Smarter Marketing with AI: How Cloud Technology is Changing Business

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy

Leveraging Artificial Intelligence for Customer Segmentation and Demand Forecasting in the Car Rental Industry

The dynamic car rental industry faces significant challenges in demand forecasting, with about 50% of companies reporting inaccuracies that result in fleet utilization rates of only 70-75% instead of the optimal 85-90%. The study integrates customer segmentation and demand forecasting into a framework using various ML models. This study utilized historical rental data from Secured Wheels Car Rental reports in Lagos and Ibadan, Nigeria. The data underwent thorough preprocessing, including cleaning, selecting relevant features, and splitting it for analysis. The study employs decision trees, random forests, and clustering algorithms such as DBSCAN, Agglomerative clustering, Fuzzy-C-Means, and Affinity Propagation for segmentation. To enhance demand forecasting in the car rental industry, key customer segmentation features such as inactivity period, number of reservations, and cluster groups were incorporated into the model. This integration allowed for more precise demand predictions by capturing segment-specific patterns. For demand forecasting, the study uses ARIMA, regression model, and Holt-Winters. Performance metrics like accuracy, precision, silhouette coefficient, and Mean Absolute Error (MAE) evaluated the models, and the framework's results were benchmarked against existing methods. Results indicate that the Agglomerative clustering achieved a silhouette coefficient of 0.9238 and a Davies-Bouldin index of 0.0031. At the same time, the HW model recorded a lower Mean Absolute Error (MAE) of 29.3641 and a Mean Squared Error (MSE) of 1183. The HW model was trained with customer segmentation features and the five cluster groups. These enhanced blended models enable more tailored marketing strategies and personalized customer experiences, increasing customer satisfaction and loyalty

Enhancing Site Reliability Engineering Through AIOps: A Framework for Next-Generation IT Operations

The increasing complexity of modern IT infrastructures has pushed traditional operational approaches beyond their limits. This paper explores the integration of Artificial Intelligence for IT Operations (AIOps) within Site Reliability Engineering (SRE) practices to address this challenge. I present a framework for enhancing core SRE concepts such as Service Level Objectives (SLOs), Service Level Indicators (SLIs), and error budgets through AI-driven capabilities. Our approach enables more dynamic reliability targets, intelligent anomaly detection, and automated remediation while maintaining the engineering rigor of SRE. Case studies demonstrate significant improvements in key operational metrics: 87% reduction in alert noise, 73% decrease in mean time to detection, and 62% of common infrastructure issues resolved automatically. The proposed framework provides a systematic path for organizations to evolve from traditional SRE to AI-enhanced reliability practices while addressing common implementation challenges including data quality issues, skills gaps, and organizational resistance. This integration represents a fundamental shift in IT operations from reactive human-centered approaches to proactive AI-augmented engineering disciplines capable of managing unprecedented scale and complexity. Aims: To develop and validate a framework that integrates Artificial Intelligence for IT Operations (AIOps) within established Site Reliability Engineering (SRE) practices, addressing the growing complexity of modern IT infrastructures. Study Design: A mixed-method research approach combining case studies, controlled experiments, and quantitative analysis across multiple industry sectors. Place and Duration of Study: The research was conducted across three major organizations in financial services, healthcare technology, and e-commerce sectors between January 2023 and February 2024. Methodology: I developed an integrated framework enhancing five core SRE functions with AI capabilities. Implementation followed a four-phase methodology addressing technical, process, and organizational aspects. Effectiveness was measured through comparative analysis of key operational metrics pre- and post-implementation, including alert volumes, detection times, resolution rates, and operational burden. Results: Implementation demonstrated significant operational improvements across all organizations. Key results include: 87% reduction in alert noise while maintaining critical issue coverage, 73% decrease in mean time to detection for system anomalies, 62% of common infrastructure issues resolved automatically without human intervention, and 47% reduction in SRE on-call burden. The financial services organization identified five previously unmonitored SLIs that significantly impacted user experience, while the e-commerce platform successfully predicted capacity-related incidents 30-45 minutes before impact. Conclusion: The integration of AIOps with SRE practices creates a powerful combination capable of managing the scale and complexity of modern IT environments. The framework enables organizations to progress from reactive to predictive operations while maintaining the engineering rigor of traditional SRE. Future research should explore incorporating emerging technologies such as large language models and developing industry-specific implementations for sectors with unique reliability requirements

EngageNet: A Model for Evaluating Student Engagement through Facial Expression and Behavior Analysis

