Introduction

Deep learning has revolutionized the field of artificial intelligence, and its applications are widespread across various industries, including healthcare, finance, and e-commerce. With the emergence of big data and the need for high-performance computing resources, deep learning has become an essential technology for scalable computing. Scalable computing, which refers to the ability of computer systems to handle an increasing amount of workloads and data, is crucial for organizations looking to scale their operations and meet growing demands. The intersection of deep learning and scalable computing has opened up new avenues for research and development. Deep learning-based scalable computing systems have the potential to provide faster and more accurate results, handle larger datasets, and enhance the performance of applications in various industries. However, there are several challenges to be addressed, such as the complexity of deep learning models, the need for massive computational resources, and the increasing demand for data storage.

This special issue aims to explore the latest research trends, challenges, and best practices in the area of deep learning-based scalable computing. The objective is to provide readers with insights into the latest advances in deep learning and how they can be applied to solve real-world problems. The scope of this special issue includes but is not limited to, deep learning-based architectures and frameworks for scalable applications, distributed computing, resource allocation and scheduling, security, privacy, and data management in the cloud.

The articles included in this special issue will present the latest research findings, insights, and perspectives on the potential applications of deep learning in scalable computing. We welcome contributions from researchers, academics, and practitioners in the field of deep learning-based scalable computing who have expertise in the above topics. This special issue aims to provide a comprehensive understanding of the latest research trends and challenges in the field of deep learning-based scalable computing and to highlight the potential impact of deep learning in scalable computing and its practical applications in various industries.

Recommended topics (but not limited to)

The following are the recommended topics for this special issue:

• Deep learning-based architectures and frameworks for scalable applications
• Distributed computing and resource management in deep learning
• Security and privacy in deep learning-based scalable computing
• Data management and analysis in deep learning-based scalable computing
• Deep learning-based edge computing and its applications in scalable computing
• Internet of Things (IoT) and deep learning-based scalable computing
• Performance optimization and evaluation of deep learning-based scalable computing systems
• Case studies and real-world applications of deep learning-based scalable computing
• Transfer learning and federated learning for scalable computing
• Future research directions and open challenges in deep learning-based scalable computing

Important dates

Submission deadline: 31 December, 2023

Authors notification: 30 January, 2024

Revised version deadline: 29 February, 2024

Final decision: 31 March, 2024

Completion of Special Issue: June, 2024

We welcome contributions from researchers, academics, and practitioners in the field of deep learning-based scalable computing who have expertise in the above topics. The articles included in this special issue will present the latest research findings, insights, and perspectives on the potential applications of deep learning in scalable computing.

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Dr. B. Nagaraj M.E., Ph.D., MIEEE, Dean - Innovation Centre, Rathinam Group of Institutions, Coimbatore, Tamilnadu, India, email: dean.sa@rathinam.in

Dr. Danilo Pelusi, Dept. of Communication Engineering, University of Teramo, Italy, email: dpelusi@unite.it

Prof. Raffaele Mascella, Dept. of Communication Engineering, University of Teramo, Italy, email: rmascella@unite.it

Dr. Hayath Thameem Basha, Department of Mathematical science, Ulsan National Institute of Science & Technology (UNIST), Ulsan, Republic of Korea, email: basha.thameem666@gmail.com

Prof. David Al-Dabass (BSc(Eng), ACGI, PhD, CEng, CMath, FIMA, FIET, FBCS), School of Computing & Informatics, Nottingham Trent University, England, email: david.al-dabass@ntu.ac.uk

