Reliable Machine Learning Research Articles

Editorial: Special Issue on Emerging Techniques in Trusted and Reliable Machine Learning

Editorial: Special Issue on Emerging Techniques in Trusted and Reliable Machine Learning

Winners of Nikolaos Bourbakis Award for 2022

International Journal on Artificial Intelligence ToolsVol. 32, No. 03, 2382001 (2023) AnnouncementNo AccessWinners of Nikolaos Bourbakis Award for 2022https://doi.org/10.1142/S0218213023820018Cited by:0 Next This article is part of the issue: Special Issue on Emerging Techniques in Trusted and Reliable Machine Learning Guest Editor: Muhammad Attique Khan AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out Notable Titles in Artificial Intelligence. FiguresReferencesRelatedDetails Recommended Vol. 32, No. 03 Metrics History PDF download

CoolPINNs: A physics-informed neural network modeling of active cooling in vascular systems

Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during the design and operational phases of these engineered systems. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. This paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation—called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs since the approach is meshless. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the most significant utility of the current framework.

Reliable machine learning for the shear strength of beams strengthened using externally bonded FRP jackets

All over the world, shear strengthening of reinforced concrete elements using external fiber-reinforced polymer jackets could be used to improve building sustainability. However, reports issued by the American Concrete Institute called for heavy scrutiny before actual field implementation. The very limited number of proposed shear equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine-learning techniques are being implemented successfully to develop strength models for complex problems including shear, flexure, and torsion. This study aims to provide a reliable machine-learning model for reinforced concrete beams strengthened in shear using externally reinforced fiber polymer sheets. The proposed model was developed and validated against the experimental database and the very limited models in existing literature. The model showed better agreement with the experimentally measured strength compared to the previous models, which accounted for the effect of various parameters including but not limited to: the element geometry, strengthening details, and configurations. The model could guide the further developments of design codes and mechanical models.

On Development of Reliable Machine Learning Systems Based on Machine Error Tolerance of Input Images

With the rapid development of machine learning technologies, more and more practical applications arise. Representative machine learning techniques that receive much attention include object detection and image classification, which can be applied to many applications, such as self-driving cars, traffic flow calculation, and detection of product defects in factories. In this article, we investigate tolerability of errors for input images in machine learning systems and develop a generic reliability enhancement methodology. This work is based on our preliminary studies on image classification, but we put major focuses in object detection applications and the comprehensive comparisons to the prior studies. The first one error-tolerability test method to support reliability enhancement of object detection applications is then proposed based on careful error-tolerability examination of input images. The experimental results show that the test accuracy of this method can achieve 93.06%, which is the state-of-the-art. One special advantage of the proposed method is that unlike the previous error-tolerance methods in the literature, no golden reference data are required for acceptability determination by the proposed method. Hence, on-line testing can be supported. Our method is also implemented and validated in hardware. The results show that the hardware performance is up to 192 frames per second (FPS), which can thus also support real-time operations.

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.

Machine learning framework for predicting the low cycle fatigue life of lead-free solders

This study explores an efficient and reliable machine learning framework for determining the low cycle fatigue life of lead-free solders, which does not necessarily separately test different series of lead-free solder. With 1387 datasets from the published experiments and formulae, five mainstream machine learning models to date are adopted for the first time to predict the low cycle fatigue life for four different series of tin-based solders by considering the composition, loading and geometry factors. Based on feature importance and Shapley values, it is confirmed that the Boosting model is capable of capturing the nonlinear relationships of factors to influence the low cycle fatigue life of lead-free solder by greatly emphasizing the effects of plastic strain amplitude and temperature.

PrecTime: A deep learning architecture for precise time series segmentation in industrial manufacturing operations

The fourth industrial revolution creates ubiquitous sensor data in production plants. To generate maximum value out of these data, reliable and precise time series-based machine learning methods like temporal neural networks are needed. This paper proposes a novel sequence-to-sequence deep learning architecture for time series segmentation called PrecTime which tries to combine the concepts and advantages of sliding window and dense labeling approaches. The general-purpose architecture is evaluated on a real-world industry dataset containing the End-of-Line testing sensor data of hydraulic pumps. We are able to show that PrecTime outperforms five implemented state-of-the-art baseline networks based on multiple metrics. The achieved segmentation accuracy of around 96% shows that PrecTime can achieve results close to human intelligence in operational state segmentation within a testing cycle.

Automated extraction of pod phenotype data from micro-computed tomography.

Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial. In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape (Brassica napus) mature pods. With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds. This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer

Mechanical properties of fine grain nanocomposites differ from those of conventional composites due to the in situ effect caused by the addition of nanoparticle reinforcement and the complexity of strengthening mechanisms, which make their prediction using conventional analytical and numerical model is relatively difficult. Therefore, this work presents a rapid reliable machine learning model based on long-short term memory model modified with beluga whale optimizer to predict the mechanical properties of ultrafine grain Al-TiO2 nanocomposite manufactured using accumulative roll bonding (ARB). The mechanical properties were evaluated using tensile tests and correlated with the composite microstructure and hardness. Experimentally, it was demonstrated that the tensile strength increases with increasing the number of ARB passes until a plateau was achieved due to the uniform distribution of TiO2 nanoparticles inside the composite and the saturation of grain refinement in the Al matrix. The maximum tensile achieved was 270 MPa for composite containing 3% TiO2 nanoparticles after 5 ARB passes compared to 90.5 MPa for the raw Al. The proposed model was able to predict the yield and ultimate strengths, elongation, and hardness for all the produced composites tested with excellent accuracy reaching R2 equal 0.9955, 0.9952, 0.9859, and 0.9975, respectively, which is way better than other models.

Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes

Abstract There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification.

Identifying an optimal machine learning model generated circulating biomarker to predict chronic postoperative pain in patients undergoing hepatectomy.

Chronic postsurgical pain (CPSP) after hepatectomy is highly prevalent and challenging to treat. Several risk factors have been unmasked for CPSP after hepatectomy, such as acute postoperative pain. The current secondary analysis of a clinical study sought to extend previous research by investigating more clinical variables and inflammatory biomarkers as risk factors for CPSP after hepatectomy and sifting those strongly related to CPSP to build a reliable machine learning model to predict CPSP occurring. Participants included 91 adults undergoing hepatectomy who was followed 3 months postoperatively. Twenty-four hours after surgery, participants completed numerical rating scale (NRS) grading and blood sample collecting. Three months after surgery, participants also reported whether CPSP occurred through follow-up. The Random Forest and Support Vector Machine models were conducted to predict pain outcomes 3 months after surgery. The results showed that the SVM model had better performance in predicting CPSP which consists of acute postoperative pain (evaluated by NRS) and matrix metalloprotease 3 (MMP3) level. What's more, besides traditional cytokines, several novel inflammatory biomarkers like C-X-C motif chemokine ligand 10 (CXCL10) and MMP2 levels were found to be closely related to CPSP and a novel spectrum of inflammatory biomarkers was created. These findings demonstrate that the SVM model consisting of acute postoperative pain and MMP3 level predicts greater chronic pain intensity 3 months after hepatectomy and with this model, intervention administration before CPSP occurs may prevent or minimize CPSP intensity successfully.

A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data.

The major mechanisms of dementia and cognitive impairment are vascular and neurodegenerative processes. Early diagnosis of cognitive impairment can facilitate timely interventions to mitigate progression. This study aims to develop a reliable machine learning (ML) model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairment in a multi-ethnic Asian population. The study consisted of 911 participants from the Epidemiology of Dementia in Singapore study (aged 60- 88 years, 49.6% male). Three ML classifiers, logistic regression, support vector machine, and gradient boosting machine, were developed. Prediction results of independent classifiers were combined in a final ensemble model. Model performances were evaluated on test data using F1 score and area under the receiver operating curve (AUC) methods. Post modelling, SHapely Additive exPlanation (SHAP) was applied on the prediction results to identify the predictors that contribute most to the cognitive impairment prediction. The final ensemble model achieved a F1 score and AUC of 0.87 and 0.80 respectively. Accuracy (0.83), sensitivity (0.86), specificity (0.74) and predictive values (positive 0.88 negative 0.72) of the ensemble model were higher compared to the independent classifiers. Age, ethnicity, highest education attainment and neuroimaging markers were identified as important predictors of cognitive impairment. This study demonstrates the feasibility of using ML tools to integrate multiple domains of data for reliable diagnosis of early cognitive impairment. The ML model uses easy-to-obtain variables and is scalable for screening individuals with a high risk of developing dementia in a population-based setting.

Retracted: An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes.

[This retracts the article DOI: 10.1155/2022/9882288.].

