K-Star Algorithm Research Articles

Using machine learning algorithms for predicting bridge vertical deflection

Machine learning (ML) is being increasingly used to ease structural health monitoring and management of bridges. Therefore, the main objective of this study is to apply two state-of-the-art ML algorithms, namely locally weighted learning (LWL) and K-Star for the prediction of vertical deflection of composite bridges. To accomplish the objective, 83 track loading tests were carried out at various bridges located in Vietnam and deflection data was collected for models’ development. Model's validation and comparison were carried out using different popular methods, namely mean absolute error, root mean squared error and R on both training (70%) and validation (30%) datasets. The results of this study indicate that the K-Star algorithm outperforms LWL in predicting the vertical deflection of composite bridges. Consequently, K-Star can serve as an effective tool for rapidly predicting vertical deflection, thereby facilitating more efficient bridge health monitoring and management. This efficiency can lead to significant time and cost savings in maintaining the structural integrity of composite bridges.

Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches

The optimal management of water resources depends on accurate and reliable streamflow prediction. Therefore, researchers have become interested in the development of hybrid approaches in recent years to enhance the performance of modeling techniques for predicting hydrological variables. In this study, hybrid models based on variational mode decomposition (VMD) and machine learning models such as random forest (RF) and K-star algorithm (KS) were developed to improve the accuracy of streamflow forecasting. The monthly data obtained between 1956 and 2017 at the Iranian Bibijan Abad station on the Zohreh River were used for this purpose. The streamflow data were initially decomposed into intrinsic modes functions (IMFs) using the VMD approach up to level eight to develop the hybrid models. The following step models the IMFs obtained by the VMD approach using the RF and KS methods. The ensemble forecasting result is then accomplished by adding the IMFs’ forecasting outputs. Other hybrid models, such as EDM-RF, EMD-KS, CEEMD-RF, and CEEMD-KS, were also developed in this research in order to assess the performance of VMD-RF and VMD-KS hybrid models. The findings demonstrated that data preprocessing enhanced standalone models’ performance, and those hybrid models developed based on VMD performed best in terms of increasing the accuracy of monthly streamflow predictions. The VMD-RF model is proposed as a superior method based on root mean square error (RMSE = 13.79), mean absolute error (MAE = 8.35), and Kling–Gupta (KGE = 0.89) indices.

Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems.

Urban Functional Zone Classification Based on POI Data and Machine Learning

The identification of urban spatial functional units is of great significance in urban planning, construction, management, and services. Conventional field surveys are labour-intensive and time-consuming, while the abundant data available via the internet provide a new way to identify urban spatial functions. A major issue is in determining point of interest (POI) weights in urban functional zone identification using POI data. Along these lines, this work proposed a recognition method based on POI data combined with machine learning. First, the relationship between POI data and urban spatial function types was mapped, and the density of each type of POI was calculated. Then, the density values of each type of POI in the study unit were used as feature vectors and combined with the Kstar algorithm to identify urban spatial functions. Finally, the identification results were validated by combining multiple sources of POI data. From the acquired sampling results, it was demonstrated that the proposed method achieved an accuracy of 86.50%. The problem of human bias was also avoided in determining POI weights. High recognition accuracy was achieved, making urban spatial function recognition more accurate and automatable.

Semi-Supervised k-Star (SSS): A Machine Learning Method with a Novel Holo-Training Approach

As one of the entropy-based methods, the k-Star algorithm benefits from information theory in computing the distances between data instances during the classification task. k-Star is a machine learning method with a high classification performance and strong generalization ability. Nevertheless, as a standard supervised learning method, it performs learning only from labeled data. This paper proposes an improved method, called Semi-Supervised k-Star (SSS), which makes efficient predictions by considering unlabeled data in addition to labeled data. Moreover, it introduces a novel semi-supervised learning approach, called holo-training, against self-training. It has the advantage of enabling a powerful and robust model of data by combining multiple classifiers and using an entropy measure. The results of extensive experimental studies showed that the proposed holo-training approach outperformed the self-training approach on 13 out of the 18 datasets. Furthermore, the proposed SSS method achieved higher accuracy (95.25%) than the state-of-the-art semi-supervised methods (90.01%) on average. The significance of the experimental results was validated by using both the Binomial Sign test and the Friedman test.

Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach

Machine Vision is an advanced and powerful imaging based technique that has been applied in various fields like robotics, inspection and process control. Machine vision, in industrial terms, is termed as a subcategory of computer vision. The primary aim of the proposed study is to distinguish various visual fault conditions that hinder the performance and life span of photovoltaic (PV) modules using computer vision and a machine learning approach. Literatures state that thermographic and electroluminescence images were used in deep learning for identifying faults in photovoltaic modules. However, the effectiveness of normal RGB images with the fusion of deep learning and machine learning techniques is less explored. This paper deals with the classification of normal RGB images of PV modules acquired using a fusion of deep learning and machine learning techniques. Visual faults like delamination, snail trail, burn marks, glass breakage and discoloration that occur in a photovoltaic module (PVM) were considered in the study. A machine learning approach was used to handle this problem which contains three stages: (i) feature extraction, (ii) feature selection and (iii) feature classification. Initially, the features from the aerial images of PVM (acquired from unmanned aerial vehicles (UAVs) equipped with digital cameras) were extracted using convolutional neural networks (CNN). Secondly, J48 decision tree algorithm was employed to select the features of utmost significance and dominance from the extracted image features. Finally, a set of lazy classifiers like locally weighted learning (LWL), K-star algorithm (KS), nearest neighbor (NN) and k-nearest neighbor (kNN) were adopted to execute the classification task on the selected image features. The classification accuracies of all the aforementioned classifiers were compared and it was found that the k-nearest neighbor classifier achieved a maximum accuracy of 98.95% with a lesser computational time of 0.04 s.

Sistem za praćenje stanja alata u više tačaka - uporedna studija

In the metal removal process, the condition of the tool plays a vital role to achieve maximum productivity. Hence, monitoring the tool condition becomes inevitable. The multipoint cutting tool used in the face milling process is taken up for the study. Cutting inserts made up of carbide with different conditions such as fault-free tool (G), flank wear (FW), wear on rake face (C) and tool with broken tip (B) are considered. During machining of mild steel, vibration signals are acquired for different conditions of the tool using a tri-axial accelerometer, and statistical features are extracted. Then, the significant features are selected using the decision tree algorithm. Support Vector Machine(SVM) algorithm is applied to classify the conditions of the tool. The results are compared with the performance of the K-Star algorithm. The classification accuracy obtained is encouraging hence, the study is recommended for real-time application.

Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm

Vibration-based fault diagnosis is one of the widely used techniques for condition monitoring of the machines equipped with a gearbox. Severe operating conditions of gearbox result in gear tooth failure. To develop an effective fault diagnosis technique for the mechanical system, a machine learning approach is highly necessary and plays a vital role in the area of condition monitoring. This paper presents the vibration-based fault diagnosis of IC engine gearbox operating under actual running condition. An Eddy current dynamometer is used to apply the external load on the output shaft of the engine. Driving gear with healthy condition and progressive tooth defect conditions are considered for the analysis. The vibration signals of engine gearbox under various gear tooth conditions are measured. Discrete wavelet transform features are extracted from the vibration signals and more contributing features for classification are selected using decision tree algorithm. The Lazy based classifiers viz, k-nearest neighbour algorithm, K-star algorithm and locally weighted learning algorithm are used for classification. A comparative study of these classifiers is made using percentage of classification accuracy. The maximum classification accuracy of about 97.5% is achieved by the K-star algorithm. Based on the experimental results, K-star algorithm and discrete wavelet transform technique can be used for diagnosing the gear faults in IC engine gearbox using vibration signals.

Driver’s black box: a system for driver risk assessment using machine learning and fuzzy logic

Risky driving behaviors can cause accidents, which may result in major material and moral damages. Due to the increase in road accidents, it has become an important issue to identify risky driving behaviors and reward people who drive safely. With the development of technology, it is now possible to model driving behavior through advanced sensors integrated into embedded systems. In this study, we modeled four major risky driving behaviors and created driver profiles using data obtained from accelerometer and gyroscope sensors and applying widely used machine learning algorithms in behavior analysis, including the C4.5 Decision Tree, Random Forest, Artificial Neural Network, Support-Vector Machine, K-Nearest Neighbor, Naive Bayes, and K-Star algorithms. Risky driving behaviors and their risk levels were evaluated in accordance with the expert opinions of traffic officers, and driver risk was modeled using the fuzzy logic method. The applied machine learning algorithms were compared using common validation metrics such as accuracy, f-measure, precision, and recall. In our experiments, the K-Star algorithm was the most successful algorithm, with 100% accuracy. As a result, a highly accurate, low-cost system which acts as the driver’s black box was developed. The system can be integrated into vehicles and it can record the driver’s behaviors and identify the risky ones. It can also open up new horizons for insurance companies to utilize usage-based policies, in which customers who drive safely are rewarded with lower car insurance premiums, encouraging others to do the same.

