Application Of Machine Learning Approaches Research Articles

Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.

Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.

Application of machine learning approach (artificial neural network) and shrinking core model in cobalt (II) and copper (II) leaching process

The leaching laboratory experiment uses the artificial neural network (ANN) to predict and evaluate copper and cobalt recovery. This study aimed to evaluate the efficacy of using the shrinking core model in conjunction with an artificial neural network (ANN) as part of a machine learning strategy to improve the leaching process of cobalt (II) and copper (II). The numerous factors in the leaching process, such as acid concentration, leaching time, temperature, soil-to-solution ratio, and stirring speed, are adjusted using an ANN with several layers, feed-forward, and back-propagation learning methods. These variables are in charge of the high cobalt recovery during the reduced sulfuric acid leaching procedure. The ANN algorithm has 10 hidden layers, 5 input variables describing the leaching parameters, and two neurons as output layers corresponding to copper and cobalt leaching recovery. The optimum conditions were found to be acid concentration of 100 g/L, leaching duration 120 min, temperature 55 °C, soil-to-solution ratio of 1:40 g/mL, and stirring speed 300 rpm. The optimized trained neural networks tested, trained, and validated steps are represented by R 2 values of 0.94, 0.99, 0.97, and 0.97, respectively, equating to 97.5% copper recovery and 95.4% cobalt recovery.

Application of machine learning approach for iron deficiency anaemia detection in children using conjunctiva images

Iron deficiency is commonly referred to as anaemia which is a general public health problem that normally occurs as a result of a reduction in red blood cells which is common in developing countries such as Africa. In this study, machine learning algorithms such as CNN, k-NN, Naïve Bayes, Decision Tree and SVM were utilized for the study to detect anaemia in children using conjunctiva images. The images were segmented into their various CIELAB colour space components and the ROI from each image was retrieved. The dataset was split randomly into 70:10:20, which were then used to train, validate, and test the models, as appropriate. The CNN achieved the highest accuracy (98.45 %). The findings of this study demonstrate that non-invasive techniques are essential for detecting anaemia in children. This study deploys a cost-effective mechanism, and result-orientated, to detect anaemia in developing communities where health facilities, resources, and personnel are scarce.

The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia

Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year’s age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, type of cooking fuel, number of family members, mothers’ educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year’s age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.

Fault detection in beam structure using adaptive immune based approach

Different structural and machine elements are used over the ages. These are subjected to various loads like static and dynamic load, temperature, corrosion etc. Due to the above-mentioned reasons, ageing of the structural elements occur. So, to enhance the designed lifetime of any structure continuous maintenance is required. One such method has been proposed in this research work and the proposed method can be employed as an online tool for the fault identification. Here dynamic analysis of structure has been conducted as the forward method to find out the modal natural frequencies related with the damage. Recently with the application of machine learning approaches and the soft computing, the damage can be detected easily. In this methodology, Clonal Section Algorithm (CSA) has been applied to find out the faults (crack locations and depth) in the structure initially. Later one such method has been developed in the concepts of adaptive immune based technique (Adaptive Clonal Section Algorithm-ACSA) which is the combination of an artificial immune (Clonal Selection Algorithm) and Regression Analysis (RA). The use of regression analysis makes the proposed method more adaptive and the residual error in the collection of vibration data is reduced. The mechanism and various steps involved in CSA, RA and ACSA are analyzed here in a precise manner. The key endeavor of this study is the development of ACSA and its implementation to condition monitoring of structure. To authenticate and check the accuracy of both the methods (CSA and ACSA), laboratory tests are carried out. The results obtained from each method are corroborated with other and found to be convergent.   

