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Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia. We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature-feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%-70% accuracy with Naïve Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature-feature correlations and their results can vary between cohort studies. By understanding these biases, we can reduce discrepancies in feature ranking and identify a minimal set of features needed for accurate classification of dementia., (© 2024 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the "best" objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of "maximizing information and general utility" extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data. Key Points: A basic problem in modeling is the choice of objective function (or performance metric)According to information theory, the "best" objective function minimizes information loss, which we evaluate using the AICLike friction or inefficiency in a system, information loss incurs additional cost however the model is used [ABSTRACT FROM AUTHOR]

AlphaFold2 (ref.  1 ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates 2 , and disease-causing point mutations often cause population changes within these substates 3,4 . We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB 5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function., (© 2023. The Author(s).)

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems. [ABSTRACT FROM AUTHOR]

The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes. [ABSTRACT FROM AUTHOR]

How to use AI to Predict Success of Email Campaigns Testing email marketing effectiveness is an active research area because email remains an important channel for customer acquisition and retention. Email open rates are a key measure of campaign effectiveness. Scholars identify three predictors of open rates: recipients' characteristics, headline characteristics, and sending time. The industry-favored A/B testing has three drawbacks: it takes hours, depletes lists available for main campaigns, and limits testable email versions because of sample size and power requirements. These limitations continue to motivate researchers to build and improve open rate prediction models. Although they reduce testing time, models developed in marketing use only recipients' past open rates as predictors. By contrast, models in computer science typically use only email headline characteristics as predictors. Consequently, current models' open rate prediction errors are high. The authors address the limitations of both literature streams and use all three predictors and machine learning to build an email open rate predictor (EMOP) based on their universal emotion detector (UED). They test EMOP on data from four brands and set state-of-the-art prediction results. Experimental validation shows that EMOP can pick the best headline from a set of professionally generated headlines. Also, UED ranked second at the SemEval 2018 Task 1 E-c competition as of January 5, 2023. History: Olivier Toubia served as the senior editor for this article. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mksc.2021.0126. [ABSTRACT FROM AUTHOR]

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires., (© 2024. The Author(s).)

Recently, the use of machine-learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures which integrate mechanistic knowledge. In this work, we use as the test case a one-compartment PK model using a scientific machine learning (SciML) framework and consider learning an unknown absorption using neural networks, while simultaneously estimating other parameters of drug distribution and elimination. We generate simulated data with different sampling strategies to show that our model can accurately predict concentrations in extrapolation tasks, including new dosing regimens with different sparsity levels, and produce reliable forecasts even for new patients. By using a scenario of fitting PK data with complex absorption, we demonstrate that including known physiological structure into an SciML model allows us to obtain highly accurate predictions while preserving the interpretability of classical compartmental models., (© 2023 AbbVie Inc and The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development. [ABSTRACT FROM AUTHOR]

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent longterm problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning ensemble modelsdRandom Forest, Adaptive Boosting (AdaBoost), and Gradient Boostingdwere used to develop the meta-ensemble models proposed in this paper, whereby the three ensemble models were adopted as base classifiers while one of them was adopted as the meta-classifier. In addition, two of the ensemble models were used as base classifiers, while the third was used as a meta-classifier to evaluate lung cancer risk prediction. Different graphs were evaluated to show that people with these features are liable to develop lung cancer. The proposed model has immensely improved prediction performance. The meta-ensemble models were simulated using the Python simulation environment, and the 5-fold cross-validation technique was used. The model validation was carried out using several known performance evaluation methodologies. The results of the experiments showed that gradient boosting achieved a maximum accuracy of 100%, an area under the curve (AUC), and a precision of 100%. The proposed model was compared with novel machine learning methods and popular state-of-the-art (SOTA) deep learning techniques. It was confirmed from the results that the model in this study had the best accuracy at lung cancer risk prediction. This study's results can be utilized to enhance the performance of actual patient risk prediction systems in the future. [ABSTRACT FROM AUTHOR]

