Network Machine Research Articles

An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

Applied Deep learning approaches on canker effected leaves to enhance the detection of the disease using Image Embedding and Machine learning Techniques

Canker, a disease that causes considerable financial losses in the agricultural business, is a small deep lesion that is visible on the leaves of many plants, especially citrus/apple trees. Canker detection is critical for limiting its spread and minimizing harm. To address this issue, we describe a computer vision-based technique that detects Canker in citrus leaves using image embedding and machine learning (ML) algorithms. The major steps in our proposed model include image embedding, and machine learning model training and testing. We started with preprocessing and then used image embedding techniques like Inception V3 and VGG 16 to turn the ROIs into feature vectors that retained the relevant information associated with Canker leaf disease, using the feature vectors acquired from the embedding stage, we then train and evaluate various ML models such as support vector machines (SVM), Gradient Boosting, neural network, and K Nearest Neighbor. Our experimental results utilizing a citrus leaf picture dataset show that the proposed strategy works. With Inception V3 as the image embedder and neural network machine learning model we have obtained an accuracy of 95.6% which suggests that our approach is effective in canker identification. Our method skips traditional image processing techniques that rely on by hand features and produces results equivalent to cutting-edge methods that use deep learning models. Finally, our proposed method provides a dependable and efficient method for detecting Canker in leaves. Farmers and agricultural specialists can benefit greatly from early illness diagnosis and quick intervention to avoid disease spread as adoption of such methods can significantly reduce the losses incurred by farmers and improve the quality of agricultural produce.

A convolutional neural network machine learning based navigation of underwater vehicles under limited communication

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate.We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN). The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.

Investigation in optimization of process parameters in turning of mild steel using response surface methodology and modified deep neural network

The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product mainly depend on its process parameters. Therefore, this study attempts to achieve a quality product from a conventional lathe machine by optimizing its process parameters for En2-BS970 mild steel. The experiments were designed with the help of response surface methodology (RSM). The most influential turning parameters, such as feed rate, spindle speed, cutting fluid flow rate, and cutting angle, are investigated experimentally. In addition, the machining responses, namely material removal rate (MRR), surface roughness (SR), cutting force (CF), and cutting time (CT), have been optimized using the RSM numerical optimization method. Furthermore, a deep neural network (DNN) machine learning prediction model based on the whale optimization algorithm (WOA) is developed for this experiment in order to predict turning performances for En2-BS970 material. The predicted DNN results showed an accuracy of about 90% compared to experimental results, indicating that the implementation of WOA significantly optimized the DNN weights during training. The RSM optimized responses are obtained as 14.608 mm3/min for MRR, 0.7504 µm for SR, 442.94 N for CF, and 2.48 seconds for CT during the turning operation with input settings of 0.24 mm/rev, 466.535 rpm, 0.075 ml/s and 60.18° for feed rate, spindle speed, cutting fluid flow rate and cutting edge angle respectively.

Alkali resistance prediction and degradation mechanism of basalt fiber: Integrated with artificial neural network machine learning model

Basalt fiber is commonly regarded as an alkaline corrosion-resistant material, capable of being transformed into various products (such as basalt fiber roving, chopped yarn and composite reinforcement). However, the main composition of the basalt fiber network skeleton consists of Si ∼ O bonds, which can still react with ∼OH in the alkaline environment. Nevertheless, there are differing viewpoints on the alkaline corrosion of basalt fibers within literatures. Therefore, this study aims to select three representative types of basalt fibers and compare them with two alkali-resistant glass fibers and one regular glass fiber. The attenuation mechanism of basalt fibers were investigated through orthogonal experiments, practical application scenarios and microscopic characterization methods. Additionally, we employed the Weibull model to analyze failure probabilities after erosion on basalt fibers while also applying a machine learning artificial neural network (ANN) model for predicting their alkali resistance. The results indicated that the tensile strength of basalt fiber tended to decrease and the alkali resistance of basalt fiber between alkali-resistant glass fiber and regular glass fiber in four alkaline solutions. The Weibull model also confirms that erosion increases failure probability of basalt fiber and glass fiber. Moreover, Si ∼ O, which forms the network reacted with OH-, resulting in a reduction in surface Si and Al elements content. It became possible to predict the alkali resistance of basalt fiber accurately by establishing an ANN model. Our study provide a theoretical foundation for predicting the lifespan of basalt fibers under corrosive conditions while offering guidance for enhancing their alkali resistance.

