Network-based Machine Learning Model Research Articles

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.

We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test). Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.

Recognizing Sign Language using Machine Learning and Deep Learning Models

Individuals with hearing impairments communicate mostly through sign language. Our goal was to create an American Sign Language recognition dataset and utilize it in a neural network-based machine learning model that can interpret hand gestures and positions into natural language. In our study, we incorporated the SVM, CNN and Resnet-18 models to enhance predictability when interpreting ASL signs through this new dataset, which includes provisions such as lighting and distance limitations. Our research also features comparison results between all the other models implemented under invariant conditions versus those using our proposed CNN model. As demonstrated by its high levels of precision at 95.10% despite changes encountered during testing procedures like varying data sets or scene configurations where losses are minimal (0.545), there exists great potential for future applications in image recognition systems requiring deep learning techniques. Furthermore, these advancements may lead to significant improvements within various fields related explicitly to speech-language therapy sessions designed specifically around helping people overcome challenges associated with deafness while building bridges towards improved social integration opportunities.

Pore fractal characteristics and ANN model of polyacrylonitrile fiber reinforced concrete under the synergistic effects of freeze-thaw and erosion

The degradation of the concrete pore structure is a major concern among scholars due to its negative impact on durability. This study conducted a salt freezing test on polyacrylonitrile fiber concrete (PANFRC) using a composite freeze-thaw medium consisting of Na2SO4, MgSO4, and NaCl (5 % + 5% + 3.5 %). The pore volume fractal dimension was determined by analyzing the T2 spectrum obtained through nuclear magnetic resonance technology. The study explored the influence of different types of pores on frost resistance and developed an artificial neural network-based machine learning model to predict macroscopic frost resistance using the pore volume fractal dimension. The experimental findings indicate that plain concrete exhibits a density loss of 4.67 % upon failure, whereas the density loss in PANFRC is reduced to less than 3 %. The frost resistance durability index is observed to improve as the fiber mass fraction increases within specific ranges. Notably, when the fiber content surpasses 1.2 kg/m³, the durability factor (DF) can be effectively doubled. Drawing from the concept of freeze-thaw cycles, a novel frost-thaw erosion damage model was developed, which incorporates the influence of fiber content under the combined action of freeze-thaw cycles and erosion. The analysis of pore dimensions reveals that the fractal characteristics become more pronounced with the increase in pore size, although this does not extend to the micro-pore range (i.e., those smaller than 0.1 μm). In comparison to other machine learning algorithms, the ANN demonstrates its superiority by establishing a meso-macro transfer prediction model with enhanced generalization capabilities.

A Comparative Study on Various ANN Optimization Algorithms for Magnetorheological Elastomer Carbonyl Iron Particle Concentration Estimation

Estimation particle composition such as particle shape, size, and concentration are crucial prior to the fabrication process of magnetorheological elastomer (MRE) to avoid process repetition due to inaccurate formulation. Currently, most of MRE prediction model were purposely used to predict the rheological properties such as shear stress and dynamic modulus, known as forward model. Nonetheless, very few studies have been reported to be capable able of predicting particle composition particularly in MR materials, which known as inverse model. Therefore, this paper proposed a carbonyl iron particle (CIP) concentration based MRE prediction model using neural network algorithm. Neural network-based machine learning model is more approachable compared to conventional mathematical modelling approach due to easily identify trends and pattern while handling multi-variety data. Various optimization algorithms have been employed such as Adam, RMSprop, SGD, AdaGrad, and Nadam throughout the modelling process. As the results, given shear strain amplitude, magnetic flux density, storage modulus, and loss factor as model input, SGD gave the maximum prediction accuracy with 0.95 and 3.038 MPa of R2 and RMSE, respectively. Hence, this model can be the basis to the MRE material and devices development particularly as the tool to reduce costing and time consuming.

Predicting Major Bleeding in Patients with Venous Thromboembolism on Extended Anticoagulation Therapy Using Follow-up Data and Long Short-Term Memory Based Recurrent Neural Network

