Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].

Routing Protocol Enhancement for handling Node Mobility in Wireless Sensor Networks

In wireless sensor networks, the routing algorithms currently available assume that the sensor nodes are stationary. Therefore when mobility modulation is applied to the wireless sensor networks, most of the current routing algorithms suffer from performance degradation. The path breaks in mobile wireless networks are due to the movement of mobile nodes, node failure, channel fading and shadowing. It is desirable to deal with dynamic topology changes with optimal effort in terms of resource and channel utilization. As the nodes in wireless sensor medium make use of wireless broadcast to communicate, it is possible to make use of neighboring node information to recover from path failure. Cooperation among the neighboring nodes plays an important role in the context of routing among the mobile nodes. This paper proposes an enhancement to an existing protocol for accommodating node mobility through neighboring node information while keeping the utilization of resources to a minimum.

Evaluation of the Power Consumption of Routing Protocols for Wireless Sensor Networks

Sensor networks are one of the fastest growing areas in broad of a packet is in transit at any one time. In GBR, each node in the network can look at itsneighbors wireless ad hoc networking (? Eld. A sensor node, typically'hop count (depth) and use this to decide which node to forward contains signal-processing circuits, micro-controllers and a the packet on to. If the nodes' power level drops below a wireless transmitter/receiver antenna. Energy saving is one certain level it will increase the depth to discourage trafiE of the critical issue forfor sensor networks since most sensors are equipped with non-rechargeable batteries that have limited lifetime.

Energy Aware Cluster-based Multihop Routing Protocol for Sensor Networks

Clustering combined with multihop communication is a promising solution to cope with the energy requirements of large scale Wireless Sensor Networks. In this work, a new cluster based routing protocol referred to as Energy Aware Cluster-based Multihop (EACM) Routing Protocol is introduced, with multihop communication between cluster heads for transmitting messages to the base station and direct communication within clusters. We propose EACM with both static and dynamic clustering. The network is partitioned into near optimal load balanced clusters by using a voting technique, which ensures that the suitability of a node to become a cluster head is determined by all its neighbors. Results show that the new protocol performs better than LEACH on network lifetime and energy dissipation

Routing Protocol Enhancement for handling Node Mobility in Wireless Sensor Networks

In wireless sensor networks, the routing algorithms currently available assume that the sensor nodes are stationary. Therefore when mobility modulation is applied to the wireless sensor networks, most of the current routing algorithms suffer from performance degradation. The path breaks in mobile wireless networks are due to the movement of mobile nodes, node failure, channel fading and shadowing. It is desirable to deal with dynamic topology changes with optimal effort in terms of resource and channel utilization. As the nodes in wireless sensor medium make use of wireless broadcast to communicate, it is possible to make use of neighboring node information to recover from path failure. Cooperation among the neighboring nodes plays an important role in the context of routing among the mobile nodes. This paper proposes an enhancement to an existing protocol for accommodating node mobility through neighboring node information while keeping the utilization of resources to a minimum.

Impact of Node Mobility on Routing Protocols for Wireless Sensor Networks

Wireless sensor networks monitor their surrounding environment for the occurrence of some anticipated phenomenon. Most of the research related to sensor networks considers the static deployment of sensor nodes. Mobility of sensor node can be considered as an extra dimension of complexity, which poses interesting and challenging problems. Node mobility is a very important aspect in the design of effective routing algorithm for mobile wireless networks. In this work we intent to present the impact of different mobility models on the performance of the wireless sensor networks. Routing characteristics of various routing protocols for ad-hoc network were studied considering different mobility models. Performance metrics such as end-to-end delay, throughput and routing load were considered and their variations in the case of mobility models like Freeway, RPGM were studied. This work will be useful to figure out the characteristics of routing protocols depending on the mobility patterns of sensors

Evaluation of the Power Consumption of Routing Protocols for Wireless Sensor Networks

Sensor networks are one of the fastest growing areas in broadwireless ad hoc networking (?Eld. A sensor node, typically'contains signal-processing circuits, micro-controllers and awireless transmitter/receiver antenna. Energy saving is oneof the critical issue for sensor networks since most sensorsare equipped with non-rechargeable batteries that have limited lifetime.In thiswork, four routing protocols for wireless sensor networks vizFlooding, Gossiping, GBR and LEACH have been simulated using Tiny OS and their power consumption is studied usingcaorwreiredTOoSuStIuMs.ingAMirceaal2izMaotitoens.of these protocols has been carried out using mica 2 motes

Techniques to Improve the word alignments in Statistical Machine Translation from English to Malayalam

In Statistical Machine Translation from English to Malayalam, an unseen English sentence is translated into its equivalent Malayalam translation using statistical models like translation model, language model and a decoder. A parallel corpus of English-Malayalam is used in the training phase. Word to word alignments has to be set up among the sentence pairs of the source and target language before subjecting them for training. This paper is deals with the techniques which can be adopted for improving the alignment model of SMT. Incorporating the parts of speech information into the bilingual corpus has eliminated many of the insignificant alignments. Also identifying the name entities and cognates present in the sentence pairs has proved to be advantageous while setting up the alignments. Moreover, reduction of the unwanted alignments has brought in better training results. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Pattern Analysis Techniques for the Recognition of Unconstrained Handwritten Malayalam Character Images

The objective of the study is to develop a hand written character recognition system that could recognisze all the characters in the mordern script of malayalam language at a high recognition rate

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis