ISSN :2582-9793
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    • Publishing date
    30 Apr, 2026

    Status
    New

    Submission deadline
    31 Dec, 2025

    Lead Editor
    Prof. Anca L. Ralescu, Editor in Chief, Professor, EECS Department, University of Cincinnati, ML 0030, Cincinnati, OH 45221-0030, USA.

    Guest Editor
    Prof. Ilesanmi Afolabi Daniyan Email: afolabiilesanmi@yahoo.com; iadaniyan@bellsuniversity.edu.ng Department: Mechatronics Engineering & Centre for Artificial Intelligence Institution: Bells

    Advances in Artificial Intelligence: Methods and Applications

    Description

    1. Description of the special issue and its purpose

    The special issue aims to explore the principles, techniques, concepts and applications of Artificial Intelligence (AI) in across different sectors including manufacturing, energy, transportation, health, service sectors amongst others. The special issue will provide practicable solutions to mitigating societal challenges and also provide useful insights for achieving manufacturing resilience and sustainability, energy efficiency, optimization of production levels, and waste minimization.

    Amongst others, the special issue focuses on artificial intelligence with emphasis on future of computing and data analytics, Natural Language Processing (NLP), Expert Systems, Robotics Process Automation (RPA), Machine and Deep Learning, Speech Recognition Knowledge Representation and Reasoning, machine vision, virtual reality (VR), predictive maintenance, automation and control, digital security and the societal consequences of digital technology development. The proposed special issue is interdisciplinary in nature, thus, there will be collaborate across different disciplines to explore the benefits of AI to effectively address the challenges faced by the manufacturing industries, business, society and to promote the quest to meet the sustainable development goals.

    2. Motivation

    According to a recent survey conducted by Gartner, organisations are more likely to make faster, informed and data-driven decisions by 35%, resulting in a 20% increase in overall productivity and projected that AI software growth will reach 297 billion USD by 2027[1]. Additionally, IDC predicts that AI investments will reach $110 billion by 2028, underscoring the growing importance of AI adoption for business success[2]. Sadly, there is dearth of knowledge diffusion in the area of development and deployment of AI solutions.

    A successfully implemented AI solution can address the challenges faced by the manufacturing industries, business and society. It can increase operational efficiency, conserve the environment, enhance customer experiences, and provide a competitive advantage. In today’s fast-paced digital landscape, harnessing the full potential of AI is critical for staying competitive and innovative. There is a critical need for research, collaboration and knowledge dissemination on the development and application of Artificial Intelligence (AI) solutions to real world scenarios. This special issue collection will address knowledge, competency, research, technology and innovation gaps in the area of AI. Thus, the special issue collection will empower the academic, business, government and non-governmental institutions to unlock the potentials of AI, thereby paving the way for a long-term sustainable growth in the data-driven future. It will also equip the university community (staff & students) with the necessary AI knowledge and skills that is crucial for success in their chosen fields.

     

    3. Short outlines of the areas to be covered by the special issue

    Table 1 presents the short outlines of the areas to be covered by the special issue.

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    Table 1: Short outlines of the areas to be covered by the special issue.

    S/N

    Proposed theme/core areas

    Description

    1.

    Introduction

    Overview of AI, sustainable development goals and challenges faced by the manufacturing, business and society

    2

    Machine and Deep Learning

    Machine Learning:

    - Supervised Learning: This will cover the implementation of various learning algorithms that can learn from labeled training data, and then apply what they have learned to new data. Some of the algorithms include Linear Regression, Decision Trees, and Support Vector Machines, Levenberg-Marquardt etc.


    - Unsupervised Learning: This will cover the implementation of various algorithms that learn from and make predictions based on unlabeled data. Some of the algorithms include Clustering Methods Like K-Means and Hierarchical Clustering, and Dimensionality Reduction Methods Like Principal Component Analysis (PCA) etc.


    - Reinforcement Learning: This will cover the implementation of various algorithms that learn to perform an action from experience. It is typically used in navigation, gaming, and real-time decisions.


    - Ensemble Methods: This will cover the techniques that combine the predictions of several base estimators to improve generalizability and robustness. These techniques include bagging, boosting, and stacking.

    - Deep Learning: This will cover deep learning techniques such as CNN, RNN, GANs etc.



    - Convolutional Neural Networks (CNNS): This will cover the application of CNNS for predictions, image and video processing tasks, like image recognition and video analysis etc.



    - Recurrent Neural Networks (RNNS): This will cover the application of RNN for sequential data tasks, such as language translation and speech recognition. It will also feature the application of Long Short-Term Memory (LSTM).

    - Generative Adversarial Networks (GANS): This will cover GANs used to generate new data that mimics some given data. It will also cover the implementation of GANS in the fields of image generation and enhancement.

