Radial Kernel Research Articles

Estimating critical depth and discharge over sloping rough end depth using machine learning

Abstract This study uses machine learning (ML) to predict the end-depth structure's discharge and critical depth (yc). Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used in this investigation. The findings indicate that the radial kernel function-based GP model is most suitable compared to other applied models with the lowest root-mean-square error = 0.0021, 0.007, normalized root-mean-square error = 0.0361, 0.0516 representing mean absolute error = 0.0015, 0.004 and the highest coefficient of correlation = 0.9912, 0.9916, Legates and McCabe's index = 0.8839, 0.9026 Willmott's index = 0.9956, 0.9956, and Nash Sutcliffe model efficiency = 0.9823, 09830 for yc for the end-depth structure (yc) and discharge (Q) with the testing stage, respectively. Results of the sensitivity study indicate that the friction coefficient is the most significant input variable compared to other parameters for predicting (yc) and flow running via the thickness model's last stage (Q) using this dataset.

A stable meshless numerical scheme using hybrid kernels to solve linear Fredholm integral equations of the second kind and its applications

The main challenge in kernel-based approximation theory and its applications is the conflict between accuracy and stability. Hybrid kernels can be used as one of the simplest and most effective tools to manage this challenge. This article uses a meshless scheme based on hybrid radial kernels (HRKs) to solve second kind Fredholm integral equations (FIEs). The method estimates the solution via the discrete collocation procedure based on a hybrid kernel that combines appropriate kernels with suitable weight parameters. The optimal parameters in the hybrid kernels can be calculated using the particle swarm optimization (PSO) algorithm based on the root mean square (RMS) error. This technique converts the problem under investigation into a system of linear equations. Moreover, the convergence of the suggested hybrid approach is studied. Lastly, some numerical experiments are included to reveal the accuracy and stability of the hybrid kernels approach. The numerical results demonstrate that the presented hybrid procedure meaningfully reduces the ill-conditioning of the operational matrices, at the same time, it maintains the accuracy and stability for all values of shape parameters.

Adjustable Jacobi-Fourier Moment for Image Representation.

The widely adopted Jacobi-Fourier moment (JFM) is limited by its inability to effectively capture spatial information. Although fractional-order JFM ( FOJFM) introduces spatial information through a fractional-order parameter, the control of spatial information remains inadequate. This limitation stems from the insufficient control over zeros distribution associated with the used moment's radial kernel. To address this issue, we generalize both JFM and FOJFM into a transformed JFM. A transformed function with four parameters is designed, and adjustable JFM (AJFM) is proposed. Two parameters correlate to increasing velocities on the left and right parts of the transformed functions, enabling zeros quantities of radial kernel fall in the left and right parts of the interval. The other two parameters segment the transformed function, adjusting regions where different quantities of zeros fall in. This refined control over the radial kernel's zero distribution enhances the versatility of feature extraction by the AJFM, governed by the introduced parameters. Experimental results demonstrate that AJFM, with properly chosen parameters, can emphasize specific regions within an image more effectively.

Machine-learning applications for differentiation across states/stages of creative thinking based on time-series and time-frequency features of EEG/ERP signals

