Adaptive Machine Research Articles

Retraction Note: Enhanced image diagnosing approach in medicine using quantum adaptive machine learning techniques

Retraction Note: Enhanced image diagnosing approach in medicine using quantum adaptive machine learning techniques

Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.MethodsClinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.ResultsBART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.ConclusionsBART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.

Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment

Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment

Enabling scalable and adaptive machine learning training via serverless computing on public cloud

In today’s production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource demands. Serverless computing is an emerging cloud paradigm that provides transparent resource management and scaling for users and has the potential to revolutionize the routine of ML design and training. However, hosting modern ML workflows on existing serverless platforms has non-trivial challenges due to their intrinsic design limitations such as stateless nature, limited communication support across function instances, and limited function execution duration. These limitations result in a lack of an overarching view and adaptation mechanism for training dynamics, and an amplification of existing problems in ML workflows.To address the above challenges, we propose SMLT, an automated, scalable and adaptive serverless framework on public cloud to enable efficient and user-centric ML design and training. SMLT employs an automated and adaptive scheduling mechanism to dynamically optimize the deployment and resource scaling for ML tasks during training. SMLT further enables user-centric ML workflow execution by supporting user-specified training deadline and budget limit. In addition, by providing an end-to-end design, SMLT solves the intrinsic problems in public cloud serverless platforms such as the communication overhead, limited function execution duration, need for repeated initialization, and also provides explicit fault tolerance for ML training. SMLT is open-sourced and compatible with all major ML frameworks. Our experimental evaluation with large, sophisticated modern ML models demonstrates that SMLT outperforms the state-of-the-art VM-based systems and existing public cloud serverless ML training frameworks in both training speed (up to 8×) and monetary cost (up to 3×).

Enhancing financial fraud detection using adaptive machine learning models and business analytics

Financial fraud continues to be a critical threat to businesses and economies worldwide, necessitating advanced detection techniques. This paper reviews the role of adaptive machine learning (ML) models and business analytics in enhancing fraud detection systems. Traditional fraud detection methods often fall short in addressing the complexity and evolving nature of fraudulent activities, making adaptive ML models, such as decision trees and neural networks, more effective in identifying subtle patterns in large datasets. Organizations can refine ML models by integrating business analytics, ensuring real-time detection and continuous improvement of fraud detection systems. The paper also explores the challenges in deploying these technologies, including data privacy concerns and model accuracy, as well as the potential impact of emerging technologies such as blockchain and quantum computing. Future directions emphasize improving the interpretability of ML models and leveraging multi-modal data for a more holistic approach to fraud prevention. The synergy between adaptive machine learning and business analytics promises a more resilient and effective framework for combating financial fraud.

Adaptive virtual machine placement: a dynamic approach for energy-efficiency, QoS enhancement, and security optimization

Adaptive virtual machine placement: a dynamic approach for energy-efficiency, QoS enhancement, and security optimization

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

ABSTRACT Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and Machine Learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X 1: 62–72% v/v), temperature (X 2: 38–48°C), ultrasonic-exposure time (X 3: 12–24 min), and particle size (X 4: 2–5 mm) were optimized. The maximum yield of phytocompounds was achieved at X 1 = 67%, X 2 = 43°C, X 3 = 18 min, and X 4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y1 = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y2 = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y3 = 66.41%), and %ABTS*sc (y4 = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L.

Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes

Background: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to compare three update strategies for predicting severe COVID-19 outcomes post-diagnosis: 'naive' (a single initial model), 'frequent' (periodic retraining), and 'context-driven' (retraining informed by clinical insights). The goal is to determine the most effective timing and approach for adapting algorithms to evolving disease dynamics and emerging data. Methods: A dataset of 1.11 million COVID-19 patients from diverse U.S. regions was used to develop and validate an XGBoost algorithm for predicting severe outcomes upon diagnosis. Data included patient demographics, vital signs, comorbidities, and immunity-related factors (prior infection and vaccination status) from January 2007 to November 2021. The study analyzed the performance of the three update strategies from March 2020 to November 2021. Results: Predictive features changed over the pandemic, with comorbidities and vitals being significant initially, and geography, demographics, and immunity-related variables gaining importance later. The 'naive' strategy had an average AUC of 0.77, the 'frequent' strategy-maintained stability with an average AUC of 0.81, and the 'context-driven' strategy averaged an AUC of 0.80, outperforming the 'naive' strategy and aligning closely with the 'frequent' strategy. Conclusion: A context-driven approach, guided by clinical insights, can enhance predictive performance and offer cost-effective solutions for dynamic public health challenges. These findings have significant implications for efficiently managing healthcare resources during evolving disease outbreaks.

