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Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

Because of rapid growth in mobile application markets, competition between companies that provide similar applications has become fierce. To improve user satisfaction for keeping existing users and attracting new users, application developers need to quickly respond to customer feedback regarding functionality and performance defects. In software engineering, specifying an accurate evolution plan according to user feedback is useful but quite difficult. Hence, we propose an approach for predicting and recommending evolution plans to application developers that includes: (1) when a new version of an App should be released; (2) which features should be updated in the next version and (3) if a new version is released, to what degree users would like or dislike it. This approach is based on an elaborate text analysis of massive numbers of user reviews and App update histories. A collocation-based mRAKE method is presented to extract requested and updated features from user reviews and update logs, and the intensity and sentiment scores of each feature are calculated to quantitatively represent time-series histories of App updates and user requests. Machine learning algorithms including linear support vector, Gaussian naïve Bayes and logistic regression are employed to discover the underlying correlation between user opinions embedded in their reviews and the App update behaviors of developers, and rich experiments were conducted on real data to validate the effectiveness of the proposed approach. Overall, our approach can achieve an average accuracy of 72.8% and 93.7% in release time recommendation and content updates of successive versions, respectively, and it can predict user reactions to a planned version with an average accuracy of above 89.0%.

Objectives: COVID-19 pandemic has severely affected the health sector in the whole world. Routine OPDs including rehabilitation centers are partially functional to minimize the risk of cross-infection. In elderly patients, rotator cuff syndrome is a common cause of shoulder pain and daily physiotherapy is the main mode of management. To minimize the risk of cross-infection (COVID-19), we introduced E-rehabilitation services via various mobile apps to our patients. In developing countries like India, E-rehabilitation is still a new concept. Methods: This study evaluated 70 patients who had been enrolled for E-rehabilitation with a minimum of 4 weeks follow-up. Every patient was asked to use the rehabilitation App as per their requirement. Results were assessed with Disabilities of the Arm, Shoulder and Hand (DASH), visual analogue scale (VAS) and active ranges of movement (forward flexion and external rotation). Results: The average age of enrolled patients at the time of surgery was 55.0 years (range, 40–65 years). In 2 and 4 weeks, the range of forward flexion and external rotation has improved significantly. DASH and VAS Score has also been decreased significantly at an average of 2 and 4 weeks with $P<0.01$. Conclusion: In this paper, we summarized the management of rotator cuff syndrome by using various mobile apps and also the various challenges faced in the elderly population with the newer concept of E-rehabilitation in this pandemic.

Context: Due to the change and advancement in technology, day by day the internet service usages are also increasing. Smartphones have become the necessity for every person these days. It is used to perform all basic daily activities such as calling, SMS, banking, gaming, entertainment, education, etc. Therefore, malware authors are developing new variants of malwares or malicious applications especially for monetary benefits.

Objective: Objective of this research paper is to develop a technique that can be used to detect malwares or malicious applications on the android devices that will work for all types of packed or encrypted malicious applications, which usually evade decompiling tools.

Method: In the proposed approach, visualization method is used for the detection of malware. In the first phase, application files are converted into images and then in second phase, texture feature of images are extracted using Grey Level Co-occurrence Matrix (GLCM). In the last phase, machine learning classification algorithms are used to classify the malicious and benign applications.

Results: The proposed approach is run on different datasets collected from various repositories. Different efficiency parameters are calculated and the proposed approach is compared with the existing approaches.

Conclusion: We have proposed a static technique for efficient detection of malwares. The proposed technique performs better than the existing technique.

Customer co-creation is a phenomenon, whose relevance for innovative technology-based services (TBS) has been acknowledged both by scientific and management practice. However, empirical research on this topic is scarce. Above all others, the lack of a good metric for this construct to establish a common ground for empirical research has hampered progress to date. Thus, the purpose of this paper is to develop and test a construct measuring the willingness of a customer to engage in co-creation (hereafer, WCC) of innovative, TBS.

This article provides a thorough literature review on customer co-creation, proposes a scale to measure the willingness to co-create (WCC) innovative, TBS and reports the results of a validation process using expert judges, an exploratory and confirmatory factor analysis. The results of our studies show that the scale has good psychometric properties and that its relationships with other constructs and consumer adoption behaviour conform to theoretical expectations.