Intelligent Model Research Articles

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Effect of Emotional Self-Awareness Coaching on Professional Burnout Among Teachers in Public Secondary Schools in Kiambu County, Kenya

Teachers play a pivotal role in the education systems of any nation. Their mental and psychological well-being is critical in achieving positive student educational outcomes. However, professional teacher burnout has been on the rise. Self-awareness is essential to helping individuals connect with their emotional state and how it affects their behavior and those they interact with. This study sought to assess the effect of self-awareness coaching on professional teacher burnout among teachers in public secondary schools in Kiambu County, Kenya. Weiner's theory of attribution and Goleman's mixed model of emotional Intelligence informed this study (Goleman, 1995). Quasi experiment Solomon four design with a target population of 3469 teachers from 277 public secondary schools of all categories in the County was used. Krejci and Morgan's table was used to sample 346 teachers, while eight principals and 40 Heads of departments were purposefully selected for the study. Maslach Burnout Inventory and an interview guide were used to collect data for professional teacher burnout. A training manual was used to coach self-awareness. Validity was achieved through expert advice and piloting, while reliability was established through the split-half method. Paired t-tests and ANOVA were used for data analysis to get the effect size of coaching. The results indicated that self-awareness coaching had statistically significant positive effects on professional teacher burnout at t = 15.779 at df=78, >.001, The effect size (Cohen d = 0.958) was high; hence, emotional self-awareness coaching significantly reduced professional teacher burnout. The study recommended that the teacher employer (TSC) develop programs to equip teachers with self-awareness skills to curb professional burnout, thereby increasing their productivity

Deep learning prediction of error and skill in robotic prostatectomy suturing.

Manual objective assessment of skill and errors in minimally invasive surgery have been validated with correlation to surgical expertise and patient outcomes. However, assessment and error annotation can be subjective and are time-consuming processes, often precluding their use. Recent years have seen the development of artificial intelligence models to work towards automating the process to allow reduction of errors and truly objective assessment. This study aimed to validate surgical skill rating and error annotations in suturing gestures to inform the development and evaluation of AI models. SAR-RARP50 open data set was blindly, independently annotated at the gesture level in Robotic-Assisted Radical Prostatectomy (RARP) suturing. Manual objective assessment tools and error annotation methodology, Objective Clinical Human Reliability Analysis (OCHRA), were used as ground truth to train and test vision-based deep learning methods to estimate skill and errors. Analysis included descriptive statistics plus tool validity and reliability. Fifty-four RARP videos (266min) were analysed. Strong/excellent inter-rater reliability (range r = 0.70-0.89, p < 0.001) and very strong correlation (r = 0.92, p < 0.001) between objective assessment tools was demonstrated. Skill estimation of OSATS and M-GEARS had a Spearman's Correlation Coefficient 0.37 and 0.36, respectively, with normalised mean absolute error representing a prediction error of 17.92% (inverted "accuracy" 82.08%) and 20.6% (inverted "accuracy" 79.4%) respectively. The best performing models in error prediction achieved mean absolute precision of 37.14%, area under the curve 65.10% and Macro-F1 58.97%. This is the first study to employ detailed error detection methodology and deep learning models within real robotic surgical video. This benchmark evaluation of AI models sets a foundation and promising approach for future advancements in automated technical skill assessment.

Emerging strategies to investigate the biology of early cancer.

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

Evaluating the Performance of ChatGPT, Gemini, and Bing Compared with Resident Surgeons in the Otorhinolaryngology In-service Training Examination.