BACKGROUND: The study presents machine-learning (ML) classification approaches for the state/stage differentiation of creative tasks using the “test-control” approach. The control tasks were considered as the initial stages of the creative activity. Time-series and time-frequency electroencephalography (EEG) data analyses were employed in three divergent thinking tasks: 1) creating endings to well-known proverbs (“PROVERBS”, event-related potential [ERP] paradigm); 2) creating stories (“STORIES”, continuous EEG); 3) free creative painting (“viART”, continuous EEG). AIM: To compare and select effective ML classification approaches for EEG signal separation at different stages or states of creative task performance. METHODS: In this study, 22 individuals participated in the “PROVERBS” (ERP paradigm), 15 in the “STORIES”, and 1 (a longitudinal case study) in the “viART” tasks. Linear and convolutional neural network (CNN) classifiers were used. EEG data were previous artifacts corrected and converted to current source density (CSD). Continuous EEGs were divided into 4-s intervals and 1500 ms after stimulus presentation, were used in ERPs. The EEG/ERP time-frequency maps (Morlet wavelet transformation) for 3–30 Hz were generated for 4-s intervals with 100 ms shift (continuous EEGs in “STORIES” and “viART”) or for 1500 ms after stimulus presentation (ERPs in “PROVERBS”) and consisted of combined images (224×224 px) for frontal (Fz) and parietal (Pz) brain zones. Image classification was carried out using the modified CNN (ResNet50, ResNet18 architectures). RESULTS: The offline classification accuracy of the four-class system (description of a picture, inventing a story plot, continuation of story’s plot, and background with open eyes) in the “STORY” creation task was up to 96.4% [±8.3 SD] with ResNet architectures (ResNet50 and ResNet18). The accuracy of the three states discrimination of the artists’ creative painting (resting state with open eyes, painting on canvas, and viewing the painting) was 86.94% for kernel naive bayes and 98.2% for CNN. For the trained and tested samples given for the CNN in consecutive order (neurointerface mode), the accuracy diminished to 70.0% [11% SD] on average. In the ERP paradigm “PROVERBS”, the classification accuracy of the three-class system (creation of “new” ending, naming of semantic synonym, and remembering of the known ending) was 80.5% [±8.7 SD] for the common spatial pattern, followed by rSVM (radial kernel basis support vector machine), compared with 43.2% [±8.8 SD] for CNN. CONCLUSION: The use of CNNs allowed better classifying of “continuous” long-term states of creative activity. In fast “transient processes” such as ERP, time-series classifiers with spatial filtering proved to be more efficient.

Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?

This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616-0.9955) and low RMSE values (0.0400-0.1031). The prediction set's relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level.The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared.Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly.The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing

We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).

Machine learning classificatory as a tool in the diagnosis of amyotrophic lateral sclerosis using diffusion tensor imaging parameters collected with 1.5T MRI scanner: A case study

The relevance of the study lies in the need to improve the diagnosis of amyotrophic lateral sclerosis (ALS) by utilizing diffusion tensor imaging (DTI) obtained from conventional 1.5 Tesla MRI scanners. The study aimed to investigate the potential of using different machine learning (ML) classifiers to distinguish between individuals with ALS. In this study, five ML classifiers (“support vector machine (SVM)”, “k-nearest neighbors (K-NN)”, naïve Bayesian classifier, “decision tree”, and “decision forest”) were used, based on two DTI parameters: fractional anisotropy and apparent diffusion coefficient, obtained from two manually selected ROIs at the level of the brain pyramids in 47 ALS patients and 55 healthy subjects. The quality of each classifier was evaluated using the confusion matrix and ROC curves. The highest accuracy in differentiating ALS patients from healthy individuals based on DTI data was demonstrated by the radial kernel support vector method (77% accuracy [p=0.01]), while K-NN and “decision tree” classifiers had slightly lower performance, and “decision forest” classifier was overtrained to the training set (AUC=1). The authors have shown a sufficiently accuracy of ML classifier “SVM” in detecting radiological characteristics of ALS in pyramidal tracts.

Solitary Wave Propagation of the Generalized Rosenau–Kawahara–RLW Equation in Shallow Water Theory with Surface Tension

This paper addresses a numerical approach for computing the solitary wave solutions of the generalized Rosenau–Kawahara–RLW model established by coupling the generalized Rosenau–Kawahara and Rosenau–RLW equations. The solution of this model is accomplished by using the finite difference approach and the upwind local radial basis functions-finite difference. Firstly, the PDE is transformed into a nonlinear ODEs system by means of the radial kernels. Secondly, a high-order ODE solver is implemented for discretizing the system of nonlinear ODEs. The main advantage of this technique is its lack of need for linearization. The global collocation techniques impose a significant computational cost, which arises from calculating the dense system of algebraic equations. The proposed technique estimates differential operators on every stencil. As a result, it produces sparse differentiation matrices and reduces the computational burden. Numerical experiments indicate that the method is precise and efficient for long-time simulation.

