Simple Machine Learning Algorithms Research Articles

Public Health Nurse Perspectives on Predicting Preterm Labor Using Risk Factors and Simple Machine Learning Algorithms

Public Health Nurse Perspectives on Predicting Preterm Labor Using Risk Factors and Simple Machine Learning Algorithms

Pattern recognition using spiking antiferromagnetic neurons

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry.

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 μm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the 1.5-10 μm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.

Spline-based methods for functional data on multivariate domains

Functional data analysis is typically performed in two steps: first, functionally representing discrete observations, and then applying functional methods to the so-represented data. The initial choice of a functional representation may have a significant impact on the second phase of the analysis, as shown in recent research, where data-driven spline bases outperformed the predefined rigid choice of functional representation. The method chooses an initial functional basis by an efficient placement of the knots using a simple machine-learning algorithm. The knot selection approach does not apply directly when the data are defined on domains of a higher dimension than one such as, for example, images. The reason is that in higher dimensions the convenient and numerically efficient spline spaces use tensor bases that require knots located on a lattice. This fundamentally limits flexible knot placement which is fundamental for the approach. The goal of this research is two-fold: first, to propose modified approaches that circumvent the issue by coding the irregular knot selection into the topology of the spaces of tensor-based splines; second, to apply the approach to a classification problem workflow for functional data that utilizes knot selection. The performance is preliminarily accessed on a benchmark dataset and shown to be comparable to or better than the previous methods.

Quantifying the Criteria Used to Identify Zircons from Ore-Bearing and Barren Systems in Porphyry Copper Exploration

Abstract Zircon is a common mineral in igneous rocks, which is resistant to both chemical weathering and physical abrasion. Its chemistry can potentially be used to distinguish ore-forming porphyry magmas from barren magma systems. This study compiles >23,000 zircon analyses from >30 porphyry deposits, barren intrusions, and rivers to determine the principal geochemical characteristics of fertile zircons using predictive modeling, and compares them with traditional geochemical thresholds. The results show that the Eu/Eu* and Dy/Yb ratios, P content, and the curvature at the end of rare earth element (REE) patterns (λ3) are the most diagnostic characteristics of fertile zircons. The use of geochemical thresholds, as Boolean conditions, reach their maximum performance for Eu/Eu* and Dy/Yb (sensitivity [sens] = 0.73, specificity [spec] = 0.90), but it is outperformed by the random forest model (sens = 0.91, spec = 0.93) in the testing set. Explanatory analysis of the models shows that the fertility signal in zircons becomes stronger as the porphyry system evolves and is accompanied by an overall decrease in the middle to light REE and P content, characteristics that are absent in barren zircons. We attribute the observed difference in λ3 to the co-crystallization of other accessory phases, suggesting that the changes in the zircon Ce anomaly is controlled by the depletion of light and middle REE. The low P content in fertile zircons is caused by extensive crystallization of apatite. Fertile zircons have an excess of (REE + Y)3+, which we attribute to charge-balance by H+ in hydrous magmas. Simple machine learning algorithms outperform the traditional geochemical discriminators in their predictions and provide insights into characteristics that have not previously been considered for evaluating porphyry copper fertility using zircon geochemistry. We propose simplified methods that can be easily incorporated into exploration workflows.

Step Length Estimation for Blind Walkers.

Wayfinding systems using inertial data recorded from a smartphone carried by the walker have great potential for increasing mobility independence of blind pedestrians. Pedestrian dead-reckoning (PDR) algorithms for localization require estimation of the step length of the walker. Prior work has shown that step length can be reliably predicted by processing the inertial data recorded by the smartphone with a simple machine learning algorithm. However, this prior work only considered sighted walkers, whose gait may be different from that of blind walkers using a long cane or a dog guide. In this work, we show that a step length estimation network trained on data from sighted walkers performs poorly when tested on blind walkers, and that retraining with data from blind walkers can dramatically increase the accuracy of step length prediction.

