International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
A Survey on Blockchain-Enabled ERP Systems for Secure Supply Chain Processes and Cloud Integration
Modern businesses are significantly reliant on integrated technologies to enhance the performance of their supply chains, operational efficiency, and decision-making capabilities. To manage client relationships, finances, inventory, procurement, and other tasks, businesses require Enterprise Resource Planning (ERP) systems. But in complicated and dispersed supply chains, typical ERP systems struggle with transparency, security, and scalability. A new answer has arisen: blockchain technology. It provides immutability, decentralization, and safe data interchange. Smart contracts built on the blockchain enable automated processes, enforce compliance, and foster improved confidence among stakeholders. Cloud computing has revolutionized ERP implementation with its scalable, affordable, and real-time accessible solutions. However, it does come with some new integration and security problems. The convergence of blockchain, cloud computing, and ERP systems provides a framework for secure, transparent, and adaptable supply chain processes, enhancing procurement, logistics, financial management, and cross-organizational collaboration. This paper presents a systematic survey of blockchain-enabled ERP systems, examining technology foundations, integration architectures, application domains, and critical challenges such as interoperability, scalability, and user adoption. By synthesizing recent literature, this study highlights research gaps, offers insights into practical implementation, and identifies opportunities for future research, including the integration of AI and IoT to further enhance automation, predictive analytics, and trust in supply chain ecosystems.
Blockchain, Enterprise Resource Planning (ERP), Cloud Integration, Supply Chain Management, Smart Contracts, Digital Transformation.
Role of Enterprise Architecture in Adoption of AI in Enterprises
The fast development of the Artificial Intelligence (AI) technologies has changed the ways the enterprise works, makes decisions and forms the value. Nevertheless, a systematic implementation of AI in businesses needs to be adopted on a methodology that integrates technological strengths with business strategy. Enterprise Architecture (EA) can be regarded as one of the key facilitators in this process, as it provides a full-fledged framework to enable a seamless incorporation of AI solutions into the current organizational systems. In this paper, the discussion will focus on how EA can enable the adoption of AI through the provision of governance infrastructure, interoperability, data management design, and alignment of IT and business goals. It brings out the role of EA frameworks, including the TOGAF and Zachman, in facilitating organizations to address issues associated with scalability, data silos and integration of legacy systems. Moreover, the paper highlights the need to tie AI efforts with enterprise architecture concepts in order to achieve agility, transparency, and long-term sustainability. Combining the use of architecture-driven design and AI innovation, companies will obtain operational excellence, decision intelligence, and competitive advantage.
Enterprise Architecture, Artificial Intelligence, Digital Transformation, IT-Business Alignment, AI Integration, Organizational Strategy, Technological Innovation]
Re-Architecting Networks with AI-Enabled SAP S/4HANA and Quantum Circuit Optimization in Cloud Environments with Oracle EBS Integration
The convergence of Artificial Intelligence (AI), quantum computing, and cloud-native enterprise systems is redefining the design and management of modern data networks. This paper proposes a network re-architecture framework that integrates AI-driven analytics, SAP S/4HANA intelligence, and quantum circuit optimization to enhance operational efficiency, scalability, and resilience. Quantum optimization algorithms are employed to accelerate computationally intensive network tasks such as traffic routing, resource allocation, and predictive anomaly detection, while AI models support real-time monitoring, adaptive policy enforcement, and intelligent automation. Oracle E-Business Suite (EBS) integration provides seamless enterprise resource management, enabling cohesive workflows across financial, administrative, and operational domains. The framework leverages cloud-native infrastructure for dynamic scalability, high availability, and secure multi-tenant operations, while adhering to data governance and compliance standards. By unifying AI, quantum computing, and SAP–Oracle enterprise integration, the proposed architecture offers a next-generation paradigm for cloud-enabled, intelligent network ecosystems, supporting strategic decision-making, reduced latency, and end-to-end business continuity.
Artificial Intelligence (AI); Quantum Circuit Optimization; SAP S/4HANA; Oracle E-Business Suite (EBS); Cloud Computing; Network Re-Architecture; Predictive Analytics; Intelligent Automation; Resource Optimization; Enterprise Integration; Data Governance; Scalable Infrastructure.
AI-Driven BMS Optimization and DC-DC Converter Design in a Cloud-Enabled Cognitive Ecosystem: SAP-Powered Architecture with Secure Data Vaulting and Policy-Based Content Governance
The rapid convergence of Artificial Intelligence (AI), cloud-native cognitive ecosystems, and intelligent power electronics is reshaping the landscape of Building Management Systems (BMS) and digital infrastructure optimization. This paper presents an integrated framework for AI-driven BMS optimization and DC–DC converter design within a cloud-enabled, SAP-powered cognitive architecture. The proposed system combines machine learning (ML), predictive analytics, and policy-based content governance with secure data vaulting mechanisms to achieve energy-efficient, reliable, and cyber-resilient operations. The architecture employs AI algorithms to optimize real-time energy flow, fault detection, and adaptive power regulation in multi-layered BMS environments. The DC–DC converter design integrates AI-enhanced control loops that autonomously tune parameters for improved conversion efficiency, reduced switching losses, and stable output regulation across dynamic load conditions. This optimization process is further augmented through cloud-based simulation environments, enabling model refinement and fault-tolerant validation across distributed systems. Secure Cyber Data Vaults ensure the immutability and encryption of operational data, protecting critical power and system information against ransomware and compliance breaches. The SAP-powered governance layer introduces policy-based access controls, audit trail management, and forensic analytics to guarantee transparency, traceability, and adherence to global data protection regulations such as HIPAA and GDPR. By integrating AI-driven optimization, power electronics design, and intelligent cloud governance, this research establishes a blueprint for energy-aware, self-healing, and secure BMS infrastructures. The proposed model supports scalable deployment across healthcare, industrial, and enterprise ecosystems—enhancing operational efficiency, sustainability, and resilience in the era of digital transformation.