Background: It is often challenging for physicians to decide on the duration of anticoagulation treatment for patients with venous thromboembolism (VTE), as they need to weigh the risk of recurrent thrombosis and bleeding at the same time. Several clinical models have been developed to help physicians identify patients with high risk of bleeding. However, these tools use only the baseline clinical information and are not able to incorporate the clinical conditions and events that occur over time that may influence the risk of bleeding. Therefore, capturing the patterns and relationships in the continuously changing time series of clinical data could be a more powerful approach to developing predictor models than just relying on the baseline clinical information. Nonetheless, creating predictive models from follow-up clinical information is challenging given that time series clinical data are non-uniform, high-dimensional, multivariate observations. Here, we present the first attempt at creating a model that uses the time series follow-up information to predict major bleeding over time using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells. Methods: 2542 patients diagnosed with VTE were enrolled in a prospective cohort study over 8 years. In addition to recording their clinical information at baseline, 6-month follow-up interviews were conducted using a standard script to monitor bleeding status and record clinical information. Major bleeding was defined by the International Society on Thrombosis and Haemostasis, with suspected bleeding events classified by an independent adjudication committee. Overall, 118 patients had major bleeding - a 4.6% incidence rate. The median and mode of the clinical variables were used to impute missing numerical and categorical values, respectively, and for patients who had no follow-up information, for whom bleeding occurred before the first follow-up, an artificial follow-up data point was generated from their corresponding baseline data. Thereafter, the data was divided into two stratified sets: 70% for training, and 30% for testing. Five supervised neural network-based machine learning models with different architectures were trained on the baseline dataset, or the follow-up dataset, or both to predict major bleeding. After training, these machine learning models were tested on the testing set and compared to the conventional clinical models, modified to make them compatible with the available predictor variables in our dataset, including the CHAP, the HAS-BLED, the VTE-BLEED, the RIETE, the ACCP, and the OBRI, which only use the baseline information. Results: Overall, the models that used the follow-up information had a higher area under the Receiver Operating Curve (AUROC) or c-statistic compared to the other models that only relied on the baseline dataset. In particular, the LSTM RNN model was able to achieve AUROC of 81.3% that is more than 10% higher compared to the best performing clinical model. We discovered that the LSTM RNN model mostly relied on features such as number of concomitant medications, years since baseline visit, use of specific antibiotics or antiplatelet agents, and presence of new hypertension to predict bleeding from the follow-up dataset. Furthermore, half of the bleeding events occurred within the first year after patients' baseline visits - a trend reflected in the predictions made by LSTM RNN model. Finally, the models that used both the baseline and the follow-up datasets showed different results depending on their architectures; that is, the simpler ensemble model achieved AUROC of 82.5% while the more complex model had AUROC of 70.8% due to overfitting. Conclusion: We have shown that using time series follow-up data can improve bleeding risk prediction in patients with VTE who are on extended anticoagulant therapy compared to just using the baseline data, and clinicians might benefit from using such an approach. Furthermore, our results indicate that LSTM RNN is a suitable architecture to model routine clinical follow-up data. Finally, we believe using time series data could improve the performance of the other clinical models that are currently based on one-time baseline measurements.

A graph neural network-based machine learning model for sentiment polarity and behavior identification of COVID patients

A graph neural network-based machine learning model for sentiment polarity and behavior identification of COVID patients

Multimodal Three-Dimensional Printing for Micro-Modulation of Scaffold Stiffness Through Machine Learning.

The ability to precisely control a scaffold's microstructure and geometry with light-based three-dimensional (3D) printing has been widely demonstrated. However, the modulation of scaffold's mechanical properties through prescribed printing parameters is still underexplored. This study demonstrates a novel 3D-printing workflow to create a complex, elastomeric scaffold with precision-engineered stiffness control by utilizing machine learning. Various printing parameters, including the exposure time, light intensity, printing infill, laser pump current, and printing speed were modulated to print poly (glycerol sebacate) acrylate (PGSA) scaffolds with mechanical properties ranging from 49.3 ± 3.3 kPa to 2.8 ± 0.3 MPa. This enables flexibility in spatial stiffness modulation in addition to high-resolution scaffold fabrication. Then, a neural network-based machine learning model was developed and validated to optimize printing parameters to yield scaffolds with user-defined stiffness modulation for two different vat photopolymerization methods: a digital light processing (DLP)-based 3D printer was utilized to rapidly fabricate stiffness-modulated scaffolds with features on the hundreds of micron scale and a two-photon polymerization (2PP) 3D printer was utilized to print fine structures on the submicron scale. A novel 3D-printing workflow was designed to utilize both DLP-based and 2PP 3D printers to create multiscale scaffolds with precision-tuned stiffness control over both gross and fine geometric features. The described workflow can be used to fabricate scaffolds for a variety of tissue engineering applications, specifically for interfacial tissue engineering for which adjacent tissues possess heterogeneous mechanical properties (e.g., muscle-tendon).

