Special Issues
Table of Content

Artificial Intelligence Algorithms and Applications

Submission Deadline: 28 February 2025 (closed) View: 1454 Submit to Special Issue

Guest Editors

Dr. Antonio Sarasa-Cabezuelo

Email: asarasa@ucm.es

Affiliation: Dpt. Sistemas Informáticos y Computación, Complutense University of Madrid, Madrid, 28040, Spain

Homepage:

Research Interests: artificial intelligence, machine learning, medical informatics, public health, deep learning, generative artificial intelligence

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Summary

Artificial Intelligence (AI) has become a transformative force in technology, driving innovation across diverse sectors. AI algorithms, which form the backbone of intelligent systems, are increasingly applied in areas such as healthcare, robotics, and beyond. The continuous evolution of these algorithms has enabled more accurate predictions, efficient data processing, and the development of autonomous systems, making AI a critical research area. Understanding and advancing AI algorithms is essential for addressing complex real-world challenges, fostering technological growth, and enhancing human-machine collaboration.


This Special Issue aims to explore the latest advancements in AI algorithms and their wide-ranging applications. The focus is on cutting-edge research that contributes to the development, optimization, and practical deployment of AI algorithms. By gathering contributions from experts in the field, this issue seeks to highlight innovative approaches and emerging trends that can drive future developments in AI. The scope includes both theoretical explorations and real-world applications, providing a comprehensive view of the current state and potential of AI technologies.


Suggested Themes:

· Machine learning and deep learning algorithms

· AI in healthcare and medical diagnostics

· Robotics and autonomous systems

· Natural language processing and understanding

· AI-driven cybersecurity solutions 

· Reinforcement learning and decision-making systems 

· Computer vision and image recognition 

· Explainable AI and transparency in algorithms 

· AI for smart cities and urban planning 

· Human-computer interaction and AI 

· AI in supply chain management and logistics 

· AI in entertainment and media content creation 

· Evolutionary algorithms and optimization techniques 

· AI for predictive maintenance and industrial automation 

· AI in agriculture and food security


Keywords

Artificial Intelligence, Machine Learning, Deep Learning, Autonomous Systems, Natural Language Processing, Robotics, AI Applications

Published Papers


  • Open Access

    ARTICLE

    Lightweight YOLOM-Net for Automatic Identification and Real-Time Detection of Fatigue Driving

    Shanmeng Zhao, Yaxue Peng, Yaqing Wang, Gang Li, Mohammed Al-Mahbashi
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4995-5017, 2025, DOI:10.32604/cmc.2025.059972
    (This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
    Abstract In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network… More >

  • Open Access

    ARTICLE

    Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

    Yahia Said, Yahya Alassaf, Refka Ghodhbani, Taoufik Saidani, Olfa Ben Rhaiem
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928
    (This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
    Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

  • Open Access

    ARTICLE

    Enhancing User Experience in AI-Powered Human-Computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

    Ahmed Alhussen, Arshiya Sajid Ansari, Mohammad Sajid Mohammadi
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2909-2929, 2025, DOI:10.32604/cmc.2024.059382
    (This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
    Abstract Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the… More >

  • Open Access

    ARTICLE

    Coordinate Descent K-means Algorithm Based on Split-Merge

    Fuheng Qu, Yuhang Shi, Yong Yang, Yating Hu, Yuyao Liu
    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4875-4893, 2024, DOI:10.32604/cmc.2024.060090
    (This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
    Abstract The Coordinate Descent Method for K-means (CDKM) is an improved algorithm of K-means. It identifies better locally optimal solutions than the original K-means algorithm. That is, it achieves solutions that yield smaller objective function values than the K-means algorithm. However, CDKM is sensitive to initialization, which makes the K-means objective function values not small enough. Since selecting suitable initial centers is not always possible, this paper proposes a novel algorithm by modifying the process of CDKM. The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the… More >

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