AI-driven BMS optimization, DC–DC converter design, Cloud-enabled cognitive ecosystem, SAP-powered architecture, Secure data vaulting, Policy-based content governance, Machine learning (ML), Predictive analytics, Cyber resilience, Energy efficiency, Healthcare and enterprise modernization.
Enhancing ERP Scalability and Software Lifecycle Efficiency through AI-Enabled Citrix and Cyber Risk-Informed Incident Management in Cloud-Native Ecosystems
Enterprise Resource Planning (ERP) systems are becoming more central to organizational operations, supporting finance, supply chain, HR, and more. As software development cycles accelerate and deployments increasingly shift to cloud-native architectures, ERP systems must scale reliably, maintain security, and manage risk proactively. This paper proposes a framework that integrates AI-driven incident and risk management, Citrix-based virtualization for secure remote access and delivery, and cloud-native engineering best practices to enhance the scalability and resilience of ERP systems in software development environments. The proposed framework includes: (1) AI modules to monitor logs, metrics, user behavior, and system performance for anomaly detection, predictive risk scoring, and incident triage; (2) Citrix Virtual Apps / Desktops (or similar virtualization layer) to allow secure, centralized delivery of ERP modules, ensuring that endpoint security, role-based access, and controlled session environments are maintained; (3) cloud-native infrastructure (microservices, containers, orchestration, DevOps / CI/CD pipelines) to support rapid deployment, horizontal scaling, and resilience; and (4) built-in risk-oriented incident management processes to reduce detection and resolution times. Evaluation metrics include: incident detection latency; accuracy (precision, recall) of AI risk models; scalability (number of concurrent users / sessions, transaction throughput); performance overhead due to virtualization and risk-monitoring; deployment frequency; mean time to recovery; user satisfaction; and system uptime. In prototype and simulated ERP development environments, the framework shows benefits: incident detection latency reduced by ~40-50%; risk model accuracy (on synthetic/historical data) around 88-92%; the virtualization layer adds a performance overhead of ~10-20% under moderate load, but scalability gains offset the cost; deployment frequency improved by ~20% via integrated CI/CD with risk gating; mean time to recovery reduced; users report improved trust in system stability and risk transparency. Challenges include complexity of integrating AI risk detection, potential false positives / negatives, overheads of virtualization and monitoring, the learning curve for development teams, and ensuring governance / policy consistency. In conclusion, combining AI-based incident and risk management, Citrix virtualization, and cloud-native development practices provides a promising path for scalable, secure ERP software development. Future work will validate in large real-world ERP deployments, improve risk model interpretability, reduce overheads, and refine policy and governance integration.
ERP scalability, AI-driven incident management, Risk detection, Citrix virtualization, Cloud-native ERP, DevOps / CI-CD, Performance overhead, System reliability, Software development acceleration, Secure remote access.
Copyright (c) 2024 International Journal of Technology, Management and Humanities
Leveraging Artificial Intelligence for Predictive and Adaptive Decision-Making in Construction Project Management
The construction sector is characterized by systematic problems of project delays, cost, safety hazards, as well as inefficiencies in decision making processes. The classical methods of project management fail to react to new circumstances and become more complex in reaction to the constantly evolving construction projects. This paper discusses how artificial intelligence (AI) can be used to help in improving predictive and adaptive decision-making within the construction project management. Using data-led models and incorporating real-time analytics, AI allows project managers to be proactive regarding the prospect of risks, control costs and timeframes and adjust resource allocation dynamically. The study focuses on how machine learning, predictive analytics, and reinforcement learning have been applied to enhance the accuracy of planning, performance monitoring, and responsiveness of operation. A conceptual framework is created that allows showing how AI systems are capable of learning on the basis of the data stream on an ongoing project to support proactive and adaptive management strategies. Results indicate the transformative AI potential to diminish uncertainty, enhance efficiency, and maximize project results, whereas indicating the implementation issues of data integration, model interpretability, and organizational readiness. The research paper is one step towards the improvement of intelligent decision-support systems in the construction industry, which encourages a transition toward more information-intensive and adaptive and resilient project management practices.
Artificial Intelligence; Predictive Analytics; Adaptive Decision-Making; Construction Project Management; Machine Learning; Risk Forecasting; Data-Driven Management; Reinforcement Learnin
Optimizing Africa’s Semiconductor Supply Chains Through AI-Powered Predictive Logistics and Anomaly Detection
Africa’s entry into the global semiconductor value chain is gathering momentum. Countries such as South Africa, Kenya, Morocco, Egypt, and Rwanda are beginning to establish design hubs, assembly and packaging plants, and logistics corridors that connect them to global markets. Yet, the continent’s semiconductor supply chains face significant structural challenges. Long lead times, fragmented logistics networks, unpredictable border delays, volatile demand, and limited end-to-end visibility are persistent obstacles. Unlike other industries, semiconductor manufacturing depends on highly precise, time-sensitive movements of materials and components. A single delayed shipment of packaged ICs or wafers can halt production lines worth millions of dollars. To address these challenges, African semiconductor stakeholders are turning to artificial intelligence—specifically, predictive logistics and anomaly detection. By embedding these technologies into their supply chain operations, African chipmakers, OSAT facilities, logistics providers, and policymakers can move from reactive problem-solving to proactive decision- making, dramatically increasing reliability, efficiency, and resilience.
Africa; semiconductor value chain; supply chain resilience; predictive logistics; artificial intelligence; anomaly detection; manufacturing efficiency; logistics networks.
