The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), autonomous computing, and distributed intelligent systems has transformed predictive decision-making across domains such as healthcare, finance, manufacturing, transportation, cybersecurity, and IoT. However, traditional centralized machine learning models often lack adaptability, scalability, and real-time responsiveness in dynamic environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates autonomous agents, distributed intelligence, machine learning, reinforcement learning, explainable AI (XAI), and continuous learning into a unified architecture. The framework enables decentralized decision-making through intelligent agent collaboration, adaptive model updates, predictive learning, and continuous feedback optimization. Supporting supervised, unsupervised, reinforcement, and deep learning techniques, the proposed framework continuously monitors environmental changes, updates prediction models, and optimizes decision strategies. It is applicable to smart manufacturing, healthcare, finance, cybersecurity, intelligent transportation, and industrial automation. Compared with conventional centralized approaches, ABML-APDS enhances prediction accuracy, scalability, explainability, computational efficiency, fault tolerance, and autonomous decision-making, providing a robust foundation for Industry 5.0, cyber-physical systems, and AI-driven digital transformation.
Louis Pouzin, J. Arsac· International Journal of Int...· 0 citations
Autonomous mobile robots operating in dynamically changing, unstructured environments require high-precision, drift-free localization capabilities to achieve robust operational safety and navigational efficacy. While visual Simultaneous Localization and Mapping (vSLAM) and Inertial Navigation Systems (INS) serve as foundational technologies in intelligent automation, standalone implementations encounter significant vulnerabilities, specifically optical occlusion and cumulative dead-reckoning drift. This paper presents a comprehensive study on an intelligent, optimization-based, tightly-coupled vision-inertial sensor fusion framework designed for robust localization in challenging environments. The proposed system integrates high-frequency inertial measurements from an Inertial Measurement Unit (IMU) with high-fidelity visual landmarks extracted from a monocular camera, utilizing an artificial intelligence-driven adaptive Extended Kalman Filter (EKF) state estimation matrix to dynamically adjust measurement noise weights. By analyzing the structural characteristics of feature tracking alongside high-frequency acceleration profiles, the intelligent layer dampens sensor anomalies caused by aggressive motion or lightning fluctuations. Experimental validations conducted using the EuRoC MAV public benchmark dataset indicate that the proposed intelligent fusion architecture provides superior performance across dynamic trajectories, reducing the Absolute Trajectory Error (ATE) by up to 34% compared to classical loosely-coupled filtering methods while maintaining sub-centimeter positional drift thresholds.
J. Arsac· International Journal of Int...· 0 citations
The rapid growth of enterprise systems and cloud computing has transformed data management across hybrid environments integrating on-premise databases, private clouds, and public cloud infrastructures. However, challenges such as data consistency, latency, conflict resolution, security, and fault tolerance remain critical in distributed heterogeneous systems. Traditional synchronization methods are often inadequate for dynamic real-time workloads. This study reviews intelligent data synchronization techniques for hybrid data platforms, emphasizing AI- and machine learning-based approaches that enhance synchronization efficiency, scalability, and reliability. The proposed framework includes four layers: Data Acquisition, Intelligent Synchronization Engine, Adaptive Conflict Management, and Distributed Analytics. Predictive learning algorithms optimize synchronization timing and resource allocation, while adaptive conflict resolution mechanisms minimize inconsistencies. Experimental results show that intelligent synchronization methods reduce delay, improve throughput, enhance scalability, and strengthen failure recovery compared to traditional approaches. The study concludes that AI-driven synchronization is essential for real-time analytics, distributed transactions, and scalable cloud-native applications in modern enterprise environments.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