Shaping the future of tunneling with data and emerging technologies

Abstract The increase in global population and urbanization is presenting significant challenges to society: space is becoming increasingly scarce, demand is exceeding capacity for deteriorating infrastructure, transportation is fraught with congestion, and environmental impacts are accelerating. Underground space, and particularly tunnels, has a key role to play in tackling these challenges. However, the cost, risk, uncertainty, and complexity of the tunneling process have impeded its growth. In this paper, we envision several technological advancements that can potentially innovate and transform the mechanized tunneling industry, including artificial intelligence (AI), autonomous, and bio-inspired systems. The proliferation of AI may assist human engineers and operators in making informed decisions systematically and quantitatively based on massive real-time data during tunneling. Autonomous tunneling systems may enable precise and predictable tunneling operations with minimal human intervention and facilitate the construction of massive and large-scale underground infrastructure projects that were previously challenging or unfeasible using conventional methods. Bio-inspired systems may provide valuable references and strategies for more efficient tunneling design and construction concepts. While these technological advancements can offer great promise, they also face considerable challenges, such as improving accessibility to and shareability of tunneling data, developing robust, reliable, and explainable machine learning systems, as well as scaling the mechanics and ensuring the applicability of bio-inspired systems from the prototype level to real-world applications. Addressing these challenges is imperative to ensure the successful implementation of these innovations for future tunneling.

Classification and Prediction of Skyrmion Material Based on Machine Learning.

The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology. The database of 196 materials, including 64 skyrmions, was established and predicted based on machine learning. A variety of intrinsic features are classified to optimize the model, and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials, such as support vector machines, k-nearest neighbor, and ensembles of trees. It is found that magnetic materials can be more accurately divided into skyrmion and non-skyrmion classes by using the classification of electronic layer. Note that the rare earths are the key elements affecting the production of skyrmion. The accuracy and reliability of random undersampling bagged trees were 87.5% and 0.89, respectively, which have the potential to build a reliable machine learning model from small data. The existence of skyrmions in LaBaMnO is predicted by the trained model and verified by micromagnetic theory and experiments.

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks.

Evaluating the Effectiveness of Interactive Video Learning by Examining Machine Learning Classifiers Models: Graduate Students’ Perspectives

Important elements had an impact on how traditional learning was implemented and motivated researchers to develop Interactive Video Learning Effectiveness (IVL-E). These variables range from price to learning-environment to learner perspective, among others. This paper’s major objectives are to: (i) assess the effectiveness of Interactive Video Learning (IVL-E) using classification techniques and considering graduate students’ viewpoints, (ii) establish appropriate classification parameters to choose the optimum classifier model, and (iii) review prior works pertaining to IVL-E assessment. The study dataset is a sample of 63 datapoints randomly chosen by a survey performed at the College of Education, University of Bisha, Bisha, Saudi Arabia. A total of 123 registered postgraduate students made up the study population when using Google’s online questionnaire method, after all the respondents voluntarily agreed to fill out and submit the questionnaire. This study develops a reliable machine learning classifier’s model for classifying IVL-E. The created models use a backpropagation algorithm and are a type of multilayer classification perceptron. The best classification output was “interactive video learning performance measure”, which provided the highest results under: 1) support vector machine-based classifier (SVC), 2) decision tree (DT), and 3) light gradient-boosting machine classifier (lgb.LGBMClassifier). Regarding classification measures like balanced accuracy (high BCCR = 0.875), balanced error rate (low BER = 0.125), and optimization precision (highest OP = 0.999), our models performed extremely well according to the literature review.

Applications of Machine Learning for Fake News Detection in Social Networks

The value of online media for getting news is questionable. People seek out and devour news from online media because it is convenient, inexpensive, and widely disseminated. In contrast, it facilitates the widespread distribution of "counterfeit news," or news of lower quality that includes fabricated data. Many people and institutions are negatively impacted by the widespread circulation of false information. As a result, detecting fake news via social media has emerged as a topic of interest for academics. Searching for and reading the news is becoming increasingly convenient as a result of the widespread availability, quick expansion, and widespread dissemination of traditional news outlets and social media. Nowadays, there is a plethora of information that can be found on social media, and it can be difficult to tell what is real and what is not. The distribution costs of releasing news via social media are inexpensive, and anyone can do it. The widespread circulation of false information could have devastating effects on both individuals and communities. Developing a reliable machine learning method for spotting fake news is the focus of this work.

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments.

Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successful combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations will lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by ∼10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, still in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIXE measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in turn brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.