Class Size and Availability of Laboratory Facility on Students’ Performance in Chemistry in Selected Private Secondary Schools in Ethiope West, Delta State
Authors-Umanah F. I. , Obruche E.K
Keyword-Class Size, Laboratory Facility, Students’ Performance, Chemistry, Secondary Schools.
Abstract-This study focuses on the effect of class size and the availability of laboratory facilities on students’ academic performance in Chemistry in selected private secondary schools in Ethiope West Local Government Area of Delta State. The study adopted a survey research design. The population of the study is 970 which consisted of teachers and students in selected private secondary schools in Ethiope West Local Government Area, Delta State. From this population, a sample size of 175 respondents was randomly selected to participate in the study. The research instrument used for data collection was a structured questionnaire designed by the researcher. The instrument was subjected to face and content validation by experts in educational research to ensure its appropriateness and relevance to the study objectives. The reliability of the instrument was established using a reliability testing method to ensure consistency which gave a co-efficient value of 0.68 indicated that the research instrument was relatively reliable in the responses obtained. Data for the study were collected through the administration of the questionnaire to the selected respondents. Mean, Pearson Product Moment Correlation coefficient(r) and coefficient of determination (r2) were to answer the research questions and hypotheses were tested using t-test and Pearson Product Moment Correlation coefficient(r) at 0.05 level of significance. The findings of the study revealed that smaller class sizes increase teacher–student interaction, encourage active participation among students, and promote more effective learning activities in the classroom, thereby improving students’ academic performance in Chemistry.
Doi-[http://doi.org/10.5281/zenodo.19110839]
Designing AI-Driven SAP Systems for Intelligent Supply Chain Optimization Across Cloud and IoT Platforms
Authors-Hayk Sargsyan
Keyword-Artificial Intelligence, SAP S/4HANA, Supply Chain Optimization, Internet of Things (IoT), Cloud Computing, SAP Business Technology Platform (BTP), Predictive Analytics.
Abstract-Modern supply chain management has entered an era of permanent volatility, requiring a fundamental transition from reactive automation to proactive, intelligent orchestration. This review article investigates the design and implementation of AI-driven SAP systems that leverage the convergence of Cloud computing and the Internet of Things (IoT) to achieve autonomous optimization. Central to this architecture is the SAP Business Technology Platform (BTP), which serves as a unified data fabric for harmonizing high-velocity telemetry from IoT sensors with the transactional integrity of the S/4HANA digital core. The study evaluates the application of diverse Machine Learning (ML) models, ranging from LSTM-based demand sensing in SAP Integrated Business Planning (IBP) to reinforcement learning for real-time logistics rerouting. A significant focus is placed on the emergence of Agentic AI and the SAP Joule copilot, which move beyond traditional decision support to execute multi-step, self-healing workflows across procurement and warehouse management. Furthermore, the article examines the role of MLOps in managing model drift within dynamic global markets and the strategic importance of a "Clean Core" approach to ensure long-term system agility. By synthesizing implementation best practices with future directions such as quantum-assisted routing and the "Green Ledger" for carbon-aware accounting, this research provides a comprehensive framework for architecting resilient, transparent, and self-optimizing supply chain ecosystems. We conclude that the successful integration of AI, Cloud, and IoT is the primary prerequisite for operational excellence and competitive survival in the contemporary digital economy.
Doi-[http://doi.org/10.5281/zenodo.19470023]
Cloud-First SAP Implementations Enhanced by Artificial Intelligence and Machine Learning Pipe-lines
Authors-Dilshan Wickramasinghe
Keyword-Cloud-first, SAP S/4HANA, Artificial Intelligence, Machine Learning pipelines, SAP Business Technology Platform, BTP, Clean Core, MLOps, Data Orchestration, SAP.
Abstract-The transition from legacy on-premises ERP systems to cloud-based environments represents the most significant architectural shift in the history of enterprise resource planning. This review article explores the emergence of cloud-first SAP implementations, specifically focusing on how artificial intelligence and machine learning pipelines are no longer optional additions but foundational components of the modern deployment lifecycle. Historically, SAP migrations were plagued by high costs, manual data cleansing, and the customization trap where bespoke code hindered future upgrades. In the 2026 landscape, the clean core strategy, powered by the SAP Business Technology Platform, allows organizations to maintain a standard ERP core while offloading complex logic to AI-driven sidecar applications. This methodology en-sures that the primary system remains agile and easily updatable while the heavy lifting of data processing and prediction happens in dedicated, scalable environments. We analyze the role of MLOps in managing the lifecycle of these intelligent extensions, ensuring that predictive models remain accurate as business conditions fluctuate. The review highlights how AI pipelines accel-erate data migration through automated mapping and improve post-go-live operations via predic-tive analytics and generative assistants. By automating the extraction, transformation, and load-ing phases of a cloud migration, these pipelines reduce the risk of human error and significantly compress the project timeline. Furthermore, the integration of machine learning allows for a more nuanced understanding of business health, moving beyond descriptive reporting to pre-scriptive action. Ultimately, this article demonstrates that integrating AI and ML into the cloud implementation strategy reduces total cost of ownership by up to thirty percent and transforms the ERP from a passive system of record into an active system of intelligence. This intelligence is capable of autonomous decision-making, anomaly detection, and self-healing, which are es-sential for maintaining a competitive edge in an increasingly digital and fast-paced global market.
Doi-[http://doi.org/10.5281/zenodo.19470064]
Machine Learning Models for Enhancing SAP Business Intelligence in Distributed Cloud Environments
Authors-Arman Petrosyan
Keyword-Machine Learning, SAP Business Intelligence, Distributed Cloud, SAP Datasphere, Predictive Analytics, SAP Analytics Cloud, Federated Data, MLOps.
Abstract-The paradigm of enterprise analytics is undergoing a fundamental shift from central-ized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Inte-grated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to miti-gate model drift in volatile global markets. The review also addresses critical technical and stra-tegic barriers, including data latency across distributed cloud nodes, the harmonization of struc-tured and unstructured data, and the evolving landscape of global data sovereignty. By synthe-sizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of ma-chine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.
Doi-[http://doi.org/10.5281/zenodo.19470095]
SAP Intelligent Enterprise Evolution Through Ar-tificial Intelligence, Cloud Computing, and Ad-vanced Analytics Integration
Authors-Gor Hakobyan
Keyword-SAP Intelligent Enterprise, S/4HANA, Business Technology Platform (BTP), Artificial Intelligence, Cloud Computing, Advanced Analytics, Digital Transformation, SAP Joule.
Abstract-The modern corporate landscape is undergoing a fundamental transformation as tradi-tional Enterprise Resource Planning (ERP) systems evolve into self-aware, intelligent entities. This review article explores the systematic evolution of the SAP Intelligent Enterprise, analyzing how the integration of Artificial Intelligence (AI), Cloud Computing, and Advanced Analytics creates a frictionless operational environment. At the center of this evolution is the SAP Busi-ness Technology Platform (BTP), which serves as the architectural backbone, enabling a "clean core" strategy while facilitating rapid innovation at the network edge. We examine the transition from standard automation to augmented intelligence, specifically highlighting the role of genera-tive AI and natural language copilots in democratizing data access for the "citizen developer." Furthermore, the article evaluates the shift in data management from siloed warehouses to uni-fied data fabrics through SAP Datasphere, and the impact of augmented analytics in providing real-time strategic foresight. A critical focus is placed on vertical industry evolution, particularly the emergence of "The Green Ledger" for sustainability and Industry 4.0 manufacturing. We also address the significant strategic barriers to this evolution, including the technical debt of legacy customizations, the widening digital skill gap, and the complexities of multi-cloud cyber-security. By synthesizing modern implementation methodologies like SAP Activate with emerg-ing trends such as autonomous process correction and quantum-assisted optimization, this study provides a comprehensive roadmap for organizations navigating the path to digital maturity. We conclude that the intelligent enterprise is not merely a technological upgrade but a fundamental shift toward a proactive, resilient, and sustainable business model designed for the complexities of the twenty-first century.
Doi-[http://doi.org/10.5281/zenodo.19470122]
The Role of Innovation in Sustainable Business Growth
Authors-Prof. (Dr.) Vellayan Srinivasan, Dr. Viji R, Dr.V.O.Kavitha
Keyword-Innovation, Sustainable Growth, Business Strategy, SMEs, Competitive Advantage
Abstract-Innovation has become a critical driver of sustainable business growth in an increasingly competitive and dynamic global environment. This study explores the role of innovation in enhancing long-term organizational performance, environmental sustainability, and competitive advantage. Using a descriptive research design and secondary data analysis supported by a structured survey of 100 small and medium enterprises (SMEs), the study examines how product, process, and business model innovations contribute to sustainable growth. The findings reveal that organizations adopting innovative practices demonstrate higher operational efficiency, improved customer satisfaction, and stronger financial performance. The paper concludes that integrating innovation strategies with sustainability goals is essential for long-term business success.
