Optimal Machining Research Articles

Experimental investigation on cutting force and machining parameters optimization in in-situ laser-assisted machining of glass–ceramic

Glass-ceramic is a typical hard and brittle material that is difficult to use for ultra precision machining. In order to improve the machining quality of glass–ceramic, orthogonal experiments on in-situ laser-assisted machining (LAM) of glass–ceramic were carried out with cutting force as the characteristic value. The maximum reduction in the resultant force of cutting force in in-situ LAM was 56.13%. The contribution order of each machining parameter to reducing cutting force is: spindle speed > laser power > feed speed > cutting depth. Response surface methodology (RSM) was used to conduct experiments and establish a regression model for predicting cutting force. The influence law of four machining parameters on cutting force was studied through three-dimensional response surface. The optimal combination of machining parameters to minimize cutting force obtained through RSM optimization is: spindle speed 400.51 rpm, feed speed 0.03 mm/rev, cutting depth 18.91 μm, laser power 74.78 W. The RSM experiments were trained, predicted, and optimized using artificial neural network (ANN) and genetic algorithms (GA). The fitting values of the ANN model are highly consistent with the experimental values of RSM. The optimal machining parameter combination obtained through GA optimization is: spindle speed 400 rpm, feed speed 0.01 mm/rev, cutting depth 20 μm, laser power 75 W. Experiments were conducted using the optimal machining parameters of RSM and ANN respectively, and the results showed that ANN is more ideal for optimizing machining parameters and predicting minimum cutting force. This study provides guidance and reference for glass–ceramic in-situ LAM and parameter optimization method for cutting force.

Boundary delineation in transrectal ultrasound images for region of interest of prostate

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.

Unravelling the use of silica recovered from waste foundry sand in sustainable water jet machining of a copper-based ternary shape memory alloy

The progressive growth of manufacturing industries has resulted in a consequential increase in waste generation, posing significant challenges to environmental sustainability. Within the realm of manufacturing, waste emerges as an inevitable byproduct, accumulating at an unprecedented rate and exerting a profound strain on ecological equilibrium. By harnessing the transformative potential of recycling, manufacturing facilities embrace a paradigm shift, transcending the mundane constraints of waste disposal. Thus, recycling fosters building nations’ capabilities by incorporating an array of exquisite and comprehensible linguistic prowess. One such change can be brought by reusing the foundry sand after the casting process to power the abrasive water jet machining (AWJM) to carry out the machining process. Therefore, in this research work, the aim was to promote sustainable development and indulge in the simultaneous cutting of a copper-based shape memory alloy. Shape memory alloys are perceived to be the most potential product in the aerospace and biomedical industries. There are only fewer indagations that analyze and investigate the effects of AWJM on the CuAlFe shape memory alloys. The effects of the cutting process on the surface and shape memory characteristics of the CuAlFe shape memory alloy were analyzed by performing tests like scanning electron microscopy, differential scanning calorimetry, and finally X-ray diffraction. The shape reverting temperature of the alloy before and after machining was 416.57 °C and 415.43 °C indicating the loss of shape memory effect even after AWJM is very less. Apart from this, optimal machining parameters were also deduced using Taguchi-Grey relational analysis of the L27 orthogonal design matrix. Of the chosen 3 parameters, the selected combination from this method was A1B1C2. From this analysis, the optimal material removal rate and surface roughness was found to be 1.873 (cm3/min) and 2.965 (μm) which are far good values in the industry. The outcomes proved to be effective in examining the optimal parameters and the surface properties of the CuAlFe Shape memory alloy thus ensuring sustainable development.

