Now a day's usage of Two-wheeler is exponentially increasing and the accidents met by them also increases every year. Even though the Safety precautions and traffic rules are there the riders are not showing interest in following the safety measures and rules. An intelligent Bike Safety and Surveillance (IBSS) is a real time system which makes a rider to follow safety measures and the rules. The System Comprise of Helmet unit & Vehicle unit with antitheft and Speed Control mode. The helmet unit ensures whether the Rider is wearing a helmet, Alcohol Consumption of the rider and the proper release of Side stand. The Vehicle unit Comprises of the Surveillance system which check and intimate if an Accident is occurred. The antitheft mode protects the vehicle from unauthorized users. The Speed control mode monitors the speed and automatically control the speed of the Vehicle. The System is implemented using Arduino UNO and ATmega 328 Microcontroller with different sensors. 

An intelligent bike safety and surveillance system

Traffic safety in the area of Intelligent Transportation System can be improved by predicting driver behavior automatically. Aggressive driving is significant indicator for collision. Several deep learning algorithms are developed to predict the behavior of driver and existing algorithm’s hyper parameters are not optimized to get efficient and accurate solution in predicting driver’s behavior. Hyper parameter optimization of LSTM based model has been proposed to predict the driver behavior accurately by determining optimal hyper parameters which includes window size, learning rate, Number of hidden layers and number of hidden units. Bayesian optimization is built to optimize the hyper parameters. 97.02% of accuracy has been achieved for the proposed model. The result reveals that the proposed model outperforms the existing algorithms and hyper parameters have been optimized. The hyper parameter optimization can improve the accuracy of the LSTM based driver’s aggressive behavior prediction model. The proposed model minimizes False alarm rate and missed alarm rate dramatically compared to traditional algorithms. The proposed model can be implemented in collision avoidance system which helps to present warning to aggressive drivers.

Hyperparameter optimization of LSTM based Driver’s Aggressive Behavior Prediction Model

Research on false review detection Methods: A state-of-the-art review

Emissions from burning biomass have become a problem in Indonesia. As found on the Indonesian island of Lombok, agricultural waste is burned for traditional industrial activities. On the other hand, biomass burning emissions contain many PMs (particulates) in different size distributions recognized to have a significant correlation to health impact. This study is conducted to predict the impact of the PM exposure on blood using a ANN (artificial neural network) model as well as a histological examination. The relationship between both methods is determined to estimate the impact of biomass burning emissions on the blood. This study used male mice as the experimental animals exposed to PM emissions (PM 0.1 , PM 2.5 , and PM 10 ) produced from the burning of various biomass (rice straw, rice husks, corn cobs, corn stalks, and tobacco) taken from Lombok Island. The sample exposure was conducted in a chamber for 100 s for ten sequence days. The blood samples were observed using a microscope with the 400 x magnification. The cell deformation was examined histologically by calculating the normal and abnormal cells. The percentage of the erythrocyte deformation was assessed using a fixed back and forth propagation ANN. The result shows that the biomass burning PM emissions have a significant impact on the erythrocyte deformation depending on the type of biomass and the particulate matter emissions. The ANN model confirms the erythrocyte deformation data obtained by the histological examination method.

/pdf/a-study-of-erythrocyte-deformation-level-related-to-biomass-3chl5nua.pdf

A Study of Erythrocyte Deformation Level Related
to Biomass Burning Emission Exposures
Using Artificial Neural Networks

The Almost 1.3 million casualties are reported round a calendar year due to road accidents. Advanced collision avoidance systems play major role in predicting the collision risk to avoid accidents. The existing deep learning algorithms are unable to predict the crash risk efficiently. In the existing system, Long Short Term Memory algorithm is used to predict the crash risk where weights are not optimized. The objective is to predict the rear end collision risk with optimized weight by combining Long Short Term Memory(LSTM) with Levenberg–Marquardt (LM) algorithms. The proposed algorithm predicts the collision risk considering vehicle, driver related factors, and temporal dependencies. Next Generation Simulation Project (NGSIM) dataset is used to evaluate the proposed model. The performance of the proposed system is compared with the performance of Long Short Term Memory and Back Propagation Neural Network. 95.6% of accuracy is achieved by LM-LSTM based Time series Deep Network Model. The prediction accuracy has been improved considerably than the existing algorithms. There is the drastic improvement in minimization of false alarm and missed alarm rate. The main advantage of the proposed system is that it will present warning at the time of high collision risk and it helps drivers to prevent from accident.