AI-Enhanced Software Engineering for Scalable ERP Systems: A DevOps-Driven Cloud-Native Evaluation Planning Framework
Enterprise Resource Planning (ERP) systems are the backbone of modern organizations, but their scale, configurability, and integration requirements present significant engineering and operational challenges. This paper presents an AI-enhanced software engineering framework for evaluating and improving the scalability, reliability, and maintainability of cloud-native ERP systems under a DevOps-driven lifecycle. The proposed framework combines automated runbook-mining and intent extraction (NLP), predictive performance modelling (time-series and graph neural networks), adaptive CI/CD pipelines, and chaos-driven resilience testing to create an integrated evaluation loop. AI agents assist at multiple stages: (1) code and configuration analysis to surface risky customizations and anti-patterns; (2) workload synthesis and demand forecasting to generate realistic, high-fidelity load profiles; (3) predictive scaling policies that map workload forecasts to resource plans; and (4) automated root-cause triage using observability traces and causal-inference modules. The framework is cloud-native by design: test harnesses run as ephemeral Kubernetes workloads, telemetry is captured with open standards (OpenTelemetry), and infrastructure is declared via IaC to enable reproducible experiments. Importantly, the framework embeds governance and explainability: AI-driven recommendations include provenance, confidence, and suggested mitigations; policy gates and human-in-the-loop approval are required for high-impact changes. We validate the framework with a representative ERP reference deployment across three cloud configurations and show empirical improvements in stress-test outcomes: improved throughput under peak load, reduced tail latency, fewer manual interventions during incidents, and faster mean time to resolution (MTTR) in post-failure repair. The paper describes the architecture, evaluation methodology, and a roadmap for integrating the framework into continuous delivery flows, arguing that AI augmentation combined with DevOps practices materially improves ERP scalability and operational resilience.
ERP scalability; DevOps; cloud-native; AI-assisted software engineering; predictive performance modelling; chaos engineering; continuous delivery; observability; intent mining; infrastructure-as-code.
Artificial Intelligence-Enabled Smart Connect Ecosystem for Real-Time Optical Wireless Mobile Communication: A Scalable ERP Framework for Intelligent and Sustainable Software Maintenance
Smart connect ecosystems integrate devices, networks, sensors, and software to deliver continuous, reliable services in mobile communications. With the growing interest in Optical Wireless Communication (OWC) as a high‐bandwidth, low-interference, spectrum-efficient complement or alternative to RF, new architectures are needed to ensure real-time performance, reliability, scalability, and maintainability of software components. This paper proposes a framework combining real-time OWC for mobile platforms with a scalable Enterprise Resource Planning (ERP)–based system for intelligent software maintenance. The aim is to provide a unified ecosystem where optical wireless links deliver high throughputs and low latency, while the ERP backbone supports monitoring, versioning, fault-handling, and predictive maintenance of the software stack across devices and nodes. We first survey the state of real-time OWC technologies, examine the constraints (mobility, alignment, latency, reliability), and then describe how ERP systems can be adapted to support software maintenance: configuration management, deployment orchestration, logging, diagnostics, and predictive analytics. Experimental or simulation results using a prototypical OWC link (mobile transmitter/receiver, visible light or free-space optical channel) show that our approach achieves end-to-end latency under 2 ms in ideal conditions, with software rollback time reduced by ~50% in maintenance scenarios and defect detection earlier via monitoring. Key challenges include environmental interference in optical paths, alignment issues during mobility, and ensuring the ERP system scales without becoming a bottleneck. The paper contributes a reference architecture, a set of evaluation metrics, and comparison with baseline RF-based mobile communication systems. We conclude that a smart connect ecosystem combining real-time OWC and ERP-driven software maintenance can deliver superior responsiveness and robustness, if the system is carefully designed with redundancy, predictive analytics, and modular software components.
Optical Wireless Communication; Real-Time Communication; Mobile Platforms; ERP; Intelligent Software Maintenance; Scalability; Low Latency; Reliability; Predictive Monitoring.
Machine Learning-Driven Optimization of Wireless Communication in Smart Ecosystems: Comparative Analysis of Image Denoising on Oracle Cloud Platforms
Wireless mobile communication within Smart Connect ecosystems (such as IoT networks, smart cities, connected vehicles, mobile sensor arrays) is often challenged by noise in imaging data, limited bandwidth, latency constraints, and resource limitations on client devices. This paper investigates how machine learning (ML) techniques combined with Oracle Cloud database services can optimize mobile wireless communication, especially when image data transmitted over noisy or constrained channels must be denoised efficiently. We perform a comparative study of several image denoising techniques — traditional filters (Gaussian, median, bilateral), non-local means (NLM), BM3D, and deep learning methods (DnCNN, FFDNet) — in the context of mobile-edge-cloud pipelines. We propose a system architecture in which denoising can be done either on the mobile device, at the edge, or in the Oracle Cloud, depending on network conditions, energy budgets, and latency requirements. Oracle’s Autonomous Database and Oracle Machine Learning services are used to store denoised and raw image data, run ML model inference, monitor performance, and support adaptive decision-making for where denoising should take place. The experimental evaluation is conducted using real and synthetic image datasets corrupted with different noise types (Gaussian, salt-and-pepper, speckle), across different mobile wireless link qualities (varying SNR, bandwidth) and with variations in compute capabilities on mobile/edge nodes. Key metrics include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), inference latency, energy consumption, and total communication cost (data transmitted). Results show that deep learning models (FFDN etc.) provide superior image quality (up to ~2–4 dB PSNR gain over traditional filters) but at higher computational cost. Hybrid schemes (e.g. partial denoising on mobile + refinement in cloud) can balance trade-offs, achieving near-cloud quality while reducing transmitted data by up to ~50% and reducing end-to-end latency under moderate network degradation. We discuss benefits and drawbacks, trade-offs between image quality, energy, latency, and cloud vs edge processing, and propose guidelines for deployment.
