single edge device. Criticality-aware canvas-based processing is a promising paradigm that decomposes multiple concurrent video streams into Regions of Interest (RoI) and spatially channels the limited computational resources to selected RoI with higher “resolution”, thereby moderating the trade-off between computational load, task fidelity, and processing throughput. RA-MOSAIC (Resource Adaptive MOSAIC) employs such canvas-based processing, while further tuning the incoming video streams and available resources on-demand to allow the system to adapt to dynamic changes in workload (often arising from variations in the number or size of relevant objects observed by individual cameras). RA-MOSAIC utilizes two distinct and synergistic concepts. First, at the camera sensor, a bandwidth-adaptive and lightweight Bandwidth Aware Camera Transmission (BACT) method applies differential down-sampling to create mixed-resolution individual frames that preferentially preserve resolution for critical ROIs, before being transmitted to the edge node. Second, at the edge, BACT video streams received from multiple cameras are decomposed into multi-scale RoI tiles and spatially packed using a novel workload-adaptive bin-packing strategy into a single ‘canvas frame’. Notably, the canvas frame itself is dynamically sized such that the edge device can opportunistically provide higher processing throughput for selected high-priority tiles during periods of lower aggregate workloads. To demonstrate RA-MOSAIC’s gains in processing throughput and perception fidelity, we evaluate RA-MOSAIC on a single NVIDIA Jetson TX2 edge device for two benchmark tasks: Drone-based Pedestrian Detection and Automatic License Plate Recognition. In a bandwidth-constrained wireless environment, RA-MOSAIC employs a batch size of 1 to pack up to 6 concurrent video streams on a dynamically sized canvas frame to provide (i) 14.3% gain in object detection accuracy and (ii) 11.11% gain in throughput on average (up to 20 FPS per camera, cumulatively 120 FPS), over our previous work MOSAIC, a naïve canvas-based baseline. Compared to prior state of the art baselines such as batched inference over extracted RoI, RA-MOSAIC provides a very-significant, 29.6% gain in accuracy for a comparable throughput. Similarly, RA-MOSAIC dramatically outperforms bandwidth adaptive baselines, such as FCFS ( \(\leq 1\%\) accuracy gain but \(5.6\) x or 566.67% throughput gain) and uniform grid packing (17% accuracy improvement and 5% throughput gain).

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care. The objective of this study is to provide an alternative method for distinguishing between radiologists by means of captured eye-tracking data such that the raw gaze (or processed fixation data) can be used to discriminate users based on subconscious behavior in visual inspection. We present a novel discretized feature encoding based on spatiotemporal binning of fixation data for efficient geometric alignment and temporal ordering of eye movement when reading chest x-rays. The encoded features of the eye-fixation data are used by machine learning classifiers to discriminate between faculty and trainee radiologists. A clinical trial case study was conducted using metrics such as the area under the curve, accuracy, F1-score, sensitivity, and specificity to evaluate the discriminability between the 2 groups regarding their level of experience. The classification performance was then compared with state-of-the-art methodologies. In addition, a repeatability experiment using a separate dataset, experimental protocol, and eye tracker was performed with 8 participants to evaluate the robustness of the proposed approach. The numerical results from both experiments demonstrate that classifiers using the proposed feature encoding methods outperform the current state-of-the-art in differentiating between radiologists in terms of experience level. An average performance gain of 6.9% is observed compared with traditional features while classifying experience levels of radiologists. This gain in accuracy is also substantial across different eye tracker-collected datasets, with improvements of 6.41% using the Tobii eye tracker and 7.29% using the EyeLink eye tracker. These results signify the potential impact of the proposed method for identifying radiologists' level of expertise and those who would benefit from additional training. The effectiveness of the proposed spatiotemporal discretization approach, validated across diverse datasets and various classification metrics, underscores its potential for objective evaluation, informing targeted interventions and training strategies in radiology. This research advances reliable assessment tools, addressing challenges in perception-related errors to enhance patient care outcomes.

Deep Learning Model Compression With Rank Reduction in Tensor Decomposition.

Large neural network models are hard to deploy on lightweight edge devices demanding large network bandwidth. In this article, we propose a novel deep learning (DL) model compression method. Specifically, we present a dual-model training strategy with an iterative and adaptive rank reduction (RR) in tensor decomposition. Our method regularizes the DL models while preserving model accuracy. With adaptive RR, the hyperparameter search space is significantly reduced. We provide a theoretical analysis of the convergence and complexity of the proposed method. Testing our method for the LeNet, VGG, ResNet, EfficientNet, and RevCol over MNIST, CIFAR-10/100, and ImageNet datasets, our method outperforms the baseline compression methods in both model compression and accuracy preservation. The experimental results validate our theoretical findings. For the VGG-16 on CIFAR-10 dataset, our compressed model has shown a 0.88% accuracy gain with 10.41 times storage reduction and 6.29 times speedup. For the ResNet-50 on ImageNet dataset, our compressed model results in 2.36 times storage reduction and 2.17 times speedup. In federated learning (FL) applications, our scheme reduces 13.96 times the communication overhead. In summary, our compressed DL method can improve the image understanding and pattern recognition processes significantly.

