Tianming Zhang¹, Zebin Chen¹, Haonan Guo², Bojun Ren¹, Quanmin Xie^3,*, Mengke Tian^4,*, Yong Wang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2139-2154, 2024, DOI:10.32604/cmes.2023.043822 - 29 January 2024

Abstract The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers’ interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people’s interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the More >

Mostafa Diba^*, Hossein Khosravi

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590 - 27 February 2024

Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories… More >

Soonshin Seo^1,2, Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816 - 26 December 2023

Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly,… More >

Abdennasser Dahmani¹, Yamina Ammi², Nadjem Bailek^3,4,*, Alban Kuriqi^5,6, Nadhir Al-Ansari^7,*, Salah Hanini², Ilhami Colak⁸, Laith Abualigah^{9,10,11,12,13,14}, El-Sayed M. El-kenawy¹⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625 - 29 November 2023

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 >

Peidong Wu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.010491

Abstract The material anisotropy of an aluminum sheet alloy is determined by performing tensile tests at different angles with respect to the rolling direction (RD). To study the effect of pre-straining on the evolution of material anisotropy, a very wide sheet is stretched to different strains in the transverse direction (TD). The material in the central region is very close to a state of in-plane plane strain tension. Small tensile samples are cut from the central region of the pre-strained wide sample. Tensile tests are then performed on these small tensile samples. By comparing the differences More >

Bilal Khan¹, Muhammad Arshad², Sarwar Shah Khan^3,4,*

Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501 - 21 August 2023

Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for More >

Mohammed Al Jaffal^*

International Journal of Mental Health Promotion, Vol.25, No.8, pp. 881-890, 2023, DOI:10.32604/ijmhp.2023.028982 - 06 July 2023

Abstract Many individuals with autism spectrum disorder (ASD) experience delays in the development of social and communications skills, which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life. This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions (VBIs) for those with ASD during the critical years of adolescence and young adulthood. The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational, daily living, and academic skills. In addition, VBIs allow… More >

Zhicong Qiu^1,2, Xianmin Wang^1,*, Huawei Ma¹, Songcao Hou¹, Jing Li^1,2,*, Zuoyong Li²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241 - 21 June 2023

Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where… More >

Mingyong Li, Zheng Jiang^*, Zongwei Zhao, Longfei Ma

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900 - 21 June 2023

Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text… More >

Tianzi Zhao^1,2,3,4, Liang Jin^1,2,3,*, Xiaofeng Zhou^1,2,3, Shuai Li^1,2,3, Shurui Liu^1,2,3,4, Jiang Zhu^1,2,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 329-346, 2023, DOI:10.32604/cmc.2023.038595 - 08 June 2023

Abstract The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory… More >