Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364 - 30 October 2020

Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them More >

Clément Chagnaud^1,3, Philippe Garat², Paule-Annick Davoine^1,3, Guylaine Brun-Trigaud⁴

Revue Internationale de Géomatique, Vol.30, No.1, pp. 67-83, 2020, DOI:10.3166/rig.2020.00107

Abstract Nous proposons une procédure d’analyse statistique multidimensionnelle couplant des méthodes de projection et de classification pour identifier des ensembles cohérents au sein d’un corpus d’entités géographiques surfaciques que l’on appelle aires de dispersion. La méthodologie intègre un facteur géographique dans la construction de l’espace de représentation pour la projection des données. En appliquant ces méthodes sur des données géolinguistiques, nous pouvons identifier et expliquer de nouvelles structures spatiales au sein d’un corpus d’aires de dispersion de traits linguistiques. More >

Rubina Aslam^1,*, Manzoor Illahi Tamimy¹, Waqar Aslam²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086 - 24 December 2020

Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking,… More >

Ujala Yasmeen¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Ghulam Jillani Ansari¹, Saeed ur Rehman¹, Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775 - 24 December 2020

Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image.… More >

Haifeng Song¹, Weiwei Yang^1,*, Haiyan Yuan², Harold Bufford³

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988 - 24 December 2020

Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual… More >

Ayyaz Hussain¹, Mohammed Alawairdhi², Fayez Alazemi³, Sajid Ali Khan⁴, Muhammad Ramzan^2,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 857-871, 2020, DOI:10.32604/iasc.2020.010120

Abstract The Computer Aided Diagnosis (CAD) systems are gaining more recognition and being used as an aid by clinicians for detection and interpretation of diseases every passing day due to their increasing accuracy and reliability. The lung(s) nodule detection is a very crucial and difficult step for CAD systems. In this paper, a hybrid approach for the lung nodule detection using a deformable model and distance transform has been proposed. The proposed method has the ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and the juxta-vescular, along with the non-solid nodules More >

Umid Suleymanov¹, Behnam Kiani Kalejahi^1,2,*, Elkhan Amrahov¹, Rashid Badirkhanli¹

Computer Systems Science and Engineering, Vol.35, No.6, pp. 467-475, 2020, DOI:10.32604/csse.2020.35.467

Abstract Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For More >

Yu Xue, Sow Alpha Amadou^*, Yan Zhao

Journal of Cyber Security, Vol.2, No.4, pp. 191-196, 2020, DOI:10.32604/jcs.2020.014045 - 07 December 2020

Abstract Attracted numerous analysts’ consideration, classification is one of the primary issues in Machine learning. Numerous evolutionary algorithms (EAs) were utilized to improve their global search ability. In the previous years, many scientists have attempted to tackle this issue, yet regardless of the endeavors, there are still a few inadequacies. Based on solving the classification problem, this paper introduces a new optimization classification model, which can be applied to the majority of evolutionary computing (EC) techniques. Firework algorithm (FWA) is one of the EC methods, Although the Firework algorithm (FWA) is a proficient algorithm for solving More >

Pallabi Sharma^1,*, Kangkana Bora², Kunio Kasugai³, Bunil Kumar Balabantaray¹

Oncologie, Vol.22, No.3, pp. 129-145, 2020, DOI:10.32604/oncologie.2020.013870

Abstract In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide cancer statistics, colorectal cancer is one of the most common cancers. The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date. The traditional manual process is dependent on the expertise of the medical practitioner. In this paper, a two-stage classification is proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video are extracted and are rated as significant if More >

Tao Liao^1,‡, Weicheng Fu^1,†, Shunxiang Zhang^1,*, Zongtian Liu^2,§

Computer Systems Science and Engineering, Vol.35, No.5, pp. 311-319, 2020, DOI:10.32604/csse.2020.35.311

Abstract Event trigger recognition is a sub-task of event extraction, which is important for text classification, topic tracking and so on. In order to improve the effectiveness of using word features as a benchmark, a new event trigger recognition method based on positive and negative weight computing is proposed. Firstly, the associated word feature, the part-of-speech feature and the dependency feature are combined. Then, the combination of these three features with positive and negative weight computing is used to identify triggers. Finally, the text classification is carried out based on the event triggers. Findings from our More >