Xiong Xu¹, Chun Zhou^2,*, Chenggang Wang¹, Xiaoyan Zhang², Hua Meng²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656

Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering… More >

Pham Huy Thong^1,2,3, Florentin Smarandache⁴, Phung The Huan⁵, Tran Manh Tuan⁶, Tran Thi Ngan^6,*, Vu Duc Thai⁵, Nguyen Long Giang², Le Hoang Son³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1981-1997, 2023, DOI:10.32604/csse.2023.035692

Abstract Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees… More >

Qianlei Wang^1,2,3,*, Zaijun Wang³, Renhe Xiong⁴, Xingbin Liao^1,2, Xiaojun Tan⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1999-2020, 2023, DOI:10.32604/csse.2023.034183

Abstract How to accurately recognize the mental state of pilots is a focus in civil aviation safety. The mental state of pilots is closely related to their cognitive ability in piloting. Whether the cognitive ability meets the standard is related to flight safety. However, the pilot's working state is unique, which increases the difficulty of analyzing the pilot's mental state. In this work, we proposed a Convolutional Neural Network (CNN) that merges attention to classify the mental state of pilots through electroencephalography (EEG). Considering the individual differences in EEG, semi-supervised learning based on improved K-Means is used in the model training… More >

Ibrar Amin¹, Saima Hassan¹, Samir Brahim Belhaouari^2,*, Muhammad Hamza Azam³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860

Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results.… More >

Vivian Lee Lay Shan, Gan Keng Hoon^*, Tan Tien Ping, Rosni Abdullah

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4801-4818, 2023, DOI:10.32604/cmc.2023.033752

Abstract Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heavily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally useful; thus identifying the informative… More >

G. Kavipriya^*, D. Manjula

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1675-1689, 2023, DOI:10.32604/iasc.2023.027670

Abstract Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this motivation, this paper presents a… More >

Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4245-4260, 2022, DOI:10.32604/cmc.2022.026012

Abstract X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to… More >

Yun Tan^1,2, Weizhao Wu², Ling Tan³, Haikuo Peng², Jiaohua Qin^2,*

Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113

Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >

Ayman Mohamed Mostafa^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1015-1034, 2023, DOI:10.32604/iasc.2023.028041

Abstract Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets… More >

Anas Al-Ragehi¹, Said Jadid Abdulkadir^1,2,*, Amgad Muneer^1,2, Safwan Sadeq³, Qasem Al-Tashi^4,5

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2169-2186, 2022, DOI:10.32604/cmc.2022.027885

Abstract Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce satisfactory results. Therefore, this study… More >