The Application of Machine Learning to Quasar and Seyfert Classification

Machine learning can be utilized to classify spectra flagged as Active Galactic Nuclei (AGNs) belonging to Seyferts or Quasars, expediting data collection and aiding in analyzing the AGN types. While many properties of Seyferts and Quasars can be used as feature points in training a machine learning model, one relatively available property with high information density is the spectra of the AGN types. This paper aims to describe the training and results of a K-Nearest Neighbors and a Dense Neural Network machine learning model built to classify AGNs as Seyfert type 1s, Seyfert type 2s, or Quasars.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

BackgroundThe proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.MethodsThis study used EHR data for children and youth aged 4–17 seeking services at McMaster Children’s Hospital’s Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores.ResultsThe GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value.ConclusionsThis study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber

Road vehicles, particularly cars, are one of the primary sources of CO2 emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R2. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest.

Abstract TP14: SocialBit: A Novel Smartwatch Sensor to Detect Social Interaction Frequency in Stroke Survivors

Introduction: Stroke survivors experience high levels of social isolation which hinder their rehabilitation and well-being. A gap in the field that impedes intervention development is a lack of real-time social interaction measurement tailored for this population. In response, we developed SocialBit, a smartwatch-based machine learning algorithm to discreetly and passively detect social interactions through privacy-preserving acoustic analysis. In this observational study, we estimated the accuracy of SocialBit to detect social interactions compared to human observers in stroke survivors. Methods: SocialBit was built as a neural network machine learning algorithm using features from YAMNet and a Transformer classifier. It was then trained and tested in stroke survivors for up to 8 days in hospital. Independently, human observers tallied the presence or absence of social interactions every minute to establish the ground truth. SocialBit performance of detecting social interactions was compared against the ground truth. Results: We analyzed 1137.7 hours of ambient audio data in 90 patients within 14 days of stroke onset. 16% of patients had aphasia, 25% reported symptoms of clinical depression, and 28% reported moderate to severe loneliness. SocialBit had an accuracy of 0.83 (SD = 0.2), sensitivity of 0.86 (SD = 0.2), specificity of 0.81 (SD = 0.02), and an area under the curve (AUC) of 0.90 (SD = 0.2) (Figure 1). Conclusion: SocialBit accurately detected the frequency of social interactions in stroke survivors in a hospital setting. These results will drive future planned studies to examine SocialBit's performance in patients’ home communities. SocialBit shows the potential to be the first real-time social sensor to quantify social interactions and social isolation in stroke survivors.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Balancing urban energy considering economic growth and environmental sustainability through integration of renewable energy

This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real-Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.

WDM C-band four channel demultiplexer using cascaded multimode interference on SiN strip waveguide structure

Back reflection challenges significantly constrain the efficiency of optical communication networks employing dense wavelength division multiplexing (DWDM) technology, particularly those based on Silicon (Si) Multimode Interference (MMI) waveguides. To mitigate this limitation, we present a novel 1×4 optical demultiplexer design using MMI within a Silicon-Nitride (SiN) strip waveguide configuration, operating within the C-band spectrum. Our design was optimized using AI-enhanced RSoft-CAD simulations that combined the Beam Propagation Method (BPM) and Finite-Difference Time-Domain (FDTD) techniques, integrated with convolutional neural network (CNN) machine learning algorithms. The simulation results demonstrate that the proposed device efficiently transmits four channels with 10 nm spacing in the C-band, showing low power loss ranging from 1.98-2.35 dB, a broad bandwidth of 7.68-8.08 nm, and excellent crosstalk suppression between 20.8-23.4 dB. Leveraging the low refractive index of SiN, we achieve ultra-low back reflection of 40.58 dB without requiring specialized angled MMI designs, which are often necessary in Si-based MMI technology. Consequently, this SiN-based MMI demultiplexer provides an effective solution for DWDM systems, enabling high data transmission rates with minimal back reflection in optical communication networks.

