Powerful Algorithm Research Articles

Enhancing neural network applications in phonetic classification through population-based incremental learning

Significant progress has been achieved in the field of Automatic Speech Recognition (ASR) thanks to the development of powerful algorithms. Research based on simplified biological models has led to the emergence of a new class called Estimation of Distribution Algorithms (EDA), which preserves significant partial solutions. Population-Based Incremental Learning (PBIL) is a technique that combines stochastic search and optimization. It is a statistical approach with evolutionary computation similar to EDAs, aimed at adapting recognition systems based on artificial Neural Networks (NN). Our main contribution lies in the improvement achieved by PBIL, which outperforms Genetic Algorithms (GA) by 16% and Evolutionary Strategies (ES) by 9.93%.

Rethreading the needle: A novel molecular index of soil health (MISH) using microbial functional genes to predict soil health management

Soil health relies on the actions and interactions of an abundant and diverse biological community. Current soil health assessments rely heavily on a suite of soil biological, chemical, and physical indicators, often excluding molecular information. Soil health is critical for sustainable agricultural production, and a comprehensive understanding of how microbial communities provide ecosystem services can help guide management practices. To explore the role of microbial function in soil health, 536 soil samples were collected from 26 U.S. states, representing 52 different crops and grazing lands, and analyzed for various soil health indicators. The bacterial functional profile was characterized using 16S ribosomal RNA gene sequencing paired with PICRUSt2 to predict metagenome functions. Functional data were used as predictors in eXtreme Gradient Boosting (XGBoost), a powerful machine learning algorithm, and enzymes important to soil health indicators were compiled into a Molecular Index of Soil Health (MISH). The overall MISH score significantly correlated with non-molecular measures of soil health and management practice adoption. Additionally, several new enzymes were identified as potential targets to better understand microbial mediation of soil health. This low-cost, DNA-based approach to measuring soil health is robust and generalizable across climates.

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm

The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, periprocedural mortality, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair. All patients who underwent elective open or endovascular repair of AAA from 1 January 2008 to 31 March 2021 were reviewed. They were assessed using the Vascular Surgery Quality Improvement Program pathway involving cardiopulmonary exercise testing, contrast enhanced computerised tomography scan, and multidisciplinary assessment. Patients were followed up for at least two years. A RFC was developed using 70% of the dataset and validated using 30% to predict survival for at least two years following AAA repair. A total of 925 patients (n = 836 men; n = 89 women) underwent elective repair of AAA; 126 (13.6%) died during the first two years; 11 (1.2%) died from periprocedural mortality. Variable importance analysis suggested that anaerobic threshold, pre-operative haemoglobin, maximal O2 consumption, body mass index, risk category, and forced expiratory volume in 1 second - forced vital capacity ratio were the most important contributors to the model. Sensitivity and specificity of the RFC for prediction of two year survival following surgery was 96.7% (95% CI 94.4 - 99%) and 67.1% (95% CI 61 - 72%); overall accuracy: 92.6% (95% CI 88 - 95%) (positive predictive value: 0.93, negative predictive value: 0.80); 10-fold cross validation revealed area under the receiver operator characteristic curve of 0.88. RFCs based on readily available clinical data can successfully predict survival in the first two years following elective repair of AAA. Such information can contribute to the risk benefit assessment when deciding to electively repair AAAs.

Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms

Abstract Breast cancer (BC) poses a significant worldwide health challenge, necessitating the identification of its molecular origins and the development of possible treatment approaches. In this investigation, we introduce a pioneering methodology integrating genomic data and machine learning algorithms to identify genes associated with BC and their corresponding drugs. Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). The results were validated based on accuracy, sensitivity, specificity, precision, and F-score. Moreover, we identified the drugs corresponding to DEGs using the DepMap database. Our results revealed that genes such as OC90, KLK9, CXCL10, CDRT1, LCN6, GOLGA7B, and ZNF223 were commonly identified by all three machine learning algorithms. We found that the drugs DIHYDROROTENONE, 4-IODO-6-PHENYLPYRIMIDINE, BMS-754807, ARRY- 886, ESTRAMUSTINE-PHOSPHATE, FR-122047, and PIK 93 produced high correlation values on gene-drug interaction. The present study emphasizes the significance of utilizing genetic data and powerful machine learning algorithms to decipher the intricacies of BC biology and expedite the creation of tailored therapeutic approaches.

