Ananthakrishnan Balasundaram^1,2, Ayesha Shaik^1,2,*, Japmann Kaur Banga², Aman Kumar Singh²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362

Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >

Tao Yi^1,3, Xingshu Chen^1,2,*, Mingdong Yang³, Qindong Li¹, Yi Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793

Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >

Arpit Jain¹, Nageswara Rao Moparthi¹, A. Swathi², Yogesh Kumar Sharma¹, Nitin Mittal³, Ahmed Alhussen⁴, Zamil S. Alzamil^5,*, MohdAnul Haq⁵

Computer Systems Science and Engineering, Vol.48, No.2, pp. 341-362, 2024, DOI:10.32604/csse.2023.036973

Abstract Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face… More >

Xinyu Hu, Defeng Kong^*, Xiyang Liu, Junwei Zhang, Daode Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376

Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet… More >

P. Nandakumar, R. Subhashini^*

Computer Systems Science and Engineering, Vol.48, No.1, pp. 57-75, 2024, DOI:10.32604/csse.2023.042294

Abstract Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was… More >

Akmalbek Abdusalomov¹, Alpamis Kutlimuratov², Rashid Nasimov³, Taeg Keun Whangbo^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2915-2933, 2023, DOI:10.32604/cmc.2023.044466

Abstract The performance of a speech emotion recognition (SER) system is heavily influenced by the efficacy of its feature extraction techniques. The study was designed to advance the field of SER by optimizing feature extraction techniques, specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients (MFCC). This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches. Ultimately, the primary objective was to elevate both the intricacy and effectiveness of our SER model, with a focus on augmenting its proficiency in the accurate identification of emotions… More >

Yunru Wu¹, Xiangbo Liu¹, Haibo Huang^1,2,*, Yudong Wu¹, Weiping Ding^1,2, Mingliang Yang^1,2,*

Sound & Vibration, Vol.57, pp. 73-95, 2023, DOI:10.32604/sv.2023.044601

Abstract Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH (Noise, Vibration, and Harshness). When analyzing the NVH performance of the vehicle body, the traditional SEA (Statistical Energy Analysis) simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions. In order to effectively solve these shortcomings, based on the analysis of the vehicle noise transmission path, a multi-level objective decomposition architecture of the interior noise at the driver’s right ear is established. Combined with the data-driven method, the ResNet neural network model… More >

Jigna Patel¹, Anand Ruparelia¹, Sudeep Tanwar^1,*, Fayez Alqahtani², Amr Tolba³, Ravi Sharma⁴, Maria Simona Raboaca^5,6,*, Bogdan Constantin Neagu⁷

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1281-1301, 2023, DOI:10.32604/cmc.2023.038796

Abstract The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images.… More >

Jianfeng Lu^1,*, Xinyi Liu¹, Mengtao Shi¹, Chen Cui^1,2, Mahmoud Emam^1,3

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2865-2882, 2023, DOI:10.32604/iasc.2023.039742

Abstract Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.… More >