Sequential Floating Forward Selection Research Articles

A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays

Early detection of faults in photovoltaic (PV) systems is crucial for improving their lifespan and reliability. Conventional protection devices may not be able to detect electrical faults under critical conditions. This usually occurs due to (i) the current-limiting nature and non-linear output characteristics of PV arrays, (ii) the changing environmental conditions (such as irradiance and temperature variations), and (iii) the influence of the maximum power point tracker (MPPT), particularly in the presence of critical fault impedance values or mismatch levels. Therefore, modern data-driven methods are required for fault detection and classification in PV arrays. However, careful investigation in previous studies reveals the existing gaps and limitations, such as poor accuracy, and incomprehensive models which have not considered various electrical faults, low mismatch levels, and critical fault impedance values. To this end, in this study, a comprehensive multilayer model is proposed which consists of six layers for diagnosis, classification and severity identification of electrical faults in PV arrays. Each layer adopts a weighted ensemble learning (WEL) algorithm consisting of three classifiers namely Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR). Besides, a genetic algorithm (GA) is employed in each layer to find the optimal weights for each classifier (i.e., SVM, NB and LR). The initial dataset is acquired through an investigation into the PV array current–voltage (I-V) characteristic curve and extraction of several features under various faulty and normal conditions. To reduce the dataset dimensionality thus computationally simplifying the training process, the sequential floating forward selection (SFFS) algorithm is utilized in each layer as a powerful feature selection technique. The results show a highly accurate performance in fault diagnosis, classification, and severity assessment, with an average simulation and experimental accuracies of 98.9% and 98.37%, respectively.

Efficient and Secure Communication in Vanets Using Block Chain

This project describes the design and implementation of a non-contact vibration picker for capturing data from spinning machinery in order to detect bearing faults early. The Hilbert transform is used to denoise the collected vibration signals, and the dataset is then analyzed by Principal Component Analysis (PCA) and Sequential Floating Forward Selection (SFFS) for dimensionality reduction and feature selection, respectively. The most essential attributes are then used to identify and categorize various bearing issues using Support Vector Machines (SVM) and Artificial Neural Network (ANN) algorithms. The entire methodology provides an effective and proactive way for bearing health monitoring and maintenance, emphasizing fast defect identification and leading to significant savings in time, effort, and equipment maintenance costs.

Grade identification of rice eating quality via a novel flow-injection voltammetric electronic tongue combined with SFFS-BO-SVM

Given the importance and complexity of grade identification for rice eating quality, this study aimed to use a self-established flow-injection voltammetric electronic tongue (FIE-tongue) combined with SFFS-BO-SVM to replace human sensory evaluation. A novel FIE-tongue was firstly designed and established in this work, and the optimal rice pretreatment conditions were determined by fractional factorial design (FFD) which used for FIE-tongue detection. Based on optimized detection data, the optimized features and hyperparameters of 16 samples in three eating quality grades for SVM model were obtained by Sequential Floating Forward Selection (SFFS) algorithm and Bayesian Optimization (BO) algorithm, respectively. Compared to the original SVM model established by all features, the accuracy of SFFS-BO-SVM model for different grades were all higher than 92%, and have the increasement of 5.5%, 18.8%, and 11.4%, respectively. Therefore, the FIE-tongue combined with SFFS-BO-SVM method should be promising for rice eating quality grading and other food quality control.

Fault Prediction Using Fuzzy Convolution Neural Network on IOT Environment with Heterogeneous Sensing Data Fusion

Because of the developing worldwide familiarity with natural issues, the expansion of sun-based power plants has turned into an unmistakable element of the energy scene. Nonetheless, keeping up with these sunlights-based offices, especially with regards to recognizing breaking down photovoltaic (PV) cells inside huge scope or far off establishments, presents critical difficulties. The focal goal of our exploration project is to resolve this issue by empowering the convenient identification of flaws in PV cells, consequently possibly saving significant time, exertion, and upkeep costs, especially as for the pivoting gear ordinarily utilized in sun-based power plants. I have developed a non-contact vibration pickup system that makes it possible to collect vibration data from PV cells operating at various speeds and loads without having to physically connect them to machine tools. In addition, I rank and select the most relevant features for accurate fault detection using the Sequential Floating Forward Selection (SFFS) method and Principal Component Analysis (PCA) to reduce the extracted features dimensionality. This thorough methodology offers a promising answer for improve the effectiveness and dependability of sun-oriented power plant upkeep while adding to the more extensive objectives of supportable energy creation and natural safeguarding.

