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Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control machines for Geometrical Accuracy Prediction Through machine learning

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SENSORS 2025年第1期25卷 169页

作者： Berus, Lucijano Hernavs, Jernej Potocnik, David Sket, Kristijan Ficko, Mirko Univ Maribor Fac Mech Engn Prod Engn Inst Intelligent Mfg Lab Smetanova Ul 17 SLO-2000 Maribor Slovenia Rudolfovo Sci & Technol Ctr Novo Mesto Lab Lab3D Podbrezn 15 Novo Mesto 8000 Slovenia

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards.

关键词： smart production machines data-driven manufacturing machine learning algorithms CNC controller data geometrical accuracy

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Predicting Insomnia Response to Acupuncture With the Development of Innovative machine learning

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IEEE ACCESS 2025年 13卷 45964-45984页

作者： Wan, Qingyun Liu, Kai Bo, Yuyang Yuan, Xiya Li, Mufeng Wang, Xiaoqiu Chen, Chuang Liu, Lanying Wu, Wenzhong Nanjing Univ Chinese Med Affiliated Hosp Jiangsu Prov Hosp Chinese Med Nanjing 210029 Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Shanghai 200071 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Insomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To address this, an innovative machine learning algorithm, Relief-NDPGWO-WSVM, is developed to predict insomnia response to acupuncture. Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. Experimental results demonstrate that this model outperforms existing models in terms of precision, recall, F1-score, accuracy, and area under the curve (AUC). These results highlight the model's potential in accurately predicting acupuncture treatment outcomes for insomnia and suggest its broader applicability for enhancing clinical decision-making and optimizing healthcare resources for other related conditions.

关键词： Acupuncture machine learning Sleep apnea Medical diagnostic imaging Hospitals Predictive models Accuracy machine learning algorithms Diseases Brain modeling Insomnia disorder acupuncture machine learning efficacy evaluation swarm intelligence algorithm

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Newton-Raphson AC Power Flow Convergence Based on Deep learning Initialization and Homotopy Continuation

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2037-2046页

作者： Okhuegbe, Samuel N. Ademola, Adedasola A. Liu, Yilu Univ Tennessee Bredesen Ctr Interdisciplinary Res & Grad Educ Knoxville TN 37996 USA Univ Tennessee Ctr Ultrawide Area Resilient Elect Network Knoxville TN 37996 USA Dominion Energy Virginia Richmond VA 23219 USA Univ Tennessee Bredesen Ctr Interdisciplinary Res & Grad Educ Knoxville TN 37996 USA Univ Tennessee Ctr Ultrawide Area Resilient Elect Network Knoxville TN 37996 USA Oak Ridge Natl Lab Bredesen Ctr Interdisciplinary Res & Grad Educ Knoxville TN 37996 USA

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. The combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

关键词： Load flow Newton method Convergence Generators Reactive power machine learning algorithms Switches Mathematical models Deep learning Prediction algorithms homotopy continuation neural network newton-raphson power flow convergence power flow initialization

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Smell-ML: A machine learning Framework for Detecting Rarely Studied Code Smells

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IEEE ACCESS 2025年 13卷 12966-12980页

作者： Hamouda, Esraa El-Korany, Abeer Makady, Soha Cairo Univ Fac Comp & Artificial Intelligence Giza 12613 Egypt

Code smells are design flaws that reduce the software quality and maintainability. machine learning classification models have been used to detect different code smells. However, such studies targeted code smells in depth, while leaving other under-explored smells;even so, such smells have a significant impact on source code quality. Recent surveys have highlighted a group of code smells that has rarely been studied by researchers. Furthermore, some machine learning classification models were evaluated on a subset of the source code features while ignoring significant features during classification. This paper proposes a novel approach, called Smell-ML, for detecting five rarely studied code smells: Middle Man (MM), Class Data Should Be Private (CDSBP), Inappropriate Intimacy (II), Refused Bequest (RB), and Speculative Generality (SG). The novelty of this approach stems from the improvement in both the data preparation and classification phases. During data preparation, Smell-ML relies on data balancing and an extended source code feature list to improve accuracy. In the classification phase, different classifiers were assessed, including traditional, ensemble, and multi-level classifiers. We evaluated Smell-ML on a dataset composed of 13 open source Java projects with 125 versions per project. The results show that Smell-ML's detection F1-score values surpass those of previous studies with significant improvements across various code smells. The F1-score measure of the 11 machine learning classifiers improved after using the extended feature list. Data balancing and multi-level classification notably boosted accuracy.

