Home / Journals / JAI / Vol.5, No.1, 2023
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  • 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… 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… 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… 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… 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 the 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… More >

  • Open AccessOpen Access

    ARTICLE

    K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

    Rufus Gikera1,*, Jonathan Mwaura2, Elizaphan Muuro3, Shadrack Mambo3
    Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229
    Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

    Tian Lu, Bohong Liu*
    Journal on Artificial Intelligence, Vol.5, pp. 113-129, 2023, DOI:10.32604/jai.2023.043970
    Abstract To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating More >

  • Open AccessOpen Access

    ARTICLE

    Research on the Application of Reinforcement Learning Model in Vocational Education System

    Fei Xue*
    Journal on Artificial Intelligence, Vol.5, pp. 131-143, 2023, DOI:10.32604/jai.2023.046293
    Abstract Vocational education can effectively improve the vocational skills of employees, improve people’s traditional concept of vocational education, and focus on the training of vocational skills for students by using new educational methods and concepts, so that they can master key vocational skills and develop key abilities. In this paper, three different learning models, Deep Knowledge Tracing (DKT), Dynamic Key-Value Memory Networks (DKVMN) and Double Deep Q-network (DDQN), are used to evaluate the indicators in the vocational education system. On the one hand, the influence of learning degree on the performance of the model is compared, More >

  • Open AccessOpen Access

    ARTICLE

    Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

    Dinesh Kalla1,*, Sivaraju Kuraku2
    Journal on Artificial Intelligence, Vol.5, pp. 145-162, 2023, DOI:10.32604/jai.2023.043366
    Abstract Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used More >

  • Open AccessOpen Access

    ARTICLE

    An Example of a Supporting Combination by Using GA to Evolve More Advanced and Deeper CNN Architecture

    Bah Mamoudou*
    Journal on Artificial Intelligence, Vol.5, pp. 163-180, 2023, DOI:10.32604/jai.2023.045324
    Abstract It has become an annual tradition for Convolutional Neural Networks (CNNs) to continuously improve their performance in image classification and other applications. These advancements are often attributed to the adoption of more intricate network architectures, such as modules and skip connections, as well as the practice of stacking additional layers to create increasingly complex networks. However, the quest to identify the most optimized model is a daunting task, given that state of the art Convolutional Neural Network (CNN) models are manually engineered. In this research paper, we leveraged a conventional Genetic Algorithm (GA) to craft More >

  • Open AccessOpen Access

    ARTICLE

    Study on Two-Tier EV Charging Station Recommendation Strategy under Multi-Factor Influence

    Miao Liu, Lei Feng, Yexun Yuan, Ye Liu, Peng Geng*
    Journal on Artificial Intelligence, Vol.5, pp. 181-193, 2023, DOI:10.32604/jai.2023.046066
    Abstract This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method. The electric vehicles (EVs) are classified into high-level alerts and general alerts based on their state of charge (SOC). EVs with high-level alerts have the most urgent charging needs, so the distance to charging stations is set as the highest priority for recommendations. For users with general alerts, a comprehensive EV charging station recommendation model is proposed, taking into account factors such as charging price, charging time, charging station preference, and distance to the More >

  • Open AccessOpen Access

    ARTICLE

    Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

    Mohamed Ammar1,*, Hamed Al-Raweshidy2,*
    Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199
    Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK… More >

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