Inversion analysis of rock mass permeability coefficient of dam engineering based on particle swarm optimization and support vector machine: A case study

In order to efficiently and reasonably determine the permeability coefficient of engineering rock mass, the two key problems of complex seepage field simulation and objective function solution in permeability coefficient inversion are studied and improved. Firstly, a 3D refined seepage numerical model is established considering the main fault fracture zones and dam seepage control system. Secondly, combined with the support vector machine (SVM) algorithm, the complex mapping relationship between permeability coefficient of each rock layer and impervious structure and piezometer head is constructed. Finally, the particle swarm optimization (PSO) algorithm is introduced, and then the efficient optimization of permeability coefficient inversion of engineering rock mass and impervious structure is realized based on PSO-SVM. The case study of LJH arch dam shows that the inversion calculation framework based on PSO-SVM not only completes the rapid proxy calculation of the numerical model of complex seepage field, but also realizes the accurate optimization of the inversion objective function. The simulation results under the final inversion parameters reproduce the variation characteristics of the piezometric tube water level of each typical measuring point under the change of reservoir water level. In addition, with the correlation analysis between the permeability coefficient and the measured value of the corresponding piezometer, the parameter inversion method is endowed with new application value. This study is of great significance for clarifying the seepage characteristics of complex rock mass and improving the inversion efficiency of dam seepage monitoring data.

A design of experiments based on the Normal‐boundary‐intersection method to identify optimum machine settings in manufacturing processes

AbstractFinding the appropriate machine settings for a given manufacturing process is an important issue in industrial production. A set of minimum and maximum machine settings correspond to the lower and upper quality limits that are specified for the produced product, and by this define the boundaries of all appropriate machine settings. This paper shows that these boundaries are the solution of a multi‐objective optimisation problem, which is called the optimum machine settings problem. However, for most processes there is no mathematical model of the manufacturing process available, which maps the setting parameters on the quality key figures in a way that allows to compute the optimisation problem. In this case, experiments may provide the required empirical data simultaneously while executing the optimisation procedure. Using a case study on heat sealing in industrial packaging, the paper shows, how to develop a design of experiments based on the Normal‐boundary‐intersection method (NBI), and how to generate the Pareto‐frontier by executing test according to this test plan. It addresses the specific limitations inherent in solving an optimisation problem by experiments. The behaviour of the method towards discrete and binary objectives and constraints is discussed.

Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm

Surface finish and temperature rise are the crucial machining outcomes since it determines the quality of the machining and the tool life. During machining operations, choosing optimal machining parameters is critical since it affects the machining outcome. In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. Cutting depth, cutting speed, and feed rate are the machining variables that are used in the process of optimization. From the results, it shows that the minimum temperature rise is 243.333 ℃ with a surface roughness of 1.975 μm during machining of 20 hardness. It also observed that the hardness of the material significantly affects the surface roughness and temperature rise. The outcome shows that as the hardness of the material is increasing the temperature is increasing while the surface roughness is decreasing. This research also revealed that using a MOGA to optimize multi-objective replies produces positive outcomes.

Use of modern algorithms for multi-parameter optimization and intelligent modelling of sustainable battery performance

The focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavg and MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (α) is analyzed. SOO gives highest fitness of Nuavg and lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavg with MaxT in the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of α is important during training and testing. The best Nuavg obtained from SOO using MVO algorithm is around 82.06 while MaxT is 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavg and MaxT around 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxT while it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxT and Nuavg respectively.

Predicting the Temperature-Dependent Long-Term Creep Mechanical Response of Silica Sand-Textured Geomembrane Interfaces Based on Physical Tests and Machine Learning Techniques.

Preciously assessing the creep mechanical response of sand-geomembrane interfaces is vital for the design of relevant engineering applications, which is inevitable to be influenced by temperature and stress statuses. In this paper, based on the self-developed temperature-controlled large interface shear apparatus, a series of long-term creep shear tests on textured geomembrane-silica sand interfaces in different temperatures, normal pressure, and creep shear pressure were conducted, and a database compiled from the physical creep shear test results is constructed. By adopting the database, three disparate machine learning algorithms of the Back Propagation Artificial Neural Network (BPANN), the Support Vector Machine (SVM) and the Extreme Learning Machine (ELM) were adopted to assess the long-term creep mechanical properties of sand-geomembrane interfaces while also considering the influence of temperature. Then, the forecasting results of the different algorithms was compared and analyzed. Furthermore, by using the optimal machine learning model, sensitivity analysis was carried out. The research indicated that the BPANN model has the best forecasting performance according to the statistics criteria of the Root-Mean-Square Error, the Correlation Coefficient, Wilmot's Index of Agreement, and the Mean Absolute Percentage Error among the developed models. Temperature is the most important influence factor on the creep interface mechanical properties, followed with time. The research findings can support the operating safety of the related engineering facilities installed with the geomembrane.

