Asian Journal of Research in Computer Science
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Cloud Migrated Continuous Testing in DevOps: A Game-Changer for P&C Insurers
No full textAim: The study examines Continuous Testing (CT) in a DevOps environment for cloud migration within the Property & Casualty (P&C) insurance industry and InsurTech companies. The study evaluates the impact of AI/ML-driven test automation, security testing, and performance validation using tools like Selenium like Selenium, JUnit, and TestNG; CI/CD pipelines such as Jenkins, GitHub Actions, and Azure DevOps. Experimental testing with comparative evaluations shows that a CT structured approach simplifies any cloud migration project and improves defect propagation and compliance ratios in regulated industries.
Industry and Scientific Application: Beyond insurance, this study applies to other industries and scientific research. Continuous Testing drives innovation by detecting real-time defects, reducing deployment risks, and ensuring regulatory compliance. These findings can serve as a model for organizations integrating DevOps-driven testing in cloud migration.
Benefits of Cloud Migration: CT in DevOps optimizes cloud migration by automating testing, reducing errors, and improving deployment efficiency. Companies using CT see 40-60% faster deployments and 35% fewer defects post-implementation. Automated security checks enhance compliance, while test automation lowers costs. These benefits make CT essential for a smooth, secure, and cost-effective cloud transition, especially in regulated sectors like finance, healthcare, and insurance.
Case Studies and Real-World Application: Case studies of Liberty Mutual and Progressive Insurance have shown that Continuous Testing is effective and accelerates DevOps-centered cloud migration. Liberty Mutual used cloud-embedded automated test frameworks to reduce the release time period by 50% and achieve compliance, thereby cutting time-to-market. Progressive Insurance streamlined the testing of APIs and mobile applications, using CI/CD-integrated testing automation to produce faster claims processing at the rate of up to 30% and a drop of around 90% in API failures. Here, in these case studies, one sees how Continuous Testing massively contributes to deployment efficiency, system resilience, and adherence to regulations in real world case applications in insurance.
Study Design: "This study takes a mixed methods approach that incorporates case studies, industry surveys, and experimental testing to assess the efficiency of Continuous Testing in cloud-migration strategies," instead. Its research targets insurance companies which have recently adopted DevOps-driven cloud migration and investigate their testing frameworks.
Place and Duration of Study: This study is based on a review of industry practices, integration strategies and analysis of cloud migration strategies in global insurance firms across various companies in North America and Asia-Pacific, focusing on solutions implemented between 2018 and 2024.
Methodology: The study employs a multiple research method approach, including reviews of the literature, case studies, surveys, and experimentation, to assess the impact of continuous testing (CT) for the DevOps-driven cloud migration of P&C insurers. Following a detailed literature review about the extant state of the research into CT and cloud adoption in insurance, case studies are available to demonstrate insurance URLs using CT-based frameworks. Surveys and interviews with IT and DevOps in-house experts underline the challenges and good practices. Through experimentation with automated testing tools such as Selenium, JUnit, and Jenkins, we measure the improvements in efficiency. A comparative analysis will measure the performance indicators prior to and after the CT implementation.
Results: Continuous testing (CT) substantially enhances cloud-migration efficiency for P&C insurance. Companies that have had CT in their version of DevOps have executed a 40-60% increase in software release cycles, leading to faster deployments. Automated testing dragged post-deployment issues down by 35%, thereby increasing the reliability of the software. Compliance with industry requirements was much better because continuous security checks lessen risks. Another benefit included a reduction of about 20-30% in testing costs due to automation that replaced human testing. On top of this, the way for more applications to be resilient to system failures was opened; applications were supported and maintained. The post-migration data should always specify that applications have 99.9% up time. In the heavily regulated insurance sector, continuous testing thus becomes a much faster, more secure, and cost-effective measure for moving to the cloud.