A Reduced Order Model for Sea Water Intrusion Simulation Using Proper Orthogonal Decomposition.

Sea water intrusion (SWI) simulators are essential tools to assist the sustainable management of coastal aquifers. These simulators require the solution of coupled variable-density partial differential equations (PDEs), which reproduce the processes of groundwater flow and dissolved salt transport. The solution of these PDEs is typically addressed numerically with the use of density-dependent flow simulators, which are computationally intensive in most practical applications. To this end, model surrogates are generally developed as substitutes for full-scale aquifer models to trade off accuracy in exchange for computational efficiency. Surrogates represent an attractive option to support groundwater management situations in which fast simulators are required to evaluate large sets of alternative pumping strategies. Reduced-order models, a sub-category of surrogate models, are based on the original model equations and may provide quite accurate results at a small fraction of computational cost. In this study, a variable-density flow reduced-order model based on proper orthogonal decomposition (POD) and utilizing a fully coupled flow and solute-transport model is implemented with a finite-difference (FD) approach for simulating SWI in coastal aquifers. The accuracy and computational efficiency of the FD-POD approach for both homogeneous and-more realistic-heterogeneous systems are investigated using test cases based on the classic Henry's problem (Henry 1964). The findings demonstrate that the combined FD-POD approach is effective in terms of both accuracy and computational gain and can accommodate the output of the most popular variable-density flow models, such as those from USGS's MODFLOW family.

Adaptive Tip Selection for DAG-Shard-Based Federated Learning with High Concurrency and Fairness

To cope with the challenges posed by high-concurrency training tasks involving large models and big data, Directed Acyclic Graph (DAG) and shard were proposed as alternatives to blockchain-based federated learning, aiming to enhance training concurrency. However, there is insufficient research on the specific consensus designs and the effects of varying shard sizes on federated learning. In this paper, we combine DAG and shard by designing three tip selection consensus algorithms and propose an adaptive algorithm to improve training performance. Additionally, we achieve concurrent control over the scale of the directed acyclic graph’s structure through shard and algorithm adjustments. Finally, we validate the fairness of our model with an incentive mechanism and its robustness under different real-world conditions and demonstrate DAG-Shard-based Federated Learning (DSFL)’s advantages in high concurrency and fairness while adjusting the DAG size through concurrency control. In concurrency, DSFL improves accuracy by 8.19–12.21% and F1 score by 7.27–11.73% compared to DAG-FL. Compared to Blockchain-FL, DSFL shows an accuracy gain of 7.82–11.86% and an F1 score improvement of 8.89–13.27%. Additionally, DSFL outperforms DAG-FL and Chains-FL on both balanced and imbalanced datasets.

Connectome embedding in multidimensional graph spaces.

Connectomes' topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated various connectomes (structural/functional, binary/weighted). We observed that nodal properties are correlated (i.e., they carry similar information) at whole-brain and subnetwork level. We conducted an exploratory machine learning analysis to test whether high-dimensional network information differs between sensory and association areas. Brain regions of sensory and association networks were classified with an 80-86% accuracy in a 10-dimensional (10D) space. We observed the largest gain in machine learning accuracy going from a 2D to 3D space, with a plateauing accuracy toward 10D space, and nonlinear Gaussian kernels outperformed linear kernels. Finally, we quantified the Euclidean distance between nodes in a 10D graph space. The multidimensional Euclidean distance was highest across subjects in the default mode network (in structural networks) and frontoparietal and temporal lobe areas (in functional networks). To conclude, we propose a new framework for quantifying network features in high-dimensional spaces that may reveal new network properties of the brain.

The Efficiency and Accuracy Gains of Real-Time Health Data Integration in Healthcare Management: A Comprehensive Review of Current Practices and Future Directions

Background: In the era of digital transformation, real-time health data integration has emerged as a pivotal element in enhancing healthcare management. The exponential growth of data, often referred to as "big data," presents both opportunities and challenges for healthcare providers seeking to improve efficiency and accuracy in patient care. This paper reviews the principles of big data and its implications within the healthcare sector, focusing on the integration of electronic health records (EHRs), Internet of Things (IoT) devices, and advanced data analytics methods. Methods: To investigate the impact of real-time data integration, a comprehensive literature review was conducted, examining current methodologies used in healthcare data management, including machine learning algorithms and cloud computing technologies. Case studies were analyzed to demonstrate the effectiveness of data integration in reducing medical errors, enhancing patient outcomes, and streamlining workflows. The findings indicate that healthcare institutions adopting real-time data integration report significant improvements in operational efficiency, with reductions in redundant testing and faster access to critical patient information. Moreover, the integration facilitates proactive healthcare management, allowing practitioners to identify and address health issues before they escalate. Conclusions: In conclusion, the integration of real-time health data is essential for the evolution of contemporary healthcare management. It not only enhances the quality of care delivered but also supports a more patient-centered approach by leveraging data for personalized treatment strategies. As the healthcare landscape continues to evolve, embracing advanced data integration technologies will be crucial for achieving optimal patient outcomes and operational efficiency.