Submit

Login Login

Register Register

Home / Journals / CMC / Special Issue / Optimization for Artificial Intelligence Application
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Optimization for Artificial Intelligence Application

Submission Deadline: 31 December 2023 (closed) View: 217

Guest Editors

Dr. Marwa M. Eid, Delta University for Science and Technology, Egypt.
Dr. Amel Alhussan, Princess Nourah bint Abdulrahman University, Saudi Arabia.
Dr. Doaa Sami Khafaga, Princess Nourah bint Abdulrahman University, Saudi Arabia.
Dr. Nima Khodadadi, Florida International University, USA.

Summary

Industrial activities have been changed by artificial intelligence. Reducing the computing costs of optimization is an effective use of artificial intelligence. Various methods for issue solving have been given and explored, each of which promises to solve the difficulties. Optimization, also known as Mathematical Programming, that solves a great variety of applied problems in diverse areas: manufacturing, transportation, finance, economics, artificial intelligence, etc

 

This special issue covers a wide variety of topics concerning optimization problems and its applications. We welcome the new research ideas and developments in mathematics and computing relevant to Optimization for Artificial Intelligence Application, including foundation, systems, innovative application, and other research contributions.

Keywords

Artificial intelligence applications
Deep learning
Computer-based algorithms
Time Series and Forecasting
Swarm Intelligence
Evolutionary Algorithms
Computer vision
Natural language processing (NLP)
Neural network

Published Papers

Show export options

  • Open Access

    ARTICLE

    An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

    Cheng Yang, Miaojia Lou, Xiaoyu Chen, Zixuan Ren
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4107-4126, 2024, DOI:10.32604/cmc.2024.049183
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and… More >

    View

    489

    Download

    204

  • Open Access

    ARTICLE

    LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

    Hao Chen, Runfeng Xie, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan, Kai Zhang
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4283-4296, 2024, DOI:10.32604/cmc.2024.049129
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we… More >

    View

    688

    Download

    337

  • Open Access

    ARTICLE

    Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

    Wael M. S. Yafooz, Abdullah Alsaeedi
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions,… More >

    View

    431

    Download

    309

  • Open Access

    ARTICLE

    Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

    Lifang Fu, Huanxin Peng, Changjin Ma, Yuhan Liu
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is… More >

    View

    518

    Download

    352

  • Open Access

    ARTICLE

    RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

    Lieping Zhang, Xiaoxu Shi, Liu Tang, Yilin Wang, Jiansheng Peng, Jianchu Zou
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2111-2136, 2024, DOI:10.32604/cmc.2024.047235
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping (SLAM) algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes’ deflection probability into the random sampling function so that the global… More >

    View

    754

    Download

    359

  • Open Access

    ARTICLE

    Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

    Shereen K. Refaay, Samia A. Ali, Moumen T. El-Melegy, Louai A. Maghrabi, Hamdy H. El-Sayed
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 873-897, 2024, DOI:10.32604/cmc.2023.044982
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime. Many studies have been conducted for homogeneous networks, but few have been performed for heterogeneous wireless sensor networks. This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies. The proposed algorithms lack algorithm-specific parameters and metaphorical connotations. The proposed algorithms examine the search space based on the relations of the population with the best, worst, and randomly assigned solutions. The proposed algorithms… More >

    View

    467

    Download

    248

  • Open Access

    ARTICLE

    Optical Neural Networks: Analysis and Prospects for 5G Applications

    Doaa Sami Khafaga, Zongming Lv, Imran Khan, Shebnam M. Sefat, Amel Ali Alhussan
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3723-3740, 2023, DOI:10.32604/cmc.2023.039956
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract With the capacities of self-learning, acquainted capacities, high-speed looking for ideal arrangements, solid nonlinear fitting, and mapping self-assertively complex nonlinear relations, neural systems have made incredible advances and accomplished broad application over the final half-century. As one of the foremost conspicuous methods for fake insights, neural systems are growing toward high computational speed and moo control utilization. Due to the inborn impediments of electronic gadgets, it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage. Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways… More >

    View

    927

    Download

    281

  • Open Access

    ARTICLE

    Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

    Abdulwahab Ali Almazroi, Nasir Ayub
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3921-3942, 2023, DOI:10.32604/cmc.2023.046602
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimization methods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly-Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm’s effectiveness is systematically… More >

    View

    854

    Download

    362

  • Open Access

    ARTICLE

    A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

    Naeem Ullah, Javed Ali Khan, Sultan Almakdi, Mohammed S. Alshehri, Mimonah Al Qathrady, Eman Abdullah Aldakheel, Doaa Sami Khafaga
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for… More >

    View

    834

    Download

    343

  • Open Access

    ARTICLE

    Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

    Guoqing Zhou, Liang Huang, Qiao Sun
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For… More >

    View

    512

    Download

    339

  • Open Access

    ARTICLE

    Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

    Abdennasser Dahmani, Yamina Ammi, Nadjem Bailek, Alban Kuriqi, Nadhir Al-Ansari, Salah Hanini, Ilhami Colak, Laith Abualigah, El-Sayed M. El-kenawy
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations… More >

    View

    647

    Download

    397

    Like

    1

  • Open Access

    ARTICLE

    An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

    Yuting Liu, Hongwei Ding, Zongshan Wang, Gaurav Dhiman, Zhijun Yang, Peng Hu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2385-2406, 2023, DOI:10.32604/cmc.2023.039883
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract The equilibrium optimizer (EO) represents a new, physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium. Despite its innovative foundation, the EO exhibits certain limitations, including imbalances between exploration and exploitation, the tendency to local optima, and the susceptibility to loss of population diversity. To alleviate these drawbacks, this paper introduces an improved EO that adopts three strategies: adaptive inertia weight, Cauchy mutation, and adaptive sine cosine mechanism, called SCEO. Firstly, a new update formula is conceived by incorporating an adaptive inertia weight… More >

    View

    501

    Download

    322

  • Open Access

    ARTICLE

    A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

    Peiyuan Jiang, Weijun Pan, Jian Zhang, Teng Wang, Junxiang Huang
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 911-940, 2023, DOI:10.32604/cmc.2023.041772
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract

    This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition (ASR) technology in the Air Traffic Control (ATC) field. This paper presents a novel cascaded model architecture, namely Conformer-CTC/Attention-T5 (CCAT), to build a highly accurate and robust ATC speech recognition model. To tackle the challenges posed by noise and fast speech rate in ATC, the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms. On the decoding side, the Attention mechanism is integrated to facilitate precise alignment between input features and

    … More >

    View

    560

    Download

    357

    Like

    1

  • Open Access

    ARTICLE

    Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

    Guohui Song, Yongbin Wang, Jianfei Li, Hongbin Hu
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations… More >

    View

    792

    Download

    396

  • Open Access

    ARTICLE

    Facial Expression Recognition Model Depending on Optimized Support Vector Machine

    Amel Ali Alhussan, Fatma M. Talaat, El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Doaa Sami Khafaga, Mona Alnaggar
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368
    （This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >

    View

    1131

    Download

    577

Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link