Conclusion: Continuous Testing in DevOps significantly enhances cloud migration for P&C insurers by improving speed-to-market, quality, and compliance. According to this discussion, automation is the key enabler for cloud adoption, which in turn mitigates risk and improves operational agility. One could advocate that P&C insurers\u27 ability to pursue a CT division in the cloud epoch is tantamount to ensuring the unremitting progress of digital transformation
A Review of Blockchain Technology In E-business: Trust, Transparency, and Security in Digital Marketing through Decentralized Solutions
No full textBlockchain technology is revolutionizing the world of e-commerce by providing innovative ways to improve digital marketing security, transparency, and trust. This analysis examines the use of blockchain technology in a variety of areas, highlighting how it can help solve issues such as data integrity, fraud prevention, and customer trust. Important developments include the use of smart contracts to improve business processes, blockchain-powered loyalty programs to increase customer retention, and hybrid models that combine blockchain, artificial intelligence, and the Internet of Things (IoT) to maximize operational efficiency and decision-making. Blockchain technology has the potential to improve the transparency of e-commerce transactions and encourage user engagement through secure solutions. Despite its many benefits, the adoption of blockchain in digital marketing presents several challenges, including data privacy concerns, interoperability issues, and the complexity of integrating blockchain-based solutions with existing marketing frameworks. Additionally, scalability limitations, high implementation costs, and regulatory uncertainties hinder widespread adoption. Addressing these challenges requires further research into developing scalable blockchain architectures, enhancing compatibility with traditional enterprise systems, and establishing regulatory guidelines tailored to digital marketing applications. Beyond its role in digital marketing, blockchain has broader implications for e-business by reshaping business models, improving operational security, and fostering trust in online transactions. This study provides a comprehensive examination of blockchain’s potential to revolutionize digital marketing and e-commerce systems by highlighting its strengths, weaknesses, and opportunities
Optimizing Web Development and Deployment Efficiency: The Impact of React, MongoDB, and Jenkins in Modern Software Engineering
No full textA study examines how the current web technologies React and MongoDB affect development speed and system operational outcomes. The analysis investigates how React elements improve system maintainability through its component-based design in combination with MongoDB flexibility for handling dynamic data systems. The integration of Jenkins as a Continuous Integration and Continuous Deployment (CI/CD) pipeline receives analysis to evaluate deployment speed and automation as well as software reliability. Jenkins stands apart from alternative CI/CD solutions because the study demonstrates its exclusive deployment benefits over conventional model approaches. The implemented technologies prove essential for contemporary software engineering because they minimize developmental periods and boost teamwork and deliver continuous updates
The Future of Block Chain in Healthcare Insurance: Reducing Fraud and Enhancing Claims Processing
No full textBlock chain technology is a powerful solution for advancing the security of medical services by addressing problems like extortion and sluggish case processing. This study examines how block chain can improve the framework\u27s efficiency, security, and clarity. Block chain’s decentralized and immutable structure allows for the safe storage and sharing of patient data, ensuring its accuracy and meticulous design. Brilliant agreements reduce the likelihood of misrepresentation while automating the case engagement, making it faster and more accurate. Permissioned block chain frameworks are used in the suggested structure to manage access to sensitive patient data and comply with information assurance standards. The accuracy of case information is protected by cryptoic mechanisms like hashing, and human awareness detects questionable activities. Additionally, wearable health devices can provide continuous health data to advance claims processing. Comparing this framework to existing methods, testing on healthcare protection data reveals that it reduces costs, processes claims more quickly, and detects extortion more accurately. It also does well in terms of accuracy, precision, and consistency. This analysis highlights how block chain can improve the safety of medical services, reduce deception, and expedite the processing of claims. Future efforts will focus on coordinating new developments, resolving adaptation concerns, and resolving challenges to ensure that block chain’s full potential is recognized in the protection of medical services
Adaptive Hybrid Algorithms for Real-Time Decision-Making in Autonomous Systems
No full textRecent breakthroughs in computational intelligence have enabled remarkable advances in decision-making systems operating within dynamic, complex environments. The work presented in this paper looks into the incorporation of three major techniques: Reinforcement Learning, Deep Neural Networks, and Fuzzy Logic in developing hybrid models in order to be able to tackle some major challenges of adaptability, handling uncertainty, and high-dimensionality data processing. These hybrid frameworks have applications in domains such as autonomous vehicle navigation, health care, robotics, and supply chain optimization, where classic methods do not work. Based on the adaptability given by RL, on the predictive power of DNNs, and on the interpretability provided by Fuzzy Logic, the proposed models demonstrate scalability and robustness under dynamic settings. It points to the existing challenges of computational complexity, real-time applicability, and cross-domain generalizability, and ascertains a unified hybrid framework in order to bridge these gaps. Experimental results also demonstrate improved accuracy with reduced response time for such models, proving their potential in advancing intelligent autonomous systems that could deal with ever-changing environments
Sentiment Analysis of Customer Feedback on Services Provided on Selected Banks’ Mobile Banking Applications in Nigeria
No full textThis study examines customer feedback on mobile banking applications for Access Bank, UBA Bank, and First Bank in Nigeria using sentiment analysis. By analyzing user reviews from the Google Play Store, the research identifies key themes and sentiments, positive, neutral, and negative, using Support Vector Machine (SVM) classifiers. Results show Access Bank had the highest positive sentiment (74.1%), followed by UBA Bank (66.5%) and First Bank (48.3%). Negative feedback was highest for First Bank (45.4%), pointing to significant usability issues. Common positive themes included ease of use, reliability, and security, while negative comments highlighted technical glitches, poor customer support, and transaction failures. The findings suggest improvements in technical performance, authentication processes, and customer service could enhance user satisfaction. Regular sentiment monitoring is crucial for maintaining user trust and competitiveness in Nigeria’s mobile banking sector
