Soft clustering: An overview

Document clustering has been extensively investigated as a methodology for improving document retrieval process. In Traditional clustering algorithm each documents belongs to exactly one cluster & hence cannot detect the multiple themes of a document where as soft clustering algorithm each document can belong to multiple clusters. This paper gives a comparative study of hard clustering & soft clustering algorithm

Mobile Data Offloading the Growing Need with Its Solutions and Challenges

From the last few years, the popularity of video, social media and Internet gaming across a range of new devices like smartphones and tablets has created a surge of data traffic over cellular networks. Device to device connectivity will give rise to a new universe of applications that will further create stress on network capacity [3]. In the next three years alone, it is accepted that data traffic will grow towards tenfold creating a tremendous capacity crunch for operators. While data revenues are expected to only double during this period, which will create a huge gap. As a result, different innovative solutions have emerged to man age data traffic. Some of the key technologies include Wi-Fi, LTE Small Cell and Relay, femtocells, DTN-based Network, and IP flow mobility. Therefore, telecom operators need to constantly review their implement traffic offloading mechanisms that will help them manage their network load and capacity mo re efficiently. This paper describes various data offload strategies and considers the challenges and benefits associated with each of them. This paper aims to provide a survey of mobile data offloading technologies including insights from the business per spective as well

Access of Encrypted Personal Record in Cloud

Personal record is a data, which is collected and stored in cloud computing to gain cost benefit and better access control. In maintaining Personal Record, cloud computing plays an important role, since minor organizations are not affordable to keep own servers to maintain the personal record for cost and security aims. Providing availability to various stake holders become a deadly process in isolated individual servers with encryption technology. Cloud ensures that personal record availability to the necessary user at any point of time. In any country, there is a law which governs to maintain privacy of special records, and hence maintaining recodes in cloud are subjected to privacy concerns and high risk of getting exploited. There are various encryption schemes to provide personal records security and privacy in Cloud computing. Extensive logical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme. DOI: 10.17762/ijritcc2321-8169.15016

Need of Boosted GMM in Speech Emotion Recognition System Implemented Using Gaussian Mixture Model

Speech feeling recognition is a vital issue that affects the human machine interaction. Automatic recognition of human feeling in speech aims at recognizing the underlying spirit of a speaker from the speech signal. Gaussian mixture models (GMMs) and therefore the minimum error rate classifier (i.e., theorem optimum classifier) is widespread and effective tools for speech feeling recognition. Typically, GMMs are wont to model the class-conditional distributions of acoustic options and their parameters are calculable by the expectation maximization (EM) algorithmic rule supported a coaching information set. During this paper, we have a tendency to introduce a boosting algorithmic rule for faithfully and accurately estimating the class-conditional GMMs. The ensuing algorithmic rule is known as the Boosted-GMM algorithmic rule. Our speech feeling recognition experiments show that the feeling recognition rates are effectively and considerably boosted by the Boosted-GMM algorithmic rule as compared to the EM-GMM algorithmic rule. During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This work dependent on the emotion recognition of the human beings from their speech signal. Emotion recognition from the speaker’s speech is very difficult because of the following reasons: Because of the existence of the different sentences, speakers, speaking styles, speaking rates accosting variability was introduced. The same utterance may show different emotions. Therefore, it is very difficult to differentiate these portions of utterance. Another problem is that emotion expression is depending on the speaker and his or her culture and environment. As the culture and environment gets change the speaking style also gets change, which is another challenge in front of the speech emotion recognition system.Human beings normally used their essential potentials to make communication better between themselves as well as between human and machine. During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This dissertation work dependent on the emotion recognition of the human beings from their speech signal. In this chapter introduction of the speech emotion recognition based on the problem overview and need of the system is provided. Emotional speech recognition aims at automatically identifying the emotional or physical state of a human being from his or her voice. Although feeling detection from speech could be a comparatively new field of analysis, it is several potential applications. In human-computer or human-human interaction systems, feeling recognition systems might give users with improved services by being adaptative to their emotions. The body of labor on sleuthing feeling in speech is sort of restricted. Currently, researchers area unit still debating what options influence the popularity of feeling in speech. There is conjointly appreciable uncertainty on the simplest algorithmic program for classifying feeling, and those emotions to category along.

International Journal on Recent and Innovation Trends in Computing and Communication Access of Encrypted Personal Record in Cloud __________________________________________________*****

Abstract-Personal record is a data, which is collected and stored in cloud computing to gain cost benefit and better access control. In maintaining Personal Record, cloud computing plays an important role, since minor organizations are not affordable to keep own servers to maintain the personal record for cost and security aims. Providing availability to various stake holders become a dead ly process in isolated individual servers with encryption technology. Cloud ensures that personal record availability to the necessary user at any p oint of time. In any country, there is a law which governs to maintain privacy of special records, and hence maintaining recodes in cloud are subjected to privacy concerns and high risk of getting exploited. There are various encryption schemes to provide personal records security and pr ivacy in Cloud computing. Extensive logical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme

CLOUD COMPUTING: concepts and overview Ms. Sapna V. Ambadkar

Abstract: Cloud Computing denotes the latest trend in application development for Internet services, relying on clouds of servers to handle tasks that used to be managed by individual machines. It is a relatively recent term, builds on decades of research in virtualization, distributed computing, utility computing, and more recently networking, web and software services. With Cloud Computing, developers take important services, such as email, calendars, and word processing, and host them entirely online, powered by a vast array (or cloud) of interdependent commodity servers. Cloud Computing presents advantages for organizations seeking to centralize the management of software and data storage, with guarantees on reliability and security for their users. It implies a service oriented architecture, reduced information technology overhead for the end-user, great flexibility, reduced total cost of ownership, on-demand services and many other things. Cloud Computing presents advantages for organizations seeking to centralize the management of software and data storage, with guarantees on reliability and security for their users. This paper discusses the concept of Cloud computing, how it works, overview, applications, security and Cloud implementation available today

Query Optimization in OODBMS using Query Decomposition & Query Caching

Query optimization is of great importance for the performance of databases, especially for the execution of complex query statements. A query optimizer determines the best strategy for performing each query. These decisions have a tremendous effect on quer y performance, and query optimization is a key technology for every application, from operational systems to data warehouse and analysis systems to content - management systems. For example, query optimizers transform query statements, so that these complex statements can be transformed into semantically equivalent, but better performing, query statements. The query optimizer chooses, for example, whether or not to use indexes for a given query, and which join techniques to use when joining multiple tables. Query optimizers are typically cost - based. In a cost - based optimization strategy, multiple execution plans are generated for a given query, and then an estimated cost is computed for each plan. The query optimizer chooses the plan with the lowest estimate d cost. This report is based on relatively newer approach for query optimization in object databases, which uses query decomposition and cached query results to improve execution times for a query. Multiple experiments were performed to prove the productivity of this newer way of optimizing a query . The limitation of this technique is that its useful especially in scenarios where data manipulation rate is very low as compared to data retrieval rate