Online learning has emerged as a prominent trend in modern education, driven by its flexibility, accessibility, and capacity to support personalized learning experiences. However, despite these advantages, one of the most pressing challenges it faces lies in maintaining and accurately evaluating the quality of teaching and learning. A particularly critical aspect is the assessment of learner engagement in virtual environments. In fact, traditional approaches to assessing student engagement, which depend on synchronous, face-to-face interaction, frequently prove inadequate in virtual learning environments where such real-time communication between educators and learners is restricted. Therefore, this study introduces a deep learning-based model that combines facial emotion recognition, gaze direction tracking, and eye openness analysis. By integrating these emotional and behavioral characteristics, the model offers a comprehensive and objective approach to assessing learner’s attention throughout online instruction. To support the development and validation of this model, a specialized dataset was proposed, capturing a diverse range of engagement scenarios. Experimental evaluations demonstrate that the proposed method achieves a notable accuracy of 79.76%, underscoring its effectiveness and robustness in capturing learner engagement dynamics. These findings suggest that the model holds strong potential for enhancing the monitoring and personalization of online learning experiences, thereby contributing to improved educational outcomes in virtual classrooms

The Role of Machine Learning in Enhancing Cybersecurity

The advancement of information technology is rapidly changing the face of cyber security and this makes it more important with the increasing trend of sophistication of cyber threats in the society. The authors in this research aim at analyze how AI and ML can improve cybersecurity capabilities and how these technologies can be employed to prevent cyber-attacks in real-time. By examining a few well-known cyber episodes – the SolarWinds attack and the Colonial Pipeline hack – in an exploration of the future of AI and machine learning in cybersecurity, the study underscores the potential for advancement along with the potential for obfuscation. Despite these benefits, these Integrated technologies come loaded with new risks, especially in matters concerning the ethical issues and future insecurities within the AI-based security systems. More specifically, this paper investigates the issue of maintaining the balance between the introduction of innovative technologies and the protection of networks, arguing that the only effective approach to combating modern threats is their combination and the implementation of layers based on traditional anti-virus programs and artificial intelligence. This discussion insists on the interdependence of governmental agencies, business entities, and academic organizations to mitigate growing new age cyber risks. Last but not the least, the study recommends that for the development of more resilience and ethical solutions towards AI for cybersecurity solutions, more research work has to be implemented in developing more robust cybersecurity models

From Framework to Practice: Barriers and Enablers to RMF Adoption in Mid-sized Enterprises

This study investigates the barriers and enablers influencing the adoption of the Risk Management Framework (RMF) in mid-sized software development enterprises through a quantitative research design. Four datasets were analyzed:  the Stack Overflow Developer Survey, ENISA Threat Landscape Reports, OpenSSF Secure Practices Survey, and Verizon’s Data Breach Investigations Report (DBIR). The study employed descriptive statistics to explore awareness patterns, exploratory factor analysis to identify latent barriers, binary logistic regression to model enablers, and independent t-tests to evaluate security performance outcomes. Findings reveal that RMF awareness is highest among DevOps Engineers and Software Architects (73.3%), yet practical adoption remains limited, with only 34.4% of Asian firms implementing RMF-aligned practices. Lack of leadership support emerged as the strongest barrier (loading = -0.58), while leadership endorsement was the most significant enabler (β = 1.1671, p < .001). Organizations that adopted RMF demonstrated 40% faster threat detection and 66% faster incident response compared to non-adopters. The study highlights the strategic role of leadership commitment, workforce training, and CI/CD pipeline integration in promoting RMF adoption. It recommends contextual RMF toolkits tailored for Agile environments, executive-level cybersecurity briefings, and role-specific certifications to accelerate adoption. By strengthening operational resilience and regulatory compliance, RMF offers a scalable pathway for mid-sized firms navigating the complexities of modern software security risks

A Review of Reinforcement Learning: Current Trends and Future Prospects in Autonomous Systems

This review focuses on the use of reinforcement learning (RL) for autonomous systems and current trends and future prospects. It is therefore the intended goal to critically evaluate the concept of RL for improving autonomous decision making with focus on current and emerging issues including; sample efficiency, scalability, and safety. This review methodology is a synthesis of 10 studies which has been conducted between the years 2021 and 2024. However, these are some of the challenges that seem to plague RL even as it has potential to be used in realistic applications such as robots, self-driving cars and smart grid. The review also opines that due to developments of algorithms, computer intrinsics and safety mechanism, RL perhaps holds the key to the future for autonomous systems