Wireless Mobile Communication, Smart Connect Ecosystems, Image Denoising, Oracle Cloud Database, Edge vs Cloud Processing,PSNR, SSIM, Latency & Energy Trade-off.
Artificial Intelligence and Regulatory Harmonization: Opportunities for Emerging Markets Linking to the United States
Artificial Intelligence (AI) is emerging as a transformative driver of economic growth, social development, and geopolitical competition. However, the fragmented nature of global regulatory approaches has created challenges for interoperability, trust, and international cooperation. While leading economies such as the United States and the European Union are defining ambitious frameworks for responsible and trustworthy AI, many emerging markets face significant barriers in terms of institutional capacity, policy alignment, and access to global innovation networks. The lack of harmonized standards risks reinforcing a digital divide, restricting emerging economies from fully benefiting from AI-enabled growth. This paper investigates the opportunities that regulatory harmonization presents for emerging markets seeking stronger integration with U.S. regulatory and innovation ecosystems. It explores how common standards, ethical frameworks, and trade-aligned policies can reduce compliance costs, enhance cross-border data flows, and foster greater trust in AI applications. The analysis highlights the role of international benchmarks such as global ethical guidelines, multilateral trade agreements, and U.S. executive policies as entry points for harmonization. Importantly, the paper presents the experiences of two emerging-market enterprises, Black Vitriol LLC and Eatsbueno AI, founded by Gabriel Jiménez, as practical illustrations of how startups can leverage regulatory compatibility to expand their reach into U.S. and global markets. The findings demonstrate that harmonization is not merely a legal or technical process but a strategic pathway for emerging economies to secure inclusion in the global AI economy. Harmonized frameworks can facilitate innovation diffusion, attract investment, and ensure that AI serves as a tool for sustainable development rather than deepening inequality. The paper concludes by offering actionable policy recommendations for governments, regulators, and private actors in emerging markets, emphasizing the need for balanced approaches that combine innovation, trust, and inclusivity in the future of AI governance.
Artificial Intelligence, Regulatory Harmonization, Emerging Markets, United States, AI Governance, Data Policy, Digital Trade, Innovation
Application of HWMA Control Charts with Ranked Set Sampling for Quality Monitoring: A Case Study on Pepsi Cola Fill Volume Data
This case study explores the implementation of the Homogeneously weighted moving average (HWMA) control chart, which is improved by the ranked set sampling (RSS and MRSS) to track the quality of the processes within a real industrial environment. Based on Pepsi Cola fill volume data generation, we show how sample rank set sampling is more effective at detecting process mean changes than the classical HWMA chart under simple random sampling. The comparison demonstrates that the proposed schemes make the out-of-control conditions known more in advance providing a more sensitive and reliable method of quality monitoring. The findings support the practical importance of using HWMA charts in conjunction with ranked set sampling methods, especially in those industries in which measurements are expensive but ranking is cheap and easy. The case study highlights the possibility of enhanced operational performance and early identification of failure in the production of beverages with improved statistical process control practices.
Homogeneously Weighted Moving Average (HWMA); Ranked Set Sampling (RSS); Median Ranked Set Sampling (MRSS); Statistical Process Control (SPC); Quality Monitoring; Pepsi Cola Fill Volume.
Early Adoption of AI-Enabled Route Optimization in SAP Logistics via GKE Deployment
The increasing complexity of global logistics networks demands intelligent solutions to optimize routing, reduce operational costs, and enhance delivery efficiency. This paper explores the early adoption of AI-enabled route optimization within SAP logistics, deployed on Google Kubernetes Engine (GKE), to provide scalable, real-time decision-making capabilities. The proposed framework integrates SAP logistics and transportation data with advanced machine learning algorithms to predict traffic patterns, optimize delivery routes, and minimize fuel consumption. Leveraging GKE’s container orchestration, elasticity, and high availability, the system ensures robust, fault-tolerant deployment capable of handling large-scale logistics data across multiple regions. AI-driven predictive models analyze historical and real-time operational data to identify optimal routing strategies, while prescriptive modules recommend actionable adjustments to logistics schedules and resource allocation. Experimental evaluation demonstrates improvements in delivery efficiency, route planning accuracy, and overall supply chain responsiveness. This research highlights the transformative potential of combining AI, SAP, and cloud-native architectures to enable proactive, intelligent, and adaptive logistics management in modern enterprise environments.
AI-Enabled Route Optimization, SAP Logistics, Google Kubernetes Engine (GKE), Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Prescriptive Insights, Supply Chain Optimization, Real-Time Decision Making, Cloud-Native Infrastructure, Transportation Management.
Evaluating Cybersecurity Patch Management through QA Performance Indicators
Effective patch management is a cornerstone of modern cybersecurity, yet many organizations struggle to measure the success of their patching processes in a structured and objective manner. This research evaluates cybersecurity patch management through the lens of Quality Assurance (QA) performance indicators, providing a data-driven approach to assess and improve security posture. Key QA metrics, including patch success rate, mean time to deploy (MTTD), rollback frequency, and vulnerability exposure window, are analyzed to determine their impact on system reliability and risk mitigation. The study highlights how integrating QA principles into patch management enables continuous monitoring, early detection of process inefficiencies, and faster remediation of vulnerabilities. Findings suggest that a standardized set of QA-based indicators can help security teams optimize patch deployment strategies, reduce operational risk, and enhance compliance with cybersecurity frameworks. This approach provides a repeatable, measurable pathway to improving organizational resilience against evolving cyber threats.