Intelligent Decision Support Systems for Optimizing Medical Emergency Responses

Now-a-days, in the realm of an emergency medical services-EMS, the swift and an accurate decision-making is a critical for an ensuring of timely responses and all the optimal patient outcome results. With a heavy advent of an advanced techniques and these proliferation of the data sources in the healthcare, there is a high-level growing opportunity of an Intelligent decision supporting systems-IDSS to enhance an efficiency and an effectiveness of the medical emergency responses-MER widely. This study paper explores the role of an intelligent-DDS in the optimization of the medical emergency responses through analyzing various aspects factors like of the real-time analytic, data integration, and the decision supporting-algorithms. First of all, the integration of the diverse data-sources likewise patients healthcare records, some historical incidents, and the geo-graphical information all forms the foundation of an effective IDSS. The amalgamation of the data streaming generated by an IDSS can be comprehensive awareness, enabling the res-ponders to make an informed-decision tailored to any specific needs in each emergency scenarios. Then, the real-time analytic plays a pivotal role in the interpretations and processing of an incoming data-sets to detect the patterns, trends, and the anomalies. With the help of these techniques like machine-learning as well predictive-modeling, an IDSS can simply anticipate the potential emergency, pro-actively allocated the resources, and optimized response routes too in an-order-to minimize the time-to treatments of the patients globally. Furthermore, decision-support algorithms embedded within IDSS provide respondents with an actionable insights and recommendations based-on analysis of available data-sets. These powerful IDSS algorithms considered factors like severity of medical conditions, proximity to the health-care facilities, and as well the availability of specialized equipment to guide respondent in prioritizing tasks and allocating resources efficiently and enhancing over-all systems resilience. [1-3]

Multi-Layered Algorithm for Chinese Cultural Dissemination Power Based on Feature Selection and Weight Learning

Multi-Layered Algorithm for Chinese Cultural Dissemination Power Based on Feature Selection and Weight Learning

Improved fractional order control with virtual inertia provision methodology for electric vehicle batteries in modern multi-microgrid energy systems

Providing inertia in modern electrical power grids has become a highly demanding task to mitigate reduced power system inertia of renewable energy sources (RES) based power grids. To mitigate the reduced inertia-related problems and consequences, this paper presents improved FOCs for achieving load frequency control (LFC) and the virtual inertia control (VIC) provision of electric vehicles (EVs) lithium-ion batteries. The proposed method includes a twofold contribution using a new hybrid FOC method and an optimum control parameter design method. An innovative hybrid controller is proposed by merging the characteristics of FOPID and TID methods with a fractional filtering stage, leading to hybrid tilt plus FOC integrator-derivative terms. From the VIC side, the TID is proposed to provide a fast response to lithium-ion batteries of the already available EVs. Furthermore, a metaheuristic-based design method is proposed to simultaneously determine the best control parameters set and to facilitate the design procedures using the recently presented powerful growth optimization algorithm (GO). The two interconnected areas case study with high-RES penetration is suggested for testing and investigating the new proposed controller. The results include performance evaluations and comparisons at various expected model uncertainties, renewable energy intermittency, and parametric variations. For instance, the proposed VIC method has a minimized ISE after 28 iterations of 0.001 compared to 0.003 with conventional VIC, and 0.004 without VIC. Thence,the proposed controller achieves more accurate frequency tracking with reduced deviations and fluctuations and exhibits greater robustness compared to previous controllers used in all case studies.

Codebase release 1.0 for TeNPy

TeNPy (short for ‘Tensor Network Python’) is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

Tensor network Python (TeNPy) version 1

TeNPy (short for ‘Tensor Network Python’) is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

Automated Forensic Examination of Virtual Assets Using XGBoost

The issues of identification of virtual assets and digital currencies have become more acute in recent years because of increased numbers of transactions, their increased volumes, and complexity in forensic examination. This paper is devoted to a new method of the automated forensic investigation of virtual assets with the help of a powerful machine learning algorithm called XGBoost. XGBoost has very high speed and it is quite useful when dealing with large amounts of data and used for finding patterns that may suggest unlawful operations. The given framework is based on XGBoost by identifying the transactions using their features activities, amount, frequencies, volumes and addresses. Through training the model that is given the historical data, the model is, therefore, able to separate normal and suspicious transactions and alert the decision-making process for further review. Network analysis integration takes the system capability to a new level by enabling the identification of intricate transaction characteristics and the interconnectedness of addressing. Aside from adding automation to the forensic examinations to make it more efficient and accurate, such a procedure assists in identifying the developments of virtual asset transactions. The results show the effectiveness of the XGBoost in supporting the advance of forensic investigators helping them to meet the growing roles that cybercriminals portray in today’s digital asset markets.

