Aims and scope

Announcement

We are pleased to announce that Information Retrieval Journal became fully open access (OA) on 1 January 2024 and moved into our Discover series as Discover Computing. As a result, all submissions are subject to an article publication charge (APC) if accepted (unless a waiver is applied) and will be published in Discover Computing. Please see our FAQs journal update for more information on APCs, funding options, and waivers. 

Discover Computing is a fully open access, peer-reviewed journal that supports multidisciplinary research and policy developments across all fields relevant to computer science. The journal aims to be a resource for researchers, policy makers and the general public for recent advances in computer science, and its uses in research development and society. As a fully open access journal, we ensure that our research is highly discoverable and instantly available globally to everyone. The journal particularly welcomes work that aims to address the United Nations Sustainable Development Goals, especially, Industry, Innovation and Infrastructure.
 
Topics 

Topics welcomed at Discover Computing include but are not limited to the following:

Foundational computing theories:

    Algorithms, data structures, computational complexity
    Automata theory, graph theory, formal languages
    Turing machines, P vs NP problem, lambda calculus

Modern computing architectures and systems:

    Quantum computing, distributed systems, parallel computing
    Microarchitecture, multicore processors, memory hierarchies
    Cloud computing, edge computing, serverless architectures

Human-computer interaction:

    User experience (UX), user interface (UI), accessibility
    Cognitive ergonomics, adaptive systems, virtual reality
    User-centered design, haptic feedback, gesture recognition

Artificial intelligence and machine learning:

    Neural networks, deep learning, reinforcement learning
    Natural language processing, computer vision, robotics
    Generative adversarial networks (GANs), transfer learning, explainable AI

Cybersecurity and privacy:

    Cryptography, firewall, intrusion detection systems
    Digital forensics, malware analysis, blockchain security
    Data anonymization, end-to-end encryption, zero trust architectures

Emergent technologies:

    Augmented reality (AR), virtual reality (VR), mixed reality (MR)
    Internet of Things (IoT), 5G, smart cities
    Drones, autonomous vehicles, wearable tech

Societal impacts of computing:

    Digital ethics, algorithmic bias, technological unemployment
    Digital divide, accessibility, information equity
    Digital literacy, e-governance, surveillance capitalism

Content types

Discover Computing welcomes a variety of article types – please see our submission guidelines for details. The journal also publishes guest-edited Topical Collections of relevance to all aspects of computer science and its applications. For more information, please follow up with our journal publishing contact.

Special Issue on Artificial Intelligence, Smart Environments and Applications
Día de Entrega: 2024-11-30

Welcome to our Topical Collection on Artificial Intelligence, Smart Environments and Applications! In this collection, we explore the intersection of artificial intelligence (AI) and smart environments, where intelligent systems enhance various aspects of our surroundings to improve efficiency, convenience, safety, and sustainability. This collection delves into the latest advancements, applications, and challenges in leveraging AI to create intelligent environments across domains such as healthcare, urban planning, security, and entertainment. Join us as we explore the transformative potential of AI in shaping the future of our living and working spaces.

This Collection supports and amplifies research related to SDG 11.

Keywords: Artificial Intelligence, Smart Environments, Internet of Things (IoT), Machine Learning, Context Awareness, Automation, Human-Computer Interaction, Healthcare, Urban Planning, Sustainability, Security, Personalization, Entertainment. 

Special Issue on Enhancing Smart Grid Security, Efficiency and Resilience: The Role of AI and Blockchain in EV Charging
Día de Entrega: 2024-12-31

