Prediction of viscosity of blast furnace slag based on NRBO-DNN model by Zhe Li, Meng Wang, Rui Xu, Juanjuan Jiang, Jie Li, Zunqian Zhang, Aimin Yang

“…Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. …”
Get full text

AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

“…Methods: We conducted a retrospective multicenter study on patients undergoing liver resection across three cohorts (N = 514). We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. …”
Get full text

A Survey on Review Spam Detection Methods using Deep Learning Approach by Mahmoud Aliarab, Kazim Fouladi-Ghaleh

“…In recent years, much research has been done to detect these types of reviews, and with the expansion of deep neural networks and the efficiency of these networks in various issues, in recent years, multiple types of deep neural networks have been used to identify spam reviews. …”
Get full text

BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Goal:</italic> In recent years, deep neural networks have consistently outperformed previously proposed methods in the domain of medical segmentation. …”
Get full text

Channel state information–based multi-level fingerprinting for indoor localization with deep learning by Tao Li, Hai Wang, Yuan Shao, Qiang Niu

“…In the offline training phase, deep neural networks are used to train the optimal weights. …”
Get full text

Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods by Hongqing Song, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, Qipeng Liu

“…Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. …”
Get full text

Complex Risk Statistics with Scenario Analysis by Fei Sun, Yichuan Dong

“…Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