Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...     

Green synthesis of highly stable carbon nanodots and their photocatalytic performance

The present study reports a novel, facile, biosynthesis route for the synthesis of carbon nanodots (CDs) with an approximate quantum yield of 38.5%, using Musk melon extract as a naturally derived‐precursor material. The synthesis of CDs was established by using ultraviolet–visible (UV–vis) spectroscopy, Dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy and Fourier transform infrared (FTIR) spectroscopy. The as‐prepared CDs possess an eminent fluorescence under UV–light (λ _ex  = 365 nm). The size range of CDs was found to be in the range of 5–10 nm. The authors further explored the use of such biosynthesised CDs as a photocatalyst material for removal of industrial dye. Degradation of methylene blue dye was performed in a photocatalytic reactor and monitored using UV–vis spectroscopy. The CDs show excellent dye degradation capability of 37.08% in 60 min and reaction rate of 0.0032 min⁻¹. This study shows that synthesised CDs are highly stable in nature, and possess potential application in wastewater treatment.Inspec keywords: carbon, nanostructured materials, nanofabrication, catalysis, photochemistry, ultraviolet spectra, visible spectra, photoluminescence, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, fluorescence, dyesOther keywords: green synthesis, highly stable CD, photocatalytic performance, biosynthesis route, carbon nanodots, quantum yield, Musk melon extract, naturally derived‐precursor material, ultraviolet‐visible spectroscopy, dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, FTIR spectroscopy, fluorescence, biosynthesised CD, photocatalyst material, industrial dye, methylene blue dye degradation, photocatalytic reactor, UV‐vis spectroscopy, wastewater treatment, size 5 nm to 10 nm, time 60 min     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.     

Mobility-Aware RSVP: A framework for improving the performance of multimedia services over wireless IP-based mobile networks

This paper proposes the Mobility-Aware Resource Reservation Protocol (MARSVP) in which mobility and QoS signaling are performed as a single functional block. The key concept of MARSVP is to convey mobility-specific information (binding updates and their associated acknowledgments) by using newly defined RSVP objects embedded in existing RSVP messages. An appealing feature of MARSVP is that it adheres to the current RSVP standard (RFC 2205) and thus requires minimal changes to end nodes without affecting any of the conventional RSVP routers in between. The proposed mechanism is evaluated using a simulation model for application-level performance and an analytical model for network-level signaling cost. Simulation results indicate a 27.9% improvement in QoS interruption when using Mobile IPv6 (MIPv6), 12.5% when using Hierarchical Mobile IPv6 (HMIPv6), and no improvement when using Fast Handovers for MIPv6 (FMIPv6). On the network-level, signaling cost savings of 9.4% and 11.9% are achieved for MIPv6 and HMIPv6, respectively, while FMIPv6 achieves savings of 17.9% when using Voice-over-IP traffic and 26.7% for Video-over-IP traffic. The results of the conducted studies indicate MARSVP’s superiority to conventional RSVP when deployed over wireless networks.     

Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.     

Developing a framework for analyzing IS sourcing

This paper develops a framework for the consideration of internal markets as an alternative to information systems (IS) outsourcing. It is based on an assessment of the pros and cons of both outsourcing and of insourcing based on the internal markets approach. It is formulated in terms of the operational, tactical, and strategic impacts of the choice among the alternatives. The framework, and the propositions that are developed from it, should be useful both for researchers, who can use it for developing testable research hypotheses, and for practitioners, who may use it as a basis for developing a comprehensive set of criteria for the evaluation of these sourcing options.     

Automated Cellular Modeling and Prediction on a Large Scale

We describe CHAMP (CHurn Analysis, Modeling, andPrediction), an automated system for modeling cellularsubscriber churn that is predicting which customerswill discontinue cellular phone service. We describevarious issues related to developing and deployingthis system including automating data access from aremote data warehouse, preprocessing, featureselection, model validation, and optimization toreflect business tradeoffs. Using data from GTE'sdata warehouse for cellular phone customers, CHAMP iscapable of developing churn models customized byregion for over one hundred GTE cellular phone marketstotaling over 5 million customers. Every month churnfactors are identified for each geographic region andmodels are updated to generate churn scores predictingwho is likely to churn in the short term. Learningmethods such as decision trees and genetic algorithmsare used for feature selection and a cascade neuralnetwork is used for predicting churn scores. Inaddition to producing churn scores, CHAMP alsoproduces qualitative results in the form of rules andcomparison of market trends that are disseminatedthrough a web based interface.     

A simplified approach to independent component analysis

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.     

The guardian model and primitives for exception handling in distributed systems

This work presents an abstraction called guardian for exception handling in distributed and concurrent systems that use coordinated exception handling. This model addresses two fundamental problems with distributed exception handling in a group of asynchronous processes. The first is to perform recovery when multiple exceptions are concurrently signaled. The second is to determine the correct context in which a process should execute its exception handling actions. Several schemes have been proposed in the past to address these problems. These are based on structuring a distributed program as atomic actions based on conversations or transactions and resolving multiple concurrent exceptions into a single one. The guardian in a distributed program represents the abstraction of a global exception handler, which encapsulates rules for handling concurrent exceptions and directing each process to the semantically correct context for executing its recovery actions. Its programming primitives and the underlying distributed execution model are presented here. In contrast to the existing approaches, this model is more basic and can be used to implement or enhance the existing schemes. Using several examples we illustrate the capabilities of this model. Finally, its advantages and limitations are discussed in contrast to existing approaches.