The manufacturing sector is undergoing a paradigm shift with the integration of Industry 4.0 technologies, particularly Industrial Big Data Analytics (BDA), which leverages high-velocity data from IoT sensors, PLCs, and production systems to enable real-time decision-making. This paper presents a novel edge-cloud analytics framework designed to address critical challenges in modern manufacturing, including data heterogeneity, latency bottlenecks, and cybersecurity risks. By implementing a hybrid architecture, the system processes sensor data at the edge (e.g., vibration spectra, thermal images) with <50ms latency for time-sensitive tasks like defect detection, while cloud-based machine learning models (e.g., LSTMs) perform long-term predictive maintenance with 89% accuracy. A large-scale case study conducted at an automotive assembly line demonstrated a 20% increase in production throughput and 15% reduction in unplanned downtime, translating to $2.7M annual cost savings. Key innovations include: (1) a dynamic data normalization pipeline (Eq. 1) that handles skewed industrial datasets; (2) a comparative analysis of ML models, showing Random Forest outperforms ANN/SVM in defect classification (92.4% F1-score); and (3) a priority-based edge processing system that reduces cloud bandwidth usage by 60%. Despite these advancements, the study identifies persistent hurdles such as legacy system interoperability (resolved via OPC UA gateways) and adversarial robustness in edge ML models. The paper concludes with a roadmap for future work, including federated learning for multi-plant scalability and digital twin integration for simulation-driven analytics. These findings validate BDA as a transformative tool for smart manufacturing, offering a 5.2-month ROI and actionable insights for practitioners adopting Industry 4.0 solutions.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
Artificial Intelligence (AI) and Large Language Models (LLMs) have significantly transformed knowledge management by enabling intelligent, context-aware, and automated information access. However, standalone LLMs often suffer from limitations such as outdated knowledge, hallucinated responses, lack of domain-specific expertise, and limited transparency, reducing their reliability in enterprise and research applications. Retrieval-Augmented Generation (RAG) has emerged as an effective solution by combining language models with external knowledge retrieval, allowing responses to be generated using up-to-date and relevant information. This study proposes a comprehensive Retrieval-Augmented Generation framework for intelligent knowledge management systems. The framework integrates document acquisition, preprocessing, semantic embedding generation, vector database indexing, document retrieval, prompt augmentation, LLM-based response generation, response validation, and continuous knowledge base updates. It supports diverse knowledge sources, including enterprise databases, technical documents, digital libraries, and research repositories, while incorporating sparse, dense, hybrid retrieval, and neural reranking techniques to improve retrieval accuracy. The proposed framework is evaluated using retrieval precision, recall, F1-score, response relevance, latency, grounding accuracy, and user satisfaction. Results demonstrate improved semantic understanding, reduced hallucinations, enhanced factual correctness, and real-time knowledge updates compared with conventional keyword-based knowledge management systems. The study also discusses future directions, including multimodal RAG, graph-enhanced retrieval, federated knowledge management, continual learning, and autonomous enterprise knowledge assistants, establishing RAG as a robust foundation for trustworthy and intelligent knowledge-driven AI systems.
Louis Pouzin, J. Arsac· International Journal of Mod...· 0 citations
Machine vision has become a key technology in modern industrial automation, enabling fully automated quality inspection in robotic manufacturing systems. Unlike traditional human inspection methods, machine vision offers higher accuracy, consistency, speed, and lower operational costs. These systems use cameras, lighting, lenses, image processing, pattern recognition, and AI techniques to detect defects, verify dimensions, classify products, and monitor manufacturing processes in real time. This survey reviews machine vision-based quality inspection methods in robotic manufacturing up to 2019, covering major applications in industries such as automotive, electronics, aerospace, pharmaceuticals, and food processing. It highlights advancements in feature extraction, defect detection algorithms, classification models, and robotic integration frameworks. The study shows that machine vision significantly improves inspection accuracy, reduces cycle time, and enhances manufacturing consistency, while also discussing challenges such as illumination changes, computational complexity, and system adaptability. Overall, machine vision plays a vital role in smart manufacturing and Industry 4.0 production systems.
Louis Pouzin, J. Arsac· International Journal of Int...· 0 citations
The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination to enhance transportation systems and road safety.
J. Arsac· International Journal of Mod...· 0 citations
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