Multiple Algorithms Research Articles

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas

BackgroundPancreatic adenocarcinomas (PAADs) often exhibit a “cold” or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.MethodsPatients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as ‘Downregulated in hot tumors, Prognostic, and Immune-Related Genes’ (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.ResultsUsing the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.ConclusionsBy combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

A robust low‐rank tensor completion model with sparse noise for higher‐order data recovery

AbstractThe tensor singular value decomposition‐based model has garnered increasing attention in addressing tensor recovery challenges. However, existing tensor recovery methods exhibit certain inherent limitations. Some ignore the simultaneous effects of noise and missing values, while most can't handle higher‐order tensors, which are not reflective of real‐world scenarios. The information redundancy within tensor data often leads to a prevailing low‐rank structure, making low‐rankness a vital prior in the tensor recovery process. To tackle this pressing issue, a robust low‐rank tensor recovery framework is proposed to rehabilitate higher‐order tensors corrupted by sparse noise and missing entries. In the model, the tensor nuclear norm derived for order‐d tensors (d 4) are employed as a representation of the low‐rank prior, while utilizing the ‐norm to model the sparse noise. To solve the proposed model, an efficient Alternating direction method of multipliers algorithm is developed. A series of experiments are performed on synthetic and real‐world datasets. The results show that the superior performance of the method compared with other algorithms dedicated to addressing order‐d tensor recovery challenges. Notably, in scenarios where the data is severely compromised (noise ratio 40%, sample ratio 70%), the algorithm consistently outperforms its competitors, achieving significantly improved results.

UCAN: U-shaped context aggregation network for thin crack segmentation under topological constraints

PurposeThin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.Design/methodology/approachUCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓcl Dice, which enhances the crack segmentation’s connectivity.FindingsThis paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓcl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓcl Dice in crack segmentation tasks.Originality/valueIn this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓcl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

Research on autonomous decision-making of UAV swarm based on neural network algorithm

This article focuses on typical unmanned aerial vehicle (UAV) cluster autonomous collaborative reconnaissance mission scenarios, constructs UAV classes in Matlab, and effectively constructs UAV autonomous decision-making models using two different algorithms; Using Extreme Learning Machines (ELM) and introducing neural network methods to solve autonomous decision-making problems for unmanned aerial vehicles. Test the performance of the model under multiple neural network algorithms, basically achieving unmanned aerial vehicles to complete flight targets based on multiple sensor parameters in unknown environments, using reinforcement learning algorithms to achieve autonomous decision-making of unmanned aerial vehicles, achieving multi parameter fusion (with parameters greater than 4) decision-making, with a decision-making time of less than 100ms, which can ensure timely decision-making while also considering the rationality of the model.

An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity

The optimization of multi-community integrated energy systems (MCIES), as a proficient means of integrating distributed energy resources and diverse loads, poses a compelling challenge. This challenge revolves around strategically harnessing the energy-carbon synergy advantages within these systems, while factoring in the heterogeneity among different communities and the constraints imposed by electrical connectivity devices. Facing the challenge, this paper proposes an individualized adaptive distributed approach for energy-carbon coordination in MCIES with power transformers (PT-MCIES). Firstly, a feasible region is constructed based on current and temperature limits to assess the loading capacity of power transformers. Based on this, a transactive energy (TE) based co-optimization model for PT-MCIES considering energy sharing and carbon trading is developed, where rolling horizon optimization is adopted to cope with the randomness from renewable energy and loads. For the TE problem, the alternating direction method of multipliers (ADMM) algorithm is employed to achieve distributed optimization. Uncertain renewable energy and loads, along with variable rolling horizons, make it difficult for the traditional ADMM to ensure convergence speed in the co-optimization of PT-MCIES. Accordingly, an individualized adaptive ADMM (IAADMM) embedded a spectral penalty parameter selection rule is adopted. Simulation results show that the proposed scheduling model can effectively guarantee the safe operation of power transformers with the suggestion of a feasible region, and achieve higher economic and environmental benefits than the reference one that ignores energy-carbon coordination. Furthermore, the proposed IAADMM exhibits superior performance in terms of convergence speed and robustness to the initial penalty parameter when compared to ADMM and its various variants.