CTI Integration in Contact Centers: A Comparative Analysis of Security, Scalability, and Challenges in Legacy vs. Cloud-Based Systems

AIM: Scope of this work aims to explore the integration of Computer Telephony Integration (CTI) in both legacy and cloud-based contact center systems, examining security challenges and integration methods for achieving robust Computer Telephony Integration implementations. Study Design: This study provides a comparative analysis of techniques, methodologies, and integration challenges associated with CTI in legacy on-premises systems and modern cloud-based solutions, with a particular focus on security considerations in both contexts. Place and Duration of Study: This study is based on a review of contact centers in retail versus mid-sized tech companies, focusing on solutions implemented between 2018 and 2024. Methodology: This study compares Computer Telephony Integration in both legacy and cloud-based contact center systems, focusing on integration processes, security concerns, scalability, challenges, and feature differences. It uses a combination of key evaluation criteria (covering core components, flexibility, security practices, and scalability), case studies, financial implications, and a feature comparison matrix to explore CTI integration in traditional on-premises environments versus modern cloud-based solutions. Security has been the primary concern, and this included encryption, authentication and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Results: The study found that cloud-based contact centers offer more flexible and scalable integration options, leveraging modern APIs, cloud-native services, and omnichannel support. Legacy systems, while reliable, face challenges in scalability and integration, requiring complex middleware and custom solutions. Security is a significant concern for both environments; however, cloud solutions are enhanced by regular updates, improved compliance standards, and centralized management. Data encryption, secure voice protocols, and authentication were identified as the foundation for security for all types of Computer Telephony Integrations in either category of systems. In addition, the study found that cloud-based systems are being accelerated as more flexible and economically viable solutions are being demanded. A unique contribution of this study is the development of a security-focused comparative framework for this integration in legacy and cloud-based contact centers. By analyzing security practices, scalability challenges, and integration methodologies, this study bridges a critical knowledge gap, offering practitioners a comprehensive guide to CTI decision-making. Conclusion: Computer Telephony Integration (CTI) plays a critical role in modern contact centers. While legacy systems continue to serve their purpose, cloud-based solutions offer superior flexibility, scalability, and security. Organizations must prioritize robust security practices when implementing them, regardless of the platform. Future research should focus on advancing AI-enhanced security frameworks to address evolving threats, evaluating hybrid CTI models that integrate legacy and cloud components, and testing the impact of real-time analytics on customer satisfaction. Such advancements will ensure data privacy, regulatory compliance, and the continuous improvement of customer engagement and operational efficiency

Reliability of the Type System in TypeScript in Software Development

The article examines the TypeScript type system as a critical element influencing its use in software development. The primary objective of the study is to analyze the characteristics of the type system and identify methods for improving the accuracy of type checking. The article explores the principles underlying the type system, its vulnerabilities such as the use of the any type, type assertions, issues with object and array indexing, and proposes approaches to enhancing system reliability. The methodology includes an analysis of TypeScript's structure, the principles of its compiler, and the application of tools for static code analysis. The study references academic articles available in open-access online repositories, providing a broad perspective on the topic. Additionally, examples of code presented in the work illustrate key concepts and methods for working with type systems. Results demonstrate that configuring the compiler, avoiding the any type, and leveraging libraries for data validation improve the quality of type safety. The findings, based on the analysis of relevant sources, will be useful for programmers and corporate professionals.  This review paper is a guide for software developers to understand the essence and the reliability of the Type System in TypeScript. This is a requirement to the scientific community as it guides software developers with the understanding and methods for efficiency in Type Checking. Principles, vulnerabilities and practice in code analysis are explicitly enumerated

From 'Sexiest Job' to 'Most Responsible Role': The Evolution of Data Scientists

This opinion article explores the evolving responsibilities of data scientists in the current data-driven landscape, in which ethical, privacy, and governance standards have grown considerably in importance. Although the job of data scientist initially attracted attention for its allure and high earning potential, in recent years, it has become associated with a particularly high level of responsibility, requiring practitioners to balance their technical skills with a commitment to social impact and accountability. This article examines the essential qualifications and criteria of a responsible data scientist, including a robust ethical awareness, an understanding of privacy safeguards and transparency, and a commitment to continuous learning. This article also discusses hiring practices that prioritize these qualities and outlines strategies for fostering a data-driven culture grounded in responsibility and trust. In the current landscape, responsible data scientists not only analyze data but also protect ethical data practices, which is crucial to building a transparent, fair, and sustainable digital world. This article also provides a framework and guidelines for identifying and recruiting responsible data scientists