Estimating SVCV waterborne transmission and predicting experimental epidemic development: A modeling study using a machine learning approach

Viral infectious diseases significantly threaten the sustainability of freshwater fish aquaculture. The lack of studies on epidemic transmission patterns and mechanisms inhibits the development of containment strategies from the viewpoint of veterinary public health. This study raises an epidemic mathematical model considering water transmission with the aim of analyzing the transmission process more accurately. The basic reproduction number R0 was derived by the model parameter including the water transmission coefficient and was used for the analysis of the virus transmission. Spring viremia of carp virus (SVCV) and zebrafish were used as model viruses and animals, respectively, to conduct the transmission experiment. Transmission through water was achieved by connecting two aquarium tanks with a water channel but blocking the fish movement between the tanks. With the collected experimental data, we determined the optimal hybrid machine learning algorithm to analyze the transmission process using an established mathematical model. In addition, future transmission was predicted and validated using the epidemic model and an optimal algorithm. Finally, the sensitivity of model parameters and the simulations of R0 variation were performed based on the modified complex epidemic model. This study is of significance in providing theoretical guidance for minimizing R0 by manipulating model parameters with containment measures. More importantly, since the modified model and algorithm demonstrated better performance in handling freshwater fish transmission problems, this study advances the future application of transmissible disease modeling with larger datasets in freshwater fish aquaculture.

Identification and validation of disulfidptosis-associated molecular clusters in non-alcoholic fatty liver disease.

Objective: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease in the world, and its pathogenesis is not fully understood. Disulfidptosis is the most recently reported form of cell death and may be associated with NAFLD progression. Our study aimed to explore the molecular clusters associated with disulfidptosis in NAFLD and to construct a predictive model. Methods: First, we analyzed the expression profile of the disulfidptosis regulators and immune characteristics in NAFLD. Using 104 NAFLD samples, we investigated molecular clusters based on differentially expressed disulfidptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were then identified by using the WGCNA method. We also evaluated the performance of four machine learning models before choosing the optimal machine model for diagnosis. Nomogram, calibration curves, decision curve analysis, and external datasets were used to confirm the prediction effectiveness. Finally, the expression levels of the biomarkers were assessed in a mouse model of a high-fat diet. Results: Two differentially expressed DRGs were identified between healthy and NAFLD patients. We revealed the expression profile of DRGs in NAFLD and the correlation with 22 immune cells. In NAFLD, two clusters of molecules connected to disulfidptosis were defined. Significant immunological heterogeneity was shown by immune infiltration analysis among the various clusters. A significant amount of immunological infiltration was seen in Cluster 1. Functional analysis revealed that Cluster 1 differentially expressed genes were strongly linked to energy metabolism and immune control. The highest discriminatory performance was demonstrated by the SVM model, which had a higher area under the curve, relatively small residual and root mean square errors. Nomograms, calibration curves, and decision curve analyses were used to show how accurate the prediction of NAFLD was. Further analysis revealed that the expression of three model-related genes was significantly associated with the level of multiple immune cells. In animal experiments, the expression trends of DDO, FRK and TMEM19 were consistent with the results of bioinformatics analysis. Conclusion: This study systematically elucidated the complex relationship between disulfidptosis and NAFLD and developed a promising predictive model to assess the risk of disease in patients with disulfidptosis subtypes and NAFLD.

A convolutional dispersion relation preserving scheme for the acoustic wave equation

We propose a numerical scheme for approximating the solution of the two dimensional acoustic wave problem. We use machine learning to find a stencil suitable even for high wavenumbers. The proposed scheme incorporates physics informed elements from the field of dispersion-relation-preserving (DRP) schemes into a convolutional optimization machine learning algorithm. We test the proposed method and demonstrate that it performs better than classical and DRP explicit methods for a wide range of wavenumbers.