Cybersecurity, Patch Management, Quality Assurance, QA Metrics, Vulnerability Management, System Reliability, Risk Mitigation
AI-Driven Real-Time Summarization and Action Item Extraction in Video Conferencing Platforms
The sudden transition to digital cooperation greatly contributed to the high use of video conferencing systems in the sphere of business, education, and healthcare. Nevertheless, the sheer amount of data in the form of conversations that are produced during meetings is frequently inefficient, and participants find it difficult to summarize the most important information and implement the decisions. The current paper introduces an AI-based platform of real-time summarization and action item extraction within the video conferencing setting. The system proposed is an automatic speech recognition system that incorporates high-level natural language processing algorithms to create summaries of the meetings and identify and organize actionable items. The framework offers solutions to the issues of speech disfluency, multi-speaker conversations, and domain variation by relying on transformer-based architectures and context-aware learning. The experimental outcomes on benchmark meeting datasets show an increase in the accuracy of the summarization and task identification of experimental results over the baseline solution. Moreover, the combination of real-time summarization with a conferencing platform reveals the possibility of improved productivity, accountability, and knowledge retention. The study is relevant to the emerging body of AI-based collaboration tools and it provides a solution that is scalable and practical to enhance productivity in remote and hybrid work environments.
Artificial Intelligence, Real-Time Summarization, Action Item Extraction, Video Conferencing, Natural Language Processing, Speech Recognition, Transformer Models, Collaboration Tools
The Diaspora: Survival, Sacrifices, and the Misunderstood Heartbeat Of Africa: An analysis of migration, remittances, and identity across Nigeria, Ghana, and Togo
This paper examines the complexities of the African diaspora, challenging narratives that portray migration as betrayal. It explores the structural failures—corruption, fragile health systems, and economic instability—that push Africans abroad, contrasting them with the discipline and resilience required to integrate into structured societies. Using Nigeria, Ghana, and Togo as case studies, it highlights how remittances—often surpassing foreign direct investment—sustain families and economies. The essay argues that diaspora identity is not diminished by distance, and that remittances act as lifelines of survival, education, and development. Ultimately, it calls for a rethinking of freedom, identity, and loyalty, recognizing diaspora communities as builders, not traitors, and as the heartbeat of Africa across the world.
African diaspora, migration, Nigeria, Ghana, and Togo.
Credit Risk Dynamics in Digital Lending via Mobile Apps: An Empirical Analysis of Alternative Data Utilization for SME Financing in West Africa
The emergence of mobile based lending through applications has revolutionised the access of finance by the small to medium enterprises (SMEs) in West Africa, where conventional banking infrastructure fails to satisfy credit demands. This paper empirically checks the dynamics of credit risk in digital lending, using the workings of alternative data, including mobile money transactions, airtime use, and digital payment history, as risk predictors and loan decision-makers. Based on the SME loan performance data provided by mobile lending services in the chosen markets in West Africa, the results indicate that the predictive value of credit scoring models is enhanced greatly when alternative data are incorporated in comparison with traditional ones. Findings also indicate that digital lending systems are able to decrease information asymmetry and widen credit access to underbanked SMEs, but there are still difficulties pertaining to privacy of data, access to information by borrowers, regulatory framework, etc. The research offers important insights to policy makers, financial innovators and financial institutions who want to enhance SME financing by using digital credit ecosystems within emerging economies.
Digital lending, Credit risk, Alternative data, SMEs, Mobile apps, Financial inclusion, West Africa
End-to-End AI Pipelines for Digital Twin-Enabled Vehicle Testing
Digital twins—high-fidelity virtual representations of physical systems—are emerging as transformative platforms for vehicle testing, enabling engineers to conduct repeatable, safe, and realistic assessments. However, traditional digital twin workflows often suffer from fragmentation, manual configuration, and limited scalability. To address these gaps, we propose an end-to-end AI-powered pipeline that seamlessly integrates digital twin creation, scenario synthesis, simulation, and feedback loops, enabling continuous, data-driven vehicle testing. Our pipeline encompasses three core stages: (1) Automated Digital Twin Generation, where onboard and field-collected sensor data train AI models to construct accurate virtual replicas of vehicle dynamics, sensors, and environmental contexts; (2) AI-Guided Scenario Generation, leveraging generative networks conditioned on safety, edge-case relevance, and operational targets to create diverse driving and environmental scenarios for testing; and (3) Simulation & Feedback, executing scenarios in real-time twin environments and then processing outcomes via anomaly detection and performance evaluation, which feed back into subsequent scenario generation and twin refinement. We evaluated our system using both physical vehicle data and simulation hardware-in-the-loop (HIL) setups. Results demonstrated a roughly 35% improvement in failure mode detection rate compared to manual testing workflows, while reducing setup time per test by over 50%. Human expert review rated scenario realism at 4.4/5, indicating high fidelity of generated conditions. The pipeline’s modularity supports rapid adaptation to new vehicle models and sensor configurations, enabling scalable, data-driven validation—even under extreme or rare conditions. This end-to-end pipeline establishes a foundation for more agile, comprehensive, and reliable vehicle testing using digital twins, paving the way for accelerated validation cycles, improved safety assurance, and continuous model evolution.
Digital Twin, Vehicle Testing, End-to-End AI Pipeline, Scenario Generation, Anomaly Detection, Simulation Feedback Loop, High-Fidelity Modeling
AI-Driven Quality Assurance for Secure Software Development Lifecycles
The growing complexity of current software systems has intensified the need for sound and safe development practices. Conventional quality assurance (QA) tends to be ill-equipped to cope with the fast-changing threat vectors, so it ends up with the vulnerability being unidentified and taking too long to fix. Artificial Intelligence (AI) has a disruptive potential due to the automatization of key processes of the Software Development Lifecycle (SDLC), which results in the increase of efficiency and security. Computer-assisted QA uses machine learning models and intelligent automation to conduct the code analysis (static and dynamic), anomaly detection, and prioritization of vulnerabilities in real-time. The process will reinforce continuous integration and deployment (CI/CD) pipelines, allowing the proactive process of risk modeling and proactive security. In addition, AI-based test case generation and optimization minimizes human error, increases coverage and speed. Although these advantages exist, issues like data quality, transparency of algorithms, and trust of the recommendations provided by AI have to be mitigated in order to guarantee the credibility of the results. With the introduction of AI to the QA processes, organizations are able to have safer, more resilient and cheaper software development lifecycle cycles besides being in compliance with the standards of security.
Artificial Intelligence, Quality Assurance, Secure Software Development Lifecycle, Vulnerability Detection, Machine Learning, Predictive Risk Modeling, Software Security, Automation.
Scalable Deep Learning on Cloud Platforms: Challenges and Architectures
The sheer growth in the workloads of deep learning has placed new and unprecedented demands on scalable and efficient computational infrastructure. Cloud systems have become the first providers of large-scale distributed training through elastic resources, purpose-built accelerators, and operated machine learning services. This study investigates the use of cloud-native architectures such as Kubernetes, TensorFlow on Kubernetes, and Apache Spark MLlib as the means to deploy distributed deep learning applications that could handle the performance, elasticity, and cost-effectiveness challenges. It discusses the importance of GPUs and new TPUs in training faster, analyzes the performance of auto-scaling and orchestration policies, and outlines the trade-offs between cloud providers. Additionally, the paper also names bottlenecks like the cost of data transfer, inefficiencies in schedules, and vendor lock-in, as well as provides commentary on the early trends of serverless ML and hybrid deployments. The results show that solutions based on the cloud are essential in addressing the gap in the computation requirements and the real-world application of deep learning on a scale and making the cloud infrastructure the basis of the upcoming AI.
Scalable deep learning, cloud computing, Kubernetes, TensorFlow on Kubernetes, Apache Spark MLlib, GPUs, TPUs, distributed training, elasticity, cost-efficiency.
Federated Learning on Cloud Platforms: Privacy-Preserving AI for Distributed Data
Federated learning has also become a paradigm shift to making machine learning collaborative and not centralized around sensitive data. Federated learning solves the increasing privacy, regulatory compliance, and data sovereignty concerns by preventing the transfer of model training to centralized model training clients, like hospitals, financial institutions, and IoT devices. Cloud platforms are critical to the operationalization of this paradigm as it offers scalable orchestration, secure aggregation, and communication-efficient frameworks. The paper discusses how cloud-native federated learning systems decrease the amount of communication, enhance the model convergence, and provide more robust privacy guarantees without violating regulation of systems like GDPR and HIPAA. By applying federated learning to the medical diagnostic and financial fraud detection domains, the study shows that federated learning can be successful in providing a high level of model accuracy and strong privacy protection. The results indicate the significance of supporting federated learning by cloud-native infrastructure that will allow implementing privacy-safe AI solutions that can be widely adopted in regulated industries.
Federated Learning, Cloud Platforms, Privacy-Preserving AI, Distributed Data, Secure Aggregation, GDPR, HIPAA, Healthcare AI, Financial Fraud Detection, Cloud-Native Architectures
Scalable ETL Pipelines for Autonomous Vehicle Sensor Data Management
Autonomous vehicles generate massive volumes of heterogeneous sensor data, including LiDAR, radar, cameras, GPS, and inertial measurement units, necessitating efficient data management pipelines to extract actionable insights. This paper presents a scalable Extract, Transform, Load (ETL) pipeline designed specifically for autonomous vehicle sensor data management, enabling real-time ingestion, processing, and storage of multi-modal data streams. Leveraging cloud-native architectures and distributed computing frameworks, the proposed ETL pipeline facilitates seamless integration of diverse sensor inputs, data cleansing, feature extraction, and efficient storage in data lakes and warehouses optimized for large-scale analysis. The pipeline addresses critical challenges such as data heterogeneity, synchronization, quality assurance, and low-latency requirements essential for autonomous driving applications. Experimental evaluations using real-world autonomous driving datasets demonstrate the pipeline’s ability to scale horizontally while maintaining high throughput and low latency. Key components include parallelized data ingestion, schema-aware transformation modules, and fault-tolerant streaming capabilities, which collectively ensure robustness and adaptability in dynamic driving environments. The pipeline’s modular design allows easy incorporation of advanced analytics and machine learning workflows downstream, facilitating continuous model training and validation. This approach not only optimizes resource utilization but also supports real-time monitoring and anomaly detection for vehicle sensor health. The proposed system represents a significant advancement in managing the growing complexity and volume of autonomous vehicle sensor data, providing a foundation for improved decision-making and system safety. Future directions include integrating edge computing for pre-processing and further enhancing pipeline automation. This work contributes to the development of scalable data infrastructure critical for accelerating autonomous vehicle research and deployment.
Autonomous Vehicles, Sensor Data Management, ETL Pipeline, Scalable Data Processing, Cloud-Native Architecture, Real-Time Data Ingestion, Multi-Modal Data Fusion, Distributed Computing, Data Lake, Anomaly Detection
Intelligent Data Curation Pipelines for Training Autonomous Vehicle Models
The development of robust autonomous vehicle (AV) models heavily depends on the quality and diversity of training data. However, raw sensor data collected from AVs is often noisy, redundant, and unstructured, posing significant challenges for efficient model training. This paper proposes an intelligent data curation pipeline designed to automate and optimize the preprocessing, filtering, annotation, and augmentation of autonomous vehicle datasets. The pipeline leverages AI techniques, including active learning, anomaly detection, and generative data augmentation, integrated within a scalable cloud-based infrastructure. Our approach facilitates the selection of the most informative and diverse samples, reduces labeling effort, and enhances data quality for supervised learning tasks. Experimental evaluations using real-world autonomous driving datasets demonstrate substantial improvements in model accuracy, training efficiency, and generalization to diverse driving scenarios. The pipeline's modular architecture allows seamless integration with existing AV development workflows and supports continuous data ingestion from multiple sensor sources such as LiDAR, cameras, and radar. The results indicate that intelligent data curation is crucial for addressing the increasing data volume challenges in autonomous driving, accelerating the training process, and improving the safety and reliability of AV systems. This study highlights the importance of automated data curation as a foundational step in building next-generation autonomous driving models.
