Single Hidden Layer Neural Network Research Articles

Predicting Bacterial Antibiotic Resistance using MALDI-TOF Mass Spectrometry Databases with ELM Applications.

Early detection of antibiotic resistance is a crucial task, especially for vulnerable patients under prolonged treatments with a single antibiotic. To solve this, machine learning approaches have been reported in the state of art. Researchers have used MALDI-TOF MS in order to predict antibiotic resistance and/or susceptibility in bacterial samples. Weis, et al. implemented LR, LightGBM and ANN to study the antibiotic resistance on bacterial strains of Escherichia Coli, Staphylococcus Aureus, and Klebsiella Pneumoniae. Despite promising results, the models have not achieved perfect accuracy, specifically when the classes are unbalanced. On the other hand, Extreme Learning Machine (ELM) is a training algorithm for forward propagation of single hidden layer neural networks, which converges much faster than traditional methods and offers promising performance along with less programmer intervention. In this way, this study introduced improved ELMs, including two weighted ELMs proposed by Zong, and the SMOTE technique in order to create new synthetic samples of the minority class. After heuristic optimization of ELM hiper-parameters, results demonstrated 85% in accuracy and 85% in geometric mean for the classification problem in the case of weighted ELM 1 subject to the SMOTE technique of oversampling.

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors

BackgroundNew cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhancement. ObjectiveThe main objective of this study is to construct machine learning-based CRF prediction models for lymphoma survivors to help healthcare professionals accurately identify the CRF population and better personalize treatment and care for patients. MethodsA cross-sectional study in China recruited lymphoma patients from June 2023 to March 2024, dividing them into two datasets for model construction and external validation. Six machine learning algorithms were used in this study: Logistic Regression (LR), Random Forest, Single Hidden Layer Neural Network, Support Vector Machine, eXtreme Gradient Boosting, and Light Gradient Boosting Machine (LightGBM). Performance metrics like the area under the receiver operating characteristic (AUROC) and calibration curves were compared. The clinical applicability was assessed by decision curve, and Shapley additive explanations was employed to explain variable significance. ResultsCRF incidence was 40.7 % (dataset I) and 44.8 % (dataset II). LightGBM showed strong performance in training and internal validation. LR excelled in external validation with the highest AUROC and best calibration. Pain, total protein, physical function, and sleep disturbance were important predictors of CRF. ConclusionThe study presents a machine learning-based CRF prediction model for lymphoma patients, offering dynamic, data-driven assessments that could enhance the development of automated CRF screening tools for personalized management in clinical practice.

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision.

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Optimized deep neural network to estimate orientation angles for solar photovoltaics intelligent systems

Using a single hidden layer neural network in estimating orientation angles for solar photovoltaics lacks the complexity required to model nonlinear relationships between input variables and the optimal orientation angles for solar photovoltaics. It struggles to generalize well to new and unseen data. More sophisticated neural network architectures such as deep learning with multi-hidden perceptron (MLP) can solve these issues by changing the architecture by deepening the network. Deepening the network will increase complexity, energy consumption, and time complexity. The study uses a novel approach to outperform traditional MLP models with two, three, four, and five hidden layers. An innovative approach was proposed by enhancing a single hidden layer MLP with a quadratic polynomial function, utilizing two robust methodologies, Least Absolute Residuals (LAR) and Bisquare methods. The results demonstrate that these approaches yield significant improvements in Root Mean Square Error (RMSE) and coefficient of determination (R squared). LAR-based MLP showed superiority over both bisquare-based and conventional MLPs methods in R2 and RMSE, ranging from 1.13 to 1.18 and 2.53 to 3.06, respectively. The study outperformed conventional MLP architectures with five hidden layers regarding accuracy and efficiency. The proposed model offers a more effective and less complex solution for data prediction tasks.

Flat minima generalize for low-rank matrix recovery

Abstract Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly impacts its performance. Flat minima—those around which the loss grows slowly—appear to generalize well. This work takes a step towards understanding this phenomenon by focusing on the simplest class of overparameterized nonlinear models: those arising in low-rank matrix recovery. We analyse overparameterized matrix and bilinear sensing, robust principal component analysis, covariance matrix estimation and single hidden layer neural networks with quadratic activation functions. In all cases, we show that flat minima, measured by the trace of the Hessian, exactly recover the ground truth under standard statistical assumptions. For matrix completion, we establish weak recovery, although empirical evidence suggests exact recovery holds here as well. We complete the paper with synthetic experiments that illustrate our findings.

