10.64886/oajea.0102.002

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Abstract: Emerging research ecosystems, particularly within developing regions, continue to face significant challenges in accessing, indexing, and disseminating scholarly knowledge. Existing global discovery platforms, such as Scopus, Web of Science, and Google Scholar, frequently underrepresent locally produced research outputs due to incomplete metadata coverage, limited interoperability, and linguistic barriers. This paper presents a conceptual design and implementation framework for an AI-powered Scholarly Discovery Platform (AI-SDP) aimed at enhancing the visibility, accessibility, and discoverability of academic resources from underrepresented regions. The proposed framework integrates artificial intelligence, natural language processing (NLP), and semantic graph technologies to enable advanced metadata enrichment, hybrid semantic search, citation graph analytics, and personalized recommendation services. The conceptual architecture is organized into five layers—data source, ingestion, intelligence, application, and user interface—each designed for interoperability, scalability, and inclusivity. By adopting open standards such as Dublin Core and Schema.org, the system ensures compatibility with institutional repositories and open-access data sources. Furthermore, the platform promotes transparency, explainable AI, and FAIR (Findable, Accessible, Interoperable, Reusable) data principles to foster equitable participation in global scholarly communication. This conceptual study contributes to the digital transformation of academic discovery infrastructures by providing a sustainable, AI-driven model that bridges the knowledge visibility gap and empowers emerging research communities to participate effectively in the global scientific ecosystem.

10.64886/oajea.0102.003

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Abstract: The rapid expansion of the Internet of Things (IoT) has increased the demand for low-cost, high-performance, and energy-efficient microcontrollers with integrated wireless communication capabilities. Among the available platforms, the ESP32 microcontroller has emerged as a widely adopted solution for IoT and embedded system applications due to its dual-core processing architecture, built-in Wi-Fi and Bluetooth connectivity, rich peripheral support, and flexible software ecosystem. This paper presents a comprehensive review of the ESP32 microcontroller, focusing on its hardware architecture, supported communication protocols, development frameworks, and application domains. The study systematically examines the use of ESP32 in diverse areas such as smart agriculture, healthcare and wearable systems, smart city infrastructure, industrial IoT, and environmental monitoring. In addition, a comparative analysis with other commonly used microcontrollers is provided to highlight the strengths and limitations of ESP32-based systems. Key research challenges related to power consumption, security, scalability, real-time performance, and memory constraints are critically discussed. Finally, future research directions are outlined, emphasizing opportunities in edge intelligence, hybrid communication architectures, energy-efficient designs, and secure IoT deployments. This review aims to serve as a valuable reference for researchers and practitioners in selecting, designing, and optimizing ESP32-based solutions for modern IoT applications.