关键词： Codes Feature extraction Accuracy Data models Surveys Software measurement Ensemble learning Random forests machine learning algorithms Java Code smell detection machine learning data balancing ensemble learning multi-level classification

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An efficient AI algorithm for fault diagnosis in nuclear power plants based on machine deep learning techniques

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PROGRESS IN NUCLEAR ENERGY 2025年 180卷

作者： Elbordany, Ayman A. Kandil, Magy M. Youness, Hassan A. Abdelaal, Hammam M. Minia Univ Fac Engn Comp & Syst Engn Dept Al Minya Egypt Egyptian Atom Energy Author EAEA Cairo Egypt Luxor Univ Fac Comp & Informat Informat Technol Dept Luxor Egypt

Artificial intelligence (AI) techniques and human intelligence are inextricably linked when it comes to operating Nuclear Power Plants (NPPs) with cutting-edge control rooms that have humans acting as actors. Using AI in fault diagnosis and prediction of accidents is valuable and crucial at NPPs. This research proposes an AI fault diagnosis algorithm for NPPs that uses AI techniques to integrate cutting-edge machine learning (ML) algorithms such as Support Vector machine, Random Forest, k-Nearest Neighbors, Light Gradient Boosting machine, and Long Short-Term Memory as deep learning (DL) algorithm to improve decision-making of operators to reduce the human errors in NPPs for increasing their safety. The proposed algorithm seeks to fault diagnosis by predicting and identifying normal and abnormal operations for Turbine trip with Anticipated Transient Without Scram (ATWS), Loss of Flow Accident (LOFA) and Loss of Coolant Accident (LOCA) with different break sizes (1,10,20,30,40,50,60,70,80,90,100 cm2) at hot leg. The dataset is pre-processed and enhanced to get complete and suitable inputs of the used ML and DL algorithms. The results show that the proposed AI fault diagnosis algorithm is effectively capable of anticipating and identifying risk according to predetermined cases since the accuracy is 98%. Moreover, validation of the proposed fault diagnosis algorithm with the previous model results also shows that the outputs are within range.

关键词： Nuclear power plants Loss of coolant accident Neural networks machine learning algorithms Deep learning algorithm Fault diagnosis

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Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 327卷 125305页

作者： Mitura, Przemyslaw Paja, Wieslaw Klebowski, Bartosz Plaza, Pawel Kuliniec, Iga Bar, Krzyszof Depciuch, Joanna Med Univ Lublin Dept Urol & Oncol Urol Jaczewskiego 8 PL-20954 Lublin Poland Univ Rzeszow Inst Comp Sci Dept Artificial Intelligence Pigon 1 PL-35310 Rzeszow Poland Polish Acad Sci Inst Nucl Phys Walerego Eljasza Radzikowskiego 152 PL-31342 Krakow Poland Med Univ Lublin Dept Biochem & Mol Biol Chodzki 1 PL-20093 Lublin Poland

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels. Accordingly, a more sensitive methodology must still be identified. It is imperative that new diagnostic methods should be non-invasive, cost-effective, rapid, and highly sensitive. Fourier transform infrared spectroscopy (FTIR) is a technique that fulfils all of the aforementioned criteria. Consequently, the present study used FTIR to assess dried serum samples obtained from a cohort of prostate cancer patients (n = 53) and a control group of healthy individuals (n = 40). Furthermore, this study proposes FTIR markers of prostate cancer obtained from serum. For this purpose, FTIR spectra of dried serum were measured and analysed using statistical, chemometric and machine learning (ML) algorithms including decision trees C5.0, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector machine (SVM). The FTIR spectra of serum collected from patients suffering from prostate cancer exhibited a reduced absorbance values of peaks derived from phospholipids, amides, and lipids. However, these differences were not statistically significant. Furthermore, principal component analysis (PCA) demonstrated that it is challenging to distinguish serum samples from healthy and non-healthy patients. The ML algorithms demonstrated that FTIR was capable of differentiating serum collected from both analysed groups of patients with high accuracy (values between 0.74 and 0.93 for the range from 800 cm-1 to 1800 cm- 1 and around 0.70 and 1 for the range from 2800 cm- 1 to 3000 cm- 1 ), depending on the ML algorithms used. The results demonstrated that the peaks at 1637 cm- 1 and 2851 cm-1 could serve as a FT

关键词： Serum Prostate cancer machine learning algorithms FTIR Cancer marker

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A Hybrid machine learning Model for Efficient XML Parsing

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IEEE ACCESS 2025年 13卷 382-393页

作者： Ali, Muhammad Khan, Minhaj Ahmad Ur Rasool, Raihan Bahauddin Zakariya Univ Dept Comp Sci Multan 60800 Pakistan IBM Technol Melbourne Vic 3006 Australia

The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid machine learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files.

