Performance Evaluation of Corporators and Authorities based on Public Grievances Redressal

People in their day-to-day life face many general problems like damaged street lights, irregular water supply, blocked drainage connection etc. In these situations they complain to the concerned authority. If the complaint is unsolved, they complain to their areas representatives i.e. corporator. But in majority of the cases complainants dont get timely delivery of the solutions to their complaint and moreover authorities and corporators are neither made accountable nor answerable for it. Hence public grievances redressal is a major issue. Another important issue is that there are no quantitative measures representing a politician. If there would be any such metrics it would assist people in selecting suitable candidate in election and would also guide political parties in selecting suitable contestant for contesting in election. There are also no quantitative measures of government employees, which if exist would be used in providing promotions and increments. This work addresses these two major issues and provides solution for it. Android app is developed which evaluates Authorities and Corporators based on whether they have solved peoples complaint and if solved how early it is solved. It provides evaluation in terms of score and rank. It also provides performance graph and statistical graphs.

Applying MCDM Techniques for Ranking Products Based on Online Customer Feedback

Text analytics is to distill out structured information from unstructured or semi-structured text. User feedback analysis or sentiment analysis on products enables to highlight the best and worst of features and recommend the product to new buyers. The model extracts the positive and negative comments and identifies the emotions in the piece of text or n-way analysis and classification like very-positive, positive, neutral, negative or very-negative. Natural Language Processing (NLP) tools play vital role in classifying the sentiment polarity of sentences while data analytics has the role in recommendation of the product. In this paper, we propose a recommender system model to rank the products based on the feedback given by the users. Features, the topics of interest, are identified from the set of review text. Sentiments are detected from each review and thus senti-score is calculated for each feature of the product. We use the Analytic Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which are Multi-Criteria Decision Making techniques to rank a set of products. This method provides a logical framework to determine the benefits of each product based on the features and thus the products are ranked.

M-Learning through Semantically Annotated Learning Objects and Ontologies

Number of mobile subscriptions has increased tremendously due to rapid development of mobile technologies. The performance and accessibility of the e-learning process can be enhanced through mobile devices which is called m-learning. M-learning makes learning resources available anywhere and anytime, provide strong search capabilities, and offers easy interaction features to the learners. M-learning also points the opportunity for interoperability than existing e-learning system. The integration of semantic web in m-learning can improve the efficiency of searching for learning objects and reduce the time and cost of learning process. Semantic web can be integrated with the help of ontologies and learning objects in semantic web. They offer rich medium to assist m-learning via semantic annotated learning objects and shared repositories. Two types of ontologies, such as learning object content ontology and learning object structure ontology are used in this system. These ontologies facilitate the reuse, sharing and retrieval of relevant learning objects which are the backbone of m-learning.

An ANN based Classification Algorithm for Swine Flu Diagnosis

Machine learning technology adds a new potential to medical diagnosis systems. This paper presents an Artificial Neural Network (ANN) based swine flu diagnosis model. The proposed model selects significant features for swine flu diagnosis by a feature selection algorithm using k- Nearest Neighbour (k-NN) classifier, which reduces the size of data to be used for training the ANN model with an objective of making the training more efficient and accurate. A threshold value is determined by ANN to identify positive and negative cases and the model classifies the test cases either positive or negative based on the threshold value. The results obtained with the proposed model demonstrate the ability of the model to provide high level of accuracy for swine flu diagnosis. The assessment (classification) ability of the proposed ANN based model is compared with that of Case Based Reasoning (CBR) approaches and is observed that the proposed model is superior to others.

A Key Management Technique to Secure Information Sharing of the Nodes within a Cluster of WSN

The nodes within a cluster of Wireless Sensor Network deployed in adverse areas face the security threats of eavesdropping and capturing. The fundamental issue in wireless sensor network security is to initialize secure communication between sensor nodes by setting up secret keys between communicating nodes. Because of limited hardware capacity, conventional network cryptography is infeasible for sensor network. In this paper a key management technique is proposed for clustered sensor network that uses some cryptographic operation to generate secret keys. This key is updated in response to the message of cluster head or base station. The key update instructions are stored in each sensor nodes before deployed in sensor field. The updated secret key is used to communicate between nodes and cluster head.

