A comprehensive dataset and neural network approach for named entity recognition in the Uzbek languageMendeley Data by Davlatyor Mengliev, Vladimir Barakhnin, Mukhriddin Eshkulov, Bahodir Ibragimov, Shohrux Madirimov

“…The study is complemented by the fact that the authors demonstrated the applications of the created dataset by training a language model using the CNN + LSTM architecture, which achieves high accuracy in NER tasks, with an F1 score of 90.8 %, precision of 93.9 %, and recall of 88.0 % on the test set. …”
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Deep learning-enhanced defects detection for printed circuit boards by Van-Truong Nguyen, Xuan-Thuc Kieu, Duc-Tuan Chu, Xiem HoangVan, Phan Xuan Tan, Tuyen Ngoc Le

“…., a type of convolutional neural network (CNN)) model. The proposed algorithm is tested in three different lighting conditions: low light, normal light, and high light conditions. …”
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Insider threat detection based on operational attention and data augmentation by Guanyun FENG, Cai FU, Jianqiang LYU, Lansheng HAN

“…In recent years, there has been an increased focus on the issue of insider threats.Insider threats are a major cause security breaches in organizations and pose an ongoing challenge.By analyzing the existing insider threat data, it was identified that the biggest challenge in insider threat detection lies in data imbalance and the limited number of labeled threat samples.In the Cert R4.2 dataset, which is a classic dataset for insider threat detection, there are over 3.22 million log data, but only 7,423 are marked as malicious operation logs.Furthermore, most of the operation types in the logs are not related to malicious behavior, and only two types of operations are highly correlated with malicious behavior, such as leaking company data, creating interference in the detection process.To address this challenge, a data processing framework was designed based on operational attention and data augmentation.Anomaly evaluation was first performed on operations by the framework, and operations with low anomaly scores were then masked.This makes the model better focus on operations related to malicious behavior, which can be considered as a hard attention mechanism for operations.Next, the characteristics of the insider threat dataset were analyzed, and three rules were designed for data augmentation on malicious samples to increase the diversity of samples and alleviate the substantial imbalance between positive and negative samples.Supervised insider threat detection was regarded as a time-series classification problem.Residual connections were added to the LSTM-FCN model to achieve multi-granularity detection, and indicators such as precision rate and recall rate were used to evaluate the model.The results indicate superior performance over existing baseline models.Moreover, the data processing framework was implemented on various classic models, such as ITD-Bert and TextCNN, and the results show that the methods effectively improve the performance of insider threat detection models.…”
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Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases by Kimi Milić Marko, Sinanović Šćepan, Prodović Tanja, Ilanković Tanja

“…Methodologically, the study relies on a systematic review of the existing literature and case studies analyzing the use of algorithms such as convolutional neural networks (CNN) and support vector machines (SVM). The results show that AI tools provide a significant advantage over classical approaches, with accuracy exceeding 90% in identifying key biomarkers and abnormalities in laboratory test results. …”
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Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax by Diego A. Ramos-Briceño, Alessandro Flammia-D’Aleo, Gerardo Fernández-López, Fhabián S. Carrión-Nessi, David A. Forero-Peña

“…Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…The benchmark experimental results show that the proposed TriCNN-CatBoost model significantly outperforms traditional Naive Bayes, Support Vector Machines, and Random Forest models in terms of accuracy, recall, and F1 score, demonstrating stronger false comment recognition ability and generalization performance. …”
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Graph-Based Feature Crossing to Enhance Recommender Systems by Congyu Cai, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou, Yuanguo Lin

“…Then, to learn as many useful features as possible for higher recommendation quality, a Convolutional Neural Network (CNN) and the Transformer model are used to parallelly learn local and global feature interactions. …”
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Predictive modeling of cattle calving time emphasizing abnormal and normal cases by using posture analysis by May Phyu Khin, Pyke Tin, Yoichiro Horii, Thi Thi Zin

“…Leveraging advanced computer vision techniques, particularly the Mask R-CNN from the Detectron2 detection and the YOLOv8-pose classification method known for their efficient training time and overall accuracy, the system analyzes the frequency of posture changes and key postures like sitting, standing, feeding, sitting with extended legs, and tail-raised to predict calving cases with high precision. …”
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Prediction of Solar Wind Speed Through Machine Learning From Extrapolated Solar Coronal Magnetic Field by Rong Lin, Zhekai Luo, Jiansen He, Lun Xie, Chuanpeng Hou, Shuwei Chen

“…In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetic field maps, considering a SW source surface of RSS = 2.5R⊙, aiming to predict the SW speed at the Lagrange‐1 (L1) point of the Sun‐Earth system. …”
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ID-UNet: A densely connected UNet architecture for infrared small target segmentation by Diankun Chen, Feiwei Qin, Ruiquan Ge, Yong Peng, Changmiao Wang

“…Existing CNN-based approaches face challenges in effectively and efficiently managing diverse scales of small infrared objects within intricate scenes, primarily as a result of the aggregation effect induced by pooling layers. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. …”
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Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. …”
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A new band selection framework for hyperspectral remote sensing image classification by B. L. N. Phaneendra Kumar, Radhesyam Vaddi, Prabukumar Manoharan, L. Agilandeeswari, V. Sangeetha

“…Then, finally, a Convolutional Neural Network (CNN) is used for effective classification by incorporating three-dimensional convolutions. …”
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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

“…The LRCT network effectively leverages the temporal learning advantages of Local Recurrent Networks (Local RNN) and the spatial learning strengths of Convolutional Neural Network (CNN) by designing the local feature extraction block (denoted as LRC Block), which extracts fine-grained local features from 2000-dimensional original sequences and reduces the dimensionality to 125. …”
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Harnessing artificial intelligence role in oral cancer diagnosis and prediction: A comprehensive exploration by Archana Behera, N. Aravindha Babu, Remya Rajan Renuka, Mukesh Kumar Dharmalingam Jothinathan

“…AI-based techniques such as SVM, CNN and capsule networks have brought the journey of tumor grading, stagewise cancer and further cancer detection at an early stage as a result of which, delays in treatment are reduced. …”
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Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds by Christopher J. Bell, Kaushallya Adhikari, Andrew Brown

“…This work also illustrates that the CNN estimators developed in this work exceed the CRLB and are biased estimators caused by the lack of unbiased constraint in the loss function during training of the CNNs.…”
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A shallow convolutional neural network for cerebral neoplasm detection from magnetic resonance imaging by Hossein Sadr, Zeinab Khodaverdian, Mojdeh Nazari, Mohammad Yamaghani

“…Accordingly, a shallow CNN model is proposed in this paper to classify MRI scans. …”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…The ECA-ResNet34 network model is used for load identification, avoiding the problems of network degradation and training difficulties caused by the excessive depth of traditional convolutional neural networks (CNN), and achieving efficient monitoring of household loads. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. …”
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The Occurrence of Noun Post-modifiers in Political News: A Corpus-based Study by Ari Murad Mohammed Salih, Hozan Hamid Ibrahim, Salih Ibrahim Ahmed

“…This research aims to investigate and observe the occurrences of noun post-modifiers in three English news websites, namely BBC News, CNN and Al Jazeera English by selecting five news articles from each to discover the frequency of noun post-modifier occurrences. …”
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