Adoption Of Computing Research Articles

Web APIs: Features, Issues, and Expectations – A Large-Scale Empirical Study of Web APIs From Two Publicly Accessible Registries Using Stack Overflow and a User Survey

With the increasing adoption of services-oriented computing and cloud computing technologies, web APIs have become the fundamental building blocks for constructing software applications. Web APIs are developed and published on the internet. The functionality of web APIs can be used to facilitate the development of software applications. There are numerous studies on retrieving and recommending candidate web APIs based on user requirements from a large set of web APIs. However, there are very limited studies on the features of web APIs that make them more likely to be used and the issues of using web APIs in practice. Moreover, users’ expectations on the development and management of web APIs are rarely investigated. In this paper, we conduct a large-scale empirical study of 20,047 web APIs published at two popular and publicly accessible web API registries: ProgrammableWeb and APIs.guru. We first extract the questions posted in Stack Overflow (SO) that are relevant to the web APIs. We then manually analyze 1,885 randomly sampled SO questions and identify 24 web API issue types (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">authorization error</i> ) that are encountered by users. Afterwards, we conduct a user survey to investigate the features of web APIs that users often consider when shortlisting a web API for testing before they adopt it, validate the identified types of web API issues, and understand users’ expectations on the development and management of web APIs. From the 191 received responses, we extract 14 important features for users to decide whether to use a web API (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">well-organized documentation</i> ). We also gain a better understanding of web API issue types and summarize 11 categories of user expectations on web APIs (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">documentation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDK/library</i> ). As the result of our study, we provide guidelines for web API developers and registry managers to improve web APIs and promote the use of web APIs.

Cloud computing communication environment security and performance: Challenges and probable solutions

The adoption of cloud computing has continued to grow, owing to its broad network access, remote accessibility, scalability, on demand self-service, rapid elasticity and resource pooling. In this environment, numerous devices are deployed to access the cloud data and services. This potentially increases the attack services, more so in public clouds. As such, the biggest challenge is the secure data exchange over insecure open network channels. This issue has seen the development of numerous security solutions over the recent past. In this paper, a survey of these security schemes, techniques and methods is provided. The findings indicate that majority of these methods have vulnerabilities that expose users to many attacks. Additionally, some of these security solutions have extremely high complexities that make the cloud communications inefficient.

How Cryptography Can Augment Zero Trust

The scope of this work presents the security architecture of the Cloud provider/consumer and showcases how “Cryptography” augments the Zero Trust Architecture security strategy. In addition, this work will delve into enforcing the minimum expected set of security controls that assures secure isolation, data protection, access control and monitoring/logging at all tiers of the hosted applications. Cryptographic solutions play an important role in the cloud environment in which customers hand over data to a cloud provider for storage, and processing. One of the biggest hurdles for the adoption of cloud computing by enterprises is security and confidentiality. The premise of the paper will be that the cloud provider acts as a distrusted black box from the client's perspective, and whose duty and interest is the protection of their information and privacy. The techniques presented are shared jointly between the service provider and client, and should protect the interests of both, in establishing a successful and trustworthy service.

Deep learning for enhancing internet of things: A comprehensive survey

The Internet of Things (IoT) has emerged as a transformative technology, connecting a wide range of devices and sensors to the internet, enabling data-driven decision-making and automation across various domains. Deep learning, a subset of machine learning, has played a pivotal role in advancing IoT applications by providing the means to process and interpret the massive volumes of data generated by IoT devices. This comprehensive survey provides an extensive review of the integration of deep learning techniques with IoT, aiming to offer a holistic understanding of the state-of-the-art research and practical implementations. The key motivations include handling complex IoT data and enabling smart applications across domains like healthcare, transportation, and industry 4.0. Real-world case studies demonstrate significant improvements on metrics like prediction accuracy, operational efficiency, and energy savings. The adoption of edge computing is further driven by the need to reduce latency in data processing and enhance real-time decisionmaking capabilities in these critical sectors. However, challenges remain around model interpretation, robustness, and computational complexity. Future IoT systems must focus on lightweight deep learning architectures tailored for edge devices, distributed and privacy-preserving analytics, and improved model transparency and fairness. Additionally, these future IoT systems should prioritize robust security measures to protect sensitive data and ensure the reliability and integrity of connected devices and networks. A systematic framework is proposed covering data curation, model development, deployment, and monitoring to guide adoption. The comprehensive nature of this survey aims to bridge the gap between theoretical knowledge and practical deployment, enabling a smoother integration of deep learning into IoT systems. By offering a structured overview of best practices and emerging trends, it empowers professionals to make informed decisions in their IoT projects.