Intelligent Data Curation, Autonomous Vehicles, Data Preprocessing, Active Learning, Data Augmentation, Autonomous Driving Models, Sensor Fusion, Cloud-Based Pipelines, Machine Learning, Model Training Efficiency.
Machine Learning for Stock Price Forecasting: LSTM vs Transformer Approaches
Stock price prediction, rightly considered one of the most complex and risk-sensitive undertakings in therealm of finance, was never quite so much in fashion. Statistical models given to financial marketprediction have never enjoyed universal acceptance because of the nonlinearities in the data-generationprocess and temporal dependence characteristic in financial time series data. This paper explores twofrontline deep learning-based approaches to stock price prediction: LSTM and Transformer. LSTM RNNsare well known for learning sequential dependencies, whereas Transformers triggered a revolution in NLPby addressing the long-term dependency problem through attention without using recurrence. This paper, taking real financial datasets from public stock exchanges, implements both LSTM andTransformer architectures and tests factors such as MSE, MAE, and R^2. Our comprehensive performance analysis is done over many stocks to assess the robustness, generalizability, andinterpretability of the tested models. The study's results reveal that while LSTM fares better with smalldatasets displaying trends that are less volatile, Transformer models outshine in volatile and highfrequencyenvironments in terms of accuracy and ability. This infers that the Transformer, at the expenseof computational effort, could be the next paradigm in time series forecasting for finance. Going further, this manuscript presents visualizations comparing predicted and actual stock prices, whilealso including a feature analysis for determining which factors contributed most to the prediction. Theconclusion provides potential trajectories for hybrid models that combine the strengths of eacharchitecture.
Stock Price Forecasting, Machine Learning, Long Short-Term Memory (LSTM), Transformer Model, Financial Time Series, Deep Learning, Attention Mechanism, Time Series Prediction, Model Comparison, Forecast Accuracy
The SME Disconnect: A Critical Analysis of the Bank-Vendor Model and a Framework for Direct-to-Merchant Financial Ecosystems
The sector of Small and Medium Enterprises (SMEs) continues to be the key to the economic development of Nigeria, yet it has a chronic lack of access to formal credit despite the unremitting demand. This gap has not been bridged by the existing bank-vendor model of SME finance where banks are dependent on third-party financial technology vendors to provide products and services. Rather, it has generated a structural disconnection: banks are motivated to focus on the product-based sales and less on the holistic solutions to business, whereas the role of a vendor is to disaggregate the operational and financial information, which is essential to proper credit evaluation. The limitations of the bank-vendor framework critically assessed in this paper result in the argument that the traditional banking system has turned into a systemic bottleneck to SME digitalization and financial inclusion. It presents the Direct-to-Merchant (D2M) financial ecosystem model as a paradigm shift - circumventing intermediaries and integrating payments with standard business management systems and converting real-time operational data into proprietary credit-scoring assets. With the Nigerian retail sector as a case study, the paper will show how the D2M model can be used to improve cash flow management, facilitate data-driven lending, and create a scalable baseline of inclusive financial ecosystems. It is concluded that the paradigm shift needs to be made beyond the bank-vendor paradigm in order to unlock SME potential, innovate financially, and ensure sustainable growth in emerging markets.
SME Finance, Bank-Vendor Model, Direct-to-Merchant (D2M) Framework, Financial Inclusion, Digitalization, Nigeria, FinTech Ecosystems
The Digital Leap Initiative: A Strategic Framework for Accelerating SME Digitalization in Emerging Economies
SMEs continue to be the engine of the Nigerian economy, but the economic and business environment still prevents their prosperity due to insufficient access to capital, a disjointed digital environment, and an increasingly large digital skills gap. The Digital Leap Initiative offers a strategic framework to speed up the process of SME digitalization by using a multi-stakeholder approach and coordination of financial institutions, technology providers and the government. It has three pillars that are interdependent, such as Accessible Digital Toolkits that give SME-specific fintech-based solutions; Integrated Skills Development that aims to offer structured capacity-building solutions to address human capital gaps; and Smart Policy Incentives that will establish an enabling regulatory environment. Based on the case study experience in India and Brazil, the paper applies global best practices to the Nigerian context showing how digital adoption has the potential to turn SMEs into sources of resilience and inclusive economic development. The results emphasize the importance of collaboration-based policy, innovation-based finance, and development of human capital in sustainable digital transformation in emerging economies.
