Najlaa Hindi Alsaedi^*, Emad Sami Jaha

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 407-427, 2022, DOI:10.32604/iasc.2022.019538

Abstract Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded… More >

FRANCISCO J. BARRANTES^#,*

BIOCELL, Vol.45, No.6, pp. 1479-1484, 2021, DOI:10.32604/biocell.2021.016502

Abstract Cholesterol is a major lipid in biological membranes. It not only plays a structural role but also modulates a wide range of functional properties of neurotransmitter and hormone receptors and ion channels. The membraneembedded segments of the paradigm neurotransmitter receptor for acetylcholine (nAChR) contain linear sequences of amino acids with the capacity to recognize cholesterol. These cholesterol consensus domains have been designated as “CARC” and its mirror sequence “CRAC”. CARC preferentially occurs in the exoplasmic-facing membrane leaflet, and CRAC, in the cytoplasmic-facing hemilayer. Both motifs are highly conserved among ion-channel and neurotransmitter receptor proteins in vertebrate nervous systems, where they… More >

Khaled Marji Alresheedi¹, Suliman Aladhadh², Rehan Ullah Khan², Ali Mustafa Qamar^1,3,*

Computer Systems Science and Engineering, Vol.40, No.1, pp. 151-166, 2022, DOI:10.32604/csse.2022.017931

Abstract There are over 200 different varieties of dates fruit in the world. Interestingly, every single type has some very specific features that differ from the others. In recent years, sorting, separating, and arranging in automated industries, in fruits businesses, and more specifically in dates businesses have inspired many research dimensions. In this regard, this paper focuses on the detection and recognition of dates using computer vision and machine learning. Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit. Classical machine learning includes the Bayesian network, Support Vector… More >

Maojian Chen^1,2,3, Xiong Luo^1,2,3,*, Hailun Shen⁴, Ziyang Huang⁴, Qiaojuan Peng^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 47-63, 2021, DOI:10.32604/cmes.2021.017491

Abstract In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the cross entropy are incorporated into… More >

Suliman Aladhadh¹, Hidayat Ur Rehman², Ali Mustafa Qamar^3,4,*, Rehan Ullah Khan¹

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724

Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution… More >

Sadia Kiran¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Yunyoung Nam^4,*, Robertas Damaševičius⁵, Muhammad Sharif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800

Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is… More >

Muhammad Rizwan Latif¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Haris Masood², Usman Tariq³, Yunyoung Nam^4,*, Seifedine Kadry⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364

Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their… More >

Jiaming Mao^1,*, Mingming Zhang¹, Mu Chen², Lu Chen², Fei Xia¹, Lei Fan¹, ZiXuan Wang³, Wenbing Zhao⁴

Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086

Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal… More >

Yujie Zhou¹, Jin Liu^1,*, Yurong Xie¹, Y. Ken Wang²

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 715-733, 2021, DOI:10.32604/iasc.2021.020184

Abstract Text recognition is a crucial and challenging task, which aims at translating a cropped text instance image into a target string sequence. Recently, Convolutional neural networks (CNN) have been widely used in text recognition tasks as it can effectively capture semantic and structural information in text. However, most existing methods are usually based on contextual clues. If only recognize a single character, the accuracy of these approaches can be reduced. For example, it is difficult to distinguish 0 and O in the traditional CNN network because they are very similar in composition and structure. To solve this problem, we propose… More >

Yabin Xu^1,2,*, Shujuan Chen², Xiaowei Xu³

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 495-511, 2021, DOI:10.32604/iasc.2021.018896

Abstract In order to quickly get the viewpoint of key opinion leaders(KOL) on public events, a method of opinion mining in Weibo is put forward. Firstly, according to the characteristics of Weibo language, the non-viewpoint sentence recognition rule is formulated, and some non-viewpoint sentence is eliminated accordingly. Secondly, based on the constructed FastText-XGBoost viewpoint sentence recognition model, the second classification is carried out to identify the opinion sentence according to the dominant and recessive features of Weibo. Finally, the group of evaluation object and evaluation word is extracted from the opinion sentence, according to our proposed multi-task learning BiLSTM-CRFs model. In… More >