Large language models (LLMs) are used in various fields for their ability to produce human-like text. They are particularly useful in medical education, aiding clinical management skills and exam preparation for residents. To evaluate and compare the performance of ChatGPT (GPT-4), Gemini, and Bing with each other and with otorhinolaryngology residents in answering in-service training exam questions and provide insights into the usefulness of these models in medical education and healthcare. Eight otorhinolaryngology in-service training exams were used for comparison. 316 questions were prepared from the Resident Training Textbook of the Turkish Society of Otorhinolaryngology Head and Neck Surgery. These questions were presented to the three artificial intelligence models. The exam results were evaluated to determine the accuracy of both models and residents. GPT-4 achieved the highest accuracy among the LLMs at 54.75% (GPT-4 vs. Gemini p=0.002, GPT-4 vs. Bing p<0.001), followed by Gemini at 40.50% and Bing at 37.00% (Gemini vs. Bing p=0.327). However, senior residents outperformed all LLMs and other residents with an accuracy rate of 75.5% (p<0.001). The LLMs could only compete with junior residents. GPT- 4 and Gemini performed similarly to juniors, whose accuracy level was 46.90% (p=0.058 and p=0.120, respectively). However, juniors still outperformed Bing (p=0.019). The LLMs currently have limitations in achieving the same medical accuracy as senior and mid-level residents. However, they outperform in specific subspecialties, indicating the potential usefulness in certain medical fields.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Exploring the potential of artificial intelligence models for triage in the emergency department

ABSTRACT Objective To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence (AI) models widely used in the daily life Materials and methods The study was prospectively conducted with patients presenting to the emergency department of a tertiary care hospital from 1 April 2024, to 7 April 2024. Among the patients who presented to the emergency department over this period, data pertaining to their primary complaints, arterial blood pressure values, heart rates, peripheral oxygen saturation values measured by pulse oximetry, body temperature values, age, and gender characteristics were analyzed. The triage categories determined by triage nurses, the abovementioned AI chatbots, and emergency medicine doctors were compared. Results The study included 500 patients, of whom 23.8% were categorized identically by all triage evaluators. Compared to the triage conducted by emergency medicine doctors, triage nurses overtriaged 6.4% of the patients and undertriaged 3.1% of the yellow-coded patients and 3.4% of the red-coded patients. Of the AI chatbots, ChatGPT exhibited the closest triage approximation to that of emergency medicine doctors; however, its undertriage rates were 26.5% for yellow-coded patients and 42.6% for red-coded patients. Conclusion The undertriage rates observed in AI models were considerably high. Hence, it does not yet seem appropriate to solely rely on the specified AI models for triage purposes in the emergency department.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

Rolling bearing fault diagnosis method based on MSDCNN in strong noise environment

Aiming at the problems of poor noise resistance, high computational complexity and insufficient generalization performance of traditional bearing fault diagnosis methods based on deep learning, a rolling bearing fault diagnosis method based on multi-scale dynamic convolutional neural network (MSDCNN) is proposed. Firstly, the one-dimensional vibration signal of the rolling bearing is converted to the frequency domain by Fourier transform, and the features are further extracted by wide convolution kernel. Secondly, a multi-scale dynamic convolution structure is proposed, and the feature information extracted by convolution kernels of different sizes is given different weights by using an improved channel attention mechanism. Then, a self-calibrating spatial attention mechanism (SCSAM) is designed, and the extracted feature information is input into the spatial attention mechanism to capture the importance of different regions. Finally, the features are further extracted by small convolution kernels, and the fault category is classified by using Softmax classifier. The fault diagnosis performance of the proposed model is verified by three different data sets. The experimental results show that compared with other intelligent models, the proposed model has higher classification accuracy, better generalization ability and stronger robustness under strong noise background.

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

A Survey of the Real-Time Metaverse: Challenges and Opportunities

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field.

A dataset of Solicited Cough Sound for Tuberculosis Triage Testing

Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.

Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study.