The convergence of electric vehicles (EVs), renewable energy, and smart grids signals a pivotal evolution in energy management. Central to this shift is the creation of secure and efficient EV charging infrastructures resilient to cyber threats and capable of managing increased load demands. This collection delves into pioneering research and innovative practices that harness Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technologies to enhance the security and efficiency of EV charging within smart grids. The objective is to compile cutting-edge research, impactful applications, and informed discussions on deploying AI/ML and Blockchain to enhance EV charging infrastructure's performance and security. Contributions will cover a broad range of topics, including AI/ML-driven anomaly detection, robust Blockchain frameworks for secure transactions, predictive maintenance strategies using data analytics, interoperability and standardization of Blockchain across smart grids, intelligent demand response and load balancing, and cybersecurity risk assessment models. The collection will also explore AI-enhanced grid cybersecurity architectures, user privacy and daspecuata security, resource allocation optimization, and real-world implementations of integrated AI/ML and Blockchain in smart charging. By offering this compendium, we aim to inspire future advancements in EV charging security, stimulate innovative smart grid solutions, and contribute to sustainable energy practices. We welcome dynamic and impactful submissions that push the boundaries of current research and practice in this emerging field.

Special Issue on Innovative AI Solutions for Low-Resource Regions
Día de Entrega: 2024-12-31

The rapid advancement of artificial intelligence has transformed various sectors globally, offering transformative changes that enhance efficiency, productivity, and overall quality of life. However, the application of AI in low-resource regions remains an underexplored and promising area with immense potential to drive significant positive impact. These regions, often characterized by limited access to advanced technology, inadequate infrastructure, and constrained economic resources, encounter unique challenges that can be effectively addressed through innovative AI solutions. Low-resource regions face a myriad of issues, including inadequate healthcare services, insufficient agricultural productivity, limited educational opportunities, and inefficiencies in public service delivery. AI, with its capability to analyze vast amounts of data, automate complex processes, and provide intelligent decision-making support, offers a powerful tool to tackle these challenges. By adapting AI technologies to the specific needs and constraints of these environments, scalable and cost-effective solutions can be developed to support sustainable development and substantially improve the quality of life for people in these areas.

This topical collection solicits high-quality original articles focusing on the following critical areas, namely agriculture, technological Innovations, and methodologies. However, the scope of the collection will cover but not be limited to the following:

· AI applications in smart agriculture

- Precision farming techniques
- AI in crop and soil monitoring
- Agricultural yield prediction
- Pest and disease management
- Supply chain and logistics optimization
- Case studies on AI-driven agricultural innovations

· Technological innovations and methodologies

- Machine learning algorithms customized for low-resource settings
- Data collection and management in developing regions
- Deployment of AI solutions in infrastructurally challenged environments
- Innovative hardware and software solutions for AI

This Collection supports and amplifies research related to SDG 2, SDG 9, SDG 10.

Keywords: Artificial intelligence; AI-powered solutions; Innovation; Smart agriculture; Precision farming; Machine learning; Crop monitoring; Agricultural yield prediction; Diseases detection; Feature recognition

Special Issue on Securing Critical Infrastructure in Next-Generation Networks: AI, Machine Learning and Cryptographic Approaches
Día de Entrega: 2025-02-28

Critical infrastructures based on next-generation networks play a vital role in sectors such as energy, transportation, healthcare and finance. However, the increasing reliance of these infrastructures on interconnected networks renders them susceptible to numerous cyber threats that frequently result in devastating repercussions. Therefore, there is a need to leverage advanced technologies such as quantum computing, artificial intelligence (AI), machine learning (ML), and cryptographic approaches to bolster their security. To facilitate real-time identification and mitigation of cyber threats, AI and ML can be applied to offer anomaly detection, pattern recognition and behavior analysis. In addition, ML algorithms have the capability to analyze vast amounts of data, aiding in the detection of malicious activities, abnormal network behavior as well as unauthorized access attempts. This can facilitate proactive response measures that can protect critical infrastructures. Moreover, AI-driven threat intelligence can boost predictive capabilities that allow infrastructure operators to anticipate and prevent potential attacks. To offer enhanced data confidentiality, integrity and authentication within next-generation networks, cryptographic approaches have been developed. For instance, techniques such as homomorphic encryption permit secure computation on encrypted data which helps to preserve privacy while allowing for data analysis. On its part, blockchain technology provides distributed and immutable ledger systems which enhance transparency and traceability in critical infrastructure operations.

