An Exploratory Framework for Intelligent Labelling of Fault Datasets

Software fault prediction (SFP) has become a pivotal aspect in realm of software quality. Nevertheless, discipline of software quality suffers the starvation of fault datasets. Most of the research endeavors are focused on type of dataset, its granularity, metrics used and metrics extractors. However, sporadic attention has been exerted on development of fault datasets and their associated challenges. There are very few publicly available datasets limiting the possibilities of comprehensive experiments on way to improvising the quality of software. Current research targets to address the challenges pertinent to fault dataset collection and development if one is not available publicly. It also considers dynamic identification of available resources such as public dataset, open-source software archieves, metrics parsers and intelligent labeling techniques. A framework for dataset collection and development process has been furnished along with evaluation procedure for the identified resources

In vitro direct regeneration in mint from different explants on half strength MS medium

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Servicing Delay Sensitive Pervasive Communication Through Adaptable Width Channelization for Supporting Mobile Edge Computing

Over the last fifteen years, wireless local area networks (WLANs) have been populated by large variety of pervasive devices hosting heterogeneous applications. Pervasive Edge computing encouraged more distributed network applications for these devices, eliminating the round-trip to help in achieving zero latency dream. However, These applications require significantly variable data rates for effective functioning, especially in pervasive computing. The static bandwidth of frequency channelization in current WLANs strictly restricts the maximum achievable data rate by a network station. This static behavior spawns two major drawbacks: under-utilization of scarce spectrum resources and less support to delay sensitive applications such as voice and video.To this point, if the computing is moved to the edge of the network WLANs to reduce the frequency of communication, the pervasive devices can be provided with better services during the communication and networking. Thus, we aim to distribute spectrum resources among pervasive resources based upon delay sensitivity of applications while simultaneously maintaining the fair channel access semantics of medium access control (MAC) layer of WLANs. Henceforth, ultra-low latency, efficiency and reliability of spectrum resources can be assured. In this paper, two novel algorithms have been proposed for adaptive channelization to offer rational distribution of spectrum resources among pervasive Edge nodes based on their bandwidth requirement and assorted ambient conditions. The proposed algorithms have been implemented on a real test bed of commercially available universal software radio peripheral (USRP) devices. Thorough investigations have been carried out to enumerate the effect of dynamic bandwidth channelization on parameters such as medium utilization, achievable throughput, service delay, channel access fairness and bit error rates. The achieved empirical results demonstrate that we can optimally enhance the network-wide throughput by almost 30% using channels of adaptable bandwidths

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity This is the peer reviewed version of the following article: Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization, which has been published in final form at https://doi.org/10.1002/ett.3729. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions

Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control

The emergence of software artifacts greatly emphasizes the need for protecting intellectual property rights (IPR) hampered by software piracy requiring effective measures for software piracy control. Software birthmarking targets to counter ownership theft of software by identifying similarity of their origins. A novice birthmarking approach has been proposed in this paper that is based on hybrid of text-mining and graph-mining techniques. The code elements of a program and their relations with other elements have been identified through their properties (i.e code constructs) and transformed into Graph Manipulation Language (GML). The software birthmarks generated by exploiting the graph theoretic properties (through clustering coefficient) are used for the classifications of similarity or dissimilarity of two programs. The proposed technique has been evaluated over metrics of credibility, resilience, method theft, modified code detection and self-copy detection for programs asserting the effectiveness of proposed approach against software ownership theft. The comparative analysis of proposed approach with contemporary ones shows better results for having properties and relations of program nodes and for employing dynamic techniques of graph mining without adding any overhead (such as increased program size and processing cost)

Ontology Evolution for Personalized and Adaptive Activity Recognition

Ontology-based knowledge driven Activity Recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognize or infer new activities. In this paper, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology based framework adapts by learning the specialized and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics and Kappa Coefficient. A well-known dataset named DAMSH has been used for having an empirical insight to the effectiveness of proposed framework that shows a significant level of accuracy for AR models This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Librar

MILK PRODUCTION POTENTIAL OF PURE BRED HOLSTEIN FRIESIAN AND JERSEY COWS IN SUBTROPICAL ENVIRONMENT OF PAKISTAN

The data on 575 records of 270 Holstein Friesian and 818 records of 326 Jersey cows maintained in Punjab, Pakistan were analyzed. The cows were grouped into imported Holstein Friesian, imported Jersey, Farm born Holstein Friesian and farm born Jersey cows. Lactation milk yield of farm born Holstein Friesian and Jersey cows was significantly (P<0.05) lower than that of imported Holstein Friesian and Jersey cows. Breed group, season of calving and lactation number had significant (P<0.05) effect on lactation milk yield. The highest lactation milk yield was observed in imported and farm born Holstein Friesian cows calved during autumn, while in imported Jersey cows maximum lactation milk yield was observed in cows calved during spring season. The maximum lactation milk yield was observed in the third lactation in imported Holstein Friesian, imported Jersey and farm born Holstein Friesian cows, while in farm born Jersey cows maximum lactation milk yield was observed in the fifth lactation. The milk yield in all breed groups increased with increase in lactation length and service period

Design and Validation of a Reduced Switching Components Step-Up Multilevel Inverter (RSCS-MLI)

A reduced switching components step-up multilevel inverter (RSCS-MLI) is presented in the paper. The basic circuit of the proposed MLI can produce 11 levels in the output voltage with a reduced number of switching components. The other features of the proposed circuit include a low value of voltage stresses and the inherent generation of the voltage levels pertaining to the negative half without the requirement of an H-bridge. Fundamental frequency switching technique, also known as Nearest Level Control (NLC) technique, is implemented in the proposed topology for generating the switching signals. The experimental total harmonic distortion (THD) in the output voltage comes out to be 9.4% for modulation index equal to 1. Based on different parameters, a comparative study has been shown in the paper, which makes the claim of the proposed MLI stronger. An experimental setup is prepared to carry out the hardware implementation of the proposed structure and monitor its performance under dynamic load conditions, which is also used to verify the simulation results. Power loss analysis, carried out by using PLECS software, helps us to gain insight into different losses occurring while operating the inverter. The different results are explained and analyzed in the paper.</jats:p

Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques