Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols.
Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.
Text tokenization is a fundamental pre-processing step for almost all the information processing applications. This task is nontrivial for the scarce resourced languages such as Urdu, as there is inconsistent use of space between words. In this paper a morpheme matching based approach has been proposed for Urdu text tokenization, along with some other algorithms to solve the additional issues of boundary detection of compound words, affixation, reduplication, names and abbreviations. This study resulted into 97.28% precision, 93.71% recall, and 95.46% F1-measure; while tokenizing a corpus of 57000 words by using a morpheme list with 6400 entries.
Risk mitigation has always been a special concern for organization's strategic management. Various tools and techniques have been developed to manage risk in an effective way. Failure Mode and Effects Analysis (FMEA) is one of the tools used for effective assessment of risk. It analyzes all potential failure modes, their causes, and effects on a product or process. Moreover it recommends actions to mitigate failures in order to enhance product reliability. Organizations spend their resources and domain experts make their efforts to complete this analysis. It further helps organizations identify the expected risks and plan strategies in advance to tackle them. But unfortunately the analysis produced after spending a lot of organizational assets and experts' struggles, is not reusable due to its natural language text based description. Information and communication technology experts proposed some solutions but they are associated with some deficiencies. Authors in [13] proposed an ontology based solution to extract and reuse FMEA knowledge from the textual documents, and this article is the first step towards its implementation. In this article we proposed our ontology for Process Failure Mode and Effects Analysis (PFMEA) for automotive domain, along with its implementation, reasoning, and data retrieval through it.
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