Classification of non-referenced continuous-wave terahertz reflection spectra for remote material identification

Commonly, terahertz spectra (both continuous-wave and pulsed) are deconvoluted by reference spectra to remove the water vapor absorption lines and other system related responses. However, in real-life applications obtaining reference spectra can be problematic and adds to the complexity of the system. Thus, a reference-free method for classification of terahertz spectra could be a welcomed advance for remote sensing applications. In this paper, we study how simple machine learning algorithms perform as a reference-free method for terahertz stand-off identification of materials. The algorithms are trained using spectra measured under controlled humidity conditions and tested by a completely independent data set measured under ambient conditions. We apply three different classification algorithms; namely a Gaussian Bayes model, the k nearest neighbors, and a support vector machine. We found that, if the terahertz spectra are processed using a supervised algorithm (Regularized Linear Discriminant Analysis), very high classification scores (¿98.6%) can be retained for the non-referenced spectra. Moreover, the high accuracy is obtained meanwhile the dimensionality is reduced by a factor larger than 160, which further reduces the computational requirements. Hence, we have demonstrated that simple supervised machine learning algorithms can serve as a highly accurate reference-free method for THz material identification. This could be of great importance for real-world remote sensing applications based on terahertz spectroscopy.

Hybrid Machine Learning Algorithms to Evaluate Prostate Cancer

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure.

A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.

Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at developing a lung ultrasound-based model for AHF diagnosis using machine learning. Random forest and decision trees were generated using the LUS data (via an 8-zone scanning protocol) in patients with acute dyspnea admitted to the Emergency Department (PLUME study, N = 117) and subsequently validated in an external dataset (80 controls from the REMI study, 50 cases from the Nancy AHF cohort). Using the random forest model, total B-line sum (i.e., in both hemithoraces) was the most significant variable for identifying AHF, followed by the difference in B-line sum between the superior and inferior lung areas. The decision tree algorithm had a good diagnostic accuracy [area under the curve (AUC) = 0.865] and identified three risk groups (i.e., low 24%, high 70%, and very high-risk 96%) for AHF. The very high-risk group was defined by the presence of 14 or more B-lines in both hemithoraces while the high-risk group was described as having either B-lines mostly localized in superior points or in the right hemithorax. Accuracy in the validation cohort was excellent (AUC = 0.906). Importantly, adding the algorithm on top of a validated clinical score and classical definition of positive LUS scanning for AHF resulted in a significant improvement in diagnostic accuracy (continuous net reclassification improvement = 1.21, P < 0.001). Our simple lung ultrasound-based machine learning algorithm features an excellent performance and may constitute a validated strategy to diagnose AHF.

Rapid standoff spectroscopic characterization of plastic waste using quartz tuning fork

Standoff molecular identification of chemicals finds immense applications in homeland security, forensic investigation and plastic recycling industries. Although, standoff detection using mid-infrared radiation offers excellent molecular selectivity, sensitive detection of mid-IR radiation using inexpensive detectors is a challenge. In the present work, we show commercially available inexpensive Quartz Tuning Fork (QTF) can be effectively used for rapid standoff detection of solid samples using mid-IR radiation. In this method, a plastic sample placed 40 cm away was excited with tunable infrared (IR) radiation pulsed at the resonance frequency of the QTF. The IR radiation scattered off the plastic sample was used for exciting the QTF into resonance. Plotting the QTF resonant amplitude as a function of irradiating IR wavelength represents the unique IR spectrum of the plastic sample under study. The resolution of spectra recorded using QTF was found to be superior to those collected with ATR-FTIR and the technique based on QTF was 103 times faster than microcantilever-based photothermal deflection techniques. By combining simple machine learning algorithms with QTF standoff resonance IR spectra, various plastic samples can be effectively classified with 100% accuracy. Also, this technique was found to be capable of identifying black plastics without any further sample preparation requirements.