Levenberg–Marquardt –LSTM based Efficient Rear-end Crash Risk Prediction System Optimization

Multivariate Time Series Crash Risk Prediction is essential in the development of Collision Avoidance Systems (CASs), which are vital components of the Intelligent Transportation System. The crash risk prediction performance is degraded with high computational cost and low accuracy. To address this issue, Attention based CNN-LSTM Hybrid model is proposed for Multivariate time series crash risk prediction through the augmentation of Convolutional Neural Network (CNN) with Attention based Long Short Term Memory (ATT-LSTM). Attention mechanism is incorporated with LSTM to learn long-term dependencies of ultra-long sequences. Modified Crash Risk Index (MCRI) is developed to label the crash and non-crash events considering Adaptive Perception Reaction Time (APRT) which enables the enhancement of the accuracy of the Multivariate time series crash risk prediction system. The problem is formulated as multivariate time series prediction and validity of proposed model is evaluated with Next Generation Simulation (NGSIM) dataset. The proposed model outperforms state-of-the-art models where MCRI and CNN-ATT-LSTM enhance accuracy of the crash risk prediction. 98.1% of accuracy has been achieved in the proposed model. The result demonstrates that the proposed model requires less computational cost, high accuracy and minimum preprocessing. The proposed model presents warning to the driver at the time of collision accurately and can be implemented in Collision Avoidance Systems.

Novel algorithm for multivariate time series crash risk prediction using CNN-ATT-LSTM model

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The\n purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the\n quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding\n such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed\n method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

Over speeding is an important factor for occurring accidents. Road accident statistics shows two wheelers account for 25% of total road crash deaths and also one third of accidents are due to over speeding. But there is lagging of research in preventing the accidents for two wheeler.\n An effective prevention mechanism is to provide safety mechanism to the drivers with the advancement of wireless communication. The Objective of this paper is to develop a automatic speed control system to avoid accidents by controlling the speed of a motor bike using intelligent based automatic\n speed control. Intelligent based automatic speed control system consist of electronic acceleration system, Ultrasonicsensor, MQ3 alcohol sensors eye blink sensors. The existing system of acceleration in two wheelers can be replaced by electronic acceleration system. The signal from ultrasonic\n sensor will be sent continuously and monitors other obstacles that are in front of motor bike. The distance coverage of ultrasonic sensor is upto 4 m. When it detects obstacle, Vehicle is controlled automatically without any manual operation. This system detects the driver if he is under the\n influence of alcohol. MQ3 alcohol sensor is used to analyze breath to determine alcohol consumption in the body The eye blink sensor detects the eye blink of driver and if eye is closed more than 30 sec, vehicle will be stopped automatically, and gives alarm to driver alert. Speed Information\n will be sent to the server and from the server authorized user can view the speed information and control the speed. This system will be more efficient to control the speed of two wheelers so that the accidents can be prevented.

Intelligent Speed Control in Motor Bikes for Accident Prevention Using Internet of Things

This is a library used to download course material from various online course sites. The current process is very tedious, the student has to download the files individually and this is really time consuming. The library will use scrappy to get the information from the website. The user will be asked to enter the URL of the course website, then the library will do rest of the work. First using the provided URL, it will make a http request, next the library will use scrappy to get extract the individual video tags. Then once the video tags are extracted the video can be downloaded using scrapy’s media pipeline. The video will be saved to the user’s computer. The library will initially have support for Open Courseware, then the future support for other websites can be provided.

/pdf/python-video-download-library-3mmrjd7f8c.pdf

D. Deva Hema

Papers

Levenberg–Marquardt –LSTM based Efficient Rear-end Crash Risk Prediction System Optimization

Novel algorithm for multivariate time series crash risk prediction using CNN-ATT-LSTM model

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

Intelligent Speed Control in Motor Bikes for Accident Prevention Using Internet of Things

Python video download library