scholarly journals Incident forecasting model for motorcycle driving based on IoT and artificial intelligence

2021 ◽  
Vol 24 (1) ◽  
pp. 444
Author(s):  
Esteban Alejandro Cárdenas-Lancheros ◽  
Nelson Enrique Vera-Parra
Keyword(s):  
Artificial Intelligence ◽  
Nearest Neighbors ◽  
Forecasting Model ◽  
Accident Risk ◽  
Classification Models ◽  
Road Accidents ◽  
K Nearest Neighbors ◽  
Accelerometer Sensor ◽  
Technological Tools ◽  
And Storage

Internet of things (IoT) and artificial intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on the analysis of dynamic behavior while driving. The development of this research began with the detection and storage of data associated with the dynamic acceleration variable of a motorcycle while driving, this with the help of a 3-axis accelerometer sensor generating a dataset, which was processed and analyzed for later be taken by three predictive classification models based on machine learning which were decision trees, K-Nearest neighbors and random forests. The performance of each model was evaluated in the task of better classifying the level of accident risk, concerning the driving style based on certain levels of acceleration. The random forest model showed a slightly better performance compared to that shown by the other two models, with 97.24% accuracy and recall, 97.16% precision and 97.17% F1 score.

scholarly journals Pengenalan Bentuk Tangan secara Real Time Menggunakan Leap Motion dan K-Nearest Neighbors sebagai Sistem Kendali Robot Beroda

2020 ◽  
Vol 5 (2) ◽  
pp. 178
Author(s):  
Supria Supria ◽  
Depandi Enda ◽  
Muhamad Nasir
Keyword(s):  
Real Time ◽  
Euclidean Distance ◽  
Nearest Neighbors ◽  
Leap Motion ◽  
K Nearest Neighbors ◽  
Accelerometer Sensor ◽  
Finger Tip

Sistem kendali robot saat ini telah banyak dibuat dengan menggunakan berbagai metode seperti sensor accelerometer, sensor suara, leap motion. Pada penelitian ini diusulkan pengenalan bentuk tangan secara real time menggunakan leap motion dan K-Nearest Neighbors (KNN) sebagai sistem kendali robot beroda. Leap motion digunakan untuk mendeteksi titik koordinat posisi tangan pada pandangan leap motion. pembentukan fitur dilakukan dengan mengukur jarak euclidean distance antara palm position dengan finger tip. KNN digunakan untuk menentukan kelas pada data testing. Uji coba dilakukan menggunakan tangan penulis dengan 5 jenis instruksi yaitu maju, mundur, stop, belok kanan, belok kiri dan setiap instruksi di ujicoba sebanyak 20 kali percobaan. Dari hasil ujicoba yang dilakukan menunjukkan bahwa sistem yang diusulkan memiliki tingkat akurasi pengenalan 94%.

scholarly journals Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  
Keyword(s):  
Artificial Intelligence ◽  
Coal Mine ◽  
Empirical Model ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
Open Pit ◽  
K Nearest Neighbors ◽  
Open Pit Mines

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

scholarly journals k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks

2021 ◽  
Author(s):  
Yiming Xu ◽  
Diego Klabjan
Keyword(s):  
Neural Network ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Sampling Technique ◽  
Nearest Neighbors ◽  
Classification Models ◽  
Feed Forward Neural Network ◽  
K Nearest Neighbors ◽  
Out Of Sample ◽  
Memory Network

k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers. We also propose 'out-of-core' versions of our models which assume that only a small portion of data can be loaded into memory. Computational experiments show that our models on structured datasets outperform k-Nearest Neighbors, a feed-forward neural network, XGBoost, lightGBM, random forest and a memory network, due to the fact that our models must produce additional output and not just the label. On image and text datasets, the performance of our model is close to many state-of-the-art deep models. As an oversampler on imbalanced datasets, the sequence to sequence kNN model often outperforms Synthetic Minority Over-sampling Technique and Adaptive Synthetic Sampling.