Analysis Of Exchange Rate Forecast Model of US Dollar Against RMB Based on BP Neural Network

Judging from the economic development situation in recent years, the uncertainty of exchange rate fluctuation will be affected by the exchange rate, not only for domestic and foreign trade but also for the cost of enterprises and individuals. Therefore, in view of this crucial problem, in this study, BP(Back Propagation) synthetic neural network is drilled by the opening price of the day in order to predict the closing price in the next day. The method is to divide 5000 groups of data into 4000 training sets and 1000 test sets. Single hidden layer neural network is adopted, and the number of nodes is determined to be 6 and 15 through the experiment of the influence of the quantity of nodes on model performance. The simulation test show that the root mean square errors in the prediction set and the verification set are very small, which are 0.0004 and 0.0006 respectively, so the neural network has good accuracy in forecasting the closing price of the next day, and some opinions on this model are also given.

Neural Network Methods Based on Efficient Optimization Algorithms for Solving Impulsive Differential Equations

In view of the fact that impulsive differential equations have the discreteness due to the impulse phenomenon, this paper proposes a single hidden layer neural network method based extreme learning machine and a physicsinformed neural network method combined with learning rate attenuation strategy to solve linear impulsive differential equations and nonlinear impulsive differential equations respectively. For the linear impulsive differential equations, firstly, the interval is segmented according to the impulse points, and a single hidden layer neural network model is constructed, the weight parameters of hidden layer are randomly set, the optimal output parameters and solution of the first segment are obtained by the extreme learning machine algorithm, then we calculate the initial value of the second segment according to the jumping equation, and the remaining segments are solved in turn in the same way. Although the single hidden layer neural network method proposed can solve linear equations with high accuracy, it is not suitable for solving nonlinear equations. Therefore, we propose the physics-informed neural network combined with learning rate attenuation strategy to solve the nonlinear impulsive differential equations, then the Adam algorithm and L-BFGS algorithm are combined to find the optimal approximate solution of each segment. Numerical examples show that the single hidden layer neural network method with Legendre polynomials as the activation function and the physics-informed neural network method combined with learning rate attenuation strategy can solve linear and nonlinear impulsive differential equations with higher accuracy.

Association between short birth spacing and child malnutrition in Bangladesh: a propensity score matching approach

ObjectivesThis study aimed to explore the effects of short birth spacing (SBS), which is defined as a period of less than 33 months between two successive births, on multiple concurrent...

A new meshless method of solving the distributed-order time-fractional mobile-immobile equations

A new method is introduced by combining neural network’s huge expressive power with technologies of the minimum residual approximate solutions (MRASs). In spatial direction, a single hidden layer neural network makes the construction of bases functions easier. The cost of calculations becomes acceptable in high dimensions space using the technologies of the MRASs. And this paper establish approximation and convergence order theories based on neural network.

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Abstract For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

A Simplified Current Feature Extraction and Deployment Method for DC Series Arc Fault Detection

DC series arc fault (DSAF) is one of the main causes of low-voltage DC (LVDC) distribution system fire accident. Traditional methods use time-frequency domain features to make judgments directly or use machine learning to further classify them. In this paper, a method is proposed to simplify the feature extraction process while ensuring accuracy. Firstly, a fully automated process is used to establish DC arc datasets. A composite bandpass filter (CBF) is designed to extract the typical frequency segment of DC arc. Besides, an arc detection neural network (ArcNN) based on temporal convolution network (TCN) is proposed to extract current waveform features. Principal component analysis (PCA) is used to process these features to reduce correlation. Finally, a single hidden layer neural network (SHLNN) is used as classifier. The database is collected from different scenarios and working conditions. By measuring the DC raw current, the arc fault detector (AFD) can achieve a test set accuracy of 99.88% at a sampling rate of 250 kHz. The model is also deployed on Jetson Nano with an average real-time detection of 0.15 s/sample.

Nonlinear Temperature Control of Additive Friction Stir Deposition Evaluated on an Echo State Network

Abstract Additive friction stir deposition is a recent innovation in additive manufacturing allowing the deposition of metallic alloys onto a metallic deposit bed, creating a purely mechanical metallic bond. The deposition can be done in a layer-by-layer manner, and the purely mechanical process eliminates the need for high energy consumption and can be deposited at a much higher rate than beam-based welding. The mechanical nature of the process allows the bonding of dissimilar alloys and a reduction in size of the heat-affected zone. The additive friction stir deposition process is difficult to model and existing literature has focused on numerical analysis, which is not amenable to online closed-loop control. In this work, a form of reservoir computing called an echo state network is used to model the additive friction stir deposition process from online process data, and validation is performed on a reserved dataset. Subsequently, a model-free controller using Lyapunov-derived combination of the robust integral of the sign error, and a single hidden layer neural network design is developed to control the additive friction stir deposition process. Control efficacy is given by way of a Lyapunov analysis which shows the system is globally exponentially stable, and simulation results with the echo state networks. Stability proof shows that under one assumption, the controller can be extrapolated to the real system. The mean squared error of the tracking result using the controller and echo state network simulation is 2.05 °C.

