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Artificial Intelligence Algorithms and Applications

Submission Deadline: 28 February 2025 (closed) View: 1666

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

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Open Access

ARTICLE

Intelligent Scheduling of Virtual Power Plants Based on Deep Reinforcement Learning
Shaowei He, Wenchao Cui, Gang Li, Hairun Xu, Xiang Chen, Yu Tai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063979
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >

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ARTICLE

AG-GCN: Vehicle Re-Identification Based on Attention-Guided Graph Convolutional Network
Ya-Jie Sun, Li-Wei Qiao, Sai Ji
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062950
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

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ARTICLE

Mitigating Fuel Station Drive-Offs Using AI: YOLOv8 OCR and MOT History API for Detecting Fake and Altered Plates
Milinda Priyankara Bandara Gamawelagedara, Mian Usman Sattar, Raza Hasan
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062826
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >

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ARTICLE

Robust Deep One-Class Classification Time Series Anomaly Detection
Zhengdao Yang, Xuewei Wang, Yuling Chen, Hui Dou, Haiwei Sang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060564
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these More >

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ARTICLE

UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images
Suhaila Abuowaida, Hamza Abu Owida, Deema Mohammed Alsekait, Nawaf Alshdaifat, Diaa Salama AbdElminaam, Mohammad Alshinwan
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3303-3333, 2025, DOI:10.32604/cmc.2025.063470
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and… More >

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Open Access

ARTICLE

Machine Learning for Smart Soil Monitoring
Khaoula Ben Abdellafou, Kamel Zidi, Ahamed Aljuhani, Okba Taouali, Mohamed Faouzi Harkat
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

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Open Access

ARTICLE

TMRE: Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models
Bushra Tayyaba, Muhammad Usman Ghani Khan, Talha Waheed, Shaha Al-Otaibi, Tanzila Saba
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2851-2864, 2025, DOI:10.32604/cmc.2025.060365
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Reference Evapotranspiration (ETo) is widely used to assess total water loss between land and atmosphere due to its importance in maintaining the atmospheric water balance, especially in agricultural and environmental management. Accurate estimation of ETo is challenging due to its dependency on multiple climatic variables, including temperature, humidity, and solar radiation, making it a complex multivariate time-series problem. Traditional machine learning and deep learning models have been applied to forecast ETo, achieving moderate success. However, the introduction of transformer-based architectures in time-series forecasting has opened new possibilities for more precise ETo predictions. In this study,… More >

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Open Access

ARTICLE

Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture
Faten S. Alamri, Tariq Sadad, Ahmed S. Almasoud, Raja Atif Aurangzeb, Amjad Khan
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1023-1039, 2025, DOI:10.32604/cmc.2025.061890
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Mango farming significantly contributes to the economy, particularly in developing countries. However, mango trees are susceptible to various diseases caused by fungi, viruses, and bacteria, and diagnosing these diseases at an early stage is crucial to prevent their spread, which can lead to substantial losses. The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture. This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer (ViT) architectures. Two datasets were used. The first, MangoLeafBD, contains data for mango leaf diseases such as… More >

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174
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 >

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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 >

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246
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 >

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271
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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Artificial Intelligence Algorithms and Applications

Guest Editors

Summary

Keywords

Published Papers

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16

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5

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13

Cited by

18

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78

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16

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116

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2

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214

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140

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58

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51

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406

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174

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668

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