Emergence of cloud computing architecture: The dynamics

The conceptualization of cloud computing is the paradigm that has revolutionized software and hardware architecture across varied technological domains. However, governments and multiple sectors’ rapid adoption of cloud computing is a significant source of cost-saving, scalability, and collaboration mechanisms. Research in cloud computing technology and advancement is yet to fully embrace the complete spectrum of potential issues confronting cloud technology. It is the fastest-growing field that has gained global adoption in the IT space since 2007. Companies like Amazon, Google, Oracle, and Microsoft provide various products via cloud computing regardless of the many challenges they face in delivering their services to establish assurance to customers demanding continuity of services, speed to market, and guaranteed security. While there are multiple discussions around cloud and data safety, only a few have a grip on the dynamism of cloud technology and the background operation. Three major conventional institutions are at the forefront of providing a clear definition of cloud computing architecture, the technology innovation and advancement driving its operation. They are Gartner, Forrester, and the National Institute of Standards and Technology (NIST). The definition of cloud computing is presented differently by Gartner and Forrester, while the NIST explanation is based on industry-standard terms. This research is an in-depth look at cloud computing architecture from the three institutions’ perspectives. It provides researcher insight to uncover the background of cloud technology, tailored toward an extensive focus area for researchers and exposing a new stream of challenges that require a quick resolution.

Perceived Usefulness, Perceived Ease of Use, Facilitating Condition, Social Influence, and Personal Innovativeness of Accounting Students Cloud Computing Adoption

This study aims to test the influence of perceived usefulness, perceived ease of use, facilitating condition, social influence, and personal innovativeness in cloud computing adoption. This research is important because of the high demands of understanding student technology which can be started by adopting cloud computing in learning. Students can use cloud computing to share information, assignments, and work that can be accessed with various devices. This research was conducted on students at two state universities in Indonesia. The study used SEM in the analysis process. This research was conducted on students at two state universities in Indonesia. The results of questionnaire to 126 students indicate that the intention to use the cloud is determined by the perceived ease of use and facilitating condition. Three other variables used, perceived usefulness, social influence, and personal innovativeness were not shown to have an effect on cloud computing adoption intentions. The implication of this research is continuous research related to the use of information systems in learning.

Data analytics as a catalyst for operational optimization: A comprehensive review of techniques in the oil and gas sector

Data analytics has emerged as a critical enabler for operational optimization in the oil and gas sector, driving efficiency and profitability through data-driven insights. This comprehensive review examines the various techniques employed in the industry, focusing on the role of predictive analytics, machine learning, and artificial intelligence (AI) in enhancing exploration, production, and distribution processes. The integration of real-time data analytics with traditional engineering methodologies allows for more accurate reservoir simulations, improved drilling precision, and predictive maintenance of critical infrastructure. Furthermore, the adoption of big data and cloud computing enables faster data processing and more scalable solutions for large and complex datasets, enhancing decision-making capabilities. The review also highlights the significance of prescriptive analytics, which aids in scenario planning and optimizing supply chain logistics, minimizing operational downtime, and improving overall asset management. Techniques such as seismic data interpretation, remote sensing analytics, and IoT-enabled sensors are discussed in the context of their application to real-time monitoring and risk mitigation in oil and gas operations. These technologies contribute to reducing environmental impact by optimizing resource allocation and minimizing operational inefficiencies, supporting the industry's transition to more sustainable practices. Moreover, the review identifies key challenges in the adoption of data analytics, such as data silos, cybersecurity risks, and the need for advanced technical literacy among the workforce. Solutions to these challenges, including the implementation of integrated data platforms and enhanced cybersecurity protocols, are explored. Finally, the review underscores the future potential of analytics-driven technologies in driving digital transformation and operational excellence in the oil and gas sector.

