Artificial Intelligence (AI) has transformed modern communication systems by enabling intelligent interactions, automated content generation, personalized recommendations, real-time translation, and conversational support. The widespread adoption of AI in areas such as social media, healthcare, education, customer service, and enterprise communication depends largely on user trust. Trust in AI communication systems is influenced by factors such as transparency, reliability, explainability, privacy, fairness, and accuracy. However, concerns regarding bias, misinformation, data privacy, and lack of explainability can reduce user confidence. This study examines technological, psychological, and social factors affecting trust in AI-based communication systems. The findings reveal that transparency, reliability, privacy protection, and explainability are critical for building long-term user trust. The study concludes that trust is a multidimensional concept involving technical performance, ethical considerations, and user experience, highlighting the need for trustworthy AI systems to support effective and responsible digital communication.
Michael Rabin· International Journal of Inn...· 0 citations
The rapid advancement of Industry 4.0 has accelerated the adoption of intelligent automation technologies that enhance productivity, flexibility, quality, and operational resilience in manufacturing. Cyber-Physical Production Systems (CPPS) have emerged as a key enabler of smart manufacturing by integrating physical production equipment with Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud and edge computing, digital twins, and autonomous control. Unlike traditional centralized automation, CPPS enables decentralized communication, real-time data exchange, intelligent decision-making, and adaptive production control. Autonomous factory automation allows machines, robots, and sensors to monitor operating conditions, predict equipment failures, and perform corrective actions with minimal human intervention. AI techniques such as machine learning, deep learning, reinforcement learning, and predictive analytics optimize production scheduling, resource allocation, predictive maintenance, and operational efficiency. Meanwhile, IIoT sensors, edge computing, and cloud platforms provide continuous monitoring, low-latency processing, and enterprise-level data analytics.This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization. The framework enables manufacturing systems to dynamically respond to equipment failures, production disturbances, and changing customer demands while maintaining operational stability and product quality. Mathematical models for production optimization and resource allocation demonstrate improvements in throughput, equipment utilization, energy efficiency, and production time. Overall, the proposed framework establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories, supporting next-generation smart manufacturing through the seamless integration of AI, IIoT, and cyber-physical engineering.
Michael Rabin, Amir Pnueli· International Journal of Int...· 0 citations