A conceptual IoT framework based on Anova-F feature selection for chronic kidney disease detection using deep learning approach

Chronic kidney disease (CKD) is becoming an increasingly significant health issue, especially in low-income countries where access to affordable treatment is limited. Additionally, CKD is associated with various dietary factors, including liver failure, diabetes, anemia, nerve damage, inflammation, peroxidation, obesity, and other related conditions. Therefore, early prediction of CKD is important to progress the functionality of the kidney. In recent times, IoT has been widely used in a diversity of healthcare sectors through the incorporation of monitoring devices such as digital sensors and medical devices for patient monitoring from remote places. To overcome the problem, this research proposed a conceptual architecture for CKD detection. The sensor layer of the architecture includes IoT devices to collect data and the proposed classifier, MLP (Multi-Layer Perceptron), utilizes the Anova-F feature selection technique to effectively detect CKD (Chronic Kidney Disease). In addition to MLP, four other classifiers including ANN (Artificial Neural Network), Simple RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), and SVM (Support Vector Machine), are employed for comparative analysis of accuracy. Furthermore, three additional feature selection techniques, namely Chi-squared, SFFS (Sequential Floating Forward Selection), and SBFS (Sequential Backward Floating Selection), are utilized to evaluate their impact on the accuracy of CKD detection. Our proposed method outperforms all other approaches with a remarkable accuracy of 99 % while maintaining efficient computational time. This advancement is crucial in developing a highly accurate machine capable of predicting CKD in remote areas with ease.

A novel committee-based framework for modeling groundwater level fluctuations: A combination of mathematical and machine learning models using the weighted multi-model ensemble mean algorithm

water level (GWL) fluctuations simulation can be divided into three general categories: Analytical solution, conceptual or physical-based models, and data-based or data-driven models. The goal of this research is to develop the MODFLOW conceptual model and the GMDH data-driven model as single models (SMs) for GWL modeling, and then to combine these models to develop a novel mathematical-machine learning model based on a weighted multi-model ensemble mean approach (MMWEM) for a more accurate GWL simulation. For this purpose, the Miqan aquifer, located in northeastern Iran, was selected as a case study. Hydrological and hydrogeological data of the Miqan wetland for ten years (September 2013 to August 2023) with a monthly time step were used to compile models. First, the MODFLOW model was developed for one month (steady-state), then calibrated for nine years (unsteady-state), and finally tested for one year. In the second step, the GMDH model was developed. The grasshopper optimization algorithm (GOA) and the singular value decomposition (SVD) algorithms were used to determine the Ivakhnenko polynomial coefficients of the GMDH (SVD-GMDH and GOA-GMDH). 80% of the data was used to develop SVD-GMDH and GOA-GMDH, and other data was used to test models. Consequently, the sequential forward floating selection (SFFS) method was used to select the best input variables for the SVD-GMDH and GOA-GMDH. The coefficient of determination (R2), root mean squared error (RMSE), and Nash-Sutcliffe Efficiency coefficient (NSE) were used to evaluate the performance of the models. Based on the results of this study, using each of the SMs, GWL can be simulated with acceptable accuracy. However, a closer examination of the results of SMs showed that the MODFLOW has the worst performance among SMs, while the GOA-GMDH has the best function for simulating GWL. In the third step, SMs was used to develop the MMWEM. The GWL of 13 of the 34 piezometers studied in this study was better simulated with the MODFLOW and GOA-GMDH than the SVD-GMDH. Therefore, these models were used to develop the MMWEM for these piezometers. For GWL simulation in 21 piezometers, the SVD-GMDH and GOA-GMDH perform better than the MODFLOW, so these models were used to develop the MMWEM in these piezometers. Finally, the results of the SMs Moreover, the MMWEM were compared with each other. Based on the results of this study, the MMWEM performs better than SMs. In many studies, only the performance of each numerical model and machine learning to simulate groundwater level (GWL) fluctuations is evaluated. Then, their results are compared with each other. However, according to this study, the combination of the results of these models is a big step in improving the accuracy of the groundwater level simulation process. In addition to simulating the groundwater level, this method can simulate other quantitative and qualitative parameters of water resources.

