AIM & SCOPE

By offering a platform for the distribution of high-quality research, reviews, and case studies, our journal aims to advance the domains of artificial intelligence (AI), artificial neural networks (ANN), machine learning (ML), and metaheuristics. We want to promote a better knowledge of these innovative technologies and their many uses therefore supporting the ongoing expansion of these exciting domains.

In the field of artificial intelligence (AI), we invite contributions that investigate theoretical foundations and innovative concepts as well as useful applications across many fields including robotics, finance, healthcare, security and beyond. Research on the ethical and social consequences of artificial intelligence as well as its integration into smart systems, Internet of Things (IoT) and Cloud services especially captures our interest.

Our journal welcomes papers on the evolution of innovative neural network topologies and sophisticated training and optimization methods for Artificial Neural Networks (ANN). We welcome entries using neural networks to tackle demanding issues in image processing, natural language processing, pattern recognition, and other fields. Furthermore appreciated are studies improving the interpretability and explainability of neural networks, thereby increasing their transparency and simplicity.

In the discipline of Machine Learning (ML), we want to publish work on a broad spectrum covering supervised, unsupervised, and semi-supervised learning. Research on transfer learning and domain adaptation as well as developments in reinforcement learning and its many applications interest us. Our journal also looks for research on the pragmatic use and scalability of ML techniques in actual environments.

Another major focus of our publication is metaheuristics. We seek papers on developing and analyzing fresh metaheuristic algorithms as well as those using these approaches to address challenging optimization issues in multiple fields. Especially appreciated is research combining metaheuristics with other artificial intelligence, ANN, and machine learning techniques to improve efficiency and performance.

Artificial Intelligence

Research on Fundamental Artificial Intelligence
• Algorithms for knowledge representation, reasoning, and decision-making development and analysis
• Cognitive computing advances incorporating simulations of human mind and behavior.
• Investigating fresh paradigms in artificial intelligence, notably neuromorphic computing and quantum computing.

Computer vision and machine perception
• Methods for segmentation, object identification, recognition, image and video processing.
• Novel ideas in scene interpretation, motion analysis, and 3D reconstruction.
• Uses in driverless cars, medical imaging, and face recognition.

Natural language processing (NLP)
• Developments in sentiment analysis, machine translating, and language modeling.
• Studies of conversational artificial intelligence, chatbots, and dialogue systems.
• Uses in systems of information retrieval, summarizing, and question-answering.

Automation and Autonomous Systems
• Intelligent robot and autonomous system development.
• Studies of robot perception, control, and planning.
• Applications in service robotics, healthcare robotics, and industrial automation.

AI for Security
• Development of artificial intelligence algorithms for cybersecurity covering threat identification, intrusion detection, and prevention.
• Studies of artificial intelligence-driven data privacy, endpoint protection, and network security solutions.
• AI's uses in cyber threat intelligence, risk analysis, and fraud detection.
• Research on the ethical and privacy ramifications of artificial intelligence in security settings.

Multi-agent systems and intelligent agents
• Designs and analyses of multi-agent systems and autonomous agents.
• Agent-based modeling, coordination, and negotiation: research.
• Uses in distributed systems, smart grids, and team robots.

Artificial intelligence in multidisciplinary contexts
• Creative uses of artificial intelligence in areas like environmental research, banking, education, agriculture, and healthcare.
• Studies on AI-driven decision support systems.
• Studies of policy, AI governance, and regulation.

Machine Learning

• Methods of supervised, unsupervised, and reinforcement learning.
• Deep learning configurations and uses.
• Methods of feature selection, dimensionality reduction, and data preparation.
• Practical ML applications in sectors like banking, healthcare, cybersecurity, and beyond.

Metaheuristics

Evolution and Enhancement of Metaheuristic Algorithms
• Improvement of current metaheuristic methods to improve scalability and performance.

Hybrid metaheuristic strategies
• Hybrid models using metaheuristic algorithms combined with machine learning methods.
• Creation of adaptive metaheuristics using ML for parameter tunning and choosing of an optimization method.
• Investigation of hybrid artificial intelligence-metaheuristic models wherein artificial intelligence methods direct the metaheuristic search.
• Uses of hybrid models for challenging real-world optimization tasks include logistics, resource allocation, and scheduling.

Studies of Theory
• Studies of metaheuristic algorithms in efficiency, effectiveness, and convergence analysis.
• Comparative analysis of many metaheuristic approaches along with their hybrid variations.
• Mathematical models and theoretical roots supporting metaheuristic methods.

Use of Metaheuristics in Practical Problems
• Engineering design, industrial process optimization, and technical innovation using metaheuristics.
• Applications in bioinformatics, financial modeling, supply chains management, logistics, and other fields.
• Case examples proving the value of metaheuristic answers in many different fields.