PureView Technology (Optimisation & Evaluation Techniques in Image Processing)

PureView Technology is the combination of a super high-resolution image sensor and high-performance optics. It further applies advanced image processing algorithms and pixel oversampling to give the best quality outputs. It uses pixel oversampling method. Pixel oversampling combines many pixels to create a single (super) pixel. When this happens, we keep virtually all the details but filter away visual noise from the image. The speckled, grainy look we tend to get in low-lighting conditions is greatly reduced. One of the major benefits of this technology is loss-less zoom. The level of pixel oversampling is highest when we are not using the zoom. It gradually decreases until we hit maximum zoom, where there is no oversampling. This technique thus allows us to have loss-less zooms even when we are using the camera for taking zoomed in photos. The core of this technology lies somewhere in the satellite imagery system which uses a similar method of pixel oversampling and high-resolution image sensors. With PureView, uses a system called oversampling, which takes the original greater number of megapixels captured with the enormous sensor and reduces them to a high-quality image consisting of only a few megapixels. Pixels are pulled together into groups of seven and those seven pixels are then condensed into one, so that even though the resulting photograph is only a few megapixel images it is of a better quality than those captured with more traditional five megapixel cameras. For example, Nokia Lumia 1020 uses a 41-megapixel camera to take the original image, however, reduces this to only an output of 5 megapixels. This thus produces a

HSTCP: A Tool for Hierarchical System Test Case Prioritization

Many constraints are imposed on the software industries that want to complete the project in time and within budget. It is a challenging task for software industries to complete the project with in time and budget due to advancement of technology. In this paper a hierarchical test case prioritization (HSTCP) (Kumar, 2013) tool is presented which implements hierarchical system test case prioritization technique. The presented tool helps to prioritize the system test case and reduce the testing cost of the software. The HSTCP tool works at three levels. At the first level it prioritizes the requirements, at the seconds level the modules of highest prioritized requirements are prioritized. Finally at third level it prioritizes the test cases of the modules in prioritized order. The presented HSTCP tool is implemented in Java language.

An Overview of Active Database: Model, Architecture and Challenges

Research in database management system results in new technology and opportunities to researchers. As a result of this researcher have developed many solutions to real time applications and advancement in hardwares. Database systems with production rules are referred to as active database systems and the field of active database systems has indeed been active. In this article we emphasize on evolution of active database system, its architecture and challenges faced by research community.

Maya: The Convergence of Open World Gaming and Virtual Reality Technologies

With the simultaneous exponential advances in various Computing fields, world is evolving at a pace unlike ever before. Virtual Reality (VR) is quite old field, but it has gained an unprecedented momentum in recent years. Despite this, the vision of the present (and the future) of virtual reality is very blurred. It is mostly made up of imaginations, and inspirations from science-fiction movies & novels, not the present truth. In this research, we try to clear that vision by throwing light on the current status of Virtual reality, and the imminent future. We describe Virtual reality, its need, immersion, and some unofficial meanings of the term. Then, we observe the future of VR as the convergence of Open World video gaming, and the VR Technologies. We derive the components of a true VR system, as the union of the essential-components of the two, and also propose a model of Virtual Reality. Finally, we compare these components of VR system with the components of Maya (i.e. our real world as a system). Surprisingly, there is sheer resemblance between the both.

Research on Ranking Algorithms in Web Structure Mining

In the past few decades, the Web has emerged as a treasure of information and web mining is a technique to handle this treasure. During recent years web mining has been a well-researched area. Web mining is the application of the data mining which is useful to extract the knowledge from web. With the progress of web, more and more data are now available for users on web. Web structure mining deals with the contents and hyperlinks on web pages. In this review paper, we have focused on three basic algorithms for evaluating the importance of pages i.e. Page Rank, Weighted Page Rank, and Hyperlink-Induced Topic Search and comparison of those algorithms. Page Rank algorithm is based on back links of the page and it calculates the rank of web pages at indexing time. Weighted Page Rank algorithm scores pages according to their relevancies and rank of a page is calculated by its number of incoming and outgoing links. Hyperlink-induced topic search algorithm is an iterative algorithm developed to quantify each pages value as an authority and as a hub. This study was done basically to explore the link structure algorithms for ranking pages.