    - Autoencoders: This will cover the use of autoencoders for unsupervised tasks, such as anomaly or pattern detection, fraud detection and dimensionality reduction

    Virtual reality (VR), Augmented reality AR) and Mixed reality

    This section will cover the development and application of the immersive technologies such as VR, AR and MR and the applications of a virtual world to model and simulate the real world scenarios.

    Computer Vision:

     

    This will cover an array of technologies that enable machines to identify, classify, and understand images and video, effectively ‘seeing’ and interpreting visual input similarly to how humans do. It will cover applications such as facial recognition, object detection, autonomous vehicles, and image restoration etc.

    3.

    Natural Language Processing (NLP)

    This will cover an array of technologies that enables machines to understand, interpret, and generate human language, including speech. It will include applications such as chatbots, sentiment analysis, and language translation.

    Expert Systems

    This will cover the use of expert systems in complex problem-solving domains, for example in diagnosis and prognosis tasks.

    Robotics, Process Automation (RPA)

    This will cover the use of software robots or “bots” to automate routine, standard tasks that were previously done by humans

    4

    Speech Recognition

     

    This will cover the technologies that allows systems to understand spoken language and convert it into text or commands. It will include voice-controlled applications like digital assistants (Siri, Alexa), transcription services, and customer service automation,

    Knowledge Representation & Reasoning:

    This will cover knowledge representation in a form that a computer system can utilize to solve complex tasks, such as determining an appropriate response to a complex query.

     Future of Computing and Data Analytics, Digital Security


    This will cover AI technology that examines the trend in computing technology and predicts the future trends while digital security will cover AI enabled security solutions that detects and prevents intrusions within the cyberspace. 

     

    4. Examples of existing literature in the field, and how the special issue will be different and provide new insights.

    This special issue be different from existing special issues on artificial intelligence in that it will cover nine (9) areas of artificial intelligence highlighted in Table 1. It will also present the conceptual and theoretical frameworks on AI as well as empirical studies. Furthermore, the special issue will present, innovations, current and future trends of AI and will discuss practical ways of developing and deploying AI solutions for meeting sustainable development goals and addressing manufacturing, business and societal challenges.

    It will be unique in that it will integrate the three different roadmaps to sustainable developments namely

    4.1 Roadmaps in the Proposed Special Issue

    The proposed special issue will cover the three major roadmaps

    • Research and development- research on addressing societal challenges via the development and deployment of AI solutions. These include: security, cyber security, democracy and elections, socioeconomic, the rule of law and privacy, employment and jobs, childhood and education, art and culture, daily life, participation and belonging.
    • Technology – research-based on the development of AI and AI-relevant technologies. This includes research on key technologies supporting or driving AI such as smart sensors, Internet of Things (IoT), big data analytics. It will cover both the improvement of existing technology and research into new methods and techniques.
    • Innovation – research on how to use AI and any other digital technologies. This includes practical research on how AI can promote creativity and innovation in various sectors such as education, health, agriculture, trade and industry, the public sector and research organisations. It will also cover the use of AI in such a way that it will have a neutral or positive contribution to the Sustainable Development Goals, especially inclusion, energy consumption, safety and responsibility. It will also include various ways by which AI technology can form the basis for new methods, practices, services, and products, as well as create new business opportunities for organisations and trade and industry.

    Thus, the proposed special issue collection will address interdisciplinary issues with potential for application in several industries, industries and sectors.

     

    4.2 Objectives of the Special Issue

    The following are the objectives of the special issue:

    1. To disseminate cutting-edge research in artificial intelligence and machine learning applications.
    2. To promote innovation in AI technologies across various disciplines.
    3. To offer AI-related knowledge diffusion and application to real world scenarios that will assist students, and professionals.
    4. To promote knowledge diffusion on AI solutions that address local and global challenges and promote the attainment of sustainable development goals.

    4.3 Significance of the Special Issue

    The following are the significance of the special issue:

    • The special issue will aid teaching and learning, and research
    • It will promote innovation, knowledge and applications in artificial intelligence (AI) space.
    • It will help readers to meet the societal, manufacturing and business challenges and exploit the opportunities offered by artificial intelligence.
    • It will provide practical roadmap to sustainable and societal needs such as unemployment..
    • The special issue collection will provide a clear roadmap by highlighting AI-relevant issues that are important to the institution and society and that are of great importance for sustainable social development.
    • It will report emerging efforts and trends in the areas highlighted in Table 1.

     

     

     

     

    5.      A description of the Special Issue Intended Audience (Academic, Professional, Classroom, etc.)

    The special issue collection will be helpful for students, lecturers, researchers, professionals, regulators, the general public, organisation policy and decision-makers, as well as government agencies and institutions, in understanding the potentials of AI so that they can be effectively harnessed for sustainable development.

     


    [1] https://www.linkedin.com/pulse/gartner-predicts-ai-software-grow-297-billion-2027-louis-columbus-okpfc

    [2] https://www.idc.com/getdoc.jsp?containerId=prAP52613324

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