Approximation error of single hidden layer neural networks with fixed weights

Neural networks with finitely many fixed weights have the universal approximation property under certain conditions on compact subsets of the d-dimensional Euclidean space, where approximation process is considered. Such conditions were delineated in our paper [26]. But for many compact sets it is impossible to approximate multivariate functions with arbitrary precision and the question on estimation or efficient computation of approximation error arises. This paper provides an explicit formula for the approximation error of single hidden layer neural networks with two fixed weights.

Feature Weighting for Parkinson's Identification using Single Hidden Layer Neural Network

The diagnosis of Parkinson has become easier with the existence of machine learning. It includes using existing features from the biometric dataset generated by the person to identify whether he has Parkinson or not. The features differ in their discrimination capability and they suffer from redundancy. Hence, researchers have recommended using feature selection for Parkinson's identification. The feature selection aims at finding the most important and relevant features to produce an efficient and effective model. In this article, we present entropy-based Parkinson classification. The goal is to select only 50% of the most relevant features for Parkinson prediction. Two variants of neural networks are used for evaluation, the first one is a feed-forward Extreme Learning Machine ELM and the second one is Fast Learning Machine FLN. Also, the K-Nearest Neighbor KNN algorithm is used for evaluation. The results show the superiority of ELM and FLN when the model of feature selection is used with an accuracy of 80% compared with only 78% when the model is not used.

An optimization-based supervised learning algorithm for PXRD phase fraction estimation

In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of each phase in the sample. Machine learning algorithms (e.g., Artificial Neural Networks) have been applied to perform such difficult tasks in powder diffraction analysis, but typically require a significant number of training samples for acceptable performance. We have developed an approach that performs well even with a small number of training samples. We apply a fixed-point iteration algorithm on the labelled training samples to estimate monophasic spectra. Then, given an unknown sample spectrum, we again use a fixed-point iteration algorithm to determine the weighted combination of monophase spectra that best approximates the unknown sample spectrum. These weights are the desired phase fractions for the sample. Our approach gave the best accuracy and lowest mean absolute error when compared with several machine learning algorithms, including a single hidden-layer neural network.

Speech emotion recognition based on syllable-level feature extraction

Speech emotion recognition systems have high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to decrease contextual differences and increase focus the attention on the structure of a formant. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives better than the state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.

An artificial electric field algorithm-based neuro-fuzzy predictor for estimation of compressive strength of concrete structures: A machine learning approach

Machine learning based forecasting are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need of exploring an effective, automated and accurate predictor for this domain. This article proposes an artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) for prediction of compressive strength of concrete structures. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using Gaussian triangular membership function with a degree of membership to different classes. The optimal number of input data, hidden neurons, bias and weights for hidden layer are decided by AEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the AEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes and analysis are in favor of the AEFA+NFN based forecasting.

Cathode Position Detection in a Transferred Arc Plasma Using Artificial Neural Network

In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this article, a machine learning technique is proposed to accurately detect the position of the cathode in a direct current (DC) transferred arc plasma system. The measured voltage signal sampled at 20 kHz is processed using a tunable Q-factor wavelet transform (TQWT) followed by statistical features extraction and a machine learning algorithm to provide accurate cathode position information. Two different machine learning algorithms are used in this work, namely, single hidden layer neural network (SHLNN) and single-layer extreme learning machine (SELM). The output of these machine learning algorithms provides accurate position information and is also compared to the traditional voltage-related position information. The experimental signal of a 30-kW DC plasma system and cathode position detection results is shown.

Low-rank kernel approximation of Lyapunov functions using neural networks

&lt;p style='text-indent:20px;'&gt;We study the use of single hidden layer neural networks for the approximation of Lyapunov functions in autonomous ordinary differential equations. In particular, we focus on the connection between this approach and that of the meshless collocation method using reproducing kernel Hilbert spaces. It is shown that under certain conditions, an optimised neural network is functionally equivalent to the RKHS generalised interpolant solution corresponding to a kernel function that is implicitly defined by the neural network. We demonstrate convergence of the neural network approximation using several numerical examples, and compare with approximations obtained by the meshless collocation method. Finally, motivated by our theoretical and numerical findings, we propose a new iterative algorithm for the approximation of Lyapunov functions using single hidden layer neural networks.&lt;/p&gt;