Machine Learning for Breast Cancer Classification Using K-Star Algorithm

Machine Learning for Breast Cancer Classification Using K-Star Algorithm

Tree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways

In this study, under the skimming flow regime, energy dissipation was investigated with data-driven methods. The data set obtained from the laboratory experiments were modelled by different machine learning (ML) methods, including support vector machine (SVM), K-star (K*) algorithm and artificial neural networks (ANN). Afterwards, for the first time in the literature, linear and nonlinear ensemble models were established in order to improve the accuracy of single models in predicting energy dissipation. Simple average (SA-E) and weighted average (WA-E) were performed as linear ensemble models while M5 Model Tree (M5-MTE) and Random Forest (RF-E) were used to establish non-linear ensemble models. The model results were evaluated according to performance metrics, such as Coefficient of Correlation (CC), Percent Bias (PBIAS), Performance Index (PI), Willmott’s index of agreement (WI) and Nash-Sutcliffe efficiency criteria (NSE). The NSE values are calculated as 0.986, 0.909 and 0.985 for SVM, K* and ANN models, respectively. Moreover, for the ensemble models, higher NSE values were obtained for both linear (NSESA-E = 0.9887, NSEWA-E = 0.9916) and tree-based non-linear (NSEM5 MT-E = 0.9963, NSERF-E = 0.9974) models. Overall, it can be stated that tree-based ensemble models make better predictions for energy dissipation calculation in step spilways compared to single ML methods.

Oil Palm Fresh Fruit Bunches Maturity Prediction by Using Optical Spectrometer

The accuracy in grading the maturity level of oil palm fruits is one of the important factors for the milling industry since it can directly affect the oil palm yield. Numerous studies have investigated for oil palm maturity classification using different approaches. However, there is a lack of clear guidance on which algorithm or technique to be applied for the oil palm maturity classification under different situations. In this research, several machine learning algorithms in the Weka data mining tool were applied for building classifier models and tested on samples of fresh fruit bunch (FFB). The maturity grade determined from the extracted sample features under the spectrometer is compared to the results of the manual standard grader of the milling industry. The validated result shows that simple lazy KStar algorithm performs better with 63% classifier model performance and the weighted average of receiver operating characteristics (ROC) curve area of 83.2%, which established the classifier model accuracy and enhances the model generalization when applied to the new set of test data for validation. The results of these studies make a contribution to the ongoing research in evaluating new techniques for oil palm maturity determination.

Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm

A belt conveyor system is a mechanical bulk material handling equipment which conveys millions of tons of material from one end to another, in core mechanical industries. Self-Aligning Troughing Roller (SATR) is one of the critical components in a belt conveyor system, It is pretty important in running the belt conveyor in good condition. Self-aligning troughing roller has machine elements like ball bearing, central shaft and the outer shell. In belt conveyor system, certain faults such as ball bearing fault (BF), central shaft fault (CSF), combined shaft and ball bearing fault (SBF) occurs frequently. For further research in this area, a prototype experimental set up has been made with the above mentioned faults, and the vibration signals were acquired from the experimental set up. From the raw vibration signals acquired, a set of significant parameters was identified, called as features. All the identified features may not be used for classification of faults. Hence, the significant features used for classification were found out using decision tree method. These descriptive statistical features such as mean, median, sum etc were calculated and used for classification. In the successive stage k-star algorithm was used for fault diagnosis. The K-star algorithm achieved 91.7% fault classification accuracy, which acknowledged that the algorithm has lots of potential in the field of fault diagnosis applications. The results were compared with the benchmarked algorithm such as Artificial Neural Network (ANN).

Tool wear rate prediction using discrete wavelet transform and K-Star algorithm

In the present study, an attempt has been made to predict flank wear during milling operation with the help of signal processing and machine learning techniques. The vibration and acoustic emission signals obtained from the spindle of milling machine with variations in feed and depth of cut are decomposed into various levels using symlet wavelet. To select the best level permutation entropy criteria were applied. Level giving minimum permutation entropy was selected for the calculation of statistical features. Eleven statistical features such as skewness, kurtosis, mean, etc. were extracted from symlet wavelet and feature vector is formed. To select the relevant features, correlation-based feature selection criterion was used for reducing the size of feature vector. Feature vector with vibration signals and acoustic emission signals is fed into machine learning techniques such as linear regression and K-Star to predict the flank wear measured during milling operation. It is observed that K-Star gives higher prediction rate of tool wear with both training and testing of the classifier and feature vector with the reduced feature set with acoustic emission signals gives better prediction accuracy compared to vibration signals.