Effectiveness of Activity Based English Learning in Indian Schools
Authors-Birajdar Suvidha Nagnath, Pritama Devi
Keyword-Activity Based Learning, English Language Teaching, Indian Schools, Language Proficiency, Student Engagement, Pedagogical Approaches, Mixed Methods, Educational Re-form, Teacher Training, Communicative Competence
Abstract-Activity Based Learning (ABL) has become interactive and dynamic in teaching English as sec-ond language especially in Indian schools whereby the traditional teaching methods are not very effective in motivating students to learn. This study examines the effects of ABL on the develop-ment of English language in Indian classrooms. In a relative study on ABL and traditional peda-gogical approaches, the study examines how ABL can enhance speaking, reading, listening, and writing of students in comparison to conventional approaches. It was a mixed-methods undertak-ing, which was represented by pre- and post-test testing, classroom observations, teacher and student interviews. The research was carried out in various public and privately-owned schools in both Karnataka and Tamil Nadu. Findings showed that the students trained on the ABL had many important gains in language proficiency, especially communicative competence, than those pro-vided with standard learning environments. Also, the student engagement, motivation, and learn-ing satisfaction reflected in ABL classes was greater. Problems like shortage of resources, train-ing of teachers and curriculum were also reported. The research comes to a conclusion that alt-hough ABL has many positive features in language learning, teacher training, support, and suffi-cient learning materials are needed to implement ABL successfully in Indian schools. The results highlight the sensitivity of the inclusion of ABL strategies in the overall educational system to improve the quality of English language teaching in India.
AI-Based Monitoring Systems for Cloud Infrastructure
Authors-Amirul Hakim
Keyword-AI-Based Monitoring, Cloud Infrastructure, Machine Learning, Anomaly Detection, Predictive Analytics, Performance Optimization, Resource Management, Fault Detection, Cloud Reliability, Self-Adaptive Systems, Data Analytics, Automated Monitoring, Cloud Performance, Scalability, Real-Time Insights.
Abstract-The rapid adoption of cloud computing has led to increasingly complex and dynamic infrastructure environments, making monitoring a critical aspect of operational efficiency, reliability, and security. AI-based monitoring systems leverage machine learning, anomaly detection, and predictive analytics to provide real-time insights into cloud infrastructure performance, resource utilization, and potential faults. This study explores the design, implementation, and benefits of AI-driven monitoring solutions for cloud environments. It examines techniques for automated data collection, performance analysis, anomaly detection, and predictive maintenance, highlighting their ability to reduce downtime, optimize resource allocation, and improve decision-making. Additionally, the research addresses challenges such as data heterogeneity, scalability, and model accuracy. By analyzing practical applications and industry case studies, this study demonstrates that AI-based monitoring is essential for achieving resilient, efficient, and self-adaptive cloud infrastructure.
Distributed System Design for Scalable Applications
Authors-Azlan Karim
Keyword-Distributed Systems, Scalable Applications, Fault Tolerance, Data Partitioning, Replication, Consistency Models, Concurrency Control, Load Balancing, Resource Management, Network Latency, Cloud Computing, Microservices, High-Performance Computing, System Reliability, Elasticity.
Abstract-The increasing demand for high-performance, scalable, and reliable applications has driven the adoption of distributed system architectures. Distributed systems divide computational workloads across multiple interconnected nodes, enabling parallel processing, fault tolerance, and elasticity to handle dynamic user demands. This study explores the principles and design strategies for building scalable applications using distributed systems, including data partitioning, replication, consistency models, and fault-tolerant mechanisms. It examines communication protocols, load balancing, and resource management techniques that optimize system performance and reliability. Additionally, the study addresses critical challenges such as network latency, synchronization, concurrency control, and system heterogeneity. By analyzing practical implementations and best practices, this research demonstrates that a well-designed distributed system architecture is essential for achieving scalable, resilient, and high-performance enterprise applications in cloud, IoT, and large-scale web environments.
An Evaluation of Hybrid Cloud Computing Models
Authors-Emmanuel Ndlovu
Keyword-Hybrid cloud, Cloud computing, Private cloud, public cloud, Interoperability, Scalability, Data security, Disaster recovery, Cloud deployment, IT infrastructure.
