Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

DevOps is a set of practices and a cultural movement that aims to break down barriers between development and operation teams to improve collaboration and communication. Different organizations have embraced DevOps principles due to the massive potential, such as a much shorter time to production, increased reliability and stability. However, despite the widespread adoption of DevOps and its infrastructure, there is a lack of understanding and literature on the key concepts, practices, tools, and challenges associated with implementing DevOps strategies. The main goal of this research paper is to explore and discuss challenges related to DevOps culture and practices. Moreover, it describes how DevOps works in an organization, provides a detailed explanation of DevOps, and investigates the cultural challenges that organizations face when implementing DevOps. The proposed paper reveals ten critical challenges that need to be addressed in adopting the DevOps culture. The challenges are further analyzed on the basis of the various continents. According to the findings, the following critical challenges are considered during the implementation of a DevOps culture: lack of collaboration and communication, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.

Offshore software development outsourcing (OSDO) is a modern business strategy for producing high-quality software at a low cost. The OSDO refers to the practice of contracting to an offshore (extrinsic) organization to perform some or all software development work of a product. For the benefit of the OSDO vendors, this paper aims to develop a “communication and coordination challenges mitigation model” (CCCMM) that provides solutions for unambiguously defined communication and coordination processes in global software development (GSD) environment. Our proposed model is based on the fuzzy multi-attribute decision-making (FMADM) approach incorporating the capability of group decision-making. The FMADM approach is used both in the ranking of survey and assessment of case studies. First, the authors undertook a systematic literature review (SLR) that identified all cited challenges from a set of 101 articles. We identified 18 problem areas faced by the GSD vendors in OSDO relationships. Of these, six were ranked as critical. For the purpose of identifying corrective interventions, a second SLR was conducted that revealed 75 remedial measures extracted from 63 chosen articles. To validate our SLR findings, we surveyed 42 outsourcing experts from six countries. We also categorized six critical challenges and 75 corrective practices into four mitigation levels based on CMMI, SOVRM, and SOPM. In addition, two case studies were conducted to evaluate CCCMM outcomes in OSDO companies. The assessment results of the first case study do not recommend Company-A for the successful implementation of level-2 of the CCCMM, so Company-A stands at level-1. We have observed from the second case study that Company-B has implemented all the critical challenges of the level-2 only; therefore, Company-B is at level-2 “success” of the proposed assessment model.

Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors’ organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms. This paper has the following content: in Section II , we have describe the Literature review; in Section III , research methodology is specified; in Section IV , the findings of the SLR and the analysis of result are discussed; in Section V , the limitations of this research are given; in Section VI , we discussed our conclusions and future work.

Security is the protection from various kinds of threats and most organizations engage in the challenge of security especially cyber-attacks. The attacks are increasing rapidly, due to which cyber security does not now change on supervised and pattern-based detection algorithms which assure continuous security observing. There are many kinds of problems in vendor organizations like cyber theft, which is the most common attack in cyberspace. This research study is developing a Cyber Security Challenges Model (CSCM) that will facilitate vendors’ organizations to identify challenges of cyber security during the development of software in a vendor organization. To find cyber security issues/challenges, a Systematic Literature Review (SLR) is conducted on 44 relevant research publications by developing a search string based on research questions. As the final selected research publications were less in number and did not complete our aim, therefore, snow bowling technique is applied to 67 relevant research publications. This relevant data was comprised of different databases/sources e.g., Google Scholar, IEEE Explore, SpringerLink, ACM Digital Library, anFffid ScienceDirect. Furthermore, for the distinctive literature review, we’ve carried out all of the steps in SLR, for example, improvement of SLR protocol, initials, and a very last collection of the applicable information, data extraction, data quality assessment, and data synthesis. Thirteen (13) critical cyber security challenges are identified which are; “Security issues/Access of Cyberattacks”, “Lack of Right Knowledge”, “Framework”, “Lack of Technical Support”, “Disaster Issues”, “Cost Security issues”, “Lack of Confidentiality and Trust”, “Lack of Management”, “Unauthorized Access issues”, “Lack of Resources”, “Lack of Metrics”, “Administrative Mistakes during Development” and “Lack of Quality, Liability, and Reliability”. The findings of our analysis study signify the similarities and dissimilarities in the recognized cybersecurity challenges in different decades, companies/firms, continents, databases, and methodologies.

Majority of software development firms are adopting the concepts of global software development (GSD) in order to develop high‐quality and low‐cost products. However, the requirements change management (RCM) becomes a significant challenge in the GSD environment because of the unavailability of proper RCM framework and taxonomy. The objective of this study is to develop a taxonomy of the challenging factors of the RCM process in GSD. The taxonomy is developed based on the results of the data collected during the survey study and the implementation of the analytical hierarchy process (AHP). Total 25 challenging factors are identified and mapped into four core categories, ie, “organizational management,” “team,” “technology,” and “process.” Moreover, the AHP analysis is performed to prioritize the challenging factors and their categories. The prioritization process highlight that “process” is the most significant category of the RCM challenging factors. The taxonomy developed in this study provides a robust framework to tackle problems associated with RCM activities in GSD environment, which is significant to the success and progression of GSD firms.