M. P. Thiruvenkatasuresh^1,*, Surbhi Bhatia², Shakila Basheer³, Pankaj Dadheech⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1767-1782, 2023, DOI:10.32604/iasc.2023.033711

Abstract The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained… More >

Gengyu Ge^1,2,*, Zhong Qin¹, Xin Chen¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6351-6369, 2023, DOI:10.32604/cmc.2023.035832

Abstract Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses… More >

S. Sandhiyaa^*, C. Gomathy

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4747-4768, 2023, DOI:10.32604/cmc.2023.032298

Abstract The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the… More >

Zahraa Tarek¹, Samaa M. Shohieb^1,*, Abdelghafar M. Elhady², El-Sayed M. El-kenawy³, Mahmoud Y. Shams⁴

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3195-3213, 2023, DOI:10.32604/csse.2023.034092

Abstract The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems. This is because eye recognition algorithms have multiple challenges, such as multi-pose variations, ocular parts, and illumination. Moreover, the modern security applications fail to detect facial expressions from eye images. In this paper, a Speeded-Up Roust Feature (SURF) Algorithm was utilized to localize the face images of the enrolled subjects. We highlighted on eye and pupil parts to be detected based on SURF, Hough Circle Transform (HCT), and Local Binary Pattern (LBP). Afterward, Deep Belief Neural Networks (DBNN)… More >

Mei Wang^1,*, Hao Xu², Yadang Chen¹

Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447

Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the multivariate Gaussian (MVG), and determine… More >

Usman Ahmad¹, Muhammad Junaid Ali², Faizan Ahmed Khan³, Arfat Ahmad Khan⁴, Arif Ur Rehman¹, Malik Muhammad Ali Shahid⁵, Mohd Anul Haq^6,*, Ilyas Khan⁷, Zamil S. Alzamil⁶, Ahmed Alhussen⁸

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008

Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

Gibrael Abosamra^*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3539-3560, 2023, DOI:10.32604/cmc.2023.032976

Abstract This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or… More >

Siguo Bi¹, Cong Wang¹, Jiajie Shen¹, Wang Xiang¹, Wei Ni², Xin Wang¹, Bochun Wu^1,*, Yi Gong^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 527-538, 2023, DOI:10.32604/cmes.2022.022298

Abstract The misreading problem of a passive ultra-high-frequency (UHF) radio frequency identification (RFID) tag is a frequent problem arising in the field of librarianship. Unfortunately, existing solutions are something inefficient, e.g., extra resource requirement, inaccuracy, and empiricism. To this end, under comprehensive analysis on the passive UHF RFID application in the librarianship scenario, a novel and judicious approach based on RFID localization is proposed to address such a misreading problem. Extensive simulation results show that the proposed approach can outperform the existing ones and can be an attractive candidate in practice. More >

Yun Fen Yong^1,*, Chee Keong Tan¹, Ian Kim Teck Tan², Su Wei Tan¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710

Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

Zakarya Farea Shaaf^1,*, Muhammad Mahadi Abdul Jamil¹, Radzi Ambar¹, Ahmed Abdu Alattab^2,3, Anwar Ali Yahya^3,4, Yousef Asiri⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900

Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from… More >