Groupers and moray eels (GME) optimization: a nature-inspired metaheuristic algorithm for solving complex engineering problems

AbstractAs engineering technology advances and the number of complex engineering problems increases, there is a growing need to expand the abundance of swarm intelligence algorithms and enhance their performance. It is crucial to develop, assess, and hybridize new powerful algorithms that can be used to deal with optimization issues in different fields. This paper proposes a novel nature-inspired algorithm, namely the Groupers and Moray Eels (GME) optimization algorithm, for solving various optimization problems. GME mimics the associative hunting between groupers and moray eels. Many species, including chimpanzees and lions, have shown cooperation during hunting. Cooperative hunting among animals of different species, which is called associative hunting, is extremely rare. Groupers and moray eels have complementary hunting approaches. Cooperation is thus mutually beneficial because it increases the likelihood of both species successfully capturing prey. The two predators have complementary hunting methods when they work together, and an associated hunt creates a multi-predator attack that is difficult to evade. This example of hunting differs from that of groups of animals of the same species due to the high level of coordination among the two species. GME consists of four phases: primary search, pair association, encircling or extended search, and attacking and catching. The behavior characteristics are mathematically represented to allow for an adequate balance between GME exploitation and exploration. Experimental results indicate that the GME outperforms competing algorithms in terms of accuracy, execution time, convergence rate, and the ability to locate all or the majority of local or global optima.

OptiFeat: enhancing feature selection, a hybrid approach combining subject matter expertise and recursive feature elimination method

Optimizing the performance of Java Virtual Machines (JVMs) (Sahin et al. in Proc IEEE Int Congr Big Data BigData Congr 410–417, 2016) is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE) (Yin et al. in J Big Data 10(1):15, 2023) model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections (Khaire and Dhanalakshmi in J King Saud Univ Comput Inf Sci 34(4):1060–1073, 2022). In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance (Menéndez and Bartlett in http://arxiv.org/abs/2310.16510, 2023). It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.

High-speed odor sensing using miniaturized electronic nose.

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results-existing solutions are either slow; or bulky, expensive, and power-intensive-limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

Inferring the dust emission at submillimeter and millimeter wavelengths using neural networks

Context. The Planck mission provided all-sky dust emission maps in the submillimeter (submm) to millimeter (mm) range at an angular resolution of 5′. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and are sometimes not delivered to the community. These ground-based observations require extensive data processing before they become available for scientific analysis, and suffer from extended emission filtering. Aims. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long (submm and mm) wavelengths. Several models have been developed to reproduce the diffuse Galactic medium, and each distinct environment requires an adjustment of the models. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5′ from Planck. In this analysis, based on supervised deep learning algorithms, we produced dust emission predictions in the two Planck bands centered at 850 µm (353 GHz) and 1.38 mm (217 GHz) at the Herschel resolution (37″). Prediction or forecasting is a frequently used term in machine learning or neural network research that refers to the output of an algorithm that has been trained on a given dataset and that is being used for modeling purposes. Methods. Herschel data of Galactic environments, ranging from 160 µm to 500 µm and smoothed to an angular resolution of 5′, were used to train the neural network. This training aimed to provide the most accurate model for reproducing Planck maps of dust emission at 850 µm and 1.38 mm. Then, using Herschel data only, the model was applied to predict dust emission maps at 37″. Results. The neural network is capable of reproducing dust emission maps of various Galactic environments with a difference of only a few percent at the Planck resolution. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains, probed by submm or mm observations, have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. For the first time, we provide to the community dust emission prediction maps at 850 µm and 1.38 mm at the 37″ of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and Very Nearby Galaxies. The ratio of these two wavelength brightness bands reveals a derived emissivity spectral index statistically close to 1 for all the surveys, which favors the hypothesis of a flattened dust emission spectrum for wavelengths larger than 850 µm. Conclusions. Neural networks appear to be powerful algorithms that are highly efficient at learning from large datasets and achieving accurate reproductions with a deviation of only a few percent. However, to fully recover the input data during the training, it is essential to sample a sufficiently large range of datasets and physical conditions.

