Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Socio-Psychological Factors of Rising Trend of Suicidal Ideation among Indigenous Workforce: Evidence from Himalayan Range

Apart from socio-economic disparities,indigenous people of the Himalayan range in Asia face an increasing trend of suicides.The tragic suicidal events usually go unaddressed,and no strategies are presently in place to mitigate suicides in the future.This study aims to explain the prevailing causes of suicidal ideation to come up with some policy recommendations.Through a preliminary survey,we identified social stigma,social isolation,lack of healthcare facilities,and domestic violence as the potential reasons for suicidal ideation.We identified individuals with suicide ideation for further data collection to test the relationships between the identified variables and suicidal ideation.A quantitative,explanatory and cross-sectional design was used to test the hypotheses.Inferential statistics were applied to the data containing 314 responses,and the results revealed that all the predictive variables influence the formation of suicidal ideation.The study uncovered some new dimensions of social stigma and social isolation that are common among the indigenous people.The results also indicate that some domestic violence is socially acceptable and considered a normal familial matter.Thefindings have theoretical and practical implications.We have identified new dimensions of social stigma,social isolation,and domestic violence that are new additions to the existing bank of literature.It provides insights to encourage public authorities,healthcare providers,and NGO managers to play their role in the right direction to mitigate the problem.This study opens further venues for researchers to investigate the complex issue of suicide more deeply.

New Fourth and Fifth-Order Iterative Methods for Solving Nonlinear Equations

In this paper, we establish two new iterative methods of order four and five by using modified homotopy perturbation technique. We also present the convergence analysis of these iterative methods. To assess the validity and performance of these iterative methods, we have applied to solve some nonlinear problems.

New Modification of Fixed Point Iterative Method for Solving Nonlinear Equations

In this paper, we have modified fixed point method and have established two new iterative methods of order two and three. We have discussed their convergence analysis and comparison with some other existing iterative methods for solving nonlinear equations.

A review of single phase adaptive auto-reclosing schemes for EHV transmission lines

Statistics shows that transients produced by lightning or momentary links with external objects, have produced more than 80% of faults in overhead lines. Reclosing of circuit breaker (CB) after a pre-defined dead time is very common however reclosing onto permanent faults may damage the power system stability and aggravate severe damage to the system. Thus, adaptive single-phase auto-reclosing (ASPAR) based on investigating existing electrical signals has fascinated engineers and researchers. An ASPAR blocks CB reclosing onto permanent faults and allows reclosing permission once secondary arc is quenched. To address the subject, there have been many ASPARs techniques proposed based on the features trapped in a faulty phase. This paper presents a critical survey of adaptive auto-reclosing schemes that have hitherto been applied to EHV transmission lines.

Warm Standard Scalar Field Modified Chaplygin Gas Inflation Inspired by Generalized Dissipative Coefficient on the Brane

We discuss the warm inflation in the presence of standard scalar field model and modified Chaplygin gas in brane-world scenario. We consider weak and strong dissipative regimes with generalized dissipative coefficient. We extract various inflationary parameters. For example, we analyze the behavior of different ratios(ratio of dissipative co-efficient and Hubble parameter Γ/3H, ratio of temperature and Hubble parameter T/H, scalar-to-tensor ratio r) with respect to spectral index ns for the weak and strong dissipative regimes through parametric plotting. It is found that T/H and Γ/3H satisfied the required conditions in both dissipative regimes. It is also noted that the spectral index(ns)attain the range n_s = 0.96_(-0.10)^(+0.10). It is remarked here that our results are consistence with observational data WMAP7,WMAP9, and recent Planck data.

Generalized Ghost Pilgrim Scalar Field Models of Dark Energy

We assume generalized ghost Pilgrim dark energy(GGPDE) model in the presence of cold dark matter in flat FRW universe.With suitable choice of interaction term between GGPDE and cold dark matter,we investigate the nature of equation of state parameter for GGPDE.Also,we investigate the natures of dynamical scalar field models(such as quintessence,tachyon,k-essence,and dilaton dark energy) and concerned potentials through the correspondence phenomenon between GGPDE and these models.