scholarly journals Intelligent Vehicle Speed Controlling and Pothole Detection System

2020 ◽  
Vol 170 ◽  
pp. 02010
Author(s):  
Aditya Anand ◽  
Rushiraj Gawande ◽  
Prathamesh Jadhav ◽  
Rima Shahapurkar ◽  
Anjali Devi ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Maximum Speed ◽  
Machine Learning Algorithm ◽  
Vehicle Speed ◽  
Safe Driving ◽  
Driving Speed ◽  
Speed Management ◽  
The Impact ◽  
Pothole Detection

Speed is the single largest killer on India’s roads. The higher the speed, the greater the impact and the more the chances of grievous injury and death. This is why speed management is something which needs to be seriously considered. Here is an approach to build a system which reduces the number of accidents due to driver’s negligence of over speeding. We are proposing a Dynamic Speed Limiter with the help of machine learning algorithm which will help in reducing the accidents caused due to over speeding and rash driving of vehicles on road. The proposed system set the maximum speed of vehicle with the help of sign board available on the roads which define the safe driving speed of vehicle for that area or road. Also to solve the potholes we are using ML algorithm and mobile phone accelerometer to detect the potholes, accelerometer vibrations are set. After detecting the vibrations, the location (i.e. longitude and latitude) is marked on the GPS, commuters will get information about potholes.

scholarly journals Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanlin Liu ◽  
Linqiang Yang ◽  
Linchao Li
Keyword(s):  
Machine Learning ◽  
Puerto Rico ◽  
Reference Frame ◽  
Learning Algorithm ◽  
Virgin Islands ◽  
Machine Learning Algorithm ◽  
Climate Factors ◽  
Helmert Transformation ◽  
The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

scholarly journals Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets

2020 ◽  
Vol 10 (3) ◽  
pp. 794 ◽  
Author(s):  
David Gonzalez-Cuautle ◽  
Aldo Hernandez-Suarez ◽  
Gabriel Sanchez-Perez ◽  
Linda Karina Toscano-Medina ◽  
Jose Portillo-Portillo ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Detection System ◽  
Optimization Procedure ◽  
Algorithm Optimization ◽  
Detection Systems ◽  
Lack Of Information ◽  
Supervised Learning Algorithms ◽  
The Impact

Presently, security is a hot research topic due to the impact in daily information infrastructure. Machine-learning solutions have been improving classical detection practices, but detection tasks employ irregular amounts of data since the number of instances that represent one or several malicious samples can significantly vary. In highly unbalanced data, classification models regularly have high precision with respect to the majority class, while minority classes are considered noise due to the lack of information that they provide. Well-known datasets used for malware-based analyses like botnet attacks and Intrusion Detection Systems (IDS) mainly comprise logs, records, or network-traffic captures that do not provide an ideal source of evidence as a result of obtaining raw data. As an example, the numbers of abnormal and constant connections generated by either botnets or intruders within a network are considerably smaller than those from benign applications. In most cases, inadequate dataset design may lead to the downgrade of a learning algorithm, resulting in overfitting and poor classification rates. To address these problems, we propose a resampling method, the Synthetic Minority Oversampling Technique (SMOTE) with a grid-search algorithm optimization procedure. This work demonstrates classification-result improvements for botnet and IDS datasets by merging synthetically generated balanced data and tuning different supervised-learning algorithms.

Impact of Encoding of High Cardinality Categorical Data to Solve Prediction Problems

2020 ◽  
Vol 17 (9) ◽  
pp. 4197-4201
Author(s):  
Heena Gupta ◽  
V. Asha
Keyword(s):  
Machine Learning ◽  
Categorical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Prediction Problem ◽  
Encoding Scheme ◽  
The Impact ◽  
Prediction Problems

The prediction problem in any domain is very important to assess the prices and preferences among people. This issue varies for different kinds of data. Data may be nominal or ordinal, it may involve more categories or less. For any category to be considered by a machine learning algorithm, it needs to be encoded before any other operation can be further performed. There are various encoding schemes available like label encoding, count encoding and one hot encoding. This paper aims to understand the impact of various encoding schemes and the accuracy among the prediction problems of high cardinality categorical data. The paper also proposes an encoding scheme based on curated strings. The domain chosen for this purpose is predicting doctors’ fees in various cities having different profiles and qualification.

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

scholarly journals Vehicle Speed Information

2020 ◽  
Vol 5 (9) ◽  
pp. 867-868
Author(s):  
Vani Valsaraj
Keyword(s):  
Maximum Speed ◽  
Vehicle Speed ◽  
Road Accidents ◽  
Driving Speed ◽  
The Given ◽  
Present World

Road accidents have been very common in the present world with prime cause being careless driving. It is very necessary to identify the careless driver. However, with the advancement in the technology, different governing bodies are demanding some sort of computerized technology to control the driving speed of drivers. At this scenario, we are proposing system to detect vehicle speed been driven the given maximum speed of vehicles the respective roads or highway limits.

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