A study of the effect of printing process parameters on the delamination and surface finish properties of aluminium-infused PLA printed via material extrusion

ABSTRACT Demand for the newer manufacturing techniques to produce materials with difference and specific properties are kept on increasing. The needed specific properties are obtained through composite materials by reinforcing components onto a base material to improve some critical properties. In that way, the polylactic acid (PLA) incorporated with aluminium was used to print the samples through fused filament fabrication (FFF) process. During printing, the printing process parameters have considerable effect on the material properties. At first, specimens are printed as per the Taguchi’s experimental design with L9 Orthogonal array design and the outputs are obtained experimentally. As an extension, this study proposed two neural network-based machine learning models to predict the influence of process parameters like printing temperature (PT), layer height (LH), raster angle (RA) and fill pattern (FP) on the surface finish (SF), surface finish near to the drilled holes (SFNDH), and delamination factor (DF). Both the proposed models recorded the overall prediction accuracy about 99.99%. To ensure the results predicted by the NN models, validation experiments are conducted and compared with the model predicted results. The experimental results are in good agreement with the predicted values which are obtained through numerical modelling.

Unconventional growth of methane hydrates: A molecular dynamics and machine learning study

Natural gas hydrate reservoirs in natural settings subjected to external driving forces are deformed and locally dissociated, while they reform due to endothermic dissociation reaction. Here we report a molecular dynamics (MD) and machine learning (ML) study of unconventional growth characteristics of methane hydrates (MH). MD results show that depending on the strain of MH substrate, methane clathrate hydrates grow via four distinct growth modes, in which diverse unconventional cages are critically involved. Interestingly, a novel new MH structure named as MH-VII composed of 51263, 51472 and 425861 polyhedral cages is discovered. Moreover, a long short-term memory (LSTM) neural network-based ML model using short-time MD information is developed to effectively predict the complex dynamic growth of MH in terms of clathrate cages and F4 order parameter. This work provides new insights and perspectives into the growth of clathrate hydrates, and as-developed LSTM-based ML model opens a critical pathway for exploring time-dependent behaviors of clathrate hydrates under complex conditions.

Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.

Technical review of supervised machine learning studies and potential implementation to identify herbal plant dataset

Abstract The use of technology in everyday life is unavoidable, considering that technological advancement occurs very quickly. The current era is also known as industry 4.0. In the industry 4.0 era, there is a convergence between the industrial world and information technology. The use of modern machines in the industry makes it possible for business actors to digitize their production facilities and open up new business opportunities. One of the developments in information technology that is being widely used in its implementation is machine learning (ML) technology and its branches such as computer vision and image recognition. In this work, we propose a customized convolutional neural network-based ML model to perform image classification technique for Indonesian herb image dataset, along with the detailed review and discussion of the methods and results. In this work, we use the transfer learning method to adopt the opensource pre-trained model, namely, Xception, developed by Google.

Machine learning accelerated search of the strongest graphene/h-BN interface with designed fracture properties

Two-dimensional lateral heterostructures exhibit novel electronic and optical properties that are induced by their in-plane interface for which the mechanical properties of the interface are important for the stability of the lateral heterostructure. Therefore, we performed molecular dynamics simulations and developed a convolutional neural network-based machine learning model to study the fracture properties of the interface in a graphene/hexagonal boron nitride lateral heterostructure. The molecular dynamics (MD) simulations show that the shape of the interface can cause an 80% difference in the fracture stress and the fracture strain for the interface. By using 11 500 training samples obtained with help of high-cost MD simulation, the machine learning model is able to search out the strongest interfaces with the largest fracture strain and fracture stress in a large sample space with over 150 000 structures. By analyzing the atomic configuration of these strongest interfaces, we disclose two major factors dominating the interface strength, including the interface roughness and the strength of the chemical bond across the interface. We also explore the correlation between the fracture properties and the thermal conductivity for these lateral heterostructures by examining the bond type and the shape of the graphene/hexagonal boron nitride interface. We find that interfaces comprised of stronger bonds and smoother zigzag interfaces can relieve the abrupt change of the acoustic velocity, leading to the enhancement of the interface thermal conductivity. These findings will be valuable for the application of the two-dimensional lateral heterostructure in electronic devices.

Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study

Machine learning has become an excellent tool for scientists and engineers to predict, design, and fabricate next-generation material. Here, we report the thermal conductivity and thermal rectification of gradient-nano-grained graphene (GNGG) by molecular dynamic simulation with machine learning. It is revealed that the thermal conductivity of GNGG is mainly determined by the average grain size, while its thermal rectification factor varies linearly with the gradient of nanograins. Deep neural network-based machine learning models are developed to estimate the thermal transport properties of GNGG using microstructural signatures, such as the location, number, and orientation of 5|7 pairs. The results stress the pivotal roles of 5|7 defects in the planar thermal transports of graphene and indicate that high-performance 2D thermal rectifiers for heat flow control and energy harvesting can be achieved by bio-inspired gradient structure engineering. The findings are expected to supply a theoretical strategy for the design of bio-inspired materials and create a method to predict the potential properties of the material candidates by using machine learning, which can save the abundant expense of developing the material by using the classical method.

