Umar Zaman¹, Junaid Khan², Eunkyu Lee^1,3, Sajjad Hussain⁴, Awatef Salim Balobaid⁵, Rua Yahya Aburasain⁵, Kyungsup Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1789-1808, 2024, DOI:10.32604/cmc.2024.056222 - 15 October 2024

Abstract Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation… More >

Qianqian Zhang^1,2,3, Yi Zhang^1,2, Yuanyuan Ma³, Yanmei Liu^1,2, Xiangyang Luo^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 105-131, 2024, DOI:10.32604/cmc.2024.055735 - 15 October 2024

Abstract The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse. Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online. Accurately discerning a steganographer from many normal users is challenging due to various factors, such as the complexity in obtaining the steganography algorithm, extracting highly separability features, and modeling the cover data. After extensive exploration, several methods have been proposed for steganographer identification. This paper presents a survey of existing studies. Firstly, we provide a concise introduction to the More >

Jiahe Wang¹, Xizhan Gao^1,*, Fa Zhu², Xingchi Chen³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 25-51, 2024, DOI:10.32604/cmc.2024.054895 - 15 October 2024

Abstract Video-based person re-identification (Re-ID), a subset of retrieval tasks, faces challenges like uncoordinated sample capturing, viewpoint variations, occlusions, cluttered backgrounds, and sequence uncertainties. Recent advancements in deep learning have significantly improved video-based person Re-ID, laying a solid foundation for further progress in the field. In order to enrich researchers’ insights into the latest research findings and prospective developments, we offer an extensive overview and meticulous analysis of contemporary video-based person Re-ID methodologies, with a specific emphasis on network architecture design and loss function design. Firstly, we introduce methods based on network architecture design and loss… More >

Zhenbing Qiao^1,2, Mengyun Chen^1,2, Wenjun Ma^1,2, Juan Zhao^1,2, Jiaju Zhu^1,2, Kaikai Zhu^1,2, Pengpeng Tan^1,2, Fangren Peng^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2349-2365, 2024, DOI:10.32604/phyton.2024.056655 - 30 September 2024

Abstract Ammonium nitrogen (NH₄⁺-N) is one of the main forms of nitrogen absorbed and utilized by plants, and mastering the regulatory mechanism of plant ammonium assimilation is a key way to improve the efficiency of plant nitrogen utilization. Glutamine synthetase (GS) and glutamate synthase (GOGAT), two key enzymes for ammonium assimilation, have rarely been studied in pecan. In this study, GS and GOGAT family members of pecan were identified and analyzed using bioinformatics methods. The results indicated that 6 GS and 4 GOGAT genes were identified. The cis-acting elements can be broadly categorized into light-responsive, hormone-responsive, and stress-responsive elements.… More >

Dongxue Su¹, Jiarui Zheng¹, Yuwei Yi¹, Shuyuan Zhang¹, Luxin Feng¹, Danzeng Quzhen², De Qiong³, Weiwei Zhang¹, Qijian Wang¹, Feng Xu^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2317-2337, 2024, DOI:10.32604/phyton.2024.055350 - 30 September 2024

Abstract The multifaceted roles of MYB transcriptional regulators are pivotal in orchestrating the complex processes of secondary metabolism, stress tolerance mechanisms, and life cycle progression and development. This study extensively examined the JrMYB genes using whole genome and transcriptomic data, focusing on identifying putative MYB genes associated with fatty acid metabolism. 126 MYB genes were identified within the walnut genome, characterized by hydrophilic proteins spanning lengths ranging from 78 to 1890 base pairs. Analysis of cis-acting elements within the promoter regions of MYB genes revealed many elements linked to cell development, environmental stress, and phytohormones. Transcriptomic data was utilized… More > Graphic Abstract

Anshul Mahajan^*, Sunil K. Singla

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1623-1649, 2024, DOI:10.32604/cmes.2024.054468 - 27 September 2024

Abstract The identification of individuals through ear images is a prominent area of study in the biometric sector. Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing, prompting the exploration of supplementary biometric measures such as ear biometrics. The research proposes a Deep Learning (DL) framework, termed DeepBio, using ear biometrics for human identification. It employs two DL models and five datasets, including IIT Delhi (IITD-I and IITD-II), annotated web images (AWI), mathematical analysis of images (AMI), and EARVN1. Data augmentation techniques such as flipping, translation, and Gaussian noise are applied to More >

Seung-Ju Han, Seong-Su Yoon, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1495-1518, 2024, DOI:10.32604/cmes.2024.053457 - 27 September 2024

Abstract The rapid proliferation of Internet of Things (IoT) technology has facilitated automation across various sectors. Nevertheless, this advancement has also resulted in a notable surge in cyberattacks, notably botnets. As a result, research on network analysis has become vital. Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods. In this paper, we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework. The results indicate that using the More >

Giada Faraco, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro^*, Arcangelo Messina

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 739-762, 2024, DOI:10.32604/sdhm.2024.052663 - 20 September 2024

Abstract The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring, such as that carried out by a series of accelerometers placed on the structure, is certainly a goal of extreme and current interest. In the present work, the results obtained from the processing of experimental data of a real structure are shown. The analyzed structure is a lattice structure approximately 9 m high, monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels. The data used refer to continuous monitoring that lasted for a total of 1… More >

Hao Xu¹, Jing Wang², Rubin Zhu², Alfred Strauss³, Maosen Cao⁴, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393 - 20 September 2024

Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >

Sheng Xu¹, Shixiong Xiang², Feiyu Meng¹, Qiang Wu^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157 - 12 September 2024

Abstract In Unsupervised Domain Adaptation (UDA) for person re-identification (re-ID), the primary challenge is reducing the distribution discrepancy between the source and target domains. This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain. Implicit construction is difficult due to the absence of intermediate state supervision, making smooth knowledge transfer from the source to the target domain a challenge. To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,… More >