The aim of this topical collection is therefore to solicit novel approaches for the integration of AI, ML, and cryptographic approaches for securing critical infrastructure in next-generation networks, with the ultimate goal of safeguarding against emerging cyber threats and ensuring the reliability and resilience of vital services. The specific areas of interest include, but are not limited to, the following:

- Network Resilience: Development of AI-driven adaptive defense approaches that can cope with evolving cyber threats landscape so as to maintain continuous operation of critical infrastructures.
- Threat Detection: Implementation of ML algorithms for detection and classification of cyber threats such as malware, insider attacks and advanced persistent threats.
- Access Control: Deployment of cryptographic mechanisms such as attribute-based access control to enforce granular access policies and thwart unauthorized access to critical infrastructures.
- Secure Communication: Utilizing techniques such as data anonymization, zero trust architectures, end-to-end encryption as well as cryptographic protocols to encipher data transmission among network endpoints.

This Collection supports and amplifies research related to SDG 9.

Keywords: AI, ML, critical infrastructure, cryptography, resilience, cybersecurity, privacy, emergent technologies

Special Issue on Towards Effective Neural Architecture Search and Its Applications
Día de Entrega: 2025-02-28

Deep neural networks (DNNs) have emerged as powerful tools across a broad spectrum of real-world applications. Their success is largely attributable to complex architectures crafted by domain experts. However, designing these architectures is often a resource-intensive task, presenting significant barriers to the continued evolution of DNNs. This bottleneck has spurred the development of Neural Architecture Search (NAS), an innovative field dedicated to automating the design of neural network architectures. NAS has quickly gained prominence by producing architectures that outperform manually designed models in numerous tasks, marking a significant milestone in deep learning research.

At its core, NAS initiates the process by defining a comprehensive search space that includes all conceivable architectures. It then applies sophisticated search strategies (e.g., evolutionary computation) to pinpoint the most effective architecture. Crucial to this process is the evaluation of each candidate architecture’s performance, which informs and refines the ongoing search strategy. The challenges inherent in NAS, such as navigating complex constraints, managing discrete and bi-level structures, and addressing the computationally intensive nature of the search, alongside the pursuit of multiple, often conflicting objectives, make it a particularly daunting domain.

To address these challenges, a variety of innovative NAS methodologies have been developed. On the optimization front, approaches encompassing multi/many-objective, multimodal, and multi-task frameworks have been proposed to streamline the NAS process. Efforts to enhance search efficiency have led to the development of strategies like weight inheritance, performance prediction, and zero-shot learning. Furthermore, the application of NAS has expanded into numerous practical domains, such as point cloud recognition and industrial defect detection, demonstrating its versatility and effectiveness. Despite these advancements, the field of Efficient NAS (ENAS) still faces unresolved issues and untapped potential, including the need for uniform representation, cross-domain prediction, and the establishment of reliable benchmarks. These areas represent promising directions for future research, underscoring the dynamic and evolving nature of NAS in driving the frontiers of deep learning.

Keywords: Deep neural networks, Neural Architecture Search (NAS), Automated design, Search space, Search strategies, Optimization, Evaluation of performance, Efficient NAS (ENAS), Industrial Prediction, Power System Applications

Special Issue on Edge-Computing AI for Medical Devices
Día de Entrega: 2025-03-28

Electronic medical devices can be implantables, wearables, and remote. These devices can collect varioius multimodal and multigrain bio-signals invasively or non-invasively. This large heterogeneous data from medical devices contains tremendous information that can be analyzed with artificial intelligence (AI). Many medical applications require edge-computing AI for real-time data processing, decision making, or feedback for unsupervised settings to seamlessly generate meaningful interpretations and actionable decisions with high degree of accuracy and reliability. Rapid progress in embedded technologies with high computing power and ubiquitous connectivity using Bluetooth, Wi-Fi, and 5G along with miniature, low-cost, flexible, and reliable sensors have paved the hardware revolution for these technologies. Advancements in edge-computing AI algorithms with machine learning (ML) and deep learning (DL) techniques with real-time interactive systems. The purpose of this collection is to address the on-going research activities in these fields with focus on edge-computing AI for a variety of applications including biomedical, assistive technologies, elderly monitoring, mobile-health, and smart-health.