In the last decades, technological advances have led to a considerable increase in computing power constraints to simulate complex phenomena in various application fields, among which are climate, physics, genomics and medical diagnosis. Often, accurate results in real time, or quasi real time, are needed, especially if related to a process requiring rapid interventions. To deal with such demands, more sophisticated approaches have been designed, including GPUs, multicore processors and hardware accelerators. Supercomputers manage high amounts of data at a very high speed; however, despite their considerable performance, their limitations are due to maintenance costs, rapid obsolescence and notable energy consumption. New processing architectures and GPUs in the medical field can provide diagnostic and therapeutic support whenever the patient is subject to risk. In this context, image processing as an aid to diagnosis, in particular pulmonary ultrasound to detect COVID-19, represents a promising diagnostic tool with the ability to discriminate between different degrees of disease. This technique has several advantages, such as no radiation exposure, low costs, the availability of follow-up tests and the ease of use even with limited resources. This work aims to identify the best approach to optimize and parallelize the selection of the most significant frames of a video which is given as the input to the classification network that will differentiate between healthy and COVID patients. Three approaches have been evaluated: histogram, entropy and ResNet-50, followed by a K-means clustering. Results highlight the third approach as the most accurate, simultaneously showing GPUs significantly lowering all processing times. [ABSTRACT FROM AUTHOR]

Introduction: Moyamoya disease (MMD) is a chronic cerebrovascular disease that can lead to ischemia and hemorrhagic stroke. The relationship between oxidative phosphorylation (OXPHOS) and MMD pathogenesis remains unknown. Methods: The gene expression data of 60 participants were acquired from three Gene Expression Omnibus (GEO) datasets, including 36 and 24 in the MMD and control groups. Differentially expressed genes (DEGs) between MMD patients MMDand control groups were identified. Machine learning was used to select the key OXPHOS-related genes associated with MMD from the intersection of DEGs and OXPHOS-related gene sets. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), gene set enrichment analysis (GSEA), Immune infiltration and microenvironments analysis were used to analyze the function of key genes. Machine learning selected four key OXPHOS-related genes associated with MMD: CSK, NARS2, PTPN6 and SMAD2 (PTPN6 was upregulated and the other three were downregulated). Results: Functional enrichment analysis showed that these genes were mainly enriched in the Notch signaling pathway, GAP junction, and RNA degradation, which are related to several biological processes, including angiogenesis, proliferation of vascular smooth muscle and endothelial cells, and cytoskeleton regulation. Immune analysis revealed immune infiltration and microenvironment in these MMD samples and their relationships with four key OXPHOS-related genes. APC co-inhibition (p = 0.032), HLA (p = 0.001), MHC I (p = 0.013), T cellco-inhibition (p = 0.032) and Type I IFN responses (p < 0.001) were significantly higher in the MMD groups than those in the control groups. The CSK positively correlated with APC co-inhibition and T cell-co-inhibition. The NARS2 negatively correlated with Type I IFN response. The SMAD2 negatively correlated with APC co-inhibition and Type I IFN response. The PTPN6 positively correlated with HLA, MHC I and Type I IFN responses. Discussion: This study provides a comprehensive understanding of the role of OXPHOS in MMD and will contribute to the development of new treatment methods and exploration of MMD pathogenesis. [ABSTRACT FROM AUTHOR]

Acoustic signals, especially those of biological source, remain unexplored in the Belgian part of the North Sea (BPNS). The BPNS, although dominated by anthrophony (sounds from human activities), is expected to be acoustically diverse given the presence of biodiverse sandbanks, gravel beds and artificial hard structures. Under the framework of the LifeWatch Broadband Acoustic Network, sound data have been collected since the spring of 2020. These recordings, encompassing both biophony, geophony and anthrophony, have been listened to and annotated for unknown, acoustically salient sounds. To obtain the acoustic features of these annotations, we used two existing automatic feature extractions: the Animal Vocalization Encoder based on Self-Supervision (AVES) and a convolutional autoencoder network (CAE) retrained on the data from this study. An unsupervised density-based clustering algorithm (HDBSCAN) was applied to predict clusters. We coded a grid search function to reduce the dimensionality of the feature sets and to adjust the hyperparameters of HDBSCAN. We searched the hyperparameter space for the most optimized combination of parameter values based on two selected clustering evaluation measures: the homogeneity and the density-based clustering validation (DBCV) scores. Although both feature sets produced meaningful clusters, AVES feature sets resulted in more solid, homogeneous clusters with relatively lower intracluster distances, appearing to be more advantageous for the purpose and dataset of this study. The 26 final clusters we obtained were revised by a bioacoustics expert. We were able to name and describe 10 unique sounds, but only clusters named as 'Jackhammer' and 'Tick' can be interpreted as biological with certainty. Although unsupervised clustering is conventional in ecological research, we highlight its practical use in revising clusters of annotated unknown sounds. The revised clusters we detailed in this study already define a few groups of distinct and recurring sounds that could serve as a preliminary component of a valid annotated training dataset potentially feeding supervised machine learning and classifier models. [ABSTRACT FROM AUTHOR]

