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Modi was very useful script in the kingdoms of medieval Maharashtra. In the reign of the great Maratha-Chhatrapati Shivaji and also in the reign of Peshwas, this script was widely incorporated in ruling the state. This script is very similar to the shorthand. At that time, it was used in Maharashtra to prepare the documents such as Property matters, Donation of Land (Dan-Patra), Land Revenue, Military...
The P300 Speller is a Brain Computer Interface that enables communication using the EEG signal. The P300 wave is an Event Related Potential that occurs as a response to a familiar stimulus. This system can be used to aid persons who are unable to communicate via conventional methods. In this paper, the P300 Speller has been modified to allow communication in three languages: English, Sinhala and Tamil...
This paper presents a user authentication system based on mouse movement data. An available logging tool named Recording User Input (RUI) is used to collect three types of mouse actions — Mouse Move, Point-and-Click on Left or Right mouse button and Drag-and-Drop. Collected data are divided into N-number of blocks consisting of specific number of actions. From each block seventy four features are...
Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's...
The major disadvantage of Support Vector Machine (SVM) happens in its training phase as it requires to solve a quadratic programming problem, making computation very costly. With the integration of LiDAR data and high spatial resolution orthophoto, more input data layers are available for object-based Support Vector Machine classification. Initially, confusion among classes arises because of the presence...
This paper describes an algorithm that parallelizes support vector machines. The data is split into subsets and optimized separately with multiple SVMs, instead of analyzing the whole training set in one optimization step. The partial results are combined and filtered in a cascade of SVMs. The process terminates when the global optimum is reached. The Cascade SVM is spread over multiple processors...
Continuously increasing ratio of spam mails has raised a serious issue regarding the Content of an e-mail and the user Consent for accepting an e-mail. The content of spam mails changes over a time period as, spammers apply different techniques to elude filters. The consent relates individual user's preferences for discriminating mails as spam or legitimate. We present personalized spam filter using...
Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated...
The Support Vector Machine (SVM) is a classical classification algorithm that has a wide range of application. With kernel function, SVM can dispose the datasets that are not linearly separable in their original feature space, making it more flexible in practical use compared with linear model. However, its complexity in training is an obstacle to large-scale dataset handling. This paper proposes...
Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is...
The paper aims at presenting an ensembling framework for Sparse Representation Classifier (SRC). The paper proposes a method of combining a series of Sparse Representation Classifier models using a network of Linear Support Vector Machines (LSVMs). One of the core ideas of the paper is that there exists a symbiosis between Sparse Representation Classifier and Linear Support Vector Machine. This symbiosis,...
The Performance of three classification methods; Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbor (k-NN) is investigated by a detailed comparison for detection of line outage. This paper presents an application of Phasor Measurement Units (PMUs) in the protection of electrical power systems that is the single line outage detection in a transmission network. Also, a detection of...
Naive Bayes classifiers are widely used to filter spam emails, however, the strong independence assumptions between features limit their performance in accurately identifying spams. To address this issue, we proposed a support machine vector based naive Bayes — SVM-NB — filtering system. The SVM-NB first constructs an optimal separating hyperplane that divides samples in the training set into two...
This paper presents a robust and optimal operation tracking Energy Management System (EMS) for Mobile Base Transceiver Station (BTS) Microgrid equipped with Battery, PV panels and Diesel Engine Generator (DEG) for unreliable grid. The contribution is particularly focused on minimizing the DEG fuel (with DEG ON/OFF frequency) at the time of power-grid outage (i.e. Blackout). The EMS is designed to...
Prediction for deck-motion is a practical measure to improve the landing/taking off safety of carrier-based aircraft when those deck-motions in six-degree freedoms cannot be effectively controlled/restrained. Deck-motions excited by waves and winds own characteristics of randomness and nonlinearity. It is generally believed those classical feed-forward neural networks, such as back propagation networks...
This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine...
Massive proliferation of social media has opened possibilities for perpetrator to conduct the crime of online child grooming. Because the pervasiveness of the problem scale, it may only be tamed effectively and efficiently by using an automatic grooming conversation detection system. Previously, Pranoto, Gunawan, and Soewito [1] had developed a logistic model for the purpose and the model was able...
Gray-Box Models which combine a phenomenological model with a black box tool are useful for determining the values of not well known parameters of the model. In this work an indirect strategy for training these gray box models using least-square support vector machine and genetic algorithms is presented. The gray box model was tested in a Continuous Stirred Tank Reactor process with good results (Index...
The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved...
This paper presents a text-dependent speaker verification using Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM). Mel-Frequency Cepstral Coefficients technique has been used to extract the characteristic from the recorded voice spoken by the user and SVM is used to classify the all models of the speakers and impostors. A Malay spoken digit database is utilized for the training...
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