Cloud Computing Adoption and Use: A Systematic Review

ABSTRACT The advent of Cloud Computing has brought tremendous growth to businesses. Enterprises, known for their cost savings, flexible and scalable features, are better managed today. Unfortunately, high dependency on service providers, data security issues, and high set-up costs are becoming major issues along with low adoption rates in developing countries. Despite the infrastructure deficit, there is a need for studies to focus on theories of adoption and use. Therefore, this study aims to systematically review empirical studies on the adoption and use of cloud computing in general to identify relevant factors determining organizational intention to use, actual use, and the impact of cloud computing services on performance. This study investigated the causal factors responsible for the adoption, use and performance of cloud computing through a systematic literature review. This study examined 150 related studies in various databases such as IEEE Xplore, Emerald, Springer link and Science Direct. Thirty-six (36) main factors were identified and the results showed that thirteen factors appeared most frequently: top management support, relative advantage, compatibility, security, complexity, technology readiness, firm size, competitive pressure, trial, cost savings, Perceived usefulness, perceived ease of use and personal innovativeness. Most of these factors are related to technology. This study serves as a foundation for future research and advancement of theory development in the adoption, use, and performance of cloud computing. It also provides inside information on user preferences and is crucial for service providers, business owners and government. Keywords: Cloud Computing, adoption, systematic literature review, determinants, utilization, performance.

Improvement of the Secure Integration of IoT and Cloud Computing using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.

Acceptance of a flipped classroom to improve university students’ learning: An empirical study on the TAM model and the unified theory of acceptance and use of technology (UTAUT)

Higher education has given the flipped classroom a lot of attention as a result of its pedagogical success. As a result of the adoption of social media, smartphones, and computers in the classroom, new strategies for providing online courses, such as flipped classrooms, have evolved. To further understand the effects of such technology integration in teaching, the study looked at the responses of 213 undergraduate students at King Faisal University. For a semester, participants took their regular classes in a flipped classroom. The participants answered a survey made expressly for this study to find out if they would still be willing to use flipped classes following this experience. A structural equation modeling approach was used to analyze the research paradigm, which is based on the technological adoption model. The findings demonstrated that each variable category had a favorable influence on perceived usefulness and perceived ease of use. Perceived utility and ease of use serve as mediating elements in the relationship between independent variables and attitudes toward adopting flipped classrooms. Additionally, the findings indicated that ATFC and BIFC have a positive influence on the acceptance of flipped classrooms. In terms of education and learning, the utilization of classroom instruction is positively impacted by both ATFC and BIFC. These findings show that attitudes toward blended learning and intentions to use flipped classrooms have the biggest impacts on the adoption of the concept in Saudi Arabia higher education.

Probing Determinants Affecting Intention to Adopt Cloud Technology in E-Government Systems

Adopting e-government services presents numerous challenges for governmental sectors in developing countries. These problems can fail some projects involving e-government. Therefore, a solution is required to address these problems. This paper presents a conceptual model and measurement to identify crucial factors that impact cloud computing technology in e-government to address the issues with e-government. According to the recent studies on technology adoption models, a theoretical model is proposed in this study. Extracting items from the literature and adapting them, creates the measurement scales for the proposed model’s structures. Through the use of face validity, pre-testing, and a pilot study, the authors confirm the scales’ content validity and reliability. The data used for this study were collected by the authors from 40 information technology IT professionals for the pilot study in the top 10 government departments in Libya who are responsible for many IT decisions in e-government. In this study, the authors first examine the reliability of the scale using Cronbach’s alpha and perform exploratory factor analysis to assess the scales’ validity. The data were analyzed using partial least squares structural equation modelling (PLS-SEM). The findings demonstrate that the scale measurements satisfy the standard requirements for the validity and reliability According to previous studies on cloud computing adoption from the IS perspectives, this paper theoretically provides a combination model for investigating the cloud-based implementation services to provide a more comprehensive model and the objective is to develop an empirical instrument for analyzing countries’ e-government adoption of cloud computing.

Intelligent data deduplication with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System

The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classification in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction; bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.

THE GAMIZED FORM OF TEACHING STUDENTS WITH DISABILITIES THROUGH THE USE AND UTILIZATION OF THE COMPUTER

Obviously, the gamified form of computer use and utilization in the teaching of students with disabilities has not been sufficiently investigated and studied regarding its contribution to their development and promotion. In this sense, the specific topic was chosen to examine to what extent this way and form of computer adoption and use affects, on the one hand, the processes and the result of the educational process and learning of these individuals/students, on the other hand, the their pedagogical and academic image and performance in all areas. Regarding the method of preparation of the specific work, the critical-interpretative review of the domestic and foreign language literature on this subject was selected and used. The results of the research demonstrate a significant trend and increase in the rates of computer use in the aforementioned form in the teaching and learning of people/students with disabilities, while regarding its contribution to the positive increase in their academic performance there is insufficient evidence from various scientific studies to support the above assumption.

