Algorithm is a peer-reviewed, open-access journal that provides a platform for the dissemination of high-quality, cutting-edge research in the fields of computer science and computational intelligence. The journal aims to bridge theory and practice by promoting algorithmic thinking, innovation, and the development of intelligent systems for solving real-world problems. We welcome original research articles, review papers, and technical notes that contribute significantly to the advancement of the field.

Scope of the Journal

1. Algorithm Design and Analysis

This area focuses on the formulation, development, and evaluation of algorithms to solve computational problems efficiently. Contributions may include exact, approximation, heuristic, or randomized algorithms that address challenges in optimization, scheduling, graph theory, or combinatorics. The journal also welcomes novel algorithmic paradigms for emerging architectures, including parallel, distributed, cloud, and quantum computing. Emphasis is placed on theoretical rigor, time-space complexity analysis, and scalability to large-scale data or systems.

2. Artificial Intelligence, Machine Learning, and Computational Intelligence

This section includes research on the design, development, and application of intelligent algorithms capable of learning and reasoning. It covers the full spectrum of machine learning approaches such as supervised, unsupervised, semi-supervised, and reinforcement learning, as well as deep learning, neural networks, and ensemble models. Topics may also include symbolic AI, fuzzy logic, evolutionary computation, and hybrid intelligent systems. We are especially interested in papers that present novel algorithmic techniques, address key challenges in scalability, interpretability, and fairness, or explore new applications in dynamic and uncertain environments. Theoretical contributions and empirical studies with strong algorithmic insights are equally welcome.

3. Theoretical Computer Science

Papers in this category explore the fundamental principles underlying computation and algorithmic processes. Topics may include automata theory, formal languages, logic in computer science, and computational complexity classes such as P, NP, and beyond. Contributions that advance the understanding of algorithmic limits, lower bounds, decidability, or the intrinsic difficulty of problems are highly encouraged. We are also interested in theoretical models that inform practical algorithm design.

4. Data Structures and Complexity Analysis

This topic highlights innovations in the construction and utilization of data structures that enhance algorithmic performance. Submissions may explore classical and modern data structures—such as trees, heaps, graphs, tries, and hash maps—as well as novel data representation techniques for specialized domains. Articles should provide detailed analysis of time and space complexity and may include empirical validation under realistic workloads.

5. Applications of Algorithms and Intelligent Systems

We encourage submissions that apply algorithmic or intelligent solutions to real-world problems across diverse domains. These may include big data processing, image recognition, speech and natural language understanding, recommender systems, robotics, autonomous systems, and smart environments. Papers should demonstrate how algorithmic innovations translate into performance gains, practical efficiency, or societal impact.

6. Cybersecurity and Cryptographic Algorithms

This scope includes secure algorithm design, cryptographic protocols, and algorithmic methods for ensuring confidentiality, integrity, and authentication. Submissions may involve symmetric/asymmetric encryption, blockchain technologies, secure multi-party computation, post-quantum cryptography, or algorithmic vulnerability analysis. Strong theoretical foundations and practical implementations with provable security guarantees are particularly welcome.

7. Bioinformatics, Computational Biology, and Health Informatics

This area covers algorithms and machine learning methods designed to handle biological data, including genome sequencing, protein structure prediction, and disease diagnosis. Submissions may include data-driven approaches for gene expression analysis, evolutionary models, or biomedical signal processing. We particularly value cross-disciplinary studies that merge biology, medicine, and computer science through algorithmic solutions.

8. Internet of Things (IoT)

This section focuses on algorithmic, architectural, and security aspects of the Internet of Things (IoT). Topics include data collection and processing in sensor-rich environments, edge and fog computing, lightweight protocols, IoT data analytics, and real-time intelligent decision-making. Submissions may also explore IoT applications in healthcare, smart cities, agriculture, transportation, and industry 4.0.

9. Emerging Technologies and Algorithmic Trends

This category invites forward-looking research exploring how algorithms evolve with emerging technologies. Relevant topics include quantum algorithms, neuromorphic computing, edge AI, federated learning, and algorithmic fairness and ethics. We seek studies that highlight how new paradigms challenge traditional algorithmic assumptions and propose novel frameworks that push the boundaries of computational intelligence.