Abstract: The Internet of Things (IoT) has revolutionized the way physical objects interact with digital systems, creating a seamlessly connected environment across various domains. This paper presents a comprehensive overview of modern IoT devices, focusing on their classification, core features, and emerging trends. It examines the technical architecture, connectivity protocols, sensing and communication capabilities, and application-specific design considerations of IoT devices. Furthermore, the study highlights their roles in smart homes, healthcare, agriculture, industrial automation, and environmental monitoring. The paper also addresses critical challenges such as interoperability, security, scalability, and energy efficiency. Finally, future research directions and technological advancements are discussed to provide a holistic understanding of the evolving landscape of IoT systems.

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.

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.

Abstract: Pattern recognition, computer vision, and image processing all are benefitted from hand-written alphabet recognition and categorization. A profusion of applications based on this domain have been created in the last few decades, such as sign identification, multilingual learning systems, and so on. This research shows how neural networks may be used to create a system that recognizes hand-written English alphabets in the air using hand gestures. Because of the acoustic similarities between the letters of the alphabet, this is a challenging undertaking to complete. The main problem is dealing with enormous different ways to write used by multiple peoples. There are a variety of alphabet-writing approaches in these complicated handwritten styles. The recognition of handwritten English alphabets has been the subject of several research studies. Several studies have been conducted on this subject, but none have proven effective in detecting English alphabets instantly moving your fingers in the breeze. Therefore, this article explains how to create an English Alphabet model of awareness that uses a Convolution Neural Network (CNN) to identify English alphabets based on hand motions the gap in the air. After a full analysis, this recommended approach achieved 93.08\% accurate responses over the EMNIST dataset.

Abstract: Electronic Health Record (EHR) exchange across heterogeneous healthcare institutions remains a persistent challenge in health informatics and biomedical engineering. Existing solutions — including IHE Cross-Enterprise Document Sharing (XDS), HL7 Direct Protocol, and FHIR Bulk Data API — each address parts of this problem but share a critical limitation: none embeds consent verification and immutable audit trail generation as first-class protocol operations. Furthermore, no globally standardised patient identifier format exists that is simultaneously unique across jurisdictions, privacy-preserving, and deployable in resource-constrained settings. This technical note introduces EHREP (Electronic Health Record Exchange Protocol), a preliminary conceptual framework for a FAIR-compliant, federated, pull-based protocol inspired by the architectural simplicity of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). EHREP proposes eight protocol verbs, a consent-first data model, a pseudonymisation-by-design identity scheme, a FAIR principle compliance mapping at the protocol specification level, and a novel Global Patient Identifier (EHREP-GPID) format based on country code, identifier type, and hashed national credentials. A cross-border medical tourism scenario — illustrating how a patient treated in Bangladesh can seamlessly share clinical records with a treating physician in Thailand — is presented to demonstrate the practical applicability of the proposed framework. A national-tier extension, the National Health Data Centre (NHDC), is also proposed as a sovereign EHREP harvester node that periodically archives pseudonymised EHR records from domestic hospital nodes and serves as the authoritative cross-border query intermediary for each participating jurisdiction. This work establishes the conceptual foundation and positions EHREP as a novel contribution to the field of interoperable health information exchange, with full protocol specification, implementation, and evaluation planned as future work.

Abstract: Road traffic accidents and overspeeding remain critical public safety challenges worldwide, disproportionately affecting rapidly urbanizing regions with limited automated enforcement capacity. This paper presents an IoT-Enabled Speed and Accident Detection Platform that incorporates multi-object tracking and deep-learning based object recognition, and calibration-based speed estimation into a unified, real-time intelligent traffic monitoring framework. The proposed system employs YOLOv8 for accurate, high-speed vehicle detection; ByteTrack for consistent persistent identity assignment across consecutive video frames; and a pixel-to-real-world-distance calibration method for precise vehicle speed computation. The platform automatically classifies vehicle types, identifies overspeeding behavior against configurable speed thresholds, generates real-time visual alerts, counts traffic volume through a configurable virtual line-crossing mechanism, and supports future IoT and cloud-platform integration for accident detection, remote monitoring, and emergency response automation. Experimental evaluation on traffic video sequences recorded at an urban intersection in Dhaka, Bangladesh, shows a mean Average Precision (mAP@0.5) as 0.82, a Multi-Object Tracking Accuracy or MOTA of 0.75, an IDF1 of 0.79, and a speed estimation Mean Absolute Error or MAE as 3.2 km/h. The reconfigurable, scalable architecture positions this platform as a practical and cost-effective foundation for intelligent transportation systems (ITS) and smart city infrastructure.

Abstract: Exploratory Data Analysis (EDA) is a critical first step in any data science workflow, yet existing tools often require software installation, programming knowledge, or lack comprehensive statistical testing capabilities. We present **EDA-Pro**, a browser-based automated system for exploratory numerical data analysis that integrates descriptive statistics, hypothesis testing, time series analysis, data transformation, and AI-powered insight generation in a unified interface. Unlike existing tools such as pandas-profiling and Sweetviz, EDA-Pro operates entirely client-side without server dependencies, supports up to 50MB datasets, and provides publication-ready HTML reports with statistical rigor. The system includes 20+ analytical modules covering univariate/bivariate/multivariate analysis, seven hypothesis tests (Shapiro-Wilk, ANOVA, Chi-Square, Kolmogorov-Smirnov, independent/paired t-tests), bootstrap confidence intervals, multiple regression, and automated data quality assessment. Comparative evaluation demonstrates 35\% faster workflow completion compared to Python-based alternatives for standard EDA tasks, with no installation overhead. EDA-Pro is freely available as open-source software, making advanced statistical analysis accessible to researchers, students, and practitioners without programming expertise.