A new distributed decomposition–reconstruction–ensemble learning paradigm for short-term wind power prediction

The rapid and accurate forecast of wind power is crucial for power grid dispatching, because wind power time series are frequently nonlinear and noisy. Currently, typical decomposition and ensemble approaches can enhance prediction precision, but at the expense of computing efficiency. In addition, existing approaches rely primarily on a single model to forecast the deconstructed sequences, which may not correspond to the physical significance of each sequence. This research presents a wind power forecasting paradigm based on distributed decomposition-reconstruction-ensemble learning to address the aforementioned issues. Initially, the wind power data are decomposed using variational mode decomposition (VMD) to reduce data noise. Second, frequency, grey correlation degree, and Pearson coefficient are employed to cluster the decomposed data into high-frequency and low-frequency sequences in order to decrease the complexity of the model. Then, the reconstructed sequences are equally divided into 10 parts and distributed model training is performed using federated learning (FL), federated averaging (FedAvg) algorithm, and differential privacy Laplace (DPLA) method in a prediction pool composed of different machine learning models. For each sequence, an optimal machine learning model is selected and combined to form the optimal hybrid model. Finally, the final wind power forecast results are obtained by adding and summing the prediction results of each sequence. The experimental results show that the root mean square error (RMSE) of the proposed method is 10.230, mean absolute error (MAE) is 5.175, R-square (R2) is 0.990, and accuracy rate (ACC) is 0.644, which is better than all the 28 compared models. Moreover, the technique protects data privacy, solves the problem of data silos, provides a new concept for the conventional decomposition-ensemble prediction method, and is more in accordance with the physical relevance of wind power prediction.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.

Mathematical Models and System of Intelligent Servo for High-Efficiency Electrical Discharge Assisted Arc Milling on Difficult-to-Cut Materials

Difficult-to-cut materials, such as superalloys and titanium alloys, have been increasingly used in aerospace and other fields. However, commonly used mechanical milling methods for difficult-to-cut materials have problems, including low processing efficiency and severe tool wear. Electrical discharge assisted arc milling (EDAAM) is a novel high-efficiency machining method proposed by the authors to solve these processing problems. The machining efficiency achieved by EDAAM when processing difficult-to-cut materials is much higher than that achieved by mechanical milling and conventional electrical discharge machining (EDM) milling. However, if EDAAM adopts the traditional EDM servo control method, it will easily form deep and large discharge craters on the surface of the workpiece, which may lead to high machining errors and cause the workpiece to be scrapped. To ensure normal processing, low-energy discharge parameters that may reduce the processing efficiency have to be adopted. To address the above issues and achieve the optimal machining condition of difficult-to-cut materials, mathematical models and system of intelligent servo for EDAAM are proposed. An EDAAM discharge information dataset is obtained, and the intelligent recognition and classification method for discharge states is proposed. The EDAAM intelligent servo control system is proposed based on mathematical models created. The experimental results of superalloy Inconel 718 by EDAAM show that, compared with the nonintelligent servo control system, the intelligent servo control system can increase the material removal rate by up to ten times, reduce the relative electrode wear rate by 37%, and avoid the scrapping of the workpiece.

Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms.