Research on Multiple Access Algorithms for Wireless Network

Research on Multiple Access Algorithms for Wireless Network

Analysis of Bubble Flow in an Inclined Tube and Modeling of Flow Prediction

The lubricating oil system is a significant component of aviation engine lubrication and cooling, and the scavenge pipe is an essential component of the lubricating oil system. Accurately identifying and understanding the flow state of the scavenge pipe is very important. This article establishes a visualization test bench for a 45-degree inclined scavenge pipe, with upward and downward flow directions, respectively. The test temperature is 370 K, and a high-speed camera captures the changes in the two-phase flow inside the pipeline. Based on high-speed photography photos, we develop software for analyzing the flow characteristics of bubbles inside the tube and explore the influence of gas phase conversion velocity and liquid phase conversion velocity on the apparent velocity of bubbles inside the tube. Multiple algorithms were used to develop the model by combining machine learning with speed and accuracy to establish a data regression prediction model for the apparent velocity of bubbles inside the tube. Through calculation and analysis, it was found that the root mean square error of the prediction model using the BP neural network algorithm was the lowest, and the decision coefficient of the prediction model using the support vector machine algorithm was the highest.

A graph decomposition-based approach for the graph-fused lasso

We propose a new algorithm for solving the graph-fused lasso (GFL), a regularized model that operates under the assumption that the signal tends to be locally constant over a predefined graph structure. The proposed method applies a novel decomposition of the objective function for the alternating direction method of multipliers (ADMM) algorithm. While ADMM has been widely used in fused lasso problems, existing works such as the network lasso decompose the objective function into the loss function component and the total variation penalty component. In contrast, based on the graph matching technique in graph theory, we propose a new method of decomposition that separates the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty. We develop an exact convergence rate of the proposed algorithm by developing a general theory on the local convergence of ADMM. Compared with the network lasso algorithm, our algorithm has a faster exact linear convergence rate (although in the same order as for the network lasso). It also enjoys a smaller computational cost per iteration, thus converges overall faster in most numerical examples.

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network’s top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

Predicting Heart Failure Survival with Machine Learning: Assessing My Risk

This study investigates the application of machine learning techniques for heart disease prediction using a comprehensive dataset of 918 patients. The research employs multiple algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks, to develop predictive models based on 11 clinical features. The dataset, compiled from five independent sources, underwent thorough preprocessing and was split into training (70%) and test (30%) sets. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Results demonstrate consistently high performance across all models, with the SVM achieving the highest overall performance (accuracy: 88.41%, precision: 89.76%, recall: 90.85%, F1-score: 90.30%, ROC-AUC: 94.97%). Key predictors identified include age, maximum heart rate, and ST depression (Oldpeak). The study's findings have significant implications for clinical practice, offering the potential for rapid, objective heart disease risk assessment. The consistent performance across different model architectures provides flexibility for implementation in various healthcare settings. Limitations include potential data collection variability and gender imbalance in the dataset. Future research directions include developing more sophisticated neural networks, incorporating additional data types, and conducting prospective studies to validate model performance in real-world clinical settings. This research contributes to the growing body of evidence supporting the use of machine learning in medical diagnostics. The developed models enhance early detection and risk stratification of heart disease, potentially improving patient outcomes through timely interventions.

A modular, composite framework for the utilization of reduced-scaling Coulomb and exchange construction algorithms: Design and implementation.

Multiple algorithms exist for calculating Coulomb (J) or exchange (K) contributions to Fock-like matrices, and it is beneficial to develop a framework that allows the seamless integration and combination of different J and K construction algorithms. In Psi4, we have implemented the "CompositeJK" formalism for this purpose. CompositeJK allows for the combination of any J and K construction algorithms for any quantum chemistry method formulated in terms of J-like or K-like matrices (including, but not limited to, Hartree-Fock and density functional theory) in a highly modular and intuitive fashion, which is simple to utilize for both developers and users. Using the CompositeJK framework, Psi4 was interfaced to the sn-LinK implementation in the GauXC library, adding the first instance of noncommercial graphics processing unit (GPU) support for the construction of Fock matrix elements to Psi4. On systems with hundreds of atoms, the interface to the CPU sn-LinK implementation displays a higher performance than all the alternative JK construction methods available in Psi4, with up to x2.8 speedups compared to existing Psi4JK implementations. The GPU sn-LinK implementation, harnessing the power of GPUs, improves the observed performance gains to up to x7.0.