SME digitalization, fintech solutions, skills development, digital policy, Nigeria, emerging economies, digital transformation framework
AI-Orchestrated Decision-Making Frameworks for Highway and Urban Mobility
Efficient decision-making frameworks are critical for managing the complexity of modern highway and urban mobility systems. As cities grow and traffic demands increase, traditional rule-based traffic management approaches struggle to adapt dynamically to evolving conditions. This paper proposes an AI-orchestrated decision-making framework that leverages advanced artificial intelligence techniques to optimize traffic flow, enhance safety, and reduce congestion in both highway and urban contexts. The framework integrates machine learning models, reinforcement learning agents, and real-time data analytics to enable adaptive traffic control, incident management, and route optimization. It synthesizes heterogeneous data streams from vehicle sensors, infrastructure devices, and mobile sources to provide comprehensive situational awareness. Key features include hierarchical decision-making layers that coordinate local intersection controls with regional highway management, multi-agent cooperation for conflict resolution, and predictive analytics for proactive congestion mitigation. The system architecture supports scalability across different traffic densities and urban layouts. Experimental evaluations, using realistic traffic simulations and datasets from metropolitan areas, demonstrate the framework’s ability to reduce average travel times by up to 20% and lower traffic-related emissions by 15%. Additionally, safety metrics improve through timely incident detection and coordinated response. This work advances the state of intelligent transportation systems by presenting an AI-driven orchestration approach that balances local autonomy with global traffic objectives. The framework provides a foundation for future smart mobility applications, supporting sustainable, efficient, and safe highway and urban traffic ecosystems.
AI Orchestration, Decision-Making Framework, Highway Mobility, Urban Traffic Management, Reinforcement Learning, Traffic Optimization, Multi-Agent Systems, Intelligent Transportation Systems (ITS)
Generative Diffusion Pipelines for Rare and Extreme Driving Event Simulation
Simulation of rare and extreme driving events is critical for the development and validation of autonomous vehicle systems, enabling them to safely navigate complex and hazardous scenarios. Traditional simulation approaches often struggle to generate realistic, diverse, and high-fidelity scenarios due to the scarcity and unpredictability of such events in real-world datasets. This paper proposes a novel generative diffusion model-based pipeline designed specifically for simulating rare and extreme driving events to enhance training and testing of autonomous driving systems. Our approach leverages the power of diffusion probabilistic models, which iteratively transform random noise into structured, high-quality driving scenarios by learning the data distribution from rare event datasets. Unlike conventional generative adversarial networks (GANs), diffusion models offer improved training stability, better mode coverage, and superior sample diversity, addressing critical challenges in rare event synthesis. The pipeline integrates multi-modal data including vehicle trajectories, environmental conditions, sensor inputs, and traffic participant behaviors, synthesizing scenarios that capture complex interactions and edge cases such as sudden obstacle appearance, adverse weather effects, and aggressive driving maneuvers. Extensive experiments conducted on benchmark datasets and simulated environments demonstrate that our diffusion-based pipeline outperforms existing generative approaches in terms of scenario realism, diversity, and relevance to real-world rare events. By enabling scalable generation of diverse edge-case scenarios, the pipeline significantly improves autonomous driving model robustness and generalization. We discuss the implications of diffusion model hyperparameters on simulation quality, computational costs, and the integration of this pipeline into broader autonomous vehicle testing frameworks. Future work aims to incorporate real-time adaptive scenario generation and multi-agent interaction modeling to further enhance simulation fidelity.
Generative diffusion models, Rare event simulation, Autonomous driving, Extreme driving scenarios, Edge case generation, Multi-modal data fusion, Scenario diversity, Vehicle trajectories, Autonomous vehicle testing, Simulation pipelines
How Volatility Regimes Affect Sector Rotation Strategies
Volatility regimes are a core aspect of financial market activity that not only affect the way they allocate assets but also determine the rotation strategies of the sector used by institutional and retail investors. This paper analyzes the effects of the changes of the low, moderate and high volatility regime on the relative performance of both defensive and cyclical sectors, carry implication on portfolio diversification, and risk-adjusted returns. Using the available theoretical frameworks, and empirical studies, the analysis will use the regime-switching framework and sectoral performance measures to evaluate the consistency and reliability of rotation strategies during different market conditions. Results show that defensive industries like utilities and consumer staples are robust in volatile settings, but those with cyclicality like technology and consumer discretionary are well-performing in stable and expanding times. The paper highlights the necessity of considering volatility regime tracking in tactical asset allocation to reduce downside risk and to realize sector or industry-specific opportunities. Moreover, the findings demonstrate the possible traps of excessively making use of the historic correlations, the continuously changing character of market structures and investor mood. This study helps understand better, by filling the gap between academic theory and the investment practice, how volatility regimes are the key factor to the sectoral change, and provide valuable information to portfolio managers who want to maximize strategies in the market that are becoming more and more uncertain.
volatility regimes, sector rotation, portfolio management, defensive sectors, cyclical sectors, tactical allocation, regime-switching models.
The Role of Confirmation Bias in Sell-Side Analyst Ratings
Sell-side analyst ratings play a pivotal role in guiding investment decisions, influencing market sentiment, and shaping capital allocation. However, cognitive biases, particularly confirmation bias, may compromise the objectivity of these evaluations. Confirmation bias occurs when analysts selectively focus on information that aligns with their pre-existing beliefs, potentially leading to skewed recommendations and market inefficiencies. This study investigates the presence and impact of confirmation bias in sell-side analyst ratings by analyzing a sample of analyst reports across multiple sectors. Utilizing a mixed-method approach that combines quantitative regression analysis with qualitative content evaluation, the research examines patterns of rating revisions, the alignment of reports with prior expectations, and the correlation with subsequent stock performance. Findings suggest that confirmation bias significantly affects analyst judgments, manifesting in a tendency to overweight supporting evidence while discounting contradictory information. The study also identifies factors that exacerbate or mitigate this bias, including analyst experience, firm culture, and market volatility. By highlighting the behavioral dimensions of financial analysis, the research underscores the importance of critical evaluation, transparency, and regulatory oversight in maintaining market efficiency. The results contribute to the literature on behavioral finance and provide actionable insights for investors, analysts, and policymakers seeking to enhance decision-making quality.
Confirmation bias, sell-side analysts, behavioral finance, analyst ratings, investment decision-making, market efficiency.