Leak detection in water distribution systems by classifying vibration signals

In addition to the issues of water quality and economic losses, leakages in pipe networks are a potential danger to public safety. Early detection of water leaks in pipe networks is crucial for implementing countermeasures to reduce structural damage and water losses. The method of data collection based on sensors installed in pipeline networks has gained a lot of attention due to its potential for application in real-time monitoring systems for leak detection. However, many models developed on data collected from simulations, engineered tests or field experiments, and few have been validated using real network data. To make further investigations in real water distribution systems (WDSs), machine learning (ML)-based leak detection models were developed and validated using signal data collected by piezoelectric accelerometers installed in WDSs over several cities of China. Nonlinear features describing the non-stationary characteristics contained in leak signals are extracted from the signals to build feature sets as inputs to the common classifiers, i.e., support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). In addition, the spectrograms obtained by the short-time Fourier transform processing are used for the convolutional neural network SqueezeNet. The effects of different features, different dataset compositions, and different models for leak detection are compared and analyzed. SqueezeNet has the best performance with 95.15% accuracy in identifying leaks, and KNN is the best of the three classifiers with superior sensitivity and 88.17% accuracy in identification. This paper demonstrates the effectiveness and practicability of using machine learning models to identify leaks in real pipe networks. The application of machine learning models can perform effective leak detection with minimal human intervention, thus providing a meaningful contribution to the study of leak detection in real WDSs. The proposed model aims to identify leakages under operational conditions in real-life pipe networks with higher efficiency and reduced manual involvement. This paper demonstrates the applicability of accelerometers based on microelectromechanical systems for leak detection and establishes real network-based machine learning models; thereby, providing meaningful contribution to the literature regarding leak detection in a real WDS.

A deep learning approach to automatic detection and profiling of dental caries in bitewing and periapical radiographs

<h3>Objective</h3> Dental caries is the most prevalent noncommunicable disease among humans, but routine misdiagnosis remains an underappreciated challenge. This study aimed to answer the challenge with the latest advances in artificial intelligence (AI), machine/deep learning, and computer vision. This study was conducted by an interdisciplinary team of academic clinicians and a dental AI startup. <h3>Study Design</h3> We curated and methodically annotated an imbalanced database of bitewing and periapical radiographs using a caries ontology derived from the ICDAS framework. Using this dataset, we leveraged machine learning algorithms to train predictive models to perform segmentation and classification of dental caries on novel radiographs. Given the trained model, we designed a comparison study to measure the qualitative and quantitative performance of the trained neural networks vs a set of experts. The objective of the study was to assess the expected effectiveness of model performance, and validate model behavior in direct comparison with humans on the same tasks. The study consisted of 3 dentists interacting with a web-based platform for collecting annotations of de-identified radiographs. Fifteen radiographs were used for the pilot study. <h3>Results</h3> In the human-human comparisons, there was 4% strong disagreement, 48% minor disagreement, and 48% agreement. In the majority of cases (69%), the neural network model was either perfectly correct or close with some nuance. In no case was the model completely incorrect (0%); it either missed one or more caries or produced a false positive (the remaining 31%). In general, it matched or exceeded the expert evaluation of radiographs. <h3>Conclusion</h3> We contend that these results show how neural network-based machine learning models hold considerable promise in providing assistance to dentists. We intend to pursue larger-scale human-machine comparison studies in subsequent efforts. <b>Statement of Ethical Review</b> Ethical review was sought and study was exempted from ethical review

Polynomial neural network based probabilistic hydrodynamic analysis of two-lobe bearings with stochasticity in surface roughness

This paper investigates the probabilistic response of two-lobe bearings considering the uncertainty in eccentricity ratio, preload value, bearing clearance, supply pressure, oil viscosity and surface roughness. To simulate stochasticity in input variables, Monte Carlo simulation (MCS) is carried out in conjunction with the Reynolds equation using finite difference method. Polynomial neural network based machine learning model is used as a surrogate model to increase the efficiency of MCS. To assess the relative importance of the stochastic input parameters, a sensitivity analysis is carried out. The physically insightful new probabilistic results, presented here covering a wide spectrum of uncertainty sources including surface roughness, make it evident that different forms of source-uncertainties have a significant effect on the critical performance of bearings.