Classification and Identification of Perfume Scents by an Enantioselective Colorimetric Sensor Array of Chiral Metal-Organic-Framework-Based Fabry-Pérot Films.

Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost-efficient (enantio-)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface-mounted metal-organic-framework (SURMOF) films in Fabry-Pérot (FP) cavities. The optical properties of the FP-SURMOF films with different chiral and achiral structures are affected by the (enantio-)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read-out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP-chiral-SURMOF-based sensor array as a powerful approach toward inexpensive selective odors sensing applications.

Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders

Ensuring the timely and precise monitoring of severe liver diseases is crucial for guiding effective therapies and significantly extending overall quality of life. However, this remains a worldwide challenge, given the high incidence rate and the presence of strong confounding clinical symptoms. Herein, we applied a convenient and high-yield method to prepare the magnetic mesoporous carbon (MMC-Fe), guided by a composite of resol and triblock copolymer. With the combination of MMC-Fe, high-throughput mass spectrometry, and a simple machine learning algorithm, we extracted N-glycan profiles from various serum samples, including healthy controls, liver cirrhosis, and liver cancer, and from which we screened specific N-glycans. Specifically, the selected N-glycans demonstrate exceptional performance with area under the curve (AUC) values ranging from 0.948 to 0.993 for the detection of liver diseases, including alpha fetoprotein (AFP)-negative liver cancer. Among them, five N-glycans holds potential in monitoring distinctions between liver cirrhosis and AFP-negative liver cancer (AUC values of 0.827–0.842). This study is expected to promote the glycan-based precise monitoring of diseases, not limited to liver disease.

A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.

Machine learning tools in the tasks of selecting the determinants of socio-economic situation and development potential of Russian regions

Subject. The article addresses the selection of determinants that are significant for assessing the level of socio-economic situation and development potential of Russian regions. Objectives. The aim is to study machine learning algorithms for the selection of determinants – predictors of the level of socio-economic situation and development potential of Russian regions, to build models of classification of regions, according to the level of socio-economic situation, using various machine learning algorithms. Methods. To build classification models, I used data from the Federal State Statistics Service, the Institute of Scientific Communications, the RIA Novosti news agency, and the TAdviser Internet portal. Procedures for data classification, model parameter estimation, selection of significant determinants and visualization of results are performed, using the basic functions of the PyCaret library. Cohen's Kappa statistics and Matthews correlation coefficient were employed as priority metrics for evaluating the model productivity. The algorithms for selecting determinants are implemented in the Google Colab analytical environment. Results. I constructed multiclass classification models based on simple and ensemble machine learning algorithms. Simple classification algorithms, including logistic and ridge regression models, naive Bayesian algorithm, decision tree, support vector machine, and k-nearest neighbor methods are characterized by accuracy values at 77%, however, Cohen's Kappa statistics and the Matthews correlation coefficient only show a satisfactory relationship between the actual and predicted value of the region class. Ensemble algorithms, including random forest, gradient boosting and extreme gradient boosting, are characterized by a close relationship between the actual and forecast estimates of the classifier at a level of more than 70%. Conclusions. The random forest algorithm is recognized as the most effective classification model. The gross regional product and investments in fixed assets are informative determinants for measuring the socio-economic status.

Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

The data analysis workflow for in situ electron microscopy experiments can require a lot of human-intensive and repetitive effort. The generation of Python-based scripts that incorporate simple machine learning algorithms are quite well established in biological sciences but not often utilised in the study of catalytic systems. Such scripted analysis is not only more efficient, but readily reproducible and allows a wide range of quantitative results to be reported, including but not limited to average and total particle size, particle counting and particle size distributions. In this work we utilise these tools to examine the effect of cycling reducing and oxidising atmospheres on CuO nanoparticles.