Anomaly Detection in Time Series Flight Parameter Data Using Machine Learning Approach

Abstract: Infrastructure monitoring is more crucial than ever, especially in the aviation industry. To attempt to overcome the difficulties brought on by the problems given by the exponential growth of connected devices and data volume, this study analyzes the application of machine learning approaches for anomaly identification in time series flight parameter data. The introduction of network telemetry, which automates data collecting, is presented as a remedy, however processing enormous data quantities in real-time still poses a challenge. With a focus on time-series data, the paper explores the role of machine learning in network telemetry anomaly detection. Statistical, proximity-based, deviation-based, and supervised classifiers are used to identify anomalies, or departures from predicted patterns, in flight parameter data. For a few examples of anomalous data, long short-term memory networks (LSTMs) are used. The objective is to provide an effective anomaly detection system that can process complex time series flight data and includes data purification, anomaly discovery, temporal reference, and value prediction. The methodology describes the univariate anomaly detection strategy, in which distinct models record particular patterns for each flight parameter. The findings offer new understanding of thresholds, repeated anomaly correction, and prediction errors. The results show that the method is accurate in separating instances of normal data from those containing anomalies, making it a useful tool for practical applications needing accurate anomaly identification.

An Improved Inspection Process and Machine-Learning-Assisted Bridge Condition Prediction Model

Bridges have a special place in transportation infrastructures and road networks due to their direct relationship with other places. These structures have the purpose of maintaining the traffic loads of the highway, crossing any obstacle, and performing effective communication between two destinations. Costs associated with bridge maintenance continue to be expensive due to their widespread use and stringent inspection requirements. Many researchers have been working on methods to use machine-learning (ML) techniques to forecast specific situations rather than physically checking bridges as part of the maintenance process in recent years. The practical value of the models has, however, been severely constrained by issues such relatively poor model evaluation results, unstable model performances, and the ambiguous application of established models in real-world scenarios. This work showed a thorough method of bridge condition prediction model building from feature engineering to model evaluation, along with a clear procedure of applying the produced model to actual usage, using data from the United States National Bridge Inventory (NBI) and the Adaboost algorithm. Multiple ML model assessment metrics’ findings revealed that the given model outperformed the majority of earlier studies in terms of values and stability. The case study demonstrated that there is a 30% reduction in the number of bridges that need to be inspected. This study serves as a crucial resource for the practical application of ML approaches in the forecast of the status of civil infrastructure. Additionally, it shows that boosted ML models may be a superior option as modeling algorithms advance. To explore the main influencing aspects of bridge conditions, a predictor importance analysis is also offered.

A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data

Smart grid is envisaged as a power grid that is extremely reliable and flexible. The electrical grid has wide-area measuring devices like Phasor measurement units (PMUs) deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability. The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data. This research proposes a transmission line fault detection and classification (FD&C) system based on an auto-encoder neural network. A comparison between a Multi-Layer Extreme Learning Machine (ML-ELM) network model and a Stacked Auto-Encoder neural network (SAE) is made. Additionally, the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models. With substantially shorter testing time, the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79% accuracy. The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms. To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio (SNR) ranging from 10 dB to 40 dB. The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BackgroundMachine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DesignSystematic review and meta-analyses.ParticipantsOlder adults (≥ 65 years) in any setting.InterventionMachine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.Outcome measuresStudies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.ResultsThirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 – 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 – 0.86) for mortality over 6 months, signifying good discriminatory power.ConclusionThe meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Knowledge Graphs and Their Applications in Drug Discovery.

Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.

Phylogenetic inference using Generative Adversarial Networks.

The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics. We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to fifteen taxa in the concatenation case and six taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics. phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.