Precursors of Cloud Computing Adoption in Selected Banks in Kenya

Purpose: The study analyzed the precursors of adoption of cloud computing in selected banks in Kenya. Specifically, the study sought to establish the effect of data security on adoption of cloud computing in selected banks in Kenya, to establish how organizational culture affects adoption of cloud computing in selected banks in Kenya, to establish how supplier lock- in affects adoption of cloud computing in selected banks in Kenya and to establish how regulatory policy affects adoption of cloud computing in selected banks in Kenya.&#x0D; Methodology: The study employed descriptive research design and inferential statistics. The target population was 366 ICT staff from 3 selected banks namely NCBA, KCB Bank and Equity bank. The selected banks had the largest market share according to published CBK supervisory report for year 2020. The sample size was 191 respondents. A structured questionnaire was used to collect the data. Responses in the questionnaires were tabulated, coded and processed by use of a computer statistical package for social sciences version 28 (SPSS) program to analyze the data. The responses from the open-ended questions were listed to obtain proportions appropriately and then reported by descriptive narrative. Descriptive statistics like mean, standard deviation were used. The ANOVA test was used to establish the findings from the study and results presented in graphs and charts.&#x0D; Findings: Cloud computing adoption was evaluated for the select commercial banks across three areas namely core-banking systems, middle office and compliance systems and back-office, data science and innovation systems over a period of five years. The respondents indicated that no more than one cloud computing project was either in planning, implementation, failed or completed stages, indicating that despite the perceived benefits of cloud computing there are precursors that need to be addressed if the commercial banks are to fully embrace cloud computing.&#x0D; Unique contribution to theory, practice and policy: In terms of policy, the findings of the study will be important to regulators like CA, CBK and other institutions that regulate financial institutions in coming up with relevant guidelines when it comes to handling customer data within Cloud Computing environment. It gives banking sector perspective of some of the issues that require attention if the envisaged benefits of clod computing are to be fully realized. This is important not only to the policy makers but also to the cloud service providers . Additionally, the findings give a theoretical basis of validating the antecedents of cloud computing adoption in the banking sector in Kenya and this can be extended to the rest of the world. The findings give a practical perspective of the real concerns the banks have to contend with from a practical perspective and therefore very critical in advancement of cloud computing technologies.

To adopt or not to adopt? The determinants of cloud computing adoption in information technology sector

This research is mainly focused on the adoption of cloud computing in the information technology (IT) industry of a developing country, Pakistan by using the theoretical lens of technology acceptance model (TAM) and Elaboration Likelihood Model (ELM). Cloud computing, being one of the latest technologies in the field of IT, has been recently adopted by organizations around the globe, although developing nations have recently started using this technology in their supply chain processes. This study involved the employees of IT industry working in the capital city of Pakistan, i.e. Islamabad. Sample respondents consisted of 213 employees of the IT based organization. Data was collected online by employing structured questionnaires based on past literature. The results revealed that there exists a positive and significant relationship between perceived security, argument advantage, source credibility and perceived usefulness. Additionally, the outcome of the study supported the significant relationships between perceived usefulness and attitude towards adoption of cloud computing, perceived usefulness and intention to adopt cloud computing, and attitude towards cloud computing and intention to adopt cloud computing. The research study has managerial and practical implications. It is one of first of its kind that explores some of the factors leading to adoption of cloud computing in of IT companies in Pakistan.

The TOEQCC Framework for Sustainable Adoption of Cloud Computing at Higher Education Institutions in the Kingdom of Jordan