Published in 2024

• Application of Genetic Algorithm in Optimization Simulation of Industrial Waste Land Reuse
• Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization
• Optimization of Nonlinear Convolutional Neural Networks based on Improved Chameleon Group Algorithm
• Channel Estimation of Urban 5G Communication System based on Improved Particle Swarm Optimization Algorithm
• Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm
• Numerical Simulation and Optimal Control of Composite Nonlinear Mechanical Parts Casting Process
• Conservation Design of Industrial Heritage based on Nonlinear GA Optimization Algorithm and Three-dimensional Reconstruction
• Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion
• Optimization of Radio Energy Transmission System Efficiency Based on Genetic Algorithm
• Intelligent Detection and Analysis of Software Vulnerabilities based on Encryption Algorithms and Feature Extraction
• Application of Control Algorithm in the Design of Automatic Crimping Device for Connecting Pipe and Ground Wire
• Linear Anti-interference Algorithm for Digital Signal Transmission in Fiber Optic Communication Networks based on Link Analysis
• Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm
• Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control
• Design of Computer Information Management System Based on Machine Learning Algorithms
• Automatic Control of Low Voltage Load in Power Systems Based on Deep Learning
• Target Image Processing Based on Super-resolution Reconstruction and Deep Machine Learning Algorithm
• Dynamic Scheduling of Multi-agent Electromechanical Production Lines based on Iterative Algorithms
• Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control
• A multi-level power grid enhanced identity authentication data management platform based on filtering algorithms
• Energy Optimization of the Multi-objective Control System for Pure Electric Vehicles based on Deep Learning
• Optimization of Unmanned Aerial Vehicle Flight Control Sensor Control System based on Deep Learning Model
• Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises
• An Intelligent Network Method for Analyzing Corporate Consumer Repurchase Behavior Using Deep Learning Neural Networks
• Application of Closed-loop Theory in Deep Learning Training Guided by High-strength Intelligent Machinery
• Deep Machine Learning-based Analysis for Intelligent Phonetic Language Recognition
• Spatial and Temporal Characteristic Analysis based Long Short-Term Memory for Detection of Sensor Fault in Autonomous Vehicles
• Application of Deep Learning Algorithm in Visual Optimization of Industrial Design
• Residual Life Prediction of Rotating Machinery Guided by Quantum Deep Neural Network
• Stability Evaluation Method of High Fill Loess Foundation Based on Numerical Simulation
• A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology
• Environmental Protection Control System Based on IoT and Deep Learning Intelligent Monitoring Sensors
• Application of Deep Learning and Computer Data Mining Technology in Electronic Information Engineering Management
• Application of CNC Robots in Deep Learning Intelligent Construction of Green Buildings
• Research on Laboratory Construction and Management Based on Internet of Things and Deep Learning
• Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning
• Research on Surveying and Mapping Data Processing Based on Nonlinear Mathematical Models and Deep Learning Optimization
• Application of Improved Genetic Algorithm and Deep Learning in Cold Chain Logistics Distribution Demand Prediction
• The Application of IoT Technology and Deep Learning in Automated Intelligent Control Systems
• RBF Neural Network for Chaotic Motion Control of Collision Vibration System
• Multi Channel Electronic Communication Signal Parameters based on Nonlinear Phase Principle Modulation and Deep Learning
• Application of Artificial Intelligence Technology and Deep Learning in Laboratory Intelligent Management Platform
• A Machine Intelligence Evaluation System Based on Internet Automation Technology and Deep Learning
• Research on Intelligent Building Integrated Cabling System Based on Internet of Things and Deep Learning
• The Application of Intelligent Robots and Deep Learning in the Construction Management Platform System of Construction Engineering
• Obstacle Avoidance Path Planning for Power Inspection Robots based on Deep Learning Algorithms
• Analysis of Frozen Data Anomaly and Update Method of Electromechanical Energy Meter Terminal based on Deep Learning
• The Application of Deep Learning Intelligent Robots in the Design and Implementation of Information Retrieval Systems
• Application of Measurement Robots based on Deep Learning in Building Tilt Stability Monitoring
• Application of Software Robots and Deep Learning in Real time Processing of E-commerce Orders
• Intelligent Algorithm Operation and Data Management of Electromechanical Engineering Power Communication Network based on the Internet of Things
• Analysis of Abnormal Freezing Data and Updating Algorithm for Electromechanical Energy Meter Terminals
• Process Testing and Algorithm Detection Analysis of Mechanical Strength of Electromechanical Coupling in the Main Drive of Rolling Mill
• Research on Power Line Communication Based on Deep Learning for Electromechanical Equipment Electricity Acquisition Terminals
• A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms
• Deep Learning Driven Real-Time Airspace Monitoring Using Satellite Imagery