Zehao Li¹, Xuting Zhang¹, Hongqi Lin¹, Wu Qin², Junyu Qi³, Zhuyun Chen^1,*, Qiang Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075436 - 26 February 2026

Abstract To ensure the safe and stable operation of rotating machinery, intelligent fault diagnosis methods hold significant research value. However, existing diagnostic approaches largely rely on manual feature extraction and expert experience, which limits their adaptability under variable operating conditions and strong noise environments, severely affecting the generalization capability of diagnostic models. To address this issue, this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning (AutoML). The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.8, pp. 65-87, 2026, DOI:10.32604/jai.2026.072857 - 13 February 2026

Abstract This paper presents a comprehensive investigation into the development and evaluation of Convolutional Neural Network (CNN) models for limited-vocabulary spoken word classification, a fundamental component of many voice-controlled systems. Two distinct CNN architectures are examined: a timeseries 1D CNN that operates directly on the temporal waveform samples of the audio signal, and a 2D CNN that leverages the richer time–frequency representation provided by spectrograms. The study systematically analyzes the influence of key architectural and training parameters, including the number of CNN layers, convolution kernel sizes, and the dimensionality of fully connected layers, on classification accuracy.… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Akbare Yaqub^1,2, Muhammad Sadiq Orakzai², Muhammad Farrukh Qureshi^3,4, Zohaib Mushtaq⁵, Imran Siddique^6,7, Taha Radwan^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2503-2533, 2025, DOI:10.32604/cmes.2025.071571 - 26 November 2025

Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating efficient diagnostic tools. This study develops and validates a deep learning framework for phonocardiogram (PCG) classification, focusing on model generalizability and robustness. Initially, a ResNet-18 model was trained on the PhysioNet 2016 dataset, achieving high accuracy. To assess real-world viability, we conducted extensive external validation on the HLS-CMDS dataset. We performed four key experiments: (1) Fine-tuning the PhysioNet-trained model for binary (Normal/Abnormal) classification on HLS-CMDS, achieving 88% accuracy. (2) Fine-tuning the same model for multi-class classification (Normal, Murmur, Extra Sound, Rhythm Disorder), which yielded… More >

Deepak Mahto¹, Sudhakar Kumar¹, Sunil K. Singh¹, Amit Chhabra¹, Irfan Ahmad Khan², Varsha Arya^3,4, Wadee Alhalabi⁵, Brij B. Gupta^6,7,8,9,*, Bassma Saleh Alsulami¹⁰

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3743-3767, 2025, DOI:10.32604/cmes.2025.063427 - 30 June 2025

Abstract Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of… More >

Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI:10.32604/cmc.2023.039683 - 30 August 2023

Abstract A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the… More >

Sonali S. Patil, Sujit S. Pardeshi, Abhishek D. Patange^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 177-199, 2023, DOI:10.32604/cmes.2023.025516 - 05 January 2023

Abstract In-process damage to a cutting tool degrades the surface finish of the job shaped by machining and causes a significant financial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The “Deep” formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform efficiently in More >

H. M. Shahzad^1,3, Sohail Masood Bhatti^1,3,*, Arfan Jaffar^1,3, Muhammad Rashid²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525 - 05 January 2023

Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal… More >

Le Quang Thao^1,2,*, Duong Duc Cuong², Tran Thi Tuong Anh³, Tran Duc Luong⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1309-1321, 2023, DOI:10.32604/csse.2023.031010 - 03 November 2022

Abstract Noise pollution tends to receive less awareness compared to other types of pollution, however, it greatly impacts the quality of life for humans such as causing sleep disruption, stress or hearing impairment. Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control, planning of the soundscape environment, or selection of safe living space. In this paper, we proposed a self-attention long short-term memory (LSTM) method that can improve sound classification compared to previous baselines. An attention mechanism… More >

Narathip Chaobankoh¹, Tallit Jumphoo¹, Monthippa Uthansakul¹, Khomdet Phapatanaburi², Bura Sindthupakorn³, Supakit Rooppakhun⁴, Peerapong Uthansakul^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1269-1282, 2022, DOI:10.32604/cmc.2022.027474 - 18 May 2022

Abstract Recently, the Muscle-Computer Interface (MCI) has been extensively popular for employing Electromyography (EMG) signals to help the development of various assistive devices. However, few studies have focused on ankle foot movement classification considering EMG signals at limb position. This work proposes a new framework considering two EMG signals at a lower-limb position to classify the ankle movement characteristics based on normal walking cycles. For this purpose, we introduce a human ankle-foot movement classification method using a two-dimensional-convolutional neural network (2D-CNN) with low-cost EMG sensors based on lower-limb motion. The time-domain signals of EMG obtained from… More >