Innovative strategies are necessary to enhance prostate cancer diagnosis whilst reducing unnecessary and invasive repeat biopsies. This study aimed to determine the significant parameters affecting repeat prostate biopsy outcomes and develop an optimal machine learning algorithm for predicting positive repeat prostate biopsy results. We analysed data from 174 men who underwent repeated prostate biopsies between January 2008 and December 2022. Systematic multiple-core, ultrasound-targeted prostate biopsies were performed, each two samples from prostatic transitional zone and peripheral zone were obtained bilaterally. Clinical characteristics were collected, including patients' age, initial prostate volume, prostate-specific antigen (PSA) level, free PSA (fPSA)/PSA ratio, biopsy core numbers, pathological result; The time interval between first and latest prostate biopsy; Latest PSA level, fPSA/PSA ratio, biopsy core numbers; And final pathological diagnosis. Six feature selection methods, namely, variable ranking, correlation matrix, random forest regression, recursive feature elimination, cross-validation and forward selection, were employed to identify key influencing factors for repeat biopsy outcomes. Subsequently, the performance of seven machine learning algorithms, namely, multivariable logistic regression (LR), K-nearest neighbour search (KNN), support vector classification (SVC), decision tree (DT), random forest classifier (RF), naïve Bayes classifier (NBC) and gradient booster tree (GB), was assessed based on accuracy, misclassification, recall, specificity, precision and receiver operating characteristic (ROC) area under the curve (AUC). About 70% of patients were used as the training dataset, meanwhile remaining 30% as validation dataset. 52 were ultimately diagnosed with prostate cancer following the final pathological examination. The remaining 122 patients were negative. Amongst six feature selection methods, the variable ranking emerged as the most effective method for identifying the essential factors influencing repeat biopsy results. Amongst the machine learning algorithms, SVC demonstrated superior accuracy (0.7365), low recall rate (0.2500) and low misclassification rate (0.2093) for both patients with cancer and healthy individuals. Meanwhile, the ROC curve of SVC showed a relatively high AUC (0.6871). We developed an SVC-based machine learning algorithm for predicting positive repeat prostate biopsy results. Our analysis revealed that initial and latest prostate volumes, initial and latest PSA levels, latest fPSA/PSA ratio and age are significant factors for this model.

Process parameters optimization in machining of monel 400 alloy using abrasive water jet machining

This research examines the optimum machining features for abrasive water jet cutting of Monel 400 alloy. This alloy system faces significant challenges in conventional machining and can be surpassed by using the coolest machining technology, such as AWJM. In this experimental investigation, the water beam jet pressure (AP), the nozzle jet traverse speed (NS), and the abrasive flow rate (AMF) were used to explore the kerf taper angle (Kta) and surface roughness (Ra) response variables. The experimental L-9 layout (Taguchi's/L-9(−|-) standard orthogonal array) was used to run the experiment. The S/N ratios for each experimental run were determined using a mono-output response optimization technique. The input characteristics, such as water beam jet pressure (AP) and abrasive flow rate (AMF), were determined to have the most substantial effects on kerf taper angle (Kta) and surface roughness (Ra). While increased water pressure (AP) caused a narrowed jet, reducing the kerf taper angle (Kta) and surface roughness (Ra). Its simultaneous abrasive mass flow rate (AMF) increase causes constant erosion on the cutting front, resulting in a smooth surface roughness (Ra) and less kerf taper angle (Kta). Confirmation tests were carried out using the optimal input parameter combinations found by the statistical program to establish effective solutions to the machining.

Dual-energy X-ray transmission identification method of multi-thickness coal and gangue based on SVM distance transformation

By weakening the influence of dual-energy X-ray transmission (DE-XRT) thickness on the coal and gangue identification, this study aims to reveal the effect of thickness, density, and coal type on sorting. Considering the imaging law of coal and gangue in R-I two-dimensional extended space, on the basis of particle swarm optimization (PSO) and support vector machine (SVM), a distance transform image processing and identification method is provided in this study. For this method, the distance between the (R, I) coordinates of coal and gangue and the SVM hyperplane is expressed as the relative density, indicating the details of the internal density change of the coal and gangue target. The average value ME of a single target distance d and the proportion RA of positive value of d can be used as sorting parameters to adjust the sorting. Both ME and RA are verified to have high correlation with density. When the gangue density is higher than 1.8 g/cm3, ME and RA are set to 0.05 and 50%, respectively, thereby realizing the gangue pre-discharge of 5 ∼ 150 mm thick raw coal. Raw coal pre-discharge has the identification rate of 100%, and the recognition rate of different densities and coal types mixed is 96.32%. Within the thickness range of 5 ∼ 150 mm, ME and RA still depend on thickness. By adjusting ME and RA, coal with large thickness and low density may be excluded by mistake. Compared with RA, ME exhibits a higher correlation with density and a lower correlation with thickness. Hence, ME can be selected to participate in regulatory sorting, thereby obtaining more accurate results. For parameter selection and regulation of DE-XRT separation of multiple thicknesses, densities, and coal types, this study provides a new method and practical guidance.