The extensive use of cloud computing influences changes in managing information technology and communication systems. The term “Cloud Computing” refers to a new management information system that emphasizes the distribution of various computer services across the internet, such as the World Wide Web. Security and privacy concerns and the deployment of resources in shared settings are only a few of the difficulties posed by cloud computing. This study’s overarching goal is to aid Jordanian higher education institutions (JHEIs) in making informed decisions about cloud computing by creating a framework that considers the most important elements influencing adoption. This study, therefore, presents a framework for the adoption of cloud computing in JHEIs by modifying the Diffusion of Innovation (DOI) theory, the Technology Acceptance Model (TAM), and the Technology, Organization, and Environment (TOE) framework. To better understand the elements influencing the adoption of cloud computing in the context of JHEIs, the authors offer a framework they call the Technology-Organization-Environment-Quality of Cloud Computing Adoption (TOEQCC), which builds on the fundamental theories and proposed models. The study aimed to solve the problem of cloud computing adoption at higher educational institutions through suggesting a new framework, which aims to improve the awareness of cloud computing factors as well as the roadmap for the adoption process. Eleven different Jordanian universities (both governmental and private) collaborated on this study. In addition to gathering data from the Jordanian Minister of Higher Education (JMHE) and the Jordanian Higher Education Accreditation Commission, the research team also performed three focus groups with Jordanian students and distributed three hundred questionnaires (JHEAC). NVivo was used to analyze the data following the steps: data reduction, data visualization, and conclusion drawing/verification. Statistics and frequency data are shown in tables, pie charts, bar charts, and column charts. Other tools were employed, such as Microsoft Visio and Microsoft Business Intelligence.

Probing the factors influencing cloud computing adoption in healthcare organizations: A three-way interaction model

The purpose of this study is to probe the influencing factors of cloud computing adoption in healthcare organizations. The study used the extended technology acceptance model (TAM) and motivation model (MM) to broaden the research model. The study proposed awareness, cost reduction, perceived security and privacy, and compatibility as extended factors of TAM. The study also proposed two-way interaction impacts of resistance to change and two-way and three-way interaction impacts of MM factors (extrinsic and intrinsic motivation). The research model was empirically tested on 512 responses gathered through a survey questionnaire by structural equation modeling. The finding concluded that all four extended factors significant predictors of TAM factors. Furthermore, resistance to change is significantly negative, and MM factors significantly positive moderate the relationship between behavioral intention and adoption of cloud computing. The MM factors also have significant three-way interaction impacts. The study's findings contributed more insight into the literature and practice of cloud computing adoption.

Is cloud computing the digital solution to the future of banking?

This study investigates the impact of banks’ strategic move to cloud computing on bank performance and risk-taking. Based on a novel index of banks’ exposure to cloud computing, we find that banks’ adoption of cloud computing is associated with lower cost efficiency, higher profit efficiency, and greater operational risk using data on Chinese banks over the period 2008–2019. We also find that cloud computing interacts with other newly emerging technologies, leading to synergy gains in cost efficiency and operational risk control but with a substitutive effect on profit efficiency from blockchain. The findings are of timely policy importance and practical relevance for regulators, policy-makers, and bank managers.

A Mixed Methods Systematic Analysis of Issues and Factors Influencing Organizational Cloud Computing Adoption and Usage in the Public Sector: Initial Findings

A Mixed Methods Systematic Analysis of Issues and Factors Influencing Organizational Cloud Computing Adoption and Usage in the Public Sector: Initial Findings

Algorithmic TCAMs: Implementing Packet Classification Algorithms in Hardware

The adoption of cloud computing and the deployment of new services require more and more computing and networking resources in large data centers. In particular, there is a need for faster, rich featured switches. Today, switching integrated circuits can handle more than 50 Tb/s and are expected to reach 100 Tb/s next year. This trend is likely to continue, approaching petabit capacities in a few years. At the same time, switches need to support more advanced packet classification features. This poses many challenges to the designers of switching integrated circuits, creating a need for innovations to move forward. One of those challenges is to support programmable packet classification with large rulesets at wire speed using limited silicon footprint. Unfortunately, traditional solutions like ternary content addressable memories (TCAMs) have high cost in area and power, and therefore are not a viable option for large on-chip rulesets, so new alternatives are needed. In the last two decades, researchers have extensively studied the problem of packet classification, proposing many algorithms based on standard memories, mostly targeting software implementations. These packet classification algorithms could be a solution to enable large on-chip classification rulesets using standard memories. However, porting the standard packet classification algorithms to a hardware implementation is not a trivial task, due to the different requirements and the different kinds of memory and computing resources available on application-specific integrated circuit (ASIC) switching chips. This article describes a packet classification design tailored for high-speed switching integrated circuits, which is currently used in the NVIDIA® Mellanox® Spectrum® switching ASIC product line, illustrating how to solve the mismatch between the usual software-based classification algorithms and the specific requirements of a hardware implementation.