MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Document image analysis analyzes the document images to extract the text and graphics information from image. Printed character recognition is important in the context of document image analysis. Machine learning Approach such as pattern recognition and matching can be applied to document image based printed character recognition. In this paper we discussed the Template Matching approach to printed character recognition. Template matching is found to be an effective technique to recognize printed character as compared to neural network and other classification techniques. © 2013 Springer.

World University root node

Engineering and Technology

Advances in Intelligent Systems and Computing

An approach for document image based printed character recognition

Random Forest is an Ensemble Supervised Machine Learning technique. Research work in the area of Random Forest aims at either improving accuracy or improving performance. In this paper we are presenting our research towards improvement in learning time of Random Forest by proposing a new approach called Disjoint Partitioning. In this approach, we are using disjoint partitions of training dataset to train individual base decision trees. This helps in creating diversity in base decision trees. Also different subsets of attributes are used at each node of decision tree to increase diversity. This approach generates Random Forest classifier which is trained efficiently and gives classification accuracy comparable to the original Random Forest approach.

Lecture Notes in Engineering and Computer Science

Efficient learning of random forest classifier using disjoint partitioning approach

International Journal of Computer Engineering \& Technology (IJCET) 4 (4 {\ldots}

On improvement of performance for transport protocol using sectoring scheme in WSN

One of the most important task of Wireless Sensor Network (WSN) is data collection. In sensor network the sensors are sensing the information and will be collected by the sink node with multi hoping. In such a scenario the energy is consumed by the node is more. In recent years the researchers are focusing on how to minimize the energy of the node and how it will help to improve the life of the node. One of the idea to do this is minimize the hop. The proposed framework is maximizing the lifetime of the wireless sensor networks (WSNs) by using a mobile sink. Proposed work is having the cluster with cluster head. The mobile sink moves towards the cluster head only upon the occurrence of an event .The cluster head is collecting the data from the sensor node and storing the same. Once the data is available to the cluster head, head will inform to the Mobile Sink. The Mobile Sink will move to respective position of the cluster head and data will be collected by Mobile sink.

International Journal of Recent Technology and Engineering(IJRTE)

Analyzing Data Collection Strategies Using Mobile Sink

International Journal of Scientific Engineering and Technology

A New Algorithm for Tracking of Multiple Moving Objects

Int. J. Emerg. Trends Technol. Comput. Sci

Symbian based rash driving detection system

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE \& ADVANCED TECHNOLOGY

COGNITIVE DISASTER INFORMATION SYSTEM: AN APPROACH USING S3C2440C

Recent few years are proved very progressive in terms of energy efficient protocols in Wireless Sensor Networks, but energy efficiency is still a point of focus in sensor networks. Among various MAC layer protocols, WiseMAC is known as one of the most energy efficient protocols. Non-Persistent Carrier Sense Multiple Access (NP-CSMA) and the preamble sampling are the main techniques used in basic WiseMAC protocol. Basically they are used to reduce the power consumption when energy is wasted in listening to idle medium. This paper investigates the WiseMAC protocol with different important schemes that are used in implementation of WiseMAC in sensor networks. These schemes basically emphasize on energy saving methods in WiseMAC. Various modifications are done to the original WiseMAC to make it more energy efficient; with the same motive of increasing energy efficiency, adaptive WiseMAC protocol with dynamic duty cycle and the adaptive Contention Window is proposed. © 2013 IEEE.

Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Various ways to implement energy efficient WiseMAC protocol for wireless sensor network

Internet has become an indispensible part of today's life. World Wide Web (WWW) is the largest shared information source. Finding relevant information on the WWW is challenging. To respond to a user query, it is difficult to search through the large number of returned documents with the presence of today's search engines. There is a need to organize a large set of documents into categories through clustering. The documents can be a user query or simply a collection of documents. Document clustering is the task of combining a set of documents into clusters so that intra cluster documents are similar to each other than inter cluster documents. Partitioning and Hierarchical algorithms are commonly used for document clustering. Existing partitioning algorithms have the limitation that the number of clusters has to be given as input and the clustering result depends on this input. If the number of clusters is not known, results are not acceptable. In this paper, we have developed a novel algorithm which generates number of clusters automatically for any unknown text dataset and clusters the documents appropriately based on Cosine Similarity between them. We have also detected zero clustering issue in partitioning algorithm and solved it using our novel algorithm. © 2013 IEEE.

Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013

A novel algorithm for automatic document clustering

Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the speech signal. Some queries are raised that which features are more responsible for depression from speech and which classifiers gives good results. By identifying proper features from speech signal system even one can save the life of a patient. In this paper, a survey of speech signal features which relates for depression analysis is presented. Specially focused on adolescence speech. After surveying it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO). Some classification methods for depression analysis from previous studies are summarized. © 2013 IEEE.