Faramarz Safi-Esfahani

Habib Larian, Faramarz Safi-Esfahani

With the rapid expansion of the Internet of Things (IoT), sensors, smartphones, and wearables have become integral to daily life, powering smart applications in home automation, healthcare, and intelligent transportation. However, these advancements face significant challenges due to latency and bandwidth constraints imposed by traditional cloud based machine learning (ML) frameworks. The need for innovative solutions is evident as cloud computing struggles with increased latency and network congestion. Previous attempts to offload parts of the ML pipeline to edge and cloud layers have yet to fully resolve these issues, often worsening system response times and network congestion due to the computational limitations of edge devices. In response to these challenges, this study introduces the InTec (Integrated Things Edge Computing) framework, a groundbreaking innovation in IoT architecture. Unlike existing methods, InTec fully leverages the potential of a three tier architecture by strategically distributing ML tasks across the Things, Edge, and Cloud layers. This comprehensive approach enables real time data processing at the point of data generation, significantly reducing latency, optimizing network traffic, and enhancing system reliability. InTec effectiveness is validated through empirical evaluation using the MHEALTH dataset for human motion detection in smart homes, demonstrating notable improvements in key metrics: an 81.56 percent reduction in response time, a 10.92 percent decrease in network traffic, a 9.82 percent improvement in throughput, a 21.86 percent reduction in edge energy consumption, and a 25.83 percent reduction in cloud energy consumption. These advancements establish InTec as a new benchmark for scalable, responsive, and energy efficient IoT applications, demonstrating its potential to revolutionize how the ML pipeline is integrated into Edge AI (EI) systems.

Faramarz Safi Esfahani, Ghassan Beydoun, Morteza Saberi et al.

Metaheuristic algorithms are widely used for solving complex optimization problems, yet their effectiveness is often constrained by fixed structures and the need for extensive tuning. The Polymorphic Metaheuristic Framework (PMF) addresses this limitation by introducing a self-adaptive metaheuristic switching mechanism driven by real-time performance feedback and dynamic algorithmic selection. PMF leverages the Polymorphic Metaheuristic Agent (PMA) and the Polymorphic Metaheuristic Selection Agent (PMSA) to dynamically select and transition between metaheuristic algorithms based on key performance indicators, ensuring continuous adaptation. This approach enhances convergence speed, adaptability, and solution quality, outperforming traditional metaheuristics in high-dimensional, dynamic, and multimodal environments. Experimental results on benchmark functions demonstrate that PMF significantly improves optimization efficiency by mitigating stagnation and balancing exploration-exploitation strategies across various problem landscapes. By integrating AI-driven decision-making and self-correcting mechanisms, PMF paves the way for scalable, intelligent, and autonomous optimization frameworks, with promising applications in engineering, logistics, and complex decision-making systems.

Soroor Malekmohammadi Faradonbeh, Faramarz Safi-Esfahani

One of the major objectives of Artificial Intelligence is to design learning algorithms that are executed on a general purposes computational machines such as human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. The attempt is made here to run a systematic review on Neural Turing Machine. First, the mind-map and taxonomy of machine learning, neural networks, and Turing machine are introduced. Next, NTM is inspected in terms of concepts, structure, variety of versions, implemented tasks, comparisons, etc. Finally, the paper discusses on issues and ends up with several future works.