Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

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Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

Cognitive radio (CR) is an emerging technology developed to overwhelm the problems like underutilization of the spectrum and spectrum scarcity based on the revolutionary ideas. To take control over the spectrum scarcity and underutilization, several bodies headed by the Federal Communications Commission (FCC), have been taking action to make new paradigm of spectrum management. In 2002 report, the FCC has pointed out that more than 70 % of the radio spectrum, is underutilized in certain times or geographic locations. Hence, spectrum scarcity is not attributable to a fundamental lack of spectrum instead because of the waste of static spectrum allocation. The energy detection spectrum sensing is used in this article that is considered to be simple and best method for sensing techniques. In this paper, two major issues of energy detection method, i.e. noise uncertainty and hidden node problem are discussed and solved. To overcome noise uncertainty, M-ary QAM technique is proposed that increases the overall performance with respect to probability of false alarm and probability of missed detection by 3 % at a same latency time. When CR experiences shadowing or fading effect; under such cases, the user cannot distinguish between an unused band and a deep fade, often consider as a hidden node problem. To overcome this problem, cooperative fusion Sensing with a combination of AND and OR logic of a primary user is done by increasing the cognitive/secondary user up to 11. Simulation results using MATLAB shows that both these techniques overcome both the issues and improves significantly the overall Cognitive system performance.

Cooperative Sensing for Cognitive Radio: A Powerful Access Method for Shadowing Environment

Cognitive radio was introduced to fill up the imbalance between spectrum scarcity and spectrum underutilization. So to make such an ideology work, a network which can utilize all the available channel in the best efficient manner, without causing any harmful interface to primary user (PU) and maintaining the Quality of Service (QoS) for cognitive user (CU) is required. In both Mesh as well as ad-hoc networks, effective utilization of the white-spaces by the CUs maintaining the QoS for both primary and CU is a challenging task due to the frequent and instant change in their channel status. In this paper an intelligent-MAC (i-MAC) for cognitive radio (CR) using two transceivers based on hybrid approach of combination of cooperative decision and contention-free approach is proposed. Cooperative decision, to overcome hidden node or the case when there is no common channel between the CU's and contention-free approach, to solve the issues in contention mechanism, where same channel is selected simultaneously by multiple CU's. Proposed CR-i-MAC permits an effective dynamic spectrum access to CUs without effecting the QoS for PU's. The simulative performance analysis of proposed CR-i-MAC is tested in various critical cases like multi-channel single-radio and multi-channel multi-radio over different on demand routing protocols like dynamic source routing, ad-hoc on demand distance vector and weighted cumulative expected transmission time using network simulator (NS-2). The performance of the network is measured on the basis of parameters like throughput, delay and interference. The analysis of the simulation results shows that the proposed CR-i-MAC outperforms various other CR MAC's in terms of both increased throughput and reduced delays thereby making the system stable and efficient.

Cognitive radio intelligent-MAC (CR-i-MAC): channel-diverse contention free approach for spectrum management

The wireless communication has undergone a revolution due to advancements in technology. For each new user or application to be a part of communication network the preliminary requirement is the allocation of frequency spectrum band. This frequency band is a limited resource and it is impossible to expand its boundaries. So the need is to employ intelligent, adaptive and reconfigurable communication systems which can investigate the requiremenmts of the end user and assign the requisite resources in contrast to the traditional communication systems which allocate a fixed amount of resource to the user under adaptive, autonomic and opportunistic cognitive radio environment. Cognitive radio technology has emerged from software defined radios wherein the key parameters of interest are frequency, power and modulation technique adopted. The role of cognitive radio is to alter these parameters under ubiquitous situations. The spectrum sensing is an important task to determine the availability of the vacant channels to be utilised by the secondary users without posing any harmful interference to the primary users. In multi carrier communication using digital signal processing techniques, filter bank multi carrier has an edge over other technologies in terms of bandwidth and spectral efficiency. The present paper deals with the multi rate FIR decimation and interpolation filter approach for physical layer of cognitive radio under binary symmetric fading channel environment.

Computer Aided BER Performance Analysis of FBMC Cognitive Radio for Physical Layer Under the Effect of Binary Symmetric Radio Fading Channel

Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.,This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.,First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.,The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.,This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.,First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.

Dynamic impact of social network on knowledge contribution loafing in mobile collaboration: a hidden Markov model

Radio (CR) is a comparatively new technology in which problems like underutilization of spectrum and spectrum scarcity is solved based on the revolutionary ideas. Cognitive Radio allows group of users to identify and access to available spectrum resources for their optimum use. Recent studies show that most of the assigned spectrum is underutilized. On the other hand, the increasing number of wireless multimedia applications leads to a spectrum scarcity. Cognitive Radio is proposed as a promising technology to solve the imbalance between spectrum scarcity and spectrum underutilization. In Cognitive Radio, spectrum sensing is done in order to locate the unused spectrum segments. This paper show the strength and capabilities of Cognitive Radio processes and what makes it more powerful over the other competitive radio. Main focus is given on Present State of Research, Architecture and Future Scope for CR. Stress is given on Application areas, where Cognitive Radio techniques can be implemented and proved out to have upper-hand than the available Intelligence and adapting Radios.

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Jai SukhPaulSingh

Papers

Cognitive Radio: State of Research Domain in Next Generation Wireless Networks - A Critical Analysis