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering. [ABSTRACT FROM AUTHOR]

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy. [ABSTRACT FROM AUTHOR]

Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology. [ABSTRACT FROM AUTHOR]

Introduction: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, longterm humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially within WRCH motifs (where W=A or T, R=A or G and H=A, C or T). However, it has been consistently observed that the mutability of WRCH motifs varies substantially, with large variations in mutation frequency even between multiple occurrences of the same motif within a single V region. This has led to the notion that the immediate sequence context of WRCH motifs contributes to mutability. Recent studies have highlighted the potential role of local DNA sequence features in promoting mutagenesis of AGCT, a commonly mutated WRCH motif. Intriguingly, AGCT motifs closer to 5' ends of V regions, within the framework 1 (FW1) sub-region1, mutate less frequently, suggesting an SHM-suppressing sequence context. Methods: Here, we systematically examined the basis of AGCT positional biases in human SHM datasets with DeepSHM, a machine-learning model designed to predict SHM patterns. This was combined with integrated gradients, an interpretability method, to interrogate the basis of DeepSHM predictions. Results: DeepSHM predicted the observed positional differences in mutation frequencies at AGCT motifs with high accuracy. For the conserved, lowly mutating AGCT motifs in FW1, integrated gradients predicted a large negative contribution of 5'C and 3'G flanking residues, suggesting that a CAGCTG context in this location was suppressive for SHM. CAGCTG is the recognition motif for Ebox transcription factors, including E2A, which has been implicated in SHM. Indeed, we found a strong, inverse relationship between E-box motif fidelity and mutation frequency. Moreover, E2A was found to associate with the V region locale in two human B cell lines. Finally, analysis of human SHM datasets revealed that naturally occurring mutations in the 3'G flanking residues, which effectively ablate the E-box motif, were associated with a significantly increased rate of AGCT mutation. Discussion: Our results suggest an antagonistic relationship between mutation frequency and the binding of E-box factors like E2A at specific AGCT motif contexts and, therefore, highlight a new, suppressive mechanism regulating local SHM patterns in human V regions. [ABSTRACT FROM AUTHOR]

Background: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. Methods: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. Results: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6–1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4–4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. Conclusions: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction. [ABSTRACT FROM AUTHOR]

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on highdimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%. [ABSTRACT FROM AUTHOR]

Employee turnover has long been a concern for businesses of all sizes and industries. To address this challenge, businesses increasingly turn to data-driven approaches to predict and prevent employee turnover. The article's authors focus on the effectiveness of processing large data through a predictive machine learning model in the context of employee turnover. The real dataset used for company research contained 14999 data points and 16 elements, while each row represented one employee. The Python programming language was used for the development of the prediction model. The study's results showed the relevance of HR analytics for companies, accurately predicting employee departures using a machine learning model and achieving significant cost savings by implementing the model. [ABSTRACT FROM AUTHOR]

Accurate estimation of the wind speed profile is crucial for a range of activities such as wind energy and aviation. The power law and the logarithmic-based profiles have been widely used as universal formulas to extrapolate the wind speed profile. However, these traditional methods have limitations in capturing the complexity of the wind flow, mainly over complex terrain. In recent years, the machine-learning techniques have emerged as a promising tool for estimating the wind speed profiles. In this study, we used the long short-term memory (LSTM) recurrent neural network and observational lidar datasets from three different sites over complex terrain to estimate the wind profile up to 230 m. Our results showed that the LSTM outperformed the power law as the distance from the surface increased. The coefficient of determination (R2) was greater than 90 % up to 100 m for input variables up to a 40 m height only. However, the performance of the model improved when the 60 m wind speed was added to the input dataset. Furthermore, we found that the LSTM model trained on one site with 40 and 60 m observational data and when applied to other sites also outperformed the power law. Our results show that the machine-learning techniques, particularly LSTM, are a promising tool for accurately estimating the wind speed profiles over complex terrain, even for short observational campaigns. [ABSTRACT FROM AUTHOR]

The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace. [ABSTRACT FROM AUTHOR]

Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented. [ABSTRACT FROM AUTHOR]

The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains. [ABSTRACT FROM AUTHOR]

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body's automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants. [ABSTRACT FROM AUTHOR]

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains. [ABSTRACT FROM AUTHOR]

