Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the a... [more]

Soil and groundwater systems have natural attenuation potential to degrade or detoxify contaminants due to biogeochemical processes. However, such potential is rarely incorporated into active remediation strategies, leading to over-remediation at many remediation sites. Here, we propose a framework for designing and searching optimal remediation strategies that fully consider the combined effects of active remediation strategies and natural attenuation potentials. The framework integrates machine-learning and process-based models for expediting the optimization process with its applicability demonstrated at a field site contaminated with arsenic (As). The process-based model was employed in the framework to simulate the evolution of As concentrations by integrating geochemical and biogeochemical processes in soil and groundwater systems under various scenarios of remedial activities. The simulation results of As concentration evolution, remedial activities, and associated remediation c... [more]

For locating the source of leaking gas in various engineering fields, several issues remain in the immediate estimation of the location of diffusion sources from limited observation data, because of the nonlinearity of turbulence. This study investigated the practical applicability of diffusion source-location prediction using a convolutional neural network (CNN) from leaking gas instantaneous distribution images captured by infrared cameras. We performed direct numerical simulation of a turbulent flow past a cylinder to provide training and test images, which are scalar concentration distribution fields integrated along the view direction, mimicking actual camera images. We discussed the effects of the direction in which the leaking gas flows into the camera’s view and the distance between the camera and the leaking gas on the accuracy of inference. A single learner created by all images provided an inference accuracy exceeding 85%, regardless of the inflow direction or the distance b... [more]

Patient-centered communication (PCC) quality is critical to increasing the quality of patient-centered care. Based on the nationally representative data of the Health Information National Trends Survey (HINTS) 2019−2020 (N = 4593), this study combined four machine learning methods, namely, Generalized Linear Models (GLM), Random Forests (Random Forests), Deep Neural Networks (Deep Learning), and Gradient Boosting Machines (GBM), to identify important PCC predictors through variable importance metrics. Fifteen variables were identified as important predictors, involving multiple dimensions, such as individual sociodemographic characteristics, health-related factors, and individual living habits. Among them, four novel potential associated variables are included, an individual’s level of verbal expression, exercise habits, etc., which significantly impacted respondents’ perceived PCC quality. This study revealed the value of combining feature selection with machine learning approaches to... [more]

The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising,... [more]

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees we... [more]

Because ships are typically operated for more than 25 years after construction, they can be considered mobile factories that require economic maintenance before being scrapped. Therefore, for stable and efficient ship operation, continuous maintenance systems and processes are required. Ships cannot be operated when defects or failures occur in any of the numerous systems configured in them, and research is urgently needed to apply predictive maintenance to propulsion engines with high maintenance costs using machine learning. Therefore, this study analyzes the operation and control characteristics of the propulsion engine, acquires engine data from the alarm monitoring system of the ship in operation, and then preprocesses the data by constructing a data preprocessing algorithm that incorporates the engine control characteristics. In addition, principal component analysis and K-nearest neighbors were used to check whether preprocessing data were classified based on engine control char... [more]

Blockchain with its numerous advantages is often considered a foundational technology with the potential to revolutionize a wide range of application domains, including enterprise applications. These enterprise applications must meet several important criteria, including scalability, performance, and privacy. Enterprise blockchain applications are frequently constructed on private blockchain platforms to satisfy these criteria. Hyperledger Fabric is one of the most popular platforms within this domain. In any privacy blockchain system, including Fabric, every organisation needs to utilise a peer node (or peer nodes) to connect to the blockchain platform. Due to the ever-increasing size of blockchain and the need to support a large user base, the monitoring and the management of different resources of such peer nodes can be crucial for a successful deployment of such blockchain platforms. Unfortunately, little attention has been paid to this issue. In this work, we propose the first-eve... [more]

The present work discusses the development and application of a machine-learning-based model to predict the enthalpy of combustion of various oxygenated fuels of interest. A detailed dataset containing 207 pure compounds and 38 surrogate fuels has been prepared, representing various chemical classes, namely paraffins, olefins, naphthenes, aromatics, alcohols, ethers, ketones, and aldehydes. The dataset was subsequently used for constructing an artificial neural network (ANN) model with 14 input layers, 26 hidden layers, and 1 output layer for predicting the enthalpy of combustion for various oxygenated fuels. The ANN model was trained using the collected dataset, validated, and finally tested to verify its accuracy in predicting the enthalpy of combustion. The results for various oxygenated fuels are discussed, especially in terms of the influence of different functional groups in shaping the enthalpy of combustion values. In predicting the enthalpy of combustion, 96.3% accuracy was ac... [more]

Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher... [more]

Considering the importance of environmental protection and renewable energy resources, particularly solar energy, the present study investigates the temperature control of a solar panel using a nanofluid (NFD) flow with eco-friendly nanoparticles (NPs) and a phase change material (PCM). The PCM was used under the solar panel, and the NFD flowed through pipes within the PCM. A number of straight fins (three fins) were exploited on the pipes, and the output flow temperature, heat transfer (HTR) coefficient, and melted PCM volume fraction were measured for different pipe diameters (D_Pipe) from 4 mm to 8 mm at various time points (from 0 to 100 min). Additionally, with the use of artificial intelligence and machine learning, the best conditions for obtaining the lowest panel temperature and the highest output NFD temperature at the lowest pressure drop have been determined. While the porosity approach was used to model the PCM melt front, a two-phase mixture was used to simulate NFD flow.... [more]

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are iden... [more]

Severe blood leakage resulting from the detachment of dialysis tubing is often difficult to detect by nurses in busy clinics. This paper presents a flexible blood leakage detection system featuring a ring-light sensor array with an operating wavelength of 500−700 nm, which is held in place by the gauze covering the dialysis puncture site. A ring-light sensor is connected to a bidirectional hetero-associative memory network, which interprets detected changes in signal strength, the output signal of which is transmitted via WiFi to a server at the nursing station where a machine learning algorithm determines whether blood leakage has occurred. The compact design of this early warning system greatly enhances the comfort and mobility of patients undergoing dialysis. The efficacy of the proposed system was demonstrated in experiments involving artificial blood.

Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from both s... [more]

Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer... [more]

Efficiently embedding and/or integrating mechanistic information within data-driven models is essentially the only approach to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive c... [more]

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge compu... [more]

In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training... [more]

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our... [more]

In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learni... [more]

This paper deals with three-objective optimization, using machine-learning-based surrogate modeling to improve the hydraulic performances of a two-vane pump for wastewater treatment. For analyzing the internal flow field in the pump, steady Reynolds-averaged Navier-Stokes equations were solved with the shear stress transport turbulence model as a turbulence closure model. The radial basis neural network model, which is an artificial neural network, was used as the surrogate model and trained to improve prediction accuracy. Three design variables related to the geometry of blade and volute were selected to optimize concurrently the objective functions with the total head and efficiency of the pump and size of the waste solids. The optimization results obtained by using the model showed highly accurate prediction values, and compared with the reference design, the optimum design provided improved hydraulic performances.

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classi... [more]

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have be... [more]

The large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The tra... [more]

This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the valid... [more]