Utilizing machine learning to enhance performance of thin-film solar cells based on Sb2(S x Se1-x )3: investigating the influence of material properties.

Antimony chalcogenides (Sb2(S x Se1-x )3) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb2(S,Se)3)-based thin-film solar cell (TFSC), where tungsten disulfide (WS2) and cuprous oxide (Cu2O) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage (V oc), short-circuit current (J sc), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu2O/Sb2(S,Se)3/WS2/FTO/Al TFSC. The optimized physical parameters for the Sb2(S,Se)3 TFSC lead to impressive PV outputs with an efficiency of 30.18%, V oc of 1.02 V, J sc of 33.65 mA cm-2, and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient (R 2) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.

English translation evaluation method based on BP neural network

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301 respectively, which is much higher than Google translation. A model of machine translation evaluation based on neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

Multimodal Machine Translation

In recent years, neural network machine translation, especially in the field of multimodality, has developed rapidly. It has been widely used in natural languages processing tasks such as event detection and sentiment classification. The existing multimodal neural network machine translation is mostly based on the autoencoder framework of the attention mechanism, which further integrates spatial-visual features. However, due to the ubiquitous lack of corpus and the semantic interaction between multimodalities, the quality of machine translation is difficult to guarantee. Therefore, this paper proposes a multi-modal machine translation model that integrates external linguistic knowledge. Specifically, on the encoder side, we adopt the pre-trained Bert model to be used as an additional encoder to integrate with the original text encoder and picture encoder. Under the cooperation of the three encoders, a better text representation and picture representation at the source end is generated. Besides, the decoder decodes and generates a translation based on the image and text representation of the source. To sum up, this paper studies the visual-text semantic interaction on the encoder side and the visual-text semantic interaction on the decoder side, and further improves the quality of translation by introducing external linguistic knowledge. We compared the performance of the multimodal neural network machine translation model with pre-trained Bert and other baseline models in English German translation tasks on the multi30k data sets. The results show that the model can significantly improve the quality of multimodal neural network machine translation, which also verifies the importance of integrating external knowledge and visual text semantic interaction.

Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals.

Machine learning is becoming a valuable tool in the characterisation and property prediction of liquid crystals. It is thus worthwhile to be aware of the possibilities but also the limitations of current machine learning algorithms. In this study we investigated a phase sequence of isotropic - fluid smecticA - hexatic smectic B - soft crystal CrE - crystalline. This is a sequence of transitions between orthogonal phases, which are expected to be difficult to distinguish, because of only minute changes in order. As expected, strong first order transitions such as the liquid to liquid crystal transition and the crystallisation can be distinguished with high accuracy. It is shown that also the hexatic SmB to soft crystal CrE transition is clearly characterised, which represents the transition from short- to long-range order. Limitations of convolutional neural networks can be observed for the fluid to hexatic SmA to SmB transition, where both phases exhibit short-range ordering.

A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows

An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM to accurately capture phase interfaces and the advantages of the LBM for easy handling of mesoscopic multi-component flow fields. Taking a single vacuole rising as an example, the input and output sets of the machine learning model are constructed using the FTM’s flow field, such as the velocity and position data from phase interface markers. Such datasets are used to train the Bayesian-Regularized Back Propagation Neural Network (BRBPNN) machine learning model to establish the corresponding relationship between the phase interface velocity and the position. Finally, the trained BRBPNN neural network is utilized within the multi-relaxation LBM pseudo potential model flow field to predict the phase interface position, which is compared with the FTM simulation. It was observed that the BRBPNN-predicted interface within the LBM exhibits a high degree of consistency with the FTM-predicted interface position, showing that the BRBPNN model is feasible and satisfies the accuracy requirements of the FT-LB coupling model.

Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments

Skin disease is a common health problem in Islamic boarding school environments. This disease can spread quickly among students due to close contact and sharing the same facilities. Preventing the spread of skin diseases is a top priority in efforts to maintain the health and welfare of students in Islamic boarding schools. In this research, we propose the use of machine learning techniques to predict skin diseases in Islamic boarding school students. The main goal of this research is to develop a predictive model that can help identify skin diseases quickly and accurately. It is hoped that this will enable the prevention of the spread of skin diseases in the Islamic boarding school environment. The method used in this research involves the following steps: skin disease image data collection, data processing and cleaning, feature extraction from patient data, and machine learning model training and evaluation. We will use a Convolutional Neural Network (CNN) machine learning algorithm to build a predictive model. The dataset used in this research consists of images of melanoma, acne and acne skin diseases. In addition, validation will be carried out using data that has never been seen before to test the performance of the predictive model. It is hoped that the results of this research can make a significant contribution in preventing the spread of skin diseases in the Islamic boarding school environment. With accurate predictive models, health workers in Islamic boarding schools can take appropriate preventive measures to control skin diseases effectively. Apart from that, this research can also be a basis for developing a health information system that supports preventive measures for skin diseases in Islamic boarding schools more widely.

Development of NaCl-MgCl2-CaCl2 Ternary Salt for High-Temperature Thermal Energy Storage Using Machine Learning.

NaCl-MgCl2-CaCl2 eutectic ternary chloride salts are potential heat transfer and storage materials for high-temperature thermal energy storage. In this study, first-principles molecular dynamics simulation results were used as a data set to develop an interatomic potential for ternary chloride salts using a neural network machine learning method. Deep potential molecular dynamics (DPMD) simulations were performed to predict the microstructure and thermophysical properties of the NaCl-MgCl2-CaCl2 ternary salt. This work reveals that DPMD simulations can accurately calculate the microstructure and thermophysical properties of ternary chloride salts. The association strength of chloride ions and cations follows the order of Mg2+ > Ca2+ > Na+, and the coordination number decreases gradually with increasing temperature, indicating a progressively looser and more disordered molten structure. Furthermore, thermophysical properties, such as density, specific heat capacity, thermal conductivity, and viscosity, are in good agreement with the experimental measurements. Machine learning molecular dynamics will provide a feasible multivariate molten salt exploration method for the design of next-generation solar power plants and thermal energy storage systems.

Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: A scope and performance analysis

To compare the Artificial Intelligence based model & conventional technique for prediction of extraction in orthodontic treatment plan. A comparative study was conducted on total 700 patients, who were divided into training set and testing set based on simple random sampling by means of computer generated random numbers. The photographs of the 630 patients [training set] along with the treatment plan finalized for them based on Arch Perimeter & Carey’s Analysis, was fed in the AI model [convolutional neural network (ResNet-50)] in order to train it for the stipulated function of eventually predicting the treatment plan in the testing set [70 patients], based on the input of the right profile photographs. The accuracy of measurement of the parameters of these seventy test set patients by the machine learning model relative to the manual method was compared eventually. Using the Statistical Package for Social Sciences, the acquired data was statistically analyzed, and p <0.05 was deemed statistically significant. The normality of the data was examined using the Shapiro-Wilks test and the Kolmogorov-Smirnov test. Depending on the collected data and normality assessed, appropriate reliability was estimated. The analysis of 70 test patients showed that 65.12% of the total extraction cases and 62.96% of the total non-extraction cases (as predicted by the AI model) were in agreement with the results of the model analysis.It is suggested that the present AI model can further be developed in order to improve the accuracy of prediction.