Xingran Kou^1,2, Qixuan Zhao², Wenwen Xu², Zuobing Xiao², Yunwei Niu², Kai Wang^3,*

Journal of Renewable Materials, Vol.9, No.7, pp. 1189-1211, 2021, DOI:10.32604/jrm.2021.015268

Abstract Several important drugs and nutritional supplements are limited by their lack of bioavailability. Nanomaterials display unique beneficial properties that might help improve the bioavailability of drugs and nutritional supplements. Unfortunately, nanomaterials produced from synthetic polymers and metals may have similar difficulties with bioavailability and toxicity. Naturally occurring biopolymers are biodegradable and non-toxic and are adaptable to the synthesis of nanoparticles. Drugs and other substances can be encapsulated or embedded in such particles with an increase in bioavailability. The search for biodegradable nanomaterials is an active research field. This review summarizes the research on nanocrystalline cellulose, starch, lignin, and other biological… More >

Suxia Li^1,2, Haiyang Zhang^1,*, Biqing Shu^1,3, Liangsong Cheng¹, Zehui Ju¹, Xiaoning Lu^1,*

Journal of Renewable Materials, Vol.9, No.7, pp. 1225-1237, 2021, DOI:10.32604/jrm.2021.014364

Abstract The wood friction welding technique with its high bonding strength, low cost, high efficiency, and without any adhesive has been increasing concern in China. Moso bamboo (Phyllostachys pubescens) and poplar (Populus sp.) are widely planted and used in the furniture industry, interior decoration, and wood structure construction in China. The aim of this work was to investigate the bonding performance of moso bamboo dowel rotation welded joints with different dowel/receiving hole diameter ratios. The results indicated that the ratio of dowel/receiving hole diameter was an important parameter that influenced the welding performance. The bonding strength of the bamboo-to-poplar welded joints… More >

Caixia Wang², Xiao Huang^1,*, Hongqing Chen², Weijie Liang²

Journal of Renewable Materials, Vol.9, No.7, pp. 1239-1252, 2021, DOI:10.32604/jrm.2021.014746

Abstract Currently, people pay more and more attention to the transitional resettlement of victims after various natural disasters. There is an urgent need for a large number of temporary houses to resettle the victims after natural disasters. Disaster-relief temporary houses (DTHs) played an important role in the post-disaster resettlement in the past, which can not only be produced on a large scale, but also can be quickly and conveniently erected, which were the main means to solve the problem of transitional resettlement. However, due to their temporary nature, there was no extra energy consuming system installed in the DTHs generally. Hence… More >

Maha M. Althobaiti^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 167-177, 2021, DOI:10.32604/iasc.2021.016660

Abstract Cybersecurity threats, including those involving machine learning, malware, phishing, and cryptocurrency, have become more sophisticated. They target sensitive information and put institutions, governments, and individuals in a continual state of risk. In 2019, phishing attacks became one of the most common and dangerous cyber threats. Such attacks attempt to steal sensitive data, such as login and payment card details, from financial, social, and educational websites. Many universities have suffered data breaches, serving as a prime example of victims of attacks on educational websites. Owing to advances in phishing tactics, strategies, and technologies, the end-user is the main victim of an… More >

Abdullah Alharbi¹, Wael Alosaimi¹, Hashem Alyami², Mohd Nadeem³, Mohd Faizan³, Alka Agrawal³, Rajeev Kumar^3,4,*, Raees Ahmad Khan³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 179-194, 2021, DOI:10.32604/iasc.2021.016646

Abstract Security risk evaluation of web-based healthcare applications is important from a design perspective. The developers as well as the users need to make sure that the applications must be secure. Citing the disastrous effects of unsecured web applications, Accuntix Online states that the IT industry has lost millions of dollars due to security theft and malware attacks. Protecting the integrity of patients’ health data is of utmost importance. Thus, assessing the security risk of web-based healthcare applications should be accorded the highest priority while developing the web applications. To fulfill the security requirements, the developers must meticulously follow the Multi-Criteria… More >

Hao Wu^1,*, Arun Kumar Sangaiah²

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 121-132, 2021, DOI:10.32604/iasc.2021.016457

Abstract In oral English teaching in China, teachers usually improve students’ pronunciation by their subjective judgment. Even to the same student, the teacher gives different suggestions at different times. Students’ oral pronunciation features can be obtained from the reconstructed acoustic and natural language features of speech audio, but the task is complicated due to the embedding of multimodal sentences. To solve this problem, this paper proposes an English speech recognition based on enhanced temporal convolution network. Firstly, a suitable UNet network model is designed to extract the noise of speech signal and achieve the purpose of speech enhancement. Secondly, a network… More >

Haoliang Cui¹, Shaozhang Niu¹, Keyue Li^1,*, Chengjie Shi², Shuai Shao³, Zhenguang Gao⁴

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 277-291, 2021, DOI:10.32604/iasc.2021.016408

Abstract At present, the research on the classification of e-commerce users is relatively mature, but with the rise of mobile social networks, the combination of social networks and e-commerce networks has become a trend and is developing rapidly. Traditional e-commerce user classification methods are not suitable for social e-commerce users. Therefore, based on the research on traditional e-commerce user classification methods, according to the characteristics of social e-commerce users, we improved data preprocessing and parameter tuning methods, and proposed a clustering method of social e-commerce users based on the K-means++ algorithm. The test on the actual data of social e-commerce users… More >

Ye Tian¹, Liguo Zhang^1,2, Linshan Shen^1,*, Guisheng Yin¹, Lei Chen³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 195-211, 2021, DOI:10.32604/iasc.2021.016314

Abstract Skin cancer is one of the most common types of cancer in the world, melanoma is considered to be the deadliest type among other skin cancers. Quite recently, automated skin lesion classification in dermoscopy images has become a hot and challenging research topic due to its essential way to improve diagnostic performance, thus reducing melanoma deaths. Convolution Neural Networks (CNNs) are at the heart of this promising performance among a variety of supervised classification techniques. However, these successes rely heavily on large amounts of class-balanced clearly labeled samples, which are expensive to obtain for skin lesion classification in the real… More >

Zhaoqing Xie^1,*, Qing Liu², Yulian Cao³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246

Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. Six representative reaches in Yangtze… More >

Peizhou Yan¹, Jiancheng Zou^2,*, Zhengzheng Li¹, Xin Yang³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 213-225, 2021, DOI:10.32604/iasc.2021.016201

Abstract Within the application of driving assistance systems, the detection of driver’s facial features in the cab for a spectrum of luminosities is mission critical. One method that addresses this concern is infrared and visible image fusion. Its purpose is to generate an aggregate image which can granularly and systematically illustrate scene details in a range of lighting conditions. Our study introduces a novel approach to this method with marked improvements. We utilize non-subsampled shearlet transform (NSST) to obtain the low and high frequency sub-bands of infrared and visible imagery. For the low frequency sub-band fusion, we incorporate the local average… More >