A Statistical Shape Model Pipeline to Enable the Creation of Synthetic 3D Liver Data

Abstract The application of machine learning approaches in medical technology is gaining more and more attention. Due to the high restrictions for collecting intraoperative patient data, synthetic data is increasingly used to support the training of artificial neural networks. We present a pipeline to create a statistical shape model (SSM) using 28 segmented clinical liver CT scans. Our pipeline consists of four steps: data preprocessing, rigid alignment, template morphing, and statistical modeling. We compared two different template morphing approaches: Laplace-Beltrami-regularized projection (LBRP) and nonrigid iterative closest points translational (N-ICP-T) and evaluated both morphing approaches and their corresponding shape model performance using six metrics. LBRP achieved a smaller mean vertex-to-nearest-neighbor distances (2.486±0.897 mm) than N-ICP-T (5.559±2.413 mm). Generalization and specificity errors for LBRP were consistently lower than those of N-ICP-T. The first principal components of the SSM showed realistic anatomical variations. The performance of the SSM was comparable to a state-of-the-art model.

Application of machine learning approaches for modelling crack growth rates

AbstractThe scientific community has widely accepted the use of machine learning techniques to tackle complex engineering problems. Among the most intriguing problems is finding the correlation between alloy steel properties and cyclic fatigue and crack growth rate. Employing machine‐learning models can provide more robust and accurate predictive models to address such challenges. This paper presents the application of four machine learning models, namely decision trees (DT), random forest (RF), adoptive boosting (AdaBoost), and gradient boosting regression tree (GBRT) to predict the crack growth rate of steel/alloys. The study utilizes a large database gathered from literature to construct the predictive models and compares the results using various statistical metrics and graphical representation. The study's findings demonstrate the effectiveness and suitability of machine learning techniques to handle complex databases related to fatigue problems.

A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies

Industrial development has contributed to carbon emissions majorly, resulting in high concentrations of greenhouse gases (GHGs) in the environment leading to climate change phenomenon. Climate change threatens people in multiple ways: threatening food security, water scarcity, frequent and extreme weather events, the spread of diseases, economic losses and migration etc. The World Health Organization (WHO) declared that climate change is the greatest threat to global health in the 21st century. Since 1970, CO2 emission has increased by about 90 per cent, making it 78 per cent of the total greenhouse gas emission. Climate change impact, carbon emission factors and social-economic attributes make the prediction of GHG emission a very complex research problem having dynamic scenarios due to a large number of factors and impacting variables. Accurate prediction of carbon emission in such a scenario makes it one of the most important and challenging research works. Artificial intelligence and machine learning approaches are increasingly being used to study complex, dynamic environmental phenomenons with high variability of time, space and other factors. The research paper proposes hybrid machine learning models for the prediction of CO2 emissions using energy and social-economic variables. The work uses energy and socioeconomic variables from 1960 to 2018 to collate them to provide a new perspective on the application of machine learning approaches in the modelling and prediction of GHG emissions. The proposed hybrid model of principal component analysis (PCA) and machine learning approaches is compared on accuracy and efficiency and performs better than other machine learning and deep learning approaches such as Linear regression variants, random forest regression (RFR), support vector regression (SVR), recurrent neural network (RNN), long short term Memory (LSTM) and Tabnet etc. The proposed hybrid model reports MAE equal to 0.0307, RMSE equal to 0.0346, MAPE equals to 5.1447 and SMAPE equal to 5.2267. In terms of efficiency of computation, the proposed hybrid machine learning model PCA+LER took 12.4 ms better than other models. This proposed research work of correlation analysis and prediction can help policymakers and governments in the mitigation and management of carbon emissions.

Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review

Directed Energy Deposition (DED) has become very popular for repair and rapid prototyping in metal manufacturing industries. However, as an anisotropic and defect-prone process, DED’s versatility and usability are currently limited. Machine Learning (ML) has been introduced to various Additive Manufacturing (AM) fields due to its functional ability to recognize complex process-structure–property (PSP) relationships. Yet, it has only been heavily employed in applications of DED very recently. Therefore, this work focuses on describing the different aspects related to DED in terms of ML and put forward a novel approach to summarize the different applications of ML approaches in DED. The methodology intends to catalog the three main aspects of the whole scenario, such as understanding the current problem domain of DED concerning the intricate phenomena and desired outcomes, visualizing the data stream, and finally, determining the suitable ML approach for the problem. This paper provides a state-of-art review of the defined problem domain based on properties, quality, defects, and process optimization, a list of external sensors, equipment, and material type required for the experiments, the ML approaches such as Supervised and Unsupervised learning with suitable algorithms and the available data types, as well as an initial detailed groundwork to provide an insight for the prospects of DED.

