Road surface classification based on LBP and  GLCM features using kNN classifier

Fauzi, Arthur Ahmad; Utaminingrum, Fitri; Ramdani, Fatwa

doi:10.11591/eei.v9i4.2348

Cited by 29 publications

(10 citation statements)

References 17 publications

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“…Accuracy is a quickly comprehensible paradigm that represents the ratios of examples that a classifier correctly recognizes the class of testing examples. To measure the accuracy of a classifier is also an easy way, and calculate according to the formula given in equation 4 [22], where TP denotes True Positive detection, the true positive is taken from the classification with the correct detection, and N denotes the number of data testing. Accuracy = TP / N * 100 (4)…”

Section: E Evaluationmentioning

confidence: 99%

Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

Cahyo

Utaminingrum

2022

JOIV : Int. J. Inform. Visualization

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The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.

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Section: E Evaluationmentioning

confidence: 99%

Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

Cahyo

Utaminingrum

2022

JOIV : Int. J. Inform. Visualization

Self Cite

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“…Meantime, Fauzi et al applied KNN to autonomous ground vehicle technology and effectively obtained accurate classification results according to the most discriminative features. [13].…”

Section: Related Workmentioning

confidence: 99%

Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

Wang

Zhao

2021

Complexity

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The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. The algorithm can select the center of the spherical region appropriately and then construct an initial classifier for the training set to improve the accuracy and time of classification.

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“…Least Absolute Shrinkage and Selection Operator (LASSO) is a linear regression-based classification method that can be used in the prediction of malignancy [29]. The K Nearest Neighbors (KNN) algorithm has found potential for use in multi-labelling problems, such as facial expression recognition and road surface classification problems [30][31][32]. With the development of machine learning methods, more and more applications of machine learning will come out, which will help people to address some of the difficult problems such as ubiquitinationsite prediction.…”

Section: Introductionmentioning

confidence: 99%

Improve Ubiquitination-site Prediction Using Physicochemical Properties and Machine Learning with Grid Search

Mo¹,

Cho²,

Lü³

2022

Preprint

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Background Ubiquitination plays an important role in protein post-translational processes and has been found to be involved in a number of regulatory functions including proteasome degradation, DNA repair, transcription, signal transduction, endocytosis, and sorting. As the identification of ubiquitination site is critical to furthering our understanding of the mechanism of ubiquitination, various experimental and machine learning methods have been used to conduct this task. It has been an important but challenging task to improve the accuracy of ubiquitination site prediction. In this research, we explore the possibility of improving the prediction performance of machine learning by incorporating grid search in the training process. Method We developed grid search procedures for each of six widely used machine learning methods including NB, LR, DT, SVM, LASSO, and KNN, and applied them to ubiquitination site prediction using the six PCP datasets that were previously developed. For each of the ML methods, we developed a set of values for each of the tunable hyperparameters available to the method. These sets of values then can be combined to form a large grid of hyperparameter settings, and each of these settings is used in the grid search. We integrated 5-fold cross-validation in grid search to train and test ML models and applied an additional independent validation procedure by conducting a pre-training 80-20 sample split. We evaluated the performance of the six methods by comparing them side by side for each of the six datasets. We also compared the grid search results with the results that were previously published without doing grid search. To optimize the prediction performance, we trained 1.1 million ML models in total through grid search. Results We compared the overall prediction performance of these six methods, as well as their prediction performance when working with balanced vs. imbalanced data, and large-scale vs. small-scale data. From the perspective of dataset, we find that the overall performance of every PCP dataset has been significantly improved in this study compared to the previous study, with the percentage increase of the average AUC of all six datasets ranging from 7.9% (PCP-4) up to 17.0% (PCP-1). From the perspective of method, we find that three out of four methods significantly benefit from grid search comparing to their previously published non-grid search results, with the maximum AUC improvement as high as 47% (LASSO on PCP-5), 43.3% (NB on PCP-1), and 33.7% (SVM on PCP-6). SVM overall ranks number one, followed by KNN as the number two performer based on their average AUCs on all datasets. But these two also ranked the top two (KNN 76 days and SVM 15 days) in terms of the total running time that they need to do grid search. We also find that SVM, KNN, and DT tend to handle small-scale and imbalanced datasets better, while LR, and LASSO are doing well with large-scale and balanced datasets. NB is more sensitive to data imbalance while less sensitive to the size of a dataset. Conclusions Our results show that using grid search has improved the performance of ubiquitination prediction significantly. We find that the performance of a method is closely related to its hyperparameter setting and the type of data it handles. Even though SVM is on average an outperformer, none of the methods can provide the best performance for all datasets. When sufficient computing resources are well accessible, grid search is an effective way to identify both a top performing model for a machine learning method and a suitable machine learning method for a particular dataset.

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Road surface classification based on LBP and GLCM features using kNN classifier

Cited by 29 publications

References 17 publications

Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

Improve Ubiquitination-site Prediction Using Physicochemical Properties and Machine Learning with Grid Search

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