Estimating Completely Remote Sensing-Based Evapotranspiration for Salt Cedar (Tamarix ramosissima), in the Southwestern United States, Using Machine Learning Algorithms

Accurate estimation of evapotranspiration (ET) is a prerequisite for water management in arid regions. Field based methods estimate point-wise ET accurately, but the challenge is in estimating ET over a region with high accuracies. Machine learning based approaches were taken to estimate ET over a large spatial scale using the Bowen Ratio Energy Balance (BREB) technique. The BREB method depends on terrestrial energy balance equations to estimate ET. Thus, remote sensing-based parameters representing variables in the energy balance equation, and vegetation index representing plant health conditions were used in the model. The study was conducted in the arid areas of the southwestern United States, where dense patches of Salt cedar consume water from the primary water source. The preliminary model used enhanced vegetation index (EVI), global horizontal irradiance (GHI), surface temperature (TS), and relative humidity (RH) as parameters. The k-nearest neighbor method consistently generated poor accuracies. When all the parameters were used, accuracies of the other models varied within 90–94%. When one predictor parameter was dropped, the best model produced accuracies between 90 to 93%, which dropped to 87–92% when a second variable was dropped. Random forest and support vector machine with radial kernel consistently produced the best predictive accuracies.

The verification of hen egg types by the classification of ultra-weak photon emission data

Examples from living systems at various levels of the biological hierarchy and also from natural food products show that ultra-weak photon emission (UPE) has potential applications in the rating of vital functions and quality testing. In this study, the UPE of chicken eggs has been tested regarding the possibility of egg quality verification. The UPE from intact eggs and separated egg parts were subjected to supervised and unsupervised classification methods according to different housing types. The results of unsupervised egg grouping substantially agreed with the types of hen rearing. The Cohen’s Kappa test score for the K-means method was up to K=0.63. Supervised Support Vector Machine (SVM) classifier with radial kernel function achieved a relatively high accuracy (AC), up to 88%, also confirmed by the value of the K-statistics up to 0.81. This study shows that the best result of egg types classification can be obtained using UPE emission data from all egg parts.

Machine learning algorithms accurately identify free-living marine nematode species.

Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903. A total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms. For both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified. These results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

Applying data mining techniques to predict vitamin D deficiency in diabetic patients.

Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.

Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine

Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.

A Study of the Effects of the Shape Parameter and Type of Data Points Locations on the Accuracy of the Hermite-Based

Theoretical approximation ideas served as the driving force behind the research. one can see that the shape parameter's behavior is driven by the kind of problem and the analytical standards that are applied. the primary issue here is not just how it impacts the interpolant's accuracy but also how quickly it converges, or how quickly the error reduces as the number of data nodes rises. Hence, this article considers two globally supported and positive radial kernels and three different patterns of data point locations on the same computational domain. The research specifically studied the effects of the shape parameter and the type of data points locations on the accurate performance of an Hermite-based symmetric approach. The two-dimensional Helmholtz equation and the two-dimensional Poisson equation were used as test functions. The problems were first solved on the three different types of data point locations using linear Laguerre-Gaussians and then, the linear Matern. In each case, the graph of the error against the shape parameter was drawn to enable easy identification of the optimal value of the shape parameter. One important result indicated that, an improved accuracy cannot be achieved without the appropriate value of the shape parameter irrespective of the type of data site used.

Comparative Study of Various Classification and Regression Techniques on Movie Datasets Using R

The entertainment industry has been exponentially growing since its inception and is primarily well-received. Creating content, in this case, Movies has been the bread and butter of the industry and has proven extremely lucrative over the years. In this comparative study, we have picked out various Machine Learning models, viz. Classification Tree, Regression Tree, AdaBoost, SVM (Linear Kernel, Polynomial Kernel, Radial Kernel), XGBoost, Random Forest, Simple Bagging, and Naive Bayes. The Classification models are used to predict the rate of a certain movie winning the Oscar whereas the Regression model implementation estimates the average earning of a film considering the various attributes.

Assessment of Escherichia coli bioreporters for early detection of fungal spoilage in postharvest grape berries

