Adjunct hexagonal array token Petri nets and hexagonal picture languages

Adjunct Hexagonal Array Token Petri Net Structures (AHPN) are re- cently introduced hexagonal picture generating devices which extended the Hexag- onal Array Token Petri Net Structures . In this paper we consider AHPN model along with a control feature called inhibitor arcs and compare it with some ex- pressive hexagonal picture generating and recognizing models with respect to the generating power

Mobile Ad hoc Networks – Dangling issues of optimal path strategy

Ad Hoc network is a collection of wireless mobile hosts forming a  temporary network without the aid of any centralized administration, in which individual nodes cooperate by forwarding packets to each other to allow nodes to communicate beyond direct wireless transmission range. Routing is a process of exchanging information from one station to other stations of the network. Routing protocols of mobile ad-hoc network tend to need different approaches from existing Internet protocols because of dynamic topology, mobile host, distributed environment, less bandwidth, less battery power. The key concern is to analyze the ability of moving nodes in the network using Random Direction Mobility model based on the path availability. Key Words: Mobile ad hoc networks, Routing, Path stabilit

A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu)

NMT (Neural Machine Translation) is an innovative approach in the field of machine translation, in contrast to SMT (statistical machine translation) and Rule-based techniques which has resulted annotable improvements. This is because NMT is able to overcome many of the shortcomings that are inherent in the traditional approaches. The Development of NMT has grown tremendously in the recent years but NMT performance remain under optimal when applied to low resource language pairs like Telugu, Tamil and Hindi. In this work a proposedmethod fortranslating pairs (Telugu to English) is attempted, an optimal approach which enhancesthe accuracy and execution time period.A hybrid method approach utilizing Long short-term memory (LSTM) and traditional Recurrent Neural Network (RNN) are used for testing and training of the dataset. In the event of long-range dependencies, LSTM will generate more accurate results than a standard RNN would endure and the hybrid technique enhances the performance of LSTM. LSTM is used during the encoding and RNN is used in decoding phases of NMT. Moth Flame Optimization (MFO) is utilized in the proposed system for the purpose of providing the encoder and decoder model with the best ideal points for training the data

Total Factor Productivity Change of Ethiopian Microfinance Institutions (MFIs): A Malmquist Productivity Index Approach (MPI)

By employing the Malmquist productivity index this study attempts to examine the total factor productivity change in the Ethiopian micro finance institutions (MFIs) using a balanced panel dataset of 114 observations from 19 micro finance institutions over the period 2004-2009. The selection of inputs and outputs for the study is based on the dual objectives of MFIs viz outreach and sustainability framework which is in line with the prior study of (Gutierrez et al 2007, 2009). Consequently, we specify two inputs and three outputs; the number of employees, and operating expenses are specified as inputs whereas the outputs are interests and fee income, gross loan portfolio, and number of loans outstanding (number). The result of the study indicated that over the period the malmquist productivity change experienced by the micro finance industry as a whole has averaged 3.8 % annually. With the exception of the year 2004-2005 (slight decline in productivity, which was 0.2 percent) the micro finance industry has reported productivity progress in the study period(i.e productivity rose of 5.5 percent, 5.8 percent,0.3 percent and 7.7 percent in the years 2005-2006, 2006-2007, 2007-2008 and 2008-2009 respectively. It is apparent from the analysis that the main source of total factor productivity (TFP) growth for the MFIs was attributed to the technical efficiency change(10.1 percent increase) as the result depicted that 16 out of 19 MFIs ( about 84 %) has shown improvement in technical efficiency changes. In contrast, only 5 out of 19 (26.3%) MFIs have shown improvement in technological change but still the industry as a whole has exhibited a decline in technological change (5.8 percent decrease over the period) and suggested that there has been a deterioration in the performance of the best practicing micro finance institutions. Further the result showed that pure technical efficiency increased by 8.9 percent while scale efficiency contributed on average 1.1 percent increase and hence suggested that during the study period the Ethiopian MFIs have experienced mainly an increment of pure technical efficiency( improvement in management practices) rather than an improvement in optimum size(scale efficiency change). Generally, an important implication for the Ethiopian micro finance industry is that they need to pursue a technological progress in order to meet the dual objectives of reaching many poor people and financial sustainability.Key words: Productivity Change, Malmquist Productivity Index, Ethiopian MFIs

Cyber Crime Detection and Prevention Techniques on Cyber Cased Objects Using SVM and Smote

Conventional cybersecurity employs crime prevention mechanisms over distributed networks. This demands crime event management at the network level where Detection and Prevention of cybercrimes is a must. A new Framework IDSEM has been introduced in this paper to handle the contemporary heterogeneous objects in cloud environment. This may aid for deployment of analytical tools over the network. A supervised machine learning algorithm like SVM has been implemented to support IDSEM. A machine learning technique Like SMOTE has been implemented to handle imbalanced classification of the sample data. This approach addresses imbalanced datasets by oversampling the minority classes. This will help to solve Social Engineering Attacks (SEA) like Phishing and Vishing. Classification mechanisms like decision trees and probability functions are used in this context. The IDSEM framework could minimize traffic across the cloud network and detect cybercrimes maximally. When results were compared with existing approaches, the results were found to be good, leading to the development of a unique SMOTE algorithm

Robust Privacy-Utility Tradeoffs Under Differential Privacy and Hamming Distortion

A privacy-utility tradeoff is developed for an arbitrary set of finite-alphabet source distributions. Privacy is quantified using differential privacy (DP), and utility is quantified using expected Hamming distortion maximized over the set of distributions. The family of source distribution sets (source sets) is categorized into three classes, based on different levels of prior knowledge they capture. For source sets whose convex hull includes the uniform distribution, symmetric DP mechanisms are optimal. For source sets whose probability values have a fixed monotonic ordering, asymmetric DP mechanisms are optimal. For all other source sets, general upper and lower bounds on the optimal privacy leakage are developed and necessary and sufficient conditions for tightness are established. Differentially private leakage is an upper bound on mutual information leakage: the two criteria are compared analytically and numerically to illustrate the effect of adopting a stronger privacy criterion