This Collection supports and amplifies research related to: SDG 3

Special Issue on AI in University Education: Transforming Learning, Teaching, and Curriculum Development
Día de Entrega: 2025-03-31

A topical collection on “AI in University Education: Transforming Learning, Teaching, and Curriculum Development” delves into how artificial intelligence is reshaping higher education, with a particular emphasis on teaching, learning, assessment and curriculum development. This collection explores AI-driven personalized learning platforms and adaptive educational systems, examining their impact on student outcomes. It highlights the development and evaluation of intelligent tutoring systems and virtual assistants that enhance student engagement and provide academic support. The focus on AI-driven curriculum development covers methods for using AI to design and optimize curricula, identifying knowledge gaps, aligning with industry needs, and ensuring relevance to job market trends. Additionally, automated assessment and feedback systems are analyzed for their potential to reduce grading workload while ensuring fairness and accuracy. The collection also discusses AI applications in administrative efficiency, predictive analytics for student retention, and streamlining processes. Ethical and privacy concerns are addressed, emphasizing data security and the balance between innovation and ethics. AI-enhanced research tools and platforms that facilitate collaboration and data management are showcased. The perspectives of students and faculty on AI integration, along with training and professional development for faculty on AI tools, are explored. Real-world case studies of successful AI integration in universities, comparative analyses of AI adoption across regions, and best practices offer practical insights. This topical collection aims to provide valuable guidance for educators, administrators, policymakers, and technology developers on leveraging AI to create dynamic, relevant, and effective educational programs while considering the broader impacts on higher education. Topics include but are not limited to:

- Use of personalized learning environments in tertiary education
- Adaptive educational tools/systems and their implementation in tertiary education
- AI-driven personalized learning platforms and their impact on student outcomes
- Intelligent tutoring systems and virtual assistants
- Virtual assistants for student support
- Enhancing student engagement and learning through conversational AI
- Leveraging big data to personalize learning experiences, optimize curriculum development, and improve student outcomes
- Methods for using AI to design and optimize curricula
- Case studies of AI-assisted curriculum redesign in universities
- Automated assessment and feedback systems
- AI in automated grading and providing personalized feedback
- Ensuring fairness and accuracy in AI-based assessment
- Training and professional development for faculty on AI tools
- Impact of AI on teaching and learning experience
- Predictive analytics for student retention and success
- Addressing ethical issues related to the use of AI in education
- Ensuring data privacy and security in AI-driven educational tools
- Balancing innovation with ethical considerations in AI deployment
- Comparative analysis of AI adoption in higher education across different regions
- Lessons learned and best practices from AI implementations in academia

This Collection supports and amplifies research related to SDG 4 and SDG 11.

Keywords: AI-driven education, personalized learning environments, intelligent tutoring systems, AI-enabled curriculum development, automated assessment

Special Issue on Advances in Large Language Models and Natural Language Processing
Día de Entrega: 2025-04-30

The advent of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP), enabling unprecedented advancements in language understanding, generation, and interaction. This Topical Collection aims to gather pioneering research on the development and application of LLMs, focusing on their transformative impact across various domains such as education, healthcare, and finance. By addressing key challenges and opportunities, the collection seeks to foster a deeper understanding of LLM capabilities and limitations, promoting innovative methodologies and practical solutions. Specific areas of interest include, but are not limited to, LLM architectures, fine-tuning techniques, ethical considerations, real-world applications, and performance evaluation metrics. This collection will serve as a comprehensive resource for scholars, practitioners, and industry experts, driving forward the conversation on the future of NLP and AI.

This Collection supports and amplifies research related to SDG 4, SDG 8, and SDG 9.

Keywords: Large Language Models, Natural Language Processing, Computational Linguistics, Text Generation, NLP Techniques.