Analysis of Data for Diabetics Patient

Diabetes, a disease responsible for different kinds of diseases such as heart attack, kidney disease, blindness and renal failure etc. The most common disorder is the endocrine (hormone) system, occurs when blood sugar levels in the body consistently stay above normal. There are two types of diabetic; one is body's inability to make insulin and another is body not responding to the effects of insulin. In our developing country Bangladesh, Diabetes is a costly disease whose risk is increasing at alarming rate. This paper evaluates the selected classification algorithms for the classification of some Diabetes patient datasets. Classification algorithms considered here are Naive Bayes classification (NBC), Bagging algorithm, KStar algorithm, Logistic algorithm and Hoeffding tree. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity. Collected datasets of diabetes affected people are firstly preprocessed then some investigation based on mentioned algorithm has been executed successfully. From the investigation result it is found that, KStar algorithm is the best as it gives high accuracy with the low error. Here it is said that, some parameters are responsible for diabetes.

Implementing K-Star Algorithm to Monitor Tyre Pressure using Extracted Statistical Features from Vertical Wheel Hub Vibrations

Implementing K-Star Algorithm to Monitor Tyre Pressure using Extracted Statistical Features from Vertical Wheel Hub Vibrations

Fault Diagnosis of Helical Gearbox Using Vibration Signals through K-Star Algorithm and Wavelet Features

Objectives: Gears are machine elements that transmit motion by successively engaging teeth. In technical terms, gears are used to transmit motion. Fault in gears can lead to major problems which may end up in affecting the gear’s functionality. Hence, fault diagnosis at an initial stage is of utmost importance to reduce losses that might occur. Continuous monitoring of the gears is very necessary. Vibration signals recorded for good and faulty conditions are used for fault detection in the helical gearbox. The fault diagnosis is done using feature extraction, feature selection and feature classification. Firstly, feature extraction was carried out using MATLAB software. Feature selection was done using J48 classifier. The classification accuracies for different conditions were calculated and compared by using K-Star classifier and the results obtained were very promising. Methods/Analysis: Vibration signals were obtained from the experimental set up of the helical gearbox. The recorded signals were then used for feature extraction using MATLAB through different wavelet features. The total number of signals extracted was 448 with each class consisting of 64 signals. The families of wavelets taken into account for fault diagnosis were Haar, Discrete Mayer, Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflet and Symlets. In wavelet selection, signals were split into different frequency components and each component was studied with a resolution matched to its scale. J48 classifier was used to carry out the feature selection process and decision tree was obtained for Sym 8 wavelet. The best combination of nodes was visualized and further feature classification was done on these nodes. By varying the global blends the optimum number of objects was selected to obtain the highest classification accuracy. Finding: The classification accuracy for the built model was 91.74%. The data extracted from the vibration signal is used for the classification purpose. This maximum classification accuracy was obtained with K star algorithm. Novelty/Improvements: Wavelet selection was different from Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes. K Star algorithm was used to carry out the fault diagnosis.

Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal

This paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis.

Prosthetic Arm Control with Statistical Features of EEG signals using K-star Algorithm

Prosthetic Arm Control with Statistical Features of EEG signals using K-star Algorithm

Implementing K-Star Algorithm to Monitor Tyre Pressure using Extracted Statistical Features from Vertical Wheel Hub Vibrations

Objectives: Tyre pressure monitoring systems are automotive electronic systems used to monitor the automobile tyre pressure. The existing systems use pressure sensors or wheel speed sensors. They depend on batteries and radio transmitters which would add up to cost and complexity. Methods/Analysis: This paper proposes a new machine learning approach to monitor the tyre pressure. Vertical vibrations are extracted from a wheel hub of a moving vehicle using an accelerometer and are classified using machine learning techniques. The statistical features are extracted from the vibration signal and the features are classified using K Star algorithm. Findings: A reasonably high classification accuracy of 89.16% was obtained. Application/Improvements: The proposed model can be used for monitoring the automobile tyre pressure successfully.