<p>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.</p>

<span lang="EN-US">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.</span>

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.

<span>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.</span>

Cloud computing has become an essential source for modern trade or market environments by abled frameworks. The exponential growth of cloud computing services in the last few years has resulted in extensive use, especially in storing and sharing the data on various cloud servers. The current trend in the cloud shows that the cloud owners use relative functions and target areas in such a way that cloud customers access or store their data either in the same servers or related servers. Simultaneously, from the security point of view, the lack of confidence about the customer's data on the cloud server is still questionable. The hour's need is to provide the cloud service in a 'single port way' by forming the joint management policy to increase customer satisfaction and profitability. In addition to this, the authentication steps also need to be improvised. This paper discusses issues on the security authentication and access provisioning of the cloud service consumers in federated clouds using subscribed user identity. This work proposes the user identity verification module (UidVM) in the cloud service consumer's authentication process to serve as a cloud broker to minimize the work overloads on the central cloud federation management system, thus enhancing the cloud security.

<span>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.</span>

Traditional way of storing such a huge amount of data is not convenient because processing those data in the later stages is very tedious job. So nowadays, Hadoop is used to store and process large amount of data. When we look at the statistics of data generated in the recent years it is very high in the last 2 years. Hadoop is a good framework to store and process data efficiently. It works like parallel processing and there is no failure or data loss as such due to fault tolerance. Job scheduling is an important process in Hadoop Map Reduce. Hadoop comes with three types of schedulers namely FIFO (First in first out), Fair and Capacity Scheduler. The schedulers are now a pluggable component in the Hadoop Map Reduce framework. This paper talks about the native job scheduling algorithms in Hadoop. Fair scheduling algorithm is analysed with its algorithm considering its response time, throughput and performance. Advantages and drawbacks of fair scheduling algorithm is discussed. Improvised fair scheduling algorithm is proposed with new strategy. Analysis is made with respect to response time, throughput and performance is calculated in naive fair scheduling and improvised fair scheduling. Improvised fair Scheduling algorithms is used in the cases where there is jobs with high and less processing time.

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