IoT based car accident detection and notification algorithm for general road accidents

With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location

Taylor series method in TDOA approach for indoor positioning system

Localisation technologies have always remained in the limelight of positioning-science as researchers have ever shown keen interest to know the exact positions of things. Ultrasonic sensors are mainly used for localisation of mobile robots since they provide high accuracy. This paper presents Taylor Series Method in Time Difference of Arrival approach using ultrasonic sensors.Signals are send from the sensors periodically.The time difference of arrival of signals from the ultrasonic sensors is used by the receiver unit to estimate the location of the mobile unit. The equations formed by using Time Difference of Approach are solved using Taylor Series Method. Taylor Series Method provides a more accurate result since they give less error compared to other methods and they ignore the measurement errors

Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion Mining

In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides  balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact

An algorithm for IoT based vehicle verification system using RFID

The verification of vehicle documents is an important role of transport department which is rising day by day due to the mass registration of the vehicles. An automated vehicle verification system can improve the efficiency of this process.  In this paper, we propose an IOT based vehicle verification system using RFID technology. As a result, the vehicle checking which is done now manually can be replaced by automation. There is a loss of a significant amount of time when the normal vehicle checking is done manually. The proposed system will make this process automated. The present verification process is using inductive loops that are placed in a roadbed for detecting vehicles as they pass through the loop of the magnetic field. Similarly, the sensing devices spread along the road can detect passing vehicles through the Bluetooth mechanism. The fixed audio detection devices that can be used to identify the type of vehicles on the road. Other measurements are fixed cameras installed in specific points of roads for categorising the vehicles. But all these mechanisms cannot verify the documents and certificates of the vehicles. In our work, we have suggested an algorithm using RFID technology to automate the documentation verification process of the vehicles like Pollution, Insurance, Rc book etc with the help of RFID reader placed at road checking areas. This documents will be updated by the motor vehicle department at specific periods

Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data