Patients often struggle with determining which outpatient specialist to consult based on their symptoms. Natural language processing models in health care offer the potential to assist patients in making these decisions before visiting a hospital. This study aimed to evaluate the performance of ChatGPT in recommending medical specialties for medical questions. We used a dataset of 31,482 medical questions, each answered by doctors and labeled with the appropriate medical specialty from the health consultation board of NAVER (NAVER Corp), a major Korean portal. This dataset includes 27 distinct medical specialty labels. We compared the performance of the fine-tuned Korean Medical bidirectional encoder representations from transformers (KM-BERT) and ChatGPT models by analyzing their ability to accurately recommend medical specialties. We categorized responses from ChatGPT into those matching the 27 predefined specialties and those that did not. Both models were evaluated using performance metrics of accuracy, precision, recall, and F1-score. ChatGPT demonstrated an answer avoidance rate of 6.2% but provided accurate medical specialty recommendations with explanations that elucidated the underlying pathophysiology of the patient's symptoms. It achieved an accuracy of 0.939, precision of 0.219, recall of 0.168, and an F1-score of 0.134. In contrast, the KM-BERT model, fine-tuned for the same task, outperformed ChatGPT with an accuracy of 0.977, precision of 0.570, recall of 0.652, and an F1-score of 0.587. Although ChatGPT did not surpass the fine-tuned KM-BERT model in recommending the correct medical specialties, it showcased notable advantages as a conversational artificial intelligence model. By providing detailed, contextually appropriate explanations, ChatGPT has the potential to significantly enhance patient comprehension of medical information, thereby improving the medical referral process.

Implementation of a semi-automatic approach for detection and extraction of oil slicks using Sentinel-1: Case study of the Moroccan exclusive economic zone

Due to its strategic geographic location, Morocco experiences heavy ship traffic, which inevitably has consequences. Ship accidents and illegal discharges, commonly referred to as oil spills, are frequent and pose significant harm to the marine ecosystem. Therefore, employing new technologies, such as satellite monitoring, is crucial and will greatly enhance the ability to monitor and assess these oil spills. The present work is based on Sentinel-1 SAR images and aims to take into evidence the extent of the hydrocarbon marine pollution in all of the Moroccan EEZ. More than 220 possible cases were detected and mapped during the last decade (2014–2024). A batch process was developed to automate the detection of oil spills by identifying areas with reduced Normalized Radar Cross Section (NRCS) values. This process incorporates corrections, masks, wind field estimation, and oil clustering. The method successfully detected approximately 80% of the cases between Kenitra and Casablanca. The closest detected spill is located 1 km from the coast, the largest spill covers an area of 90 km2, and the most extended spill measures about 145 km. This study can help raise awareness among governmental authorities about potential environmental damage and can be used to train artificial intelligence models, which could simulate oil slick propagation.

Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort.

Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. Of the 2473 images in 210 eyes, 427 had no tumor, and 2046 had iRB. After training the AI model based on race, the sensitivity and specificity for detection of RB in 2473 images were 93% and 96%, respectively. The sensitivity and specificity of the AI model were 74% and 100% for group A; 88% and 96% for group B; 88% and 100% for group C; 73% and 98% for group D, and 100% and 92% for group E, respectively. Conclusions: The AI models built on a single race do not work well for other races. When retrained for different races, our model exhibited high sensitivity and specificity in detecting RB and classifying RB.

Thermal imaging and deep learning-based fit-checking for respiratory protection

This study develops an artificial intelligence model to quickly and easily determine correct mask-wearing in real time using thermal videos that ascertained temperature changes caused by air trapped inside the mask. Five types of masks approved by the Korean Ministry of Food and Drug Safety were worn in four different ways across 50 participants, generating 5000 videos. The results showed that 3DCNN outperformed ConvLSTM in both binary and multi-classification for mask wearing methods, with the highest AUROC of 0.986 for multi-classification. Each mask type scored AUROC values > 0.9, with KF-AD being the best classified. This improved use of thermal imaging and deep learning for mask fit-checking could be useful in high-risk environments. It can be applied to various mask types, which enables easy generalizability and advantages in public and occupational health and healthcare system.

