Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

Wenlu Ji¹, Yingqi Liao^1,*, Liudong Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

Seema Sabharwal^1,2,*, Priti Singla¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2567-2582, 2023, DOI:10.32604/iasc.2023.035497

Abstract Sign language is used as a communication medium in the field of trade, defence, and in deaf-mute communities worldwide. Over the last few decades, research in the domain of translation of sign language has grown and become more challenging. This necessitates the development of a Sign Language Translation System (SLTS) to provide effective communication in different research domains. In this paper, novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm (Hybrid-AO) for image segmentation is proposed for the translation of alphabet-level Indian Sign Language (ISLTS) with a 5-layer Convolution Neural Network (CNN). The focus of this paper is to analyze various… More >

Alaa Omran Almagrabi^*

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

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 vocalization patterns of two different… More >

Hyeon Park¹, SeoYeon Kim², Seok Min Ko¹, TaeGuen Kim^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464

Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. Our experimental results revealed that… More >

Nouf Saeed Alotaibi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446

Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS… More >

Lina Al-joudi, Muhammad Arif^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1763-1780, 2023, DOI:10.32604/cmc.2023.039413

Abstract The subcellular localization of human proteins is vital for understanding the structure of human cells. Proteins play a significant role within human cells, as many different groups of proteins are located in a specific location to perform a particular function. Understanding these functions will help in discovering many diseases and developing their treatments. The importance of imaging analysis techniques, specifically in proteomics research, is becoming more prevalent. Despite recent advances in deep learning techniques for analyzing microscopy images, classification models have faced critical challenges in achieving high performance. Most protein subcellular images have a significant class imbalance. We use oversampling… More >

Asif Rahim¹, Yanru Zhong², Tariq Ahmad^3,*, Sadique Ahmad^4,*, Mohammed A. ElAffendi⁴

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340

Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and… More >

Hyun-Wook Son¹, Ali A. Al-Hamid^1,2, Yong-Seok Na¹, Dong-Yeong Lee¹, Hyung-Won Kim^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760

Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been implemented for You Only Look… More >

Sang-Soo Yeo¹, Seungmin Rho^2,*, Hyungjoon Kim³, Jibran Safdar⁴, Umar Zia⁵, Mehr Yahya Durrani⁵

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2027-2047, 2023, DOI:10.32604/csse.2023.040327

Abstract Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection, tracking, monitoring, behavioral analysis, and retrievals. In addition to that, another evolving way of surveillance systems in a particular environment is human gait-based surveillance. In the existing research, several methodological frameworks are designed to use deep learning and traditional methods, nevertheless, the accuracies of these methods drop substantially when they are subjected to covariate conditions. These covariate variables disrupt the gait features and hence the recognition of subjects becomes difficult. To… More >