Application of machine learning approaches in the computation of energy dissipation over rectangular stepped spillway

Abstract The stepped spillway of a dam is a crucial element that serves multiple purposes in the field of river engineering. Research related to flood control necessitates an investigation into the dissipation of energy over stepped spillways. Previous research has been conducted on stepped spillways in the absence of baffles, utilizing diverse methodologies. This study employs machine learning techniques, specifically support vector machine (SVM) and regression tree (RT), to assess the energy dissipation of rectangular stepped spillways incorporating baffles arranged in different configurations and operating at varying channel slopes. Empirical evidence suggests that energy dissipation is more pronounced in channels with flat slopes and increases proportionally with the quantity of baffles present. Statistical measures are employed to validate the constructed models in the experimental investigation, with the aim of evaluating the efficacy and performance of the proposed model. The findings indicate that the SVM model proposed in this study accurately forecasted the energy dissipation, in contrast to both RT and the conventional method. This study confirms the applicability of machine learning techniques in the relevant field. Notably, it provides a unique contribution by predicting energy dissipation in stepped spillways with baffle configurations.

Achieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failure.

Viral failure in people with HIV (PWH) may be influenced by multiple sociobehavioral, clinical, and context-specific factors, and supervised learning approaches may identify novel predictors. We compared the performance of two supervised learning algorithms to predict viral failure in four African countries. Cohort study. The African Cohort Study is an ongoing, longitudinal cohort enrolling PWH at 12 sites in Uganda, Kenya, Tanzania, and Nigeria. Participants underwent physical examination, medical history-taking, medical record extraction, sociobehavioral interviews, and laboratory testing. In cross-sectional analyses of enrollment data, viral failure was defined as a viral load at least 1000 copies/ml among participants on antiretroviral therapy (ART) for at least 6 months. We compared the performance of lasso-type regularized regression and random forests by calculating area under the curve (AUC) and used each to identify factors associated with viral failure; 94 explanatory variables were considered. Between January 2013 and December 2020, 2941 PWH were enrolled, 1602 had been on antiretroviral therapy (ART) for at least 6 months, and 1571 participants with complete case data were included. At enrollment, 190 (12.0%) had viral failure. The lasso regression model was slightly superior to the random forest in its ability to identify PWH with viral failure (AUC: 0.82 vs. 0.75). Both models identified CD4 + count, ART regimen, age, self-reported ART adherence and duration on ART as important factors associated with viral failure. These findings corroborate existing literature primarily based on hypothesis-testing statistical approaches and help to generate questions for future investigations that may impact viral failure.

A SURVEY OF BRAIN TUMOR DETECTION USING MACHINE LEARNING TECHNIQUES

Brain tumors are one of the most critical health concerns worldwide, requiring accurate and timely diagnosis for effective treatment planning. The advent of machine learning techniques has shown promising results in automating the detection of brain tumors, aiding medical professionals in making informed decisions. This paper presents a comprehensive review of the application of machine learning approaches for brain tumor detection. The review encompasses various machine learning algorithms and methodologies employed in brain tumor detection and the challenges associated with brain tumor detection. The review concludes by summarizing the current state-of-the-art in machine learning-based brain tumor detection, identifying potential areas for improvement, and discussing future research directions. In conclusion, machine learning approaches have demonstrated significant potential in automating brain tumor detection, offering a valuable tool for radiologists and clinicians. However, further advancements and collaborations between machine learning experts and medical professionals are crucial to develop hybrid techniques and reliable models that can assist in early and accurate brain tumor diagnosis.

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.