Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

J. S. Sujin^1,*, S. Sophia²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 157-171, 2023, DOI:10.32604/csse.2023.023747 - 01 June 2022

Abstract Digital picture forgery detection has recently become a popular and significant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying original photographic images to generate a forged image is known as digital image forging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery. In order to make the forgeries real and inconspicuous,… More >

E. Gothai^1,*, Surbhi Bhatia², Aliaa M. Alabdali³, Dilip Kumar Sharma⁴, Bhavana Raj Kondamudi⁵, Pankaj Dadheech⁶

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1231-1246, 2022, DOI:10.32604/iasc.2022.026264 - 03 May 2022

Abstract At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a… More >

Muhammad Asif Nauman^*, Muhammad Shoaib

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 925-939, 2022, DOI:10.32604/cmc.2022.022147 - 03 November 2021

Abstract Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained… More >

Chunhua Zhou^*, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1429-1440, 2020, DOI:10.32604/iasc.2020.011757 - 24 December 2020

Abstract With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation.… More >

Chunjing Wang^*, Li Liu, Yanyan Tan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1061-1083, 2020, DOI:10.32604/cmes.2020.010198 - 21 August 2020

Abstract The implementation of content-based image retrieval (CBIR) mainly depends on two key technologies: image feature extraction and image feature matching. In this paper, we extract the color features based on Global Color Histogram (GCH) and texture features based on Gray Level Co-occurrence Matrix (GLCM). In order to obtain the effective and representative features of the image, we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively. And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector… More >