scholarly journals Near-infrared spectroscopy for the distinction of wood and charcoal from Fabaceae species: comparison of ANN, KNN AND SVM models

2020 ◽  
Vol 29 (3) ◽  
pp. e020
Author(s):  
Helena Cristina Vieira ◽  
Joielan Xipaia dos Santos ◽  
Deivison Venicio Souza ◽  
Polliana D’ Angelo Rios ◽  
Graciela Inés Bolzon de Muñiz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbors ◽  
Araucaria Forest ◽  
Vector Machines ◽  
Wood Analysis ◽  
Artificial Neural Network Ann

Aim of study: The objective of this work was to evaluate the potential of NIR spectroscopy to differentiate Fabaceae species native to Araucaria forest fragments.Area of study; Trees of the evaluated species were collected from an Araucaria forest stand in the state of Santa Catarina, southern Brazil, in the region to be flooded by the São Roque hydroelectric project.Material and methods: Discs of three species (Inga vera, Machaerium paraguariense and Muellera campestris) were collected at 1.30 meters from the ground. They were sectioned to cover radial variation of the wood (regions near bark, intermediate and near pith). After wood analysis, the same samples were carbonized. Six spectra were obtained from each specimen of wood and charcoal. The original and second derivative spectra, principal component statistics and classification models (Artificial Neural Network: ANN, Support Vector Machines with kernel radial basis function: SVM and k-Nearest Neighbors: k-NN) were investigated.Main results: Visual analysis of spectra was not efficient for species differentiation, so three NIR classification models for species discrimination were tested. The best results were obtained with the use of k-NN for both wood and charcoal and ANN for wood analysis. In all situations, second derivative NIR spectra produced better results.Research highlights: Correct discrimination of wood and charcoal species for control of illegal logging was achieved. Fabaceae species in an Araucaria forest stand were correctly identified.Keywords: Araucaria forest; identification of species; classification models.Abbreviations used: Near infrared: NIR, Lages Herbarium of Santa Catarina State University: LUSC, Principal component analysis: PCA, artificial neural network: ANN, support vector machines with kernel radial basis function: SVM, k-nearest neighbors: k-NN.

The application of artificial intelligence and virtual reality in the auxiliary teaching of American science fiction literature

2021 ◽  
pp. 1-10
Author(s):  
Jin Xu ◽  
Tong Li
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
Science Fiction ◽  
Point Cloud ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Denoising Method ◽  
Virtual Technology ◽  
Teaching System ◽  
American Science

In order to improve the teaching effect of American science fiction literature, based on artificial intelligence virtual reality technology, this paper constructs an auxiliary teaching system of intelligent American science fiction literature. Moreover, this paper analyzes the time complexity and space complexity of constructing point cloud spatial topological relations and finding the nearest k neighboring points. Simultaneously, this paper uses CUDA to find k nearest neighbors on the GPU, analyzes the point cloud denoising technology, uses the KD-tree to construct the point cloud topology in the DBSCAN-based denoising method, searches for the k nearest neighbors to complete the mark of the core point and the boundary point. In addition, this paper combines artificial intelligence virtual technology and intelligent algorithms to construct the framework of the auxiliary teaching system of American science fiction literature, and analyze its functional modules. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper can meet the needs of auxiliary teaching of American science fiction literature.

Artificial intelligence-based nonlinear control of renewable energies and storage system in a DC microgrid

2021 ◽  
Author(s):  
Syeda Shafia Zehra ◽  
Aqeel Ur Rahman ◽  
Hammad Armghan ◽  
Iftikhar Ahmad ◽  
Umme Ammara
Keyword(s):  
Artificial Intelligence ◽  
Nonlinear Control ◽  
Storage System ◽  
Renewable Energies ◽  
Dc Microgrid ◽  
And Storage

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

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