Automated Data Cleaning in Large Databases Using Machine Learning Methods
No full textThe paper discusses the need for effective data cleaning processes to ensure the accuracy and reliability of datasets in machine learning and big data analytics due to the growing volume and complexity of data. Traditional manual cleaning methods are often inefficient and error-prone, compromising data quality. It explores automated techniques that utilize machine learning, particularly integrating supervised and unsupervised learning algorithms, to enhance data preparation efficiency. The study shows that these advanced methods can significantly improve data quality, reduce preparation time, and support better decision-making. Ultimately, it emphasizes the importance of robust data cleansing frameworks for effectively harnessing big data\u27s potential and improving model performance in various applications
Ensemble Machine Learning Models Based on Predictions for Sentimental Analysis on Twitter Data
No full textIn current days, web content comes from social media, multiple companies, different types of events, online products and personal data. This sentiment analysis predicts findings with the help of different methodologies. We used machine learning models for this research. In this process, the input is so simple, but deriving this information is too difficult. Internet data usage is increasing throughout the world, using this data is used for feedback purposes. Such a type of data classification and organize was most difficult for sentiments. This feedback is most important for improving the business, gaining more profit and understanding the customer’s interest. Finally, from our research, Logistic regression accuracy is 92%, XGBoost accuracy is 90%, Decision trees predict 90% accuracy, and Random forests predict 95.5% accuracy. Compared to the ensemble learning model, the Random Forest Tree model achieves a higher accuracy rate than the ensemble models
Artificial Intelligence in Scalable Content Creation for Micro-influencer Marketing Agencies
No full textThis research explores the transformative function of AI in the improved scalable content creation for micro-influencer marketing agencies. Micro influencer is defined as an individual with social media follower count between 1,000 and 100,000 which provide high engagement rate and niche audience trust, making them the choice asset for targeted marketing campaigns. Nevertheless, the flow of frequent customized content for several influencers may bury small to mid-sized agencies and its scope for client expansion.
The main aim of this study is to review the ways in which AI-based tools streamline the content creation process and as a result, agencies can boost productivity, content quality and campaign performance, while retaining their human resources. The research takes a mixed methods approach: Qualitative data were collected in the form of case studies of selected micro-influencer marketing agencies that have incorporated AI into their workflows, and quantitative data were drawn from industry reports published recently by platforms like Hootsuite, Sprout Social, Canva, and others that publish performance metrics.
Discoveries show how AI considerably cuts down content production time, approximately 30–40%, via automatic generation of captions, choice of hashtags, visual design, and video editing adapted to the influencer personae. In addition, AI-powered analytics platforms assist in identification of audience preferences, content trends, and optimum posting times leading to an additional 25% improvement in such engagement metrics as likes, shares and comments. Content relevance and brand loyalty are further increased through emotional targeting using natural language processing.
The research ends with the conclusion that AI is an essential enabler of scalability capability in micro-influencer marketing. Through automation of routine tasks and strategic decision support with data analysis that is real-time, AI unlocks potential to enable agencies to serve a wider range of clients profitably and feature efficiently. The point of the research is also the availability of AI tools, that is, free-to-use (such as AI features of Canva) and paid (such as Sprout Social), making the adoption of AI possible for agencies that have poor financial capabilities. Such results highlight the necessity of AI integration. The study looks at it as a competitive advantage in this rapidly changing digital marketing world
Examining the Human Factors in Cybersecurity Practices: Psychological, Technical, and Organisational Perspectives
No full textThe study of human factors in cybersecurity is increasingly relevant due to the growing complexity and scale of cyber threats, coupled with the paradoxical position of humans as both a key element of protection and the most vulnerable link in information security systems. This study provides a comprehensive analysis of psychological, organisational, and technical aspects of human influence on cybersecurity and characterises strategies to minimise associated risks. The research employed systematic literature review, statistical assessment, comparison, systematic-logical methods, and generalization.
Key findings revealed important contradictions, including conflicts between system safety and usability, the challenge of balancing automation with human control, and ethical dilemmas related to employee monitoring. The research found that attackers actively exploit human psychological traits to gain unauthorised access, with techniques like "pretexting" becoming increasingly sophisticated. Special attention was given to incorporating human factors in security architecture design, which requires an interdisciplinary approach. User and Entity Behaviour Analytics (UEBA) systems enable detection of anomalous behaviours that may indicate insider activity, though careful configuration is needed to minimise false positives.
Literature analysis concluded that an integrated approach to cybersecurity must account for users\u27 cognitive characteristics, organisational culture, and technological innovations. An effective cybersecurity strategy should incorporate personalised training programs, ergonomic security interfaces, and cybersecurity culture development across all organisational levels. Particular attention should focus on emotional intelligence and critical thinking to counter social engineering attacks.
These findings may benefit information security specialists, organisational leaders, security system developers, and researchers in psychology, organisational behaviour, and computer science