Abstract LB148: Knowledge graph integration of germline, primary cancer and cancer genetic dependency data prioritizes new target candidates

Abstract Background: Effective target discovery strategies are key to developing new precision medicines against cancer. Large-scale efforts to systematically characterize cancer-associated germline variants, genomic changes in tumors and cancer genetic dependencies via CRISPR and RNAi in cell lines provide valuable resources for machine learning (ML) approaches designed to identify disease-modifying targets. However, fundamental challenges remain in understanding how various data types (including primary tumor and cell line data) can be jointly modeled to decipher the molecular network contexts underpinning the biology and efficacy of new target candidates. Approach: Using a data-driven knowledge graph spanning &amp;gt;2 billion relations over &amp;gt;350,000 biomedical entities, we assembled a comprehensive target prioritization resource with 53 feature types including multi-omics (germline and tumor) and genetic dependency data. Feature categories consisted of raw feature types (e.g. germline genetic, somatic mutation, copy number variation, transcriptomic, proteomic, survival associations, CRISPR/RNAi sensitivity, selectivity, efficacy and essentiality), network-transformed features capturing the underlying molecular network context of each possible target, and multidimensional network integration features combining systems-level information from multiple feature types. Multiple ML models were trained on either omics-based features only or on both omics and genetic dependency features; and using information from each cancer type separately, as well as jointly in multi-disease models. Results: Individual features and the resulting ML models were evaluated using known drug targets in 20 cancer types. We found that a) germline genetic and survival association features along with genetic dependency (efficacy and selectivity) features were the most predictive prior to network transformation; b) network-based feature transformation raised the predictive power of individual features (e.g. increasing mean AUPRC from 0.024 to 0.064 for germline genetic and from 0.018 to 0.048 for cell line sensitivity features); c) network integration of multiple feature types increased mean AUPRC by 21% over the best individual omics features; d) ML models integrating both omics and genetic dependency data outperformed ML omics-only approaches (mean AUPRC 0.219 vs. 0.195); and e) multi-disease ML models provided the best overall performance (mean AUPRC 0.232) underlining the value of multi-dimensional data integration and information transfer across cancer types. Resulting network-based machine learning models provide a highly interpretable view into the top-scoring genes for each cancer, prioritizing established targets, targets recently being evaluated (e.g. FGFR2 and FGFR3 in lung squamous cell carcinoma) and novel target candidates. Citation Format: Janusz Dutkowski, Radosław Bielecki, Karol Nienałtowski, Michał Kukiełka, Roy Ronen. Knowledge graph integration of germline, primary cancer and cancer genetic dependency data prioritizes new target candidates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB148.

Stock Prediction Using Machine Learning

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

Machine learning regression approach to on-chip optical frequency combs analyses

We present a practical machine learning (ML) method for serving accessible nonlinear functions, which tackles a regression problem with tremendous parameters. By solving the modified Lugiato–Lefever equation, datasets for emulating the silicon-on-insulator platform and generating the on-chip optical frequency comb (OFC) are gathered. Furthermore, a feed-forward network-based ML model is used to train the datasets, and the prediction of the related parameters is implemented synchronously. Numerical results show that the model combining the finite element method with the ML technique is capable of predicting the properties of on-chip frequency combs for the first time, as far as we know, paving the way for analyzing OFCs based on integrated silicon photonics.

Data-driven application of MEMS-based accelerometers for leak detection in water distribution networks

Water scarcity is a global concern; 68 countries are facing extremely-high to medium-high risk of water stress. In this era of crisis, where water conservation is an absolute necessity, the water distribution networks (WDNs) globally are experiencing significant leaks. These leaks cause tremendous financial loss and unacceptable environmental hazards, thus further aggravating the water scarcity situation. To minimize such damage, the adoption of advanced technologies and methodologies for leak detection in the WDNs is absolutely necessary. In this regard, we have investigated the application of cost-effective MEMS-based accelerometers. Experiments were conducted on real networks (metal and non-metal pipes), over the course of ten months, and the acquired acceleration signals were analyzed using a monitoring algorithm. Monitoring index efficiencies and standard deviations for every leak and no-leak case was extracted. Two individual [KNN and Decision Tree] and two ensembles [Random Forest and Adaboost (Decision Tree)] based machine learning models were developed for the accurate classification of the leak and no-leak cases using extracted features; and separate models were developed for metal and non-metal pipes. Random Forest outperformed the other machine learning models and the overall accuracy reached 100% for metal pipes and 94.93% for non-metal pipes. The machine learning models were further validated using unseen/unlabeled cases and were highly effective in detecting leaks. This study demonstrated the applicability of MEMS-based accelerometers for leak detection and established real network-based machine learning models thereby contributing to the research scarcity in this important area.