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Article Citation - WoS: 11Citation - Scopus: 13Challenges in Agile Software Maintenance for Local and Global Development: an Empirical Assessment(Mdpi, 2023) Almashhadani, Mohammed; Mishra, Alok; Yazici, Ali; Younas, MuhammadAgile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.Article Citation - WoS: 13Citation - Scopus: 25Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network(Mdpi, 2021) Gosala, Bethany; Chowdhuri, Sripriya Roy; Singh, Jyoti; Gupta, Manjari; Mishra, AlokUnified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset.Article Citation - WoS: 8Citation - Scopus: 12Srcmimm: the Software Requirements Change Management and Implementation Maturity Model in the Domain of Global Software Development Industry(Springer, 2023) Akbar, Muhammad Azeem; Khan, Arif Ali; Mahmood, Sajjad; Mishra, AlokThe software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners' feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness.Article Citation - WoS: 10Citation - Scopus: 13Sustainability Inclusion in Informatics Curriculum Development(Mdpi, 2020) Mishra, Deepti; Mishra, Alok(1) Background: Presently, sustainability is a crucial issue for human beings due to many disasters owing to climate change. Information Technology (IT) is now part of everyday life in society due to the proliferation of gadgets such as mobile phones, apps, computers, information systems, web-based systems, etc. (2) Methods: The analysis is based on recent ACM/IEEE curriculum guidelines for IT, a rigorous literature review as well as various viewpoints and their relevance for sustainability-oriented curriculum development; it also includes an assessment of key competencies in sustainability for proposed units in the IT curriculum. (3) Results: Sustainability is a critical subject for prospective IT professionals. Therefore, it is imperative to motivate and raise awareness among students and the faculty community regarding sustainability through its inclusion in the Informatics curriculum. This paper focuses on how sustainability can be included in various courses of the Informatics curriculum. It also considers recent ACM/IEEE curriculum guidelines for IT professionals, which assert that IT students should explore IT strategies required for developing a culture of green and sustainable IT. (4) Conclusions: This paper provides guidelines for IT curriculum development by incorporating sustainable elements in courses, so that future IT professionals can learn and practice sustainability in order to develop a sustainable society.Article Citation - WoS: 18Citation - Scopus: 23A Novel Hybrid Machine Learning Based System To Classify Shoulder Implant Manufacturers(Mdpi, 2022) Sivari, Esra; Guzel, Mehmet Serdar; Bostanci, Erkan; Mishra, AlokIt is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient's previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.Article Citation - WoS: 18Citation - Scopus: 21Research Trends in Management Issues of Global Software Development: Evaluating the Past To Envision the Future(Taylor & Francis inc, 2011) Mishra, Deepti; Mishra, AlokThis paper presents research trends in management issues (project management, process management, knowledge management, requirements management, configuration management, risk management, quality management) of distributed/global information system development. The main objective is to highlight the current research and practice direction in these areas. The results are based on peer-reviewed conference papers/journal articles, published between 2000 and early 2011. The analysis revealed that most research has been done in project management, process management, knowledge management and requirements management areas while configuration, risk, and quality management issues could get only limited attention in global/distributed information system development. This indicates the need for future research (quantitative and qualitative) in these areas.Article Citation - WoS: 5Citation - Scopus: 8A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, AlokMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.Article Citation - WoS: 6Citation - Scopus: 7Predictive Effect of Gender and Sector Differences on Internet Usage Among Employees(Kaunas Univ Technol, 2010) Akman, Ibrahim; Mishra, Alok; Software Engineering; Computer EngineeringInternet has become the foundation for the world's new information infrastructure. This impact could be attributed to the Internet's universal access to information as well as its applications in all walks of life. Various services of the Internet and tools (chat rooms, e-mails, etc.) provide users with a wide range of benefits. In their study, Colley and Maltby (2008) indicated that one important research area over the last decade has been the impact of the Internet upon different social groups in the society. The differences in various aspects of Internet usage across demographic groups have also become an interesting research area (Yang and Tung, 2007; Jaeger, 2003) because demographic attributes were found to influence individuals' actions before they engage in a given behaviour (Ajzen and Fishbein, 1980; Zhang, 2005; Jaeger, 2003). Zhang (2005) reported that although studies of computer and Internet attitudes are abundant, the majority of these researches use college students (Zhang, 2005; Hwang et al., 2006; Li and Kirkup, 2007; Chen, 2008) or ordinary citizens (Fisher and Jacob, 2006; Fang and Yen, 2006; Colley and Maltby, 2008) as samples. However, employee populations constitute one of the largest groups and play a very important role in the adoption of new technologies. Additionally, employees' use of Internet services may show entirely different patterns than other groups in the society (Jin et al., 2007). Furthermore, the basic concepts of the Internet have been developed in the Western World and most of the empirical research focusing on Internet usage is either US/EU or Far East based (Teo and Lim, 2000; Usiner, 2005). Although they cover very valuable territory and provide useful insights that can provide direction in the examination of the issues from a global perspective, the results of these studies may not be applicable to other parts of the world due to the existence of social and economic differences (Bertot, McClure and Owens, 1999; Zhang, 2005). Nowadays, there is a growing divide between Western countries and the developing countries and, comparatively, very little has been researched in the field of ICT in the latter. Keeping these in view, the present study was undertaken to investigate the predictive effect of employees' gender and sector in their Internet usage and purpose of using the Internet. For the purpose of this study, "public sector" refers to national government departments and "private sector" comprises private corporations (Akman et al., 2005). Here, it is important to note that institutions providing nonprofit public services (e. g. universities, local government, etc.) have been categorized as a public sector. Our study focused on employees from private and public sector organizations. A sample of employees was used for this purpose. The independent (decision) variables were gender and sector of employees. The dependent variables were categorized into two empirical factors: (i) usage profile (average daily use of Internet and reason for using Internet) and (ii) usage pattern (average daily use of Internet for communication services, average daily use of Internet for information services and average daily use of Internet for electronic services). For this purpose, a survey was conducted among employees from public and private sector organizations. Interestingly, the results indicated that gender and sector both have significant impact on average daily use of Internet for communication services. This impact is significant for gender on average daily use of Internet for information services and for sector on average daily use of Internet. Another interesting finding is that there is no significant predictive effect of gender and sector on the reason for using Internet.Article Citation - WoS: 9Citation - Scopus: 9Proton Therapy for Mandibula Plate Phantom(Mdpi, 2021) Senirkentli, Guler Burcu; Ekinci, Fatih; Bostanci, Erkan; Guzel, Mehmet Serdar; Dagli, Ozlem; Karim, Ahmad M.; Mishra, AlokPurpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values. Methods: In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom. Results: Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47-3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased. Conclusion: These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues.Article Citation - WoS: 2Citation - Scopus: 2Cloud-Based Test Tools: a Brief Comparative View(inst information & Communication Technologies-bulgarian Acad Sciences, 2018) Kilinc, Nergiz; Sezer, Leyla; Mishra, AlokThe concept of virtualization has brought life to the new methods of software testing. With the help of cloud technology, testing has become much more popular because of the opportunities it provides. Cloud technologies provides everything as a service, hence the software testing is also provided as a service on cloud with the privileges of lower cost of testing, and relatively less effort. There are various cloud-based test tools . focusing on different aspects of software testing such as load tests, regression tests, stress tests, performance tests, scalability tests, security tests, .functional tests, browser performance tests, and latency tests. This paper investigates the cloud-based testing tools focusing on different aspects of software testing.
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