Haizhen Wang^1,2,*, Jinying Yan^1,*, Na Jia¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055 - 30 January 2024

Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the… More >

Xin Fan^1,2,3, Jingen Mao^2,3,*, Liangjue Lian^2,3, Li Yu¹, Wei Zheng^2,3, Yun Ge^2,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522 - 30 January 2024

Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable… More >

Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860 - 27 February 2024

Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940 - 05 February 2024

Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires… More >

Wenyang Tang^1,2, Cong Liu^1,*, Bo Zhang²

Energy Engineering, Vol.120, No.11, pp. 2667-2681, 2023, DOI:10.32604/ee.2023.040743 - 31 October 2023

Abstract Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain… More >

Yinghua Song^1,2, Hairong Lyu^1,2, Wei Zhang^1,2,*

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249 - 05 October 2023

Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a More >

Sakorn Mekruksavanich¹, Narit Hnoohom², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549 - 11 September 2023

Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1275-1291, 2023, DOI:10.32604/iasc.2023.038584 - 21 June 2023

Abstract In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features… More >

Zhiqiang Wu^*, Bing Xie

Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586 - 26 May 2023

Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic… More >

V. Banupriya^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696 - 29 April 2023

Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying… More >