Creation and interpretation of machine learning models for aqueous solubility prediction

Aim: Solubility prediction is an essential factor in rational drug design and many models have been developed with machine learning (ML) methods to enhance the predictive ability. However, most of the ML models are hard to interpret which limits the insights they can give in the lead optimization process. Here, an approach to construct and interpret solubility models with a combination of physicochemical properties and ML algorithms is presented. Methods: The models were trained, optimized, and tested in a dataset containing 12,983 compounds from two public datasets and further evaluated in two external test sets. More importantly, the SHapley Additive exPlanations (SHAP) and heat map coloring approaches were used to explain the predictive models and assess their suitability to guide compound optimization. Results: Among the different ML methods, random forest (RF) models obtain the best performance in the different test sets. From the interpretability perspective, fragment-based coloring offers a more robust interpretation than atom-based coloring and that normalizing the values further improves it. Conclusions: Overall, for certain applications simple ML algorithms such as RF work well and can outperform more complex methods and that combining them with fragment-coloring can offer guidance for chemists to modify the structure with a desired property. This interpretation strategy is publicly available at https://github.com/Pharmacelera/predictive-model-coloring and could be further applied in other property predictions to improve the interpretability of ML models.

An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN

Among harmful gases, carbon monoxide (CO) has posed a significant threat to human safety due to its concealment and harmfulness. Therefore, it’s imperative for the preservation of human safety to implement early detection for CO concentration. However, traditional methods for gas detection are either plagued by the limitations of insufficient accuracy like electronic nose (E-nose) equipped with relatively simple machine learning algorithms such as vanilla SVM or RNN, or limited by the incapacity to make early prediction such as sensory analysis, which requires a large amount of manpower resources and time. To address this issue, a novel technique, namely H-CRNN, is presented for E-nose to perform early prediction of low concentration CO, leveraging the strengths of convolutional and recurrent neural network to efficiently capture long-term dependencies within data. Also, shortcut connection and a novel gated attention mechanism is applied to enhance the capacity to capture crucial features. Furthermore, a linear process unit has been incorporated to handle the scale insensitivity issue. The experimental results show that the proposed H-CRNN outperforms RNN with a notable average reduction of 50.51% in the root relative squared error and 53.8% in the relative absolute error, and exhibits varying degrees of improvement when compared to state-of-the-art algorithms (TCN, TPA-LSTM, STCN, LSTNet, LSTM). Additionally, the H-CRNN achieved an average accuracy rate of 96.42%, surpassing all other algorithms and demonstrating the highest level of performance. Therefore, our work substantiates the proposed H-CRNN as an applicable method for early prediction of CO concentration.

Convolutional Neural Networks: A Simple and Functional Approach for COVID-19 Severity Prediction

Background and Aims: The Coronavirus disease 2019 (COVID-19) pandemic began in 2020. A major problem during COVID-19 was determining the clinical severity. There are a variety of markers for assessing the COVID-19 severity and outcome. So, this study aims to introduce a new approach for determining the disease severity based on the laboratory data obtained by machine learning algorithms.&#x0D; Materials and Methods: In this study, we used 100 patients for modeling. We used demographical, background disease, and laboratory data of COVID-19 patients as parameters for training the convolutional neural network model to evaluate disease severity and tried to create a predictive algorithm for future data. The sequential neural network from the Keras library by TensorFlow was used for prediction. The clinical validation of prediction by model was evaluated by the receiver operating characteristic (ROC) curve.&#x0D; Results: The mean F1 score for our current model was 0.62 (in the range of 0-1). The F1 scores for the severe group and the mild group were 0.8 and 0.45, respectively. The ROC curve for clinical validity revealed an acceptable Area Under Curve (0.085) for both severe and mild categories.&#x0D; Conclusion: The current study introduces a simple machine learning algorithm as tool for determining COVID-19 severity of by acceptable ROC. This study can lead us to use such algorithms more often in laboratory medicine and clinical decision-making. Furthermore, the present study is just a preliminary study and highlights the need for further research to validate and refine the proposed model.

Supervised learning techniques for detection of Lung Carcinoma

Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed 'quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.