Abstract-Hybrid cloud computing combines private and public cloud environments to provide organizations with enhanced flexibility, scalability, and cost efficiency. This paper evaluates the architectures, deployment strategies, and performance considerations of hybrid cloud models, highlighting their role in modern IT infrastructure. The study examines the advantages of hybrid cloud solutions, including improved resource utilization, data security, and disaster recovery capabilities, while also addressing challenges such as interoperability, compliance, and management complexity. By analyzing real-world use cases and performance metrics, the paper provides insights into best practices for implementing hybrid cloud models effectively. The findings suggest that hybrid cloud computing offers a strategic balance between control and agility, enabling organizations to optimize workloads, maintain compliance, and respond to dynamic business requirements.
Security and Compliance in Enterprise Cloud Systems
Authors-Noraini Ahmad
Keyword-Enterprise Cloud Security, Cloud Compliance, Data Protection, Identity and Access Management (IAM), Encryption, Threat Detection, Vulnerability Management, Regulatory Compliance, GDPR, HIPAA, ISO Standards, Zero-Trust Security, Cloud-Native Security, Automated Compliance, Risk Mitigation
Abstract-As enterprises increasingly migrate critical applications and data to the cloud, ensuring security and regulatory compliance has become a top priority. Cloud environments offer scalability and flexibility, but they also introduce new vulnerabilities and complexities related to data protection, access control, and governance. This study provides a comprehensive examination of security and compliance challenges in enterprise cloud systems, including identity and access management, encryption, threat detection, and vulnerability management. It also explores regulatory frameworks such as GDPR, HIPAA, and ISO standards that govern data privacy and operational compliance in cloud deployments. The study evaluates strategies and best practices for mitigating risks, maintaining continuous compliance, and implementing secure cloud architectures. Additionally, it addresses emerging trends such as zero-trust security models, cloud-native security tools, and automated compliance monitoring. By analyzing real-world use cases and industry practices, this study demonstrates that robust security and compliance measures are essential for protecting sensitive enterprise data, maintaining business continuity, and fostering trust in cloud computing environments.
Machine Learning for Cybersecurity Threat Detection
Authors-Rizky Pratama
Keyword-Machine Learning, Cybersecurity, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Malware Analysis, Network Security, Deep Learning, Big Data Analytics, Adversarial Attacks, Data Imbalance, Security Analytics, Artificial Intelligence, Real-Time Detection, Cyber Defense
Abstract-The increasing sophistication and frequency of cyber threats have made traditional security mechanisms insufficient for protecting modern digital infrastructures. Machine learning (ML) has emerged as a powerful approach for enhancing cybersecurity by enabling systems to automatically detect, analyze, and respond to potential threats in real time. This study provides a comprehensive analysis of the application of machine learning techniques in cybersecurity threat detection, focusing on anomaly detection, intrusion detection systems (IDS), malware classification, and network traffic analysis. It explores various ML models, including supervised, unsupervised, and deep learning approaches, and their effectiveness in identifying known and unknown attack patterns. The integration of ML with big data analytics and cloud-based security platforms is also examined, highlighting the ability to process large volumes of data and improve detection accuracy. Additionally, the study addresses key challenges such as data imbalance, false positives, adversarial attacks, and model interpretability, along with potential solutions to mitigate these issues. The findings demonstrate that machine learning significantly enhances the capability of cybersecurity systems to detect and prevent evolving threats, making it a critical component of modern security frameworks.
A Survey on DevOps and Continuous Integration Practices
Authors-Syed Faiz
Keyword-DevOps, Continuous Integration (CI), Software Development, Automation, Build Pipelines, Deployment, Version Control, Testing Automation, Continuous Delivery (CD), Agile Practices, Monitoring, Toolchain Integration, Software Quality, Release Management, Enterprise Software Development
Abstract-The growing demand for rapid software delivery, high-quality applications, and operational efficiency has driven the adoption of DevOps and continuous integration (CI) practices in modern software development. DevOps integrates development and operations teams, fostering collaboration, automation, and iterative improvements, while continuous integration ensures that code changes are regularly tested and merged into a shared repository. This study provides a comprehensive survey of DevOps methodologies, CI tools, and best practices, highlighting their impact on software quality, release frequency, and operational stability. It examines techniques for automated testing, version control, build automation, deployment pipelines, and monitoring. The survey also addresses challenges in implementing DevOps and CI, including cultural barriers, toolchain integration, security considerations, and scalability in enterprise environments. By analyzing current trends, industry practices, and empirical studies, this research demonstrates that effective DevOps and continuous integration practices are key enablers of agile, reliable, and high-performance software delivery.