Truck Platooning Merging Control Method in Merge Areas Based on the IDM Model and V2X Communication

Truck platooning technology has been widely studied for its advantages in traffic efficiency and energy savings. Although recent studies have made significant progress in specific scenarios, research on merging ramp trucks into the main road truck platoons remains insufficient. This paper proposes an innovative cooperative control method for merging ramp trucks and main road truck platoons based on vehicle-to-everything (V2X) technology. The method integrates the intelligent driver model (IDM), acceleration control logic for merging trucks before merging, lane-changing decision logic, steering control methods, and on-board unit (OBU) and roadside unit (RSU) communication technologies. This ensures that autonomous merging trucks can smoothly join the main road truck platoon under various insertion positions and acceleration lane lengths. After successfully joining the platoon, the merging trucks can maintain appropriate spacing and high speed, thereby improving overall traffic efficiency. Further analysis on different insertion positions and acceleration lane lengths reveals that when using the proposed method, merging trucks have the least impact on the overall platoon when they join at the head or tail of the main road truck platoon. Although the proposed method performs better with longer acceleration lanes, these lanes are costly to construct. The proposed method can also enable successful platoon joining with shorter acceleration lanes, optimizing the use of acceleration lanes to some extent.

Forecasting Construction Cost Indices: Methods, Trends, and Influential Factors

The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series, artificial intelligence, machine learning, and hybrid models. Therefore, this study seeks to reveal the complexity of CCI forecasting and identify the leading indicators, trends, and techniques for CCI prediction. A bibliometric analysis was conducted to explore the landscape in the CCI literature, focusing on co-occurrence, co-authorship, and citation analysis. These analyses revealed the frequent keywords, the most cited authors and documents, and the most productive countries. The research topics and clusters in the CCI forecasting process were presented, and directions for future research were suggested to enhance the prediction models. A case study was conducted to demonstrate the practical application of a forecasting model to validate its prediction reliability. Furthermore, this study emphasizes the need to integrate advanced technologies and sustainable practices into future CCI forecasting models. The findings are useful in enhancing the knowledge of CCI prediction techniques and serve as a base for future research in construction cost estimation.

Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation Classification Tasks in Multiple Sclerosis.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GAN) that creates synthetic patient image data during model training to improve model generalizability. Model development and external testing were performed for a given classification task, namely the detection of new fluid-attenuated inversion recovery (FLAIR) lesions on MRI during longitudinal follow-up of patients with multiple sclerosis (MS). An internal dataset of 669 patients with MS (n = 3083 examinations) was used to develop an attention-based network, trained both with and without the inclusion of the GAN-based synthetic data augmentation framework. External testing was performed on 134 patients with MS from a different institution, with MR images acquired using different scanners and protocols than images used during training. Models trained using synthetic data augmentation showed a significant performance improvement when applied on external data (AUC 83.6% without synthetic data versus AUC 93.3% with synthetic data augmentation, P = .03), achieving comparable results to the internal test set (AUC 95.5%, P = .53), whereas models without synthetic data augmentation demonstrated a performance drop upon external testing (AUC 93.8% on internal dataset versus AUC 83.6% on external data, P = .03). Data augmentation with synthetic patient data substantially improved performance of AI models on unseen MRI data and may be extended to other clinical conditions or tasks to mitigate domain shift, limit class imbalance, and enhance the robustness of AI applications in medical imaging. ©RSNA, 2024.

Detection and Prediction of Probe Mark Damage in Wafer Testing

In wafer testing, the test probe needs to contact the surface of the semiconductor wafer to measure electrical parameters such as resistance, capacitance, and current. The probe mark damage frequently occurs on the machine during wafer testing. This phenomenon causes inaccuracies in electrical parameter testing, significantly reducing the yield in IC packaging processes and resulting in substantial manufacturing cost losses. Therefore, this study proposed an effective probe mark damage detection and prediction method to prevent significant yield reduction due to inaccurate testing. This study uses importance analysis from random forests and correlation analysis to identify the critical parameters influencing probe mark damage. Introducing these parameters into a Sparse Transformer with hybrid normalization can successfully train an intelligent model for predicting the occurrence of probe mark damage. The model accurately predicts the probability of probe mark damage and promptly adjusts machine parameters to avoid inaccuracies in electrical parameter settings. The proposed approach can outperform other methods, achieving two very high accuracies of 95.1% (at room temperature) and 93.5% (at high temperature) and significantly reducing the occurrence of large-area probe mark damage.