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An Explainable AI-Driven Zero-Day Attack Detection Framework for Securing Edge Devices in Smart Cities

Santhiyakumari N., Sabarinathan S., Veerakumar S., Chandraman M. et al. • Journal of Cognitive Human-Computer Interaction (2025)

The rapid proliferation of edge computing in smart cities has enhanced real-time data processing capabilities, but it has also exposed critical vulnerabilities to sophisticated cyber threats such as zero-day attacks. Traditional signature-b...

DOAJ Open Access (DOAJ) DOI: 10.54216/JCHCI.100201

Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor

Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno, Hisahide Nakamura • Applied Sciences (2022)

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed us...

DOAJ Open Access (DOAJ) DOI: 10.3390/app12115513

An Unsupervised and End-to-End Registration Method Using Offset Field and Pseudodata for Video SAR Images

Hui Fang, Guisheng Liao, Yongjun Liu, Cao Zeng et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024)

Video synthetic aperture radar (video SAR) has been successfully applied in many fields and the registration of the video SAR images has been proven to be a crucial step in their preprocessing. However, video SAR images exhibit more severe ...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2024.3465835

Application of Convolutional Neural Network in Natural Language Processing

Wei Wang, Jianxun Gang • IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) (2018)

Convolutional neural network (Convolutionl Neural Network, CNN) is a multiple-layer neural network method to learn hierarchical characteristic of data. In recent years, CNN has developed rapidly in the design and calculation of natural lang...

OPENALEX DOI: https://doi.org/10.1109/iciscae.2018.8666928

Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding

Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi • PeerJ Computer Science (2025)

Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a cru...

DOAJ Open Access (DOAJ) DOI: 10.7717/peerj-cs.2669

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Shima Javanmardi, Ali Mohammad Latif, Mohammad Taghi Sadeghi, Mehrdad Jahanbanifard et al. • Sensors (2022)

In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have b...

DOAJ Open Access (DOAJ) DOI: 10.3390/s22218376

Comparative Study of CNN and RNN for Natural Language Processing

Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze • arXiv (Cornell University) (2017)

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1702.01923

Detecting Fake News in Urdu Language Using Machine Learning, Deep Learning, and Large Language Model-Based Approaches

Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor, Momina Shaheen • Information (2025)

Fake news is false or misleading information that looks like real news and spreads through traditional and social media. It has a big impact on our social lives, especially in politics. In Pakistan, where Urdu is the main language, finding ...

DOAJ Open Access (DOAJ) DOI: 10.3390/info16070595

A unified architecture for natural language processing

Ronan Collobert, Jason Weston • International Conference on Machine Learning (2008)

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the lik...

OPENALEX DOI: https://doi.org/10.1145/1390156.1390177

Multi-Camera Machine Vision for Detecting and Analyzing Vehicle–Pedestrian Conflicts at Signalized Intersections: Deep Neural-Based Pose Estimation Algorithms

Ahmed Mohamed, Mohamed M. Ahmed • Applied Sciences (2025)

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track veh...

DOAJ Open Access (DOAJ) DOI: 10.3390/app151910413

DETECTION AND RECOGNITION OF ROAD SIGNS USING YOLOv5

Haitam Ettazi, Najat Rafalia, Jaafar Abouchabaka • Proceedings on Engineering Sciences (2023)

In the field of deep learning, a convolutional neural network is a class of artificial neural networks that became dominant in various computer vision tasks, which is widely used to solve complex problems in various areas, including driver ...

DOAJ Open Access (DOAJ) DOI: 10.24874/PES05.03.017

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications

Yumin Zhang, Yanyong Wang • EURASIP Journal on Image and Video Processing (2025)

Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13640-025-00677-0

Identification of camouflage military individuals with deep learning approaches DFAN and SINETV2

Ali Haider, Ghulam Muhammad, Talha Ahmed Khan, Kushsairy Kadir et al. • Scientific Reports (2025)

Abstract Camouflaged object detection particularly is considered as challenging and crucial because these objects are designed to either mimic their environment or be completely hidden within it. The goal of camouflage patterns utilization ...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-18886-y

DIAGNOSTICS OF RUSTS AND SPOTTS OF WHEAT USING COMPUTER VISION METHODS

Irina V. Arinicheva, Igor V. Arinichev, Galina V. Volkova, Sergey V. Polyanskikh • Siberian Journal of Life Sciences and Agriculture (2022)

Purpose. To investigate the possibility of diagnosing yellow and brown rust (Puccinia striiformis f. sp. tritici West. Puccinia triticina f. sp. tritici Erikss), yellow spot (Pyrenophora tritici-repentis (Died.) Drechsler) on wheat leaves f...

DOAJ Open Access (DOAJ) DOI: 10.12731/2658-6649-2022-14-1-248-261

Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery

Wassim Bouachir, Koffi Eddy Ihou, Houssem-Eddine Gueziri, Nizar Bouguila et al. • IEEE Access (2019)

Mechanical site preparation by mounding is often used by the forest industry to provide optimal growth conditions for tree seedlings. Prior to planting, an essential step consists in estimating the number of mounds at each planting block, w...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2019.2923765

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Rittik Chandra Das Turjy, Fahmid Al Farid, Fahmid Al Farid, Md. Hasan Imam Bijoy et al. • Frontiers in Artificial Intelligence (2026)

Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1884567