Yi Pan¹, Lei Xie^2,*, Hongye Su²

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.038543

Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… More >

Pawat Chunhachatrachai^1,*, Chyi-Yeu Lin^1,2

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.055385

Abstract Angle detection is a crucial aspect of industrial automation, ensuring precise alignment and orientation of components in manufacturing processes. Despite the widespread application of computer vision in industrial settings, angle detection remains an underexplored domain, with limited integration into production lines. This paper addresses the need for automated angle detection in industrial environments by presenting a methodology that eliminates training time and higher computation cost on Graphics Processing Unit (GPU) from machine learning in computer vision (e.g., Convolutional Neural Networks (CNN)). Our approach leverages advanced image processing techniques and a strategic combination of algorithms, including More >

Selwyn Darryl Kessler, Kok-Why Ng^*, Su-Cheng Haw^*

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.054154

Abstract Operating System (OS) is a critical piece of software that manages a computer’s hardware and resources, acting as the intermediary between the computer and the user. The existing OS is not designed for Big Data and Cloud Computing, resulting in data processing and management inefficiency. This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes. The proposed algorithm utilizes the performance benefits from the improved Input/Output (I/O) performance. The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by… More >

Liqing Wang^*, Yiheng Xiao

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.052971

Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >

Adel Sabry Eesa^*

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.053192

Abstract While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the delicate balance between exploration and exploitation, leading to issues such as premature convergence, sensitivity to parameter settings, and difficulty in maintaining population diversity. In response to these challenges, this study introduces the Chase, Pounce, and Escape (CPE) algorithm, drawing inspiration from predator-prey dynamics. Unlike traditional optimization approaches, the CPE algorithm divides the population into two groups, each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima. By incorporating a unique search mechanism that integrates More >

Jielin Jiang^1,2,3,4,*, Chao Cui¹, Xiaolong Xu^1,2,3,4, Yan Cui⁵

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.036897

Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

M. Jayasree¹, K. A. Sunitha^2,*, A. Brindha¹, Punna Rajasekhar³, G. Aravamuthan³, G. Joselin Retnakumar¹

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.052983

Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >

Hussein Alaa Al-Kabbi^1,2, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh³

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.050452

Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… More >

Sana Jawarneh^*

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.047126

Abstract High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow More >

Youshen Jiang¹, Tongqing Zhou¹, Zhilin Wang², Zhiping Cai^1,*, Qiang Ni³

Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.030221

Abstract Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and… More >