State-of-health estimation for lithium-ion battery using model-based feature optimization and deep extreme learning machine

Accurate and reliable estimation of state of health (SOH) is essential for the safe and efficient operation of battery energy storages. However, random charging/discharging behaviors and ambient conditions complicate the online SOH estimation. Aiming at an accurate and robust online SOH estimation for lithium-ion batteries, this paper proposes a SOH estimation method using model-based feature optimization and improved machine learning. First, an empirical model-based voltage reconstruction method is proposed to reconstruct the voltage curve for solving disturbance-free different time (DT) curves under measurement noises and high C-rates and extract model-associated health features (HFs). Then, an optimal charging voltage window (CVW) determination method is proposed, which extracts the CVW-associated HFs by determining the optimal CVW from partial charging voltage ranges. Finally, a set of informative and multi-attribute HFs are extracted to train an improved deep extreme learning machine (DELM) mode for online SOH estimation. The proposed method is validated based on three datasets of batteries with different materials. The results demonstrate high accuracy and strong robustness to partial voltage range, noise corruption, and ambient temperature fluctuation.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

A Novel Political Optimizer-Based Feature Selection with an Optimal Machine Learning Model for Financial Crisis Prediction

In today’s digital environment, business intelligence advances make it difficult to stay competitive and up to date on business trends. Decision-making in the financial industry is increasingly being powered by big data and machine learning. A decision-making process may be thought of as any sequence of processes that an individual goes through in order to select the option or course of action that is most suitable to meet their needs and necessities. The ability to anticipate the onset of a financial crisis is a significant economic phenomenon. A nation’s economic development and strength can be gauged by its capacity to provide an accurate assessment of the number of failed firms and the frequency with which they fail. The economics of the globe have been ravaged by recent global crises like as the COVID-19 pandemic and other recent environmental, financial, and economic disasters, which have marginalized efforts to construct a maintainable economy and civilization. The health and growth of a nation’s economy can be determined by precisely estimating the number of enterprises that will fail and the number that will succeed. Historically, there have been numerous strategies for constructing a successful financial crisis prediction (FPC) method. Effectively predicting business failures is a gauge of a country’s economic health. Several strategies are available for effective FCP. Classification performance, forecast accuracy, and legality are insufficient for practical use. Several of the suggested methods work for some issues. The specific dataset is not expandable. To improve classification, design a good prediction model adaptable to several datasets. An effective financial crisis prediction method (FPC) requires the right qualities. ML models can also be used to classify a company’s financial health. This research presents political optimizer-based feature selection (POFS) with optimal cascaded deep forest (OCDF) for FCP in big data environments. Hadoop Map Reduce handles huge datasets. POFS reduces computing complexity by handling feature selection. POFS is an original FCP algorithm categorization using OCDF. SFO is used to optimize CDF model parameters. A thorough simulation study was performed to evaluate POFS performance on benchmark datasets OCDFs. The results confirmed the POFS-OCDF method’s superiority over state-of-the-art approaches. With an outstanding sensitivity of 0.912, specificity of 0.953, accuracy of 0.944, F-score of 0.930, and Matthews correlation coefficient (MCC) of 0.912, the proposed POFS-OCDF technique has shown optimum results. The experimental results demonstrated that the POFS-OCDF technique outperformed other recently developed strategies on a variety of criteria. As previously stated, Sunflower optimization (SFO) is also used to tune the Cascaded Deep Forest (CDF) parameters. A detailed simulation analysis is performed based on the benchmark dataset to evaluate the higher classification efficiency of the POFS-OCDF technique. The invention of the POFS algorithm for FCP exemplifies the work’s originality.