A Novel Multi-modal Sentiment Analysis Based on Multiple Kernel Learning with Margin-Dimension Constraint

Most of the sentiment analysis studies focus on the sentimental classification of pictures in the video, ignoring the spatio-temporal information of the sequence of picture frames as well as text and audio information. The multiple kernel learning is a new hotspot in the field of nuclear machine learning, capable of handling multiple modalities. For multiple kernel learning, it is easy to ignore the basic features that are not discriminative, and cannot make full use of the base features of different modes. This paper puts forward a novel multi-modal fusion model for sentiment analysis, in which a multiple kernel learning algorithm based on convolution margin-dimension constraint is proposed for feature fusion. Moreover, the 3D convolutional neural network is used to extract the features of visual information, and the multiple kernel learning algorithm based on margin-dimension constraint is used to fuse visual, text and audio sentiment features. Experiments conducted on the MOUD and IEMOCAP sentiment databases show that the proposed model outperforms existing models in the field of multi-modal sentiment analysis research.

An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems

Feature selection (FS) is the activity of defining the most contributing feature subset among all used features to improve the superiority of datasets with a large number of dimensions by selecting significant features and eliminating redundant and irrelevant ones. Therefore, this process can be seen as an optimization process. The primary goals of feature selection are to decrease the number of dimensions and enhance classification accuracy in many domains, such as text classification, large-scale data analysis, and pattern recognition. Several metaheuristics, such as the Genghis Khan Shark Optimizer Algorithm (GKSO), can assist in optimizing the FS issue. However, these methods tend to converge towards local solutions with a low convergence rate. In order to address this issue in GKSO, a more refined version called I-GKSO is implemented. The I-GKSO suggested introducing a new approach to modify solutions that have low fitness values. In addition, it employs the Enhanced Solution Quality (ESQ) strategy to enhance the exploration phase. It utilizes Quasi-opposite-based learning (QOBL) to enhance the best solution obtained and, consequently, the entire population. The algorithm presented aims to solve the FS problem and has been assessed using benchmark optimization problems from the CEC’2017 and CEC’2022. To assess the efficacy of the I-GKSO, it has been subjected to comparisons with multiple different algorithms. The trials conducted using FS datasets yield a quantitative consideration of the I-GKSO's capacity to attain the most optimal subset of features. Furthermore, Wilcoxon and Friedman's non-parametric tests were accomplished to support the performance of the proposed method.

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Abstract This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawater irradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated data were generated using an analytical model of a magnetic dipole in air and seawater placed 1.5–3.5 m above the center of the sphere in 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeable materials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receiver coil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms to noise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression, which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models. Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram data used for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heaviside step-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects, including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified using finite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion of electromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without the use of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallic spheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paper presents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis

<p>As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.</p>

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

BackgroundTimely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.MethodsFrom January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction.ResultsWe screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value.ConclusionsThe developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood.

There has been growing research interest in developing methodology to evaluate the health care providers' performance with respect to a patient outcome. Random and fixed effects models are traditionally used for such a purpose. We propose a new method, using a fusion penalty to cluster health care providers based on quasi-likelihood. Without any priori knowledge of grouping information, our method provides a desirable data-driven approach for automatically clustering health care providers into different groups based on their performance. Further, the quasi-likelihood is more flexible and robust than the regular likelihood in that no distributional assumption is needed. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. We show that the proposed method enjoys the oracle properties; namely, it performs as well as if the true group structure were known in advance. The consistency and asymptotic normality of the estimators are established. Simulation studies and analysis of the national kidney transplant registry data demonstrate the utility and validity of ourmethod.