Table grapes are popular for their taste and nutritional value, but they are vulnerable to fungal spoilage that can cause economic losses and food safety concerns. The early detection of the causal agents is essential to implement effective prevention and control measures. Escherichia coli bioreporters are cost-effective, sensitive, and non-invasive tools promising for this purpose. However, their effectiveness for discriminating multiple fungal spoilers in grape berries has not been assessed. In this study, two fungal spoilers, Aspergillus niger and Botrytis cinerea, were isolated from rotting grape berries and identified using phylogenetic analysis. E. coli bioreporters were then noninvasively exposed to inoculated grape berries by three pathogens A. niger, A. westerdijkiae and B. cinerea. SoxSp, fabAp and pspAp bioreporters were among the most inducible ones by the infection-related volatile organic compounds. The response pattern was classified using six machine-learning predictors in combination with various preprocessing techniques. The top-performing model was a random forest (RF) model that utilized the highly-correlated variables (RHCV) preprocessing technique, which achieved 100% accuracy in predicting infection stages of A. niger in grape berries. The most robust predicator to discriminate the infection stages of A. westerdijkiae and B. cinerea was generated using spatialSign preprocessing in combination with support vector machine with radial kernel (svmRadial), both achieving prediction accuracy of 92%. Lastly, RHCV preprocessing coupled with svmRadial classifier was the best method to discriminate between the three fungal spoilers on grape berries, with a prediction accuracy of 80%. The study showed that E. coli bioreporters are highly accurate in predicting early fungal spoilage in grape berries but have moderate prediction specificity in discriminating multiple fungal spoilers.

Several models combined with ultrasound techniques to predict breast muscle weight in broilers

The weight of breast muscle (WBM) is a highly monitored indicator in broiler breeding that can be obtained after slaughtering. Currently, due to the lack of accurate in vivo phenotypes for both genomic and phenotypic selection, genetic gains in WBM fall short of initial expectations. In this study, 1,006 market-age (42 d) broilers from 3 generations over 2 yr were randomly selected, and the breast width (BW), fossil bone length (FBL), breast muscle thickness (BMT), and live weight (LW) were measured exactly in vivo. Eight models, including multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN), were fitted to explore the best regression relationships between breast muscle weight and these indicators. Support vector machine (SVM) methods with both linear kernels and radial kernels were used to fit the models, while 2 decision tree-based machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost), were used to establish the prediction model. The predictive effects of different combinations of independent variables were compared, leading to the conclusion that the EN model achieves the best predictive power when all 4 live features are used as inputs and is slightly better than the other models (R2=0.7696). This method could be applied in practical production and breeding work, leading to substantial cost savings and enhancements in the breeding process.

Estimating the impact of Pregnancy, Systolic and Age on Diabetes for Women by Using Support Vector Regression Model (SVR)

In this paper we have 623 cases of diabetes patients, the data partitioned in to training dataset (500 observations) and testing dataset (123 observations), and the aim is to estimate the impact of pregnancy duration in weeks, systolic and age as factors on diabetes of the women patients for this purpose SVR has been used. According to the results radial kernel function gave highest performance compared to the other kernel functions, the R2 = 83% this implies the factors capable of explaining 83% of diabetes variable with MSE and RMSE of (0.000958 and 0.030956) respectively. And p-values of the three aforementioned variables are less than the significant level of 0.01, implying that the three factors have a statistically significant impact on the response variable. Where Pregnancy duration in weeks has an impact of 0.401 on the patient, that means if duration increase by one week, then diabetes will increase by 0.401 units, also both Systolic and age have a significant positive effect on the response variable, and the amount of impact is (0.621 and 0.557) respectively.

RSSI based Device-free Human Identification

Researchers have explored many methods and techniques for human detection and identification in diverse contexts. One such approach is studying variations of Radio Frequency (RF) signals (e.g., Received Signal Strength Indicator (RSSI)). RSSI based techniques have been widely used in human detection, but not for unique human identification due to its noisy nature. This research focused on investigating the possibilities and limitations of device-free human identification using Wi-Fi RSSI data with machine learning-based classification techniques. To inspect the characteristics of Wi-Fi RSSI data, the authors have conducted statistical analysis techniques. A Kalman filter was applied to minimize the noise in Wi-Fi RSSI data, followed by a feature extraction process. Furthermore, the authors have conducted multiple research experiments in different configurations of receivers and participant numbers. The experimental results show that the human identification accuracy level increases with the number of receivers used for the data collection. Moreover, the authors have identified that human identification accuracy can be further improved by leveraging proper noise reduction methods and feature extraction processes. For a Kalman filter applied and feature-extracted Wi-Fi RSSI dataset of 20 people, the Support Vector Machine (SVM) - Radial Kernel model recorded the highest average accuracy of 99.58%.