Application maintenance consumes a considerable amount of an organization's time and resources each year. Almost 60% of IT budget is spent alone on application maintenance. The reason of offshore outsourcing of application maintenance is not only the reduction of maintenance cost but to free up the resources and to keep the focus on core products. Offshore outsourcing is a common business strategy that is used by companies to achieve cost savings about 20-50%. However, the decision making process of application maintenance is a complex phenomenon. It is based on a set of influencing factors, clients' requirements and nature of the project. Hence, the current study is aimed at the in-depth investigation of the complex sourcing decision process of application maintenance. Accordingly, a systematic literature review is performed to determine the influencing factors and critical success factors that will be used by the decision makers for the evaluation of projects before making the outsourcing decisions. A total of 15 influencing factors out of 52 selected papers were identified. Based on the defined criteria, amongst the identified factors, only 10 factors were ranked as critical success factors, which are employees' skills, cost, legal requirements, infrastructure, communication, knowledge transfer, maturity level, project management, language barrier and frequent requirements changes. Consequently, a sourcing model was proposed based on the identified critical success factors that help the IT managers and domain experts in making appropriate outsourcing decisions. INDEX TERMS Application maintenance, critical success factors, influencing factors, offshoring, outsourcing, outsourcing decisions.

Global Software Development (GSD) has been an emerging trend in the development of software globally, for the last two decades. Information Technology (IT) outsourcing includes application development, application maintenance, infrastructure management and business process outsourcing. Software maintenance aims to keep the IT system operational and to fulfill the client requirements. The maintenance is considered the longest phase of software life cycle that consumes about 60-70% of the total software budget. Maintenance of software is not only time consuming but also requires a significant human resources' ratio. Mostly, software acquisition and maintenance consume a big portion of the total IT budget. The current study aims to evaluate the findings of the systematic literature review and to derive a list of critical success factors regarding offshore outsourcing decision of application maintenance. Thus, an empirical study is performed to validate the influencing factors that were identified by using systematic literature review. These factors are further validated by 93 outsourcing experts from 30 different countries. The collected data through online survey is analyzed based on variables such as respondents experience level, respondents' locations (continents), experts' positions. Similarly, the data is analysed based on Chi square test (linear by linear association) and Spearman Rank Correlation. Additionally, the identified factors through survey and systematic literature review are ranked by two different methods. Consequently, a project assessment model is proposed, based on the critical success factors for the sourcing decision of application maintenance.

Enhancers are short DNA regulatory elements which play a vital role in gene expression. Due to their important roles in genomics, several computational models have been proposed in the literature for identification of enhancers and their strengths using traditional machine learning algorithms, however, the proposed models are unable to identify enhancers and their strength with reasonable accuracy because of high non-linearity in DNA sequences. This paper proposes a two-level intelligent model based on Deep Neural Network (DNN) along with multiple feature extraction methods. Firstly, the proposed model represents the given DNA sequences into feature vectors using Pseudo K-tuple Nucleotide Composition (PseKNC) and FastText methods. Secondly, the features vectors are fused to make a heterogeneous features vector that considered the local and global correlation amongst the given sequences along with internal structure information. Finally, the heterogeneous feature vector is given to a DNN model to make final predictions. The proposed iEnhancer-DHF is developed using two-layer approach. The first layer predicts whether the given DNA samples are enhancers or non-enhancers whereas the second layer identifies either the enhancers are strong enhancers or weak enhancers. The outcome of the proposed model was rigorously assessed using both training and independent datasets via 10-fold cross validation method. The validation outcome demonstrated that the iEnhancer-DHF model yielded accuracies 86.07% and 69.60% at first layer and second layer respectively utilizing the training dataset. Similarly, the model yielded accuracies 83.21% and 67.54% at first layer and at second layer respectively by using the independent dataset. Additionally, the outcomes of the proposed model was initially compared with widely applied classifiers such as Support Vector Machine, Random Forest and K-nearest Neighbor and subsequently the performance is compared with the existing models using both the training and independent datasets. The comparison results exhibited that the iEnhancer-DHF model performed superior than the recently published models.

Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as "The Internet of Things". WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called "A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs". CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

ABSTRACT

High-maturity software development processes, such as the Team Software Process and the accompanying Personal Software Process (PSP), can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, there is a lack of tool support for automating that type of analysis, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality, and productivity, to enable the automated (tool based) analysis of performance data produced by PSP developers, namely, identify and rank performance problems and their root causes. A PSP data set referring to more than 30 000 projects was used to validate and calibrate the model.

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.

Selective image segmentation is one of the most important topics in medical imaging and real applications. In this paper, we propose a robust selective segmentation model using a dual-level set variational formulation based on local spatial distance. Our model aims to segment all objects with one level set function (global) and the selected object with another level set function (local). Our model is the combination of marker distance function, edge detection, local spatial distance, and active contour without edges. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.