Tool path modification based on deformation mapping relationship for the adaptive machining of aero-engine blade

Tool path modification based on deformation mapping relationship for the adaptive machining of aero-engine blade

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

Enhanced error correction for adaptive electrochemical machining of eccentric bushings

Enhanced error correction for adaptive electrochemical machining of eccentric bushings

Adaptive support vector machine for time-variant failure probability function estimation

Time variant reliability analysis introduces additional complexity due to the inclusion of time. When the time-variant failure probability function (TFPF) of the structure is of interest, it inherently involves sequential evaluations of the failure probabilities of series systems varied with time in discretized space, posing a challenge to reliability analysis. An efficient approach for the evaluation of the TFPF, called ‘Time-dependent Adaptive Support Vector Machine combined with Monte Carlo Simulation’ (TASVM-MCS), is presented to reduce the corresponding computational cost. Based on the samples from Monte Carlo simulation (MCS), an iterative strategy is proposed to actively extract the most valuable sample points from the sample pool and iteratively update the support vector machine (SVM) model. In particular, an active learning function is proposed to take into account the diversity of samples and time simultaneously. In this way, the built SVM will be more suitable for the evaluation of TFPF other than a point-wise failure probability. The proposed TASVM-MCS method is relatively less sensitive to the dimensionality of the input variables, making it a powerful and promising approach for time-variant reliability computations. Four representative examples are given to demonstrate the significant effectiveness and efficiency of the proposed method.

Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation

Virtualization is the process of sharing physical machine resources into multiple Virtual Machines (VMs) for the effective utilization of resources. The major issues are in handling physical resources such as high demand of resources, infrastructure issues and operational issues. Also violating Service Level Agreement (SLA) is another problem to lead the violations. In this case, VM consolidation method is used to migrate the VMs and optimize the efficient way to use the resources. So we get minimized energy consumptions. Existing methods has increased energy consumption and overhead issues. In this paper we propose adaptive backtracking methods to provide VM consolidation based on less energy consumptions. We use Adaptive Hill Climbing and Pursuit algorithms to consolidate the VMs and use the VMs with efficient energy consumption. We implement this simulations using Matlab in Hybrid Cloud Environment. The performance factors are obtained with less energy consumption and compare the results with existing methods. Overall our proposed system is achieving 95% of accuracy index with lower energy consumption such as 28%, 30% and 32% with multiple VMs consolidations.

Federated Incremental Learning algorithm based on Topological Data Analysis

Federated learning is a distributed learning approach aimed at preserving user’s data privacy, while incremental learning is an adaptive machine learning method that enables continuous learning of new data. The combination of these two approaches into Federated Incremental Learning (FIL) algorithms brings together their respective advantages. However, the existing federated incremental learning still faces two main challenges: (1) catastrophic forgetting of previous knowledge by local models when adapting to incremental tasks, and (2) limited capability of the server to capture critical features during federated aggregation. To address these challenges, this paper proposes a federated incremental learning algorithm based on Topological Data Analysis (TDA). Firstly, the algorithm extracts topological features from the input information and designs a Topological Stability Loss (TSL) to mitigate catastrophic forgetting of previous knowledge by local models. Secondly, a feature attention mechanism is utilized to select appropriate attention weights for each local model, enhancing the recognition performance of the global model. The experimental results on publicly available datasets, namely CIFAR10, CIFAR100, and ImageNet, demonstrate that the proposed approach in this paper achieves global accuracies of 67.23%, 65.75%, and 62.41% respectively for the federated incremental learning model. These accuracies surpass the existing Icarl incremental algorithm by improvements of 3.21%, 2.87%, and 1.34% respectively. Therefore, the algorithm presented in this paper achieves better performance, indicating its superiority over existing methods.

Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a significant challenge. Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitations. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are identified using Koopman-reweighted time-lagged independent component analysis (KTICA), which are subsequently leveraged by on-the-fly probability enhanced sampling (OPES) to efficiently explore the FES. The effectiveness of our pipeline is demonstrated and further compared with the common Markov State Model (MSM) approach on two model systems with increasing complexity: alanine dipeptide (Ala2) and deca-alanine (Ala10), underscoring its applicability across diverse biomolecular simulations.