关键词： XML Support vector machines Classification algorithms Prediction algorithms machine learning Codes machine learning algorithms Predictive models Performance evaluation Training Artificial intelligence artificial neural network eXtensible markup language framework machine learning model parsing support vector machine XML parsing optimization

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Towards More Precise Coincidental Correctness Detection With Deep Semantic learning

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第12期50卷 3265-3289页

作者： Xie, Huan Lei, Yan Yan, Meng Li, Shanshan Mao, Xiaoguang Yu, Yue Lo, David Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410003 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Coincidental correctness (CC) is a situation during the execution of a test case, the buggy entity is executed, but the program behaves correctly as expected. Many automated fault localization (FL) techniques use runtime information to discover the underlying connection between the executed buggy entity and the failing test result. The existence of CC will weaken such connection, mislead the FL algorithms to build inaccurate models, and consequently, decrease the localization accuracy. To alleviate the adverse effect of CC on FL, CC detection techniques have been proposed to identify the possible CC tests via heuristic or machine learning algorithms. However, their performance on precision is not satisfactory since they overestimate the possible CC tests and are insufficient in learning the deep semantic features. In this work, we propose a novel Triplet network-based Coincidental Correctness detection technique (i.e., TriCoCo) to overcome the limitations of the prior works. TriCoCo narrows the possible CC tests by designing three features to identify genuine passing tests. Instead of using all tests as inputs by existing techniques, TriCoCo takes the identified genuine passing tests and failing ones to train a triplet model that can evaluate their relative distance. Finally, TriCoCo infers the probability of being a CC test of the test in the rest of the passing tests by using the trained triplet model. We conduct large-scale experiments to evaluate TriCoCo based on the widely-used Defects4J benchmark. The results demonstrate that TriCoCo can improve not only the precision of CC detection but also the effectiveness of FL techniques, e.g., the precision of TriCoCo is 80.33% on average, and TriCoCo boosts the efficacy of DStar by 18%-74% in terms of MFR metric when compared to seven state-of-the-art CC detection baselines.

关键词： Feature extraction Semantics machine learning algorithms Location awareness Deep learning Clustering algorithms Prediction algorithms Benchmark testing Accuracy Partitioning algorithms Coincidental correctness fault localization deep semantic learning

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A Novel Hybrid Ordinal learning Model With Health Care Application

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 339-352页

作者： Wang, Lujia Wang, Hairong Su, Yi Lure, Fleming Li, Jing Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Banner Alzheimers Inst Phoenix AZ 85006 USA MS Technol Corp Rockville MD 20850 USA

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited in the training set due to cost or availability constraints, whereas there could be an abundance of samples with imprecise labels. We focus on imprecise labels that are intervals, i.e., one can know that the a sample belongs to an interval of labels but cannot know which unique label it has. This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout. Limited research has been done to develop OL models with imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. We also develop a tractable and efficient optimization algorithm to solve the HOL formulation. We compare HOL with several recently developed OL methods on four benchmarking datasets, which demonstrate the superior performance of HOL. Finally, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI) based on a combination of multi-modality neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in the prediction and outperforms existing methods. The capability of accurately predicting the speed of progression to AD for each individual with MCI has the potential for helping facilitate more individually-optimized interventional strategies.

关键词： Modeling Neuroimaging Classification algorithms Training Prediction algorithms Alzheimer's disease machine learning algorithms machine learning ordinal learning health care imprecise labels

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Applying machine learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset

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APPLIED SCIENCES-BASEL 2025年第3期15卷

作者： Ormeno-Arriagada, Pablo Marquez, Gaston Araya, David Rimassa, Carla Taramasco, Carla Univ Vina Del Mar Escuela Ingn & Negocios Vina Del Mar 2580022 Chile Univ Bio Bio Dept Ciencias Comp & Tecnol Informac Chillan 4081112 Chile Univ Andres Bello Fac Ingn Inst Tecnol Innovac Salud & Bienestar Vina Del Mar 2530959 Chile Univ Valparaiso Escuela Fonoaudiol Fac Med Camino Troya S-N Valparaiso 2360102 Chile Univ Andres Bello Fac Ingn Millennium Nucl Sociomed Vina Del Mar 2520000 Chile

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EPIVIGILA system and the Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers from class imbalance. To address this issue, we applied various machine learning algorithms, both with and without sampling methods, and compared them using different classification and diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, and diagnostic odds ratio. Our results showed that applying sampling methods to this dataset improved the metric values and contributed to models with better generalization. Effectively managing imbalanced data is crucial for reliable COVID-19 diagnosis. This study enhances the understanding of how machine learning techniques can improve diagnostic reliability and contribute to better patient outcomes.

关键词： machine learning algorithms COVID-19 diagnosis imbalanced data sampling methods classification metrics epidemiological dataset EPIVIGILA system

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