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature selection methods: t-test and sequential forward floating selection (SFFS). The subset of selected features was presented to the k-nearest neighbor classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.

Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks.

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons

Minimum Miscibility Pressure (MMP) plays a crucial role in subsurface gas injection processes. Hence, the accurate determination and analysis of the effective parameters on MMP are vital for a successful injection project. In this study, different Machine Learning (ML) algorithms are used to identify the most influential parameters on the MMP and develop reliable predictive models. A comprehensive database containing 812 samples (almost all the available experimental data set published from 1961 to 2022) is collected from 66 open literature studies. Six algorithms were employed for feature selection: Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Backward Floating Selection (SBFS), Lasso Regression (LR), and Random Forest Feature Importance (RFFI). These feature selection algorithms were evaluated using a Decision Tree (DT) regressor. The most important features from 42 potential features were x C₅, x C₆, x C₂-C₆, MW C₇⁺, MW Gas, TC, and T, selected using the SBFS method based on the Root Mean Squared Error (RMSE). Using the best-selected features, six predictive models were developed, including LR, DT, Random Forest (RF), Extra Trees (ET), Stacking Regressor (SR), and Voting Regressor (VR). The SR predictive model performed the best with RMSE and R2 values of 18.37 bars and 0.96, respectively, for the testing dataset. The outcomes of this research can be employed for any industrial process involving gas injection into hydrocarbon reservoirs to select the most relevant features in designing the experimental and field trials.

Classification of operational states in porous journal bearings using a semi-supervised multi-sensor Machine Learning approach

Tribosystems are designed for long-lasting operation necessitating condition monitoring to identify critical regimes in time. On the example of porous journal bearings we propose a new approach for the classification of the operational states.Tribometer data from accelerated lifetime tests were used for developing a semi-supervised multi-sensor Machine Learning (ML) classifier. To advance the state-of-the art and gain higher classification accuracy, data from multiple sensors were combined by calculating time and frequency feature sets. The frequencies found in the sensor data were traced using a 3D scanning vibrometer and originated from eigenmodes and vibration states of the system. Relevant features were selected using Sequential Forward Floating Selection (SFFS) to enhance the performance of the model. Labeling was assisted by explorative data analysis techniques, i.e., principal component analysis (PCA) and subsequent hierarchical (Ward) clustering of the feature sets.A tree-based ensemble classifier (Extra-Trees) differentiating four tribological regimes from ‘Run-in’ to ‘Critical’ yielded a classification accuracy of 0.98. The algorithm was able to reliably detect states even when applied to data from previously unseen experiments and to predict terminal failure of a bearing up to 50 h before it occurred.

FEATURE SELECTION FOR ARABIC MISPRONUNCIATION DETECTION BASED ON SEQUENTIAL FLOATING FORWARD SELECTION AND DATA MINING CLASSIFIERS

Feature selection process is used to reduce the feature vector length and identify thediscriminative features. Many acoustic-phonetic features including Mel-Frequency CepstralCoefficient (MFCC), Energy, Pitch, Zero-crossing, spectrum were tested individually for Arabicmispronunciation detection using three classifiers; Random Forest, Bayesian classifier, and BaggedSupport Vector Machine (SVM). The results for Bagged SVM were better than the other twoclassifiers. Top three individual features with highest accuracies were identified for each isolatedArabic consonant. To validate the results, a modified form of Sequential Floating Forward Selection(SFFS) process was used. Results showed that MFCC along with its first and second derivatives,energy, spectrum, and zero-crossing were the most suitable acoustic features for Arabicmispronunciation detection system. The proposed approach provided an average accuracy of 94.9%which was better than the previous best 92.95% for Arabic consonants.

Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction

Surface grinding, being one of the later stages of a manufacturing process, is crucial to achieve the desired workpiece quality by maintaining proper wheel condition. Machine learning and acoustic emission (AE) detection have demonstrated potentials for developing an effective on-line wheel wear monitoring system. However there are inconsistencies among existing studies regarding to signal sampling and feature extraction methods. In the present paper, surface grinding experiments using medium carbon steel workpieces were conducted to determine the effects of using different signal analysis window lengths, feature types, and AE sensor bandwidths on the wheel wear detection performances. In addition, the use of AE transient spikes occurred during wheel entry and exit were analyzed. Feature were extracted from time domain (TD), frequency domain (FD), and time–frequency domain (TFD) representations of the measured AE signals, which were then refined using sequential floating forward selection (SFFS) algorithm to reduce feature redundancy. Support vector machine (SVM) algorithm with linear kernel was used for pattern classification. A number of insights for future monitoring system development were obtained. Firstly, while the lower frequency AE sensor (below 100 kHz) provided adequate classification performances, the higher frequency AE sensor (above 100 kHz) produced more robust results. Secondly, 100% classification rate was achievable using features derived from Fast Fourier Transform (FFT) in combination with descriptive statistics. Thirdly, it was determined that a window length as short as 1 ms (1000 data points) could capture enough signal information using FFT with the higher frequency AE sensor. Lastly, the choice sampling instance did not significantly impact classification outcomes.

Detection of malfunctioning photovoltaic cells in solar panel

Solar energy plays a significant role in electricity generation in today's society. Solar panels are used to generate solar energy. Solar panel consists collection of PV cells which produce the solar power. The amount of electricity produced may be reduced if PV cells fail. Fortunate acknowledgment of deficiencies in Photovoltaic cells can save time, tries and upkeep costs of turning gear. In order to prevent the genuine relationship of vibration pickup to the machine instrument, a non-contact type vibration pickup has been organised and manufactured in this survey to obtain vibration data for Photovoltaic cells prosperity when considering underweight and speed assortment. The main objective is to identify the malfunctioning photovoltaic cells in solar panel. Feature extraction and dimensionality reduction are being applied to the collected data sets. The dimensionality of the removed features was diminished using Principal Component Analysis (PCA) and from that point on the picked features were situated organized by importance using the Sequential Floating Forward Selection (SFFS) method for lessening the amount of data components and noticing the best rundown of capacities.

Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods

The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.

Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment.

Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.

An effective fine grading method of BI-RADS classification in mammography.

Mammography is an important imaging technique for the detection of early breast cancer. Doctors classify mammograms as Breast Imaging Reporting and Data Systems (BI-RADS). This study aims to provide an intelligent BI-RADS grading prediction method, which can help radiologists and clinicians to distinguish the most challenging 4A, 4B, and 4C cases in mammography. Firstly, the breast region, the lesion region, and the corresponding region in the contralateral breast were extracted. Four categories of features were extracted from the original images and the images after the wavelet transform. Secondly, an optimized sequential forward floating selection (SFFS) was used for feature selection. Finally, a two-layer classifier integration was employed for fine grading prediction. 45 cases from the hospital and 500 cases from Digital Database for Screening Mammography (DDSM) database were used for evaluation. The classification performance of the support vector machine (SVM), Bayes, and random forest is very close on the 45 testing set, with the area under the receiver operating characteristic curve (AUC) of 0.978, 0.967, and 0.968. On the DDSM set, the AUC achieves 0.931, 0.938, and 0.874. Using the mean probability prediction, the AUC on the two datasets reaches 0.998 and 0.916. However, they are all significantly higher than the doctors' diagnosis, with the AUC of 0.807 and 0.725. A BI-RADS fine grading (2, 3, 4A, 4B, 4C, 5) prediction model was proposed. Through the evaluation from different datasets, the performance is proved higher than that of the doctors, which may provide great help for clinical BI-RADS classification diagnosis. Therefore, our method can produce more effective and reliable results.