3D printing food flow in different extruders based on crazy and adaptive salp swarm algorithm-deep extreme learning machine improved-lattice Boltzmann method

Extrusion is significant in achieving 3D printing emulsion. The piston and screw extruders are the common structures to achieve the extrusion. The chocolate emulsion is taken for example, two extruders are numerically investigated and compared based on the fluid dynamic analysis. To conduct the simulations, the crazy and adaptive salp swarm algorithm-deep extreme learning machine (CASSA-DELM) is proposed to predict the viscosity of the chocolate emulsion, which is used to replace the traditional fitted model. The built model can avoid non-consistency in the whole shearing rate range. Then, an improved lattice Boltzmann method (I-LBM) is introduced to process the non-Newtonian behavior of the emulsion. In the simulation, the CASSA-DELM model provides the viscosities for each iteration in I-LBM based on the obtained shearing rates. Because the attachment(s) may be generated on the walls, the no-attachment and with-attachment(s) cases are explored, and the necessary results are obtained, which indicate that the piston extruder is more suitable for extruding the single component of food fluid. The screw extruder is recommended for the multiple components of food fluid because the vortex in the X-Y cross-section contributes to further mixing action for the emulsion containing different materials, such as the investigated chocolate emulsion. The indirect experiments are conducted to validate the above conclusions. The current work can contribute to improving the extruding theory of material extrusion technologies.

Application of Adaptive Machine Learning in Non-Stationary Environments

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

Identification of anomaly of tunnel segment strain using an adaptive machine learning model

ABSTRACT Anomaly identification is a crucial issue for preventing diseases in tunnel structures. However, distinguishing whether the structure is abnormal from outliers is challenging, as the monitoring dataset composed of time series may contain non-structural abnormal data caused by operational environmental pollution. Therefore, this study aims to propose a structural anomaly identification model to distinguish the structural anomaly from the polluted dataset based on an improved autoencoder model with adaptive loss function modification (AAE). To solve the problem of insufficient abnormal samples in practice, the proposed model is applied to a time series dataset obtained from numerical simulation, where extreme conditions are introduced to generate structural abnormal data, and Gaussian noises are overlaid to pollute the raw data and generate non-structural abnormal data. Then, the anomaly identification capability of the AAE model under different intensities of noise pollution conditions is discussed, along with its reliability compared to other common methods. Experimental results demonstrate that the AAE model can effectively identify structural anomaly information from heavily polluted datasets. The accuracy of the AAE model improved by at least 4% to other models even on a serious polluted dataset. Therefore, the structural anomaly identification model presented in this study is reliable.

Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.

Colorectal Polyps are the main source of precancerous lesions in colorectal cancer. To increase the early diagnosis of tumors and improve their screening, we aimed to develop a simple and non-invasive diagnostic prediction model for colorectal polyps based on machine learning (ML) and using accessible health examination records. We conducted a single-center observational retrospective study in China. The derivation cohort, consisting of 5426 individuals who underwent colonoscopy screening from January 2021 to January 2024, was separated for training (cohort 1) and validation (cohort 2). The variables considered in this study included demographic data, vital signs, and laboratory results recorded by health examination records. With features selected by univariate analysis and Lasso regression analysis, nine machine learning methods were utilized to develop a colorectal polyp diagnostic model. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley additive explanation method (SHAP) was used to rank the feature importance and explain the final model. 14 independent predictors were identified as the most valuable features to establish the models. The adaptive boosting machine (AdaBoost) model exhibited the best performance among the 9 ML models in cohort 1, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) of 0.632 (0.618-0.646), 0.635 (0.550-0.721), 0.674 (0.591-0.758), 0.593 (0.576-0.611), 0.673 (0.654-0.691), 0.608 (0.560-0.655) and 0.687 (0.626-0.749), respectively. The final model gave an AUC of 0.675 in cohort 2. Additionally, the precision recall (PR) curve for the AdaBoost model reached the highest AUPR of 0.648, positioning it nearest to the upper right corner. SHAP analysis provided visualized explanations, reaffirming the critical factors associated with the risk of colorectal polyps in the asymptomatic population. This study integrated the clinical and laboratory indicators with machine learning techniques to establish the predictive model for colorectal polyps, providing non-invasive, cost-effective screening strategies for asymptomatic individuals and guiding decisions for further examination and treatment.

Error-controlled adaptive machining of aeronautical cabin structures by laser triangulation on-machine measurement.

Laser Triangulation On-Machine Measurement (LTOMM) is being implemented increasingly to inspect aeronautical components accurately and efficiently, with its enhanced application in adaptive machining. This work proposes an error compensation and controlling method for measuring the typical features of steps, holes, and freeform surfaces to improve accuracy. Then, the global path to inspect the cabin's structures is planned by introducing optimization algorithms, thus providing an appropriate sequence to shorten the traveling length. After these, the test piece was designed, measured, and manufactured using the adaptive machining process that integrates the LTOMM. The results show that the measurement errors of steps, holes, and freeform surfaces are +0.0092, -0.006, and +0.0406mm, respectively, and further reduced to +0.0013, -0.0019, and +0.0083mm after error controlling. The cabin's freeform surface was fabricated with the maximum positive and minimum negative errors of +0.184 and -0.123mm, which is evaluated by the mechanical probe. The measured data-driven machining process can guarantee that the error satisfies the required tolerance, promoting the application of the LTOMM process in aeronautical intelligent manufacturing.