The work detailed here is part of an international initiative on the evaluation of dam safety simulation software for internal erosion performance and best practices. The primary experiments involve simulating uncertainty in the failure events of two dams with two different models: DLBreach and WinDAM C. DLBreach is a physically-based dam/levee breach model developed by Wu. WinDAM C is also a physically based dam breach model capable of analyzing both dam overtopping and internal erosion. The dams selected for the analysis include a 1.3 m high dam tested in the lab and a larger 15.56 m high dam, which suffered a failure in the field. The findings from these experiments are extended with a further analysis on variance, sensitivity, and optimization. Finally, a regression model is trained using the results of these simulators as an inquiry into how well such a system can be captured using machine learning techniques. The results of these experiments, together with the results of the other members of the initiative, improve our understanding of the influences that users bring to using these tools. [ABSTRACT FROM AUTHOR]

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are "Project planning and design", "Organizational issues", and "Information technologies, robotics, and automation"; the lowest was "Legal and contractual issues". This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications. [ABSTRACT FROM AUTHOR]

With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project "3DPrintFEM", a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm's detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the algorithm will be integrated into an equipment developed in-house to monitor the quality of samples produced, thereby having an in-house quality assessment routine. The equipment shall be less expensive than conventional acoustic equipment, thus helping the industry cut costs when validating the quality of their components. [ABSTRACT FROM AUTHOR]

Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the permeability of concrete by replacing the laboratory tests with robust machine learning (ML)-based models. For this purpose, the potential of twelve well-known ML techniques was investigated in estimating the water penetration depth (WPD) of nano natural pozzolana (NNP)-reinforced concrete based on 840 data points. The preparation of concrete specimens was based on the different combinations of NNP content, water-to-cement (W/C) ratio, median particle size (MPS) of NNP, and curing time (CT). Comparing the results estimated by the ML models with the laboratory results revealed that the hist-gradient boosting regressor (HGBR) and K-nearest neighbors (KNN) algorithms were the most and least robust models to estimate the WPD of NNP-reinforced concrete, respectively. Both laboratory and ML results showed that the WPD of NNP-reinforced concrete decreased with the increase of the NNP content from 1 to 4%, the decrease of the W/C ratio and the MPS, and the increase of the CT. To further aid in the estimation of concrete's WPD for engineering challenges, a graphical user interface for the ML-based models was developed. Proposing such a model may be effectively employed in the management of concrete quality. [ABSTRACT FROM AUTHOR]

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86–97% and an F1 (harmonic mean of precision and recall) score of 0.83–0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling small data sets in the prediction, which is critical given the challenge of obtaining large datasets for biomarker research. The combination of self-reported logs and the GCN algorithm is a promising approach for predicting depression and warrants further investigation. [ABSTRACT FROM AUTHOR]

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration's frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications. [ABSTRACT FROM AUTHOR]

Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy. [ABSTRACT FROM AUTHOR]

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system. [ABSTRACT FROM AUTHOR]

In the telecom sector, predicting customer churn has increased in importance in recent years. Developing a robust and accurate churn prediction model takes time, but it is crucial. Early churn prediction avoids revenue loss and improves customer retention. Telecom companies must identify these customers before they leave to solve this issue. Researchers have used a variety of applied machine-learning approaches to reveal the hidden relationships between different features. A key aspect of churn prediction is the accuracy level that affects the learning model's performance. This study aims to clarify several aspects of customer churn prediction accuracy and investigate state-of-the-art techniques' performance. However, no previous research has investigated performance using a hybrid framework combining the advantages of selecting suitable data preprocessing, ensemble learning, and resampling techniques. The study introduces a proposed hybrid framework that improves the accuracy of customer churn prediction in the telecom industry. The framework is built by integrating the XGBOOST classifier with the hybrid resampling method SMOTE-ENN, which concerns applying effective techniques for data preprocessing. The proposed framework is used for two experiments with three datasets in the telecom industry. This study determines which features are most crucial and influence customer churn, introduces the impact of data balancing, compares the classifiers' pre- and post-data balancing performances, and examines a speed-accuracy trade-off in hybrid classifiers. Many metrics, including accuracy, precision, recall, F1-score, and ROC curve, are used to analyze the results. All evaluation criteria are used to identify the most effective experiment. The results of the accuracy of the hybrid framework that respects balanced data outperformed applying the classifier only to imbalanced data. In addition, the results of the proposed hybrid framework are compared to previous studies on the same datasets, and the result of this comparison is offered. Compared with the review of the latest works, our proposed hybrid framework with the three datasets outperformed these works. [ABSTRACT FROM AUTHOR]

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses. [ABSTRACT FROM AUTHOR]

The joint analysis of two datasets X and Y that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union X∪Y in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables. [ABSTRACT FROM AUTHOR]

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]