Highlighted Document Image Classification

There are many existing document image classification researches, but most of them are not designed for use in constrained computer resources, like printers, or focused on documents with highlighter pen marks. To enable printers to better discriminate highlighted documents, we designed a set of features in CIE Lch(a* b*) space to use along with the support vector machine. The features include two gamut-based features and six low-level color features. By first identifying the highlight pixels, and then computing the distance from the highlight pixels to the boundary of the printer gamut, the gamut-based features can be obtained. The low-level color features are built upon the color distribution information of the image blocks. The best feature subset of the existing and new features is constructed by sequential forward floating selection (SFFS) feature selection. Leave-one-out cross-validation is performed on a dataset with 400 document images to evaluate the effectiveness of the classification model. The cross-validation results indicate significant improvements over the baseline highlighted document classification model.

Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing

Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth.

Fast, easy-to-use, machine learning-developed models of prediction of flash point, heat of combustion, and lower and upper flammability limits for inherently safer design

This study proposes easy-to-apply machine learning-developed models, which predict four flammability properties of pure organic compounds: the flash point, heat of combustion, lower flammability limit (LFL), and upper flammability limit (UFL). These flammability properties pose a strong impact on the inherently safer design of industrial processes. Similar to quantitative structure-property relationship (QSPR) or group contribution models, machine learning algorithms are utilized in this study to establish predictive models. Compared to previous models, this study uses readily available variables (i.e., the numbers of atomic elements, molecular weights, and normal boiling points) as default variables without the analysis of detailed molecular structures or the in-depth knowledge of chemistry. This study consists of two steps: Step (1) building multiple linear regression (MLR) models by incorporating default input variables and Step (2) building MLR models by incorporating interaction and transformed variables to improve the predictions from the models in Step 1. In Step 1, an optimal subset of predictors is identified by constructing an MLR model via the sequential floating backward selection (SFBS) algorithm. As a result of Step 1, the two constructed models of the flash point and heat of combustion are found to be adequate, while the predictability of LFL and UFL are insufficient. In Step 2, MLR models incorporating nonlinearity and interaction terms are constructed via the sequential floating forward selection (SFFS) algorithm by selecting the optimal subset of default variables. The results show that all the constructed models in Step 2 are adequate as predictive models; the mean absolute errors (MAEs) of the flash point, heat of combustion, LFL, and UFL are 7.31 (5.67 via the SFBS) [K], 60.6 (61.87 via the SFBS) [kJ/mol], 0.21 (0.19 via the SFBS) [vol.%], and 2.44 (2.33 via the SFBS) [vol.%], respectively. Compared to previous models, the approved models in this study provide highly competitive performance with enhanced simplicity and interpretability.

RETRACTED: Identifying non-contact defects in fault bearing

Early diagnosis of bearing failures can save time, effort, and money on rotating machine maintenance. On this test, a non-touch type vibration pickup was designed and refined to capture vibration data for bearing fitness tracking under tight load and pace variations, avoiding the bodily connection of vibration pickup to the system tool. The signal was denoised and fault analysis was performed using a Hilbert rework. Principal Component Analysis (PCA) was used to reduce the dimensionality of the extracted capabilities, and then the chosen capabilities for lowering the amount of enter capabilities and discovering the maximum premier function set, the Sequential Floating Forward Selection (SFFS) method was used to rank them in order of significance. Finally, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to determine and classify the numerous faults in bearings. A comparison of SVM and ANN efficacy was carried out. The results reveal that vibration signatures from advanced non-touch sensors (NCS) correspond well with accelerometer data collected under the same conditions. The classification accuracy obtained by combining the advanced NCS with various sensors mentioned in the literature is comparable to that obtained by using the advanced NCS alone. The proposed method could be utilised to detect automated popularity system faults and issue early warnings, preventing unwelcome and unplanned device shutdowns due to bearing failure.