Home / Journals / JAI / Vol.5, No.1, 2023
  • Open AccessOpen Access

    ARTICLE

    AI Safety Approach for Minimizing Collisions in Autonomous Navigation

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*
    Journal on Artificial Intelligence, Vol.5, pp. 1-14, 2023, DOI:10.32604/jai.2023.039786
    Abstract Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions. These systems are developed under the term Artificial Intelligence (AI) safety. AI safety is essential to provide reliable service to consumers in various fields such as military, education, healthcare, and automotive. This paper presents the design of an AI safety algorithm for safe autonomous navigation using Reinforcement Learning (RL). Machine Learning Agents Toolkit (ML-Agents) was used to train the agent with a proximal policy optimizer algorithm with an intrinsic curiosity module (PPO + ICM). This training aims to improve AI… More >

  • Open AccessOpen Access

    ARTICLE

    Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*
    Journal on Artificial Intelligence, Vol.5, pp. 15-30, 2023, DOI:10.32604/jai.2023.041341
    Abstract The object detection technique depends on various methods for duplicating the dataset without adding more images. Data augmentation is a popular method that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization. This method is recommended in the case where the amount of high-quality data is limited, and gaining new examples is costly and time-consuming. In this paper, we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes (Car, Bus, Motorcycle, and Person). We used five different data augmentations… More >

  • Open AccessOpen Access

    ARTICLE

    Explainable AI and Interpretable Model for Insurance Premium Prediction

    Umar Abdulkadir Isa*, Anil Fernando*
    Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213
    Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation… More >

  • Open AccessOpen Access

    REVIEW

    Study of Intelligent Approaches to Identify Impact of Environmental Temperature on Ultrasonic GWs Based SHM: A Review

    Saqlain Abbas1,2,*, Zulkarnain Abbas3, Xiaotong Tu4, Yanping Zhu2
    Journal on Artificial Intelligence, Vol.5, pp. 43-56, 2023, DOI:10.32604/jai.2023.040948
    Abstract Structural health monitoring (SHM) is considered an effective approach to analyze the efficient working of several mechanical components. For this purpose, ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers. However, the working of the SHM mechanism can be affected by some sources of variations (i.e., environmental). To improve the final results of ultrasonic guided wave inspections, it is necessary to highlight and attenuate these environmental variations. The loading parameters, temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing… More >

  • Open AccessOpen Access

    REVIEW

    Embracing the Future: AI and ML Transforming Urban Environments in Smart Cities

    Gagan Deep*, Jyoti Verma
    Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329
    (This article belongs to this Special Issue: Explainable & Responsible Edge-AI for Smart Computing Technologies )
    Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI… More >

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