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

Algorithms for the global domination problem

A dominating set D in a graph G is a subset of its vertices such that every its vertex that does not belong to set D is adjacent to at least one vertex from set D. A set of vertices of graph G is a global dominating set if it is a dominating set for both, graph G and its complement. The objective is to find a global dominating set with the minimum cardinality. Neither exact nor approximation algorithm existed for the problem known to be NP-hard. We show that it remains NP-hard for restricted types of graphs. At the same time, we specify some families of graphs for which the three heuristics, that we propose here, are optimal. Given the complexity status of the problem, our aim was the development of powerful heuristic algorithms that work well in practice for large-scaled instances. To measure the efficiency of our heuristics, we formulated the problem as an integer linear program (ILP) and also we developed an alternative implicit enumeration (IE) algorithm obtaining guaranteed optimal solutions for the existing benchmark instances with up to 8000 vertices. Remarkably, for 56.75% of these instances, at least one of our heuristics also created an optimal solution, where an average absolute error for the remaining instances was a single vertex. The average approximation ratio was 1.005, whereas for the largest benchmark instances with up to 25000 vertices our heuristics delivered solutions in less than 2 min.

Neural network algorithm with transfer learning and dropout for using a UAV to search the lost target in motion

When performing the rescue mission, how to find a lost target in motion as soon as possible is a very valuable and interesting research topic. To find the optimal flight path of the UAV used to search the lost target in motion under different prior knowledge and interference intensity, this paper proposes a novel optimization technique called neural network algorithm with transfer learning and dropout (TLDNNA), which is a variant of neural network algorithm (NNA) inspired by artificial neural networks. To improve the global search ability and convergence performance of NNA, the multiple transfer mechanism, the generalized mean transfer position, and the random dropout operator driven by the principles of transfer learning and dropout are introduced to TLDNNA. To investigate the performance of TLDNNA, TLDNNA and 12 powerful population-based optimization algorithms are applied to the problem of using a UAV to search the lost target in motion in the designed 9 scenarios. Experimental results show that TLDNNA is remarkably better than the other 12 algorithms in most of the scenarios in terms of solution quality, stability, and convergence performance. In addition, TLDNNA can improve the overall search performance by 18.14% and overall computational efficiency by 3.41 times compared to NNA. The source code of TLDNNA can be obtained by https://github.com/jsuzyy/TLDNNA.

Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning

Random Forests (RFs) are powerful ensemble learning algorithms that are widely used in various machine learning tasks. However, they tend to overfit noisy or irrelevant features, which can result in decreased generalization performance. Post-hoc regularization techniques aim to solve this problem by modifying the structure of the learned ensemble after training. We propose a novel post-hoc regularization via tree smoothing for classification tasks to leverage the reliable class distributions closer to the root node whilst reducing the impact of more specific and potentially noisy splits deeper in the tree. Our novel approach allows for a form of pruning that does not alter the general structure of the trees, adjusting the influence of nodes based on their proximity to the root node. We evaluated the performance of our method on various machine learning benchmark data sets and on cancer data from The Cancer Genome Atlas (TCGA). Our approach demonstrates competitive performance compared to the state-of-the-art and, in the majority of cases, and outperforms it in most cases in terms of prediction accuracy, generalization, and interpretability.

A Feature-Weighted Support Vector Regression Machine Based on Hilbert–Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator

Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) for selecting features with minimal redundancy and maximum relevance, we propose a novel feature-weighted SVR that considers the importance of features to the output and the redundancy between features. In this approach, the HSIC is utilized to effectively measure the correlation between features as well as that between features and the output. The feature weights are obtained by solving a LASSO regression problem. Compared to other feature weighting methods, our method takes much more comprehensive consideration of weight calculation, and the obtained weighted kernel function can lead to more precise predictions for unknown data. Comprehensive experiments on real datasets from the University of California Irvine (UCI) machine learning repository demonstrate the effectiveness of the proposed method.

Reducing ACO Population Size to Increase Computational Speed

Ant Colony Optimization (ACO) is a powerful metaheuristic algorithm widely used to solve complex optimization problems in production and logistics. This paper presents a methodology for enhancing the ACO performance when applied to Traveling Salesman Problems (TSP). By reducing the number of ants in the colony, the algorithm's computational speed improves but solution quality is sacrificed. An optimal number of ants to produce the best results in the shortest time is specific to the problem at hand and can't be defined generally. This paper investigates the effect of ant population reduction relative to the problem size by measuring its impact on solution quality and execution time. Results show that for certain problem sizes ant population and execution time can be halved with practically no reduction in solution quality, or they can be reduced 5 times at the price of slightly worse solution quality. Reduction of ant population is much more impactful on reduction of execution time than it is on solution quality.