An efficient pre-analysis and optimization generation method for reference curves of automated fiber placement path planning

Complex curved composite components often rely on multiple reference curve algorithms for path planning in automated fiber placement. However, the reference curves are typically manually drawn. Moreover, designing the reference curves follows an iterative planning-analysis-improvement process, which can be inefficient. A new approach for the automatic pre-analysis and optimized generation of reference curves for fiber placement is proposed in this paper to enhance the efficiency of reference curve analysis and generation. Firstly, a pre-analysis algorithm for reference curves based on triangular meshes is proposed. This algorithm analyzes the theoretical geodesic curvature and angular deviation of the path before its planning. Subsequently, a comprehensive evaluation index for reference curve generation is formulated based on the pre-analysis algorithm, and the reference curve is optimized using genetic algorithms. The results demonstrate that the pre-analysis algorithm accurately computes the steering radius distribution of the path. Areas with over-limit steering radius can be eliminated while maintaining angular deviations within 10° by utilizing optimized reference curves for path planning.

Hybrid Inverse Weed Optimization Algorithm with Math-Flame Optimization Algorithm

In this work, two Meta-Heuristic Algorithms were hybridized, the first is the Invers Weed optimization algorithm (IWO), which is a passing multiple algorithm, and the second is the Moth-flame Optimization Algorithm (MFO). Which depend in their behavior on the intelligence of the swarm and the intelligence of society, and they have unique characteristics that exceed the characteristics of the intelligence of other swarms because they are efficient in achieving the right balance between exploration and exploitation. So the new algorithm improves the initial population that is randomly generated, A process of hybridization was made between the IWO and MFO Algorithm to call The new hybrid algorithm (IWOMFO). The new hybrid algorithm was used for 16 high-scaling optimization functions with different community sizes and 250 repetitions. The Algorithm showed access to optimal solutions by achieving the value Minority () for most of these functions and the results of this algorithm are compared with the basic algorithms IWO, MFO

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation

BackgroundOxidative stress is an unavoidable process in kidney transplantation and is closely related to the development of acute rejection after kidney transplantation. This study aimed to investigate the biomarkers associated with oxidative stress and their potential biological functions during acute rejection of kidney transplants. MethodsWe identified Hub genes using five machine learning algorithms based on differentially expressed genes (DEGs) in the kidney transplant acute rejection dataset GSE50058 and oxidative stress-related genes (OS) obtained from the MSigDB database, and validated them with the datasets GSE1563 and GSE9493, as well as with animal experiments; Subsequently, we explored the potential biological functions of Hub genes using single-gene GSEA enrichment analysis; The Cibersort algorithm was used to explore the altered levels of infiltration of 22 immune cells during acute rejection of renal transplantation, and a correlation analysis between Hub genes and immune cells was performed; Finally, we also explored transcription factors (TFs), miRNAs, and potential drugs that regulate Hub genes. ResultsWe obtained a total of 57 genes, which we defined as oxidative stress-associated differential genes (DEOSGs), after intersecting DEGs during acute rejection of kidney transplants with OSs obtained from the MSigDB database; The results of enrichment analysis revealed that DEOSGs were mainly enriched in response to oxidative stress, response to reactive oxygen species, and regulation of oxidative stress and reactive oxygen species; Subsequently, we identified one Hub gene as APOD using five machine learning algorithms, which were validated by validation sets and animal experiments; The results of single-gene GSEA enrichment analysis revealed that APOD was closely associated with the regulation of immune signaling pathways during acute rejection of kidney transplants; The Cibersort algorithm found that the infiltration levels of a total of 10 immune cells were altered in acute rejection, while APOD was found to correlate with the expression of multiple immune cells; Finally, we also identified 154 TFs, 12 miRNAs, and 12 drugs or compounds associated with APOD regulation. ConclusionIn this study, APOD was identified as a biomarker associated with oxidative stress during acute rejection of kidney transplants using multiple machine learning algorithms, which provides a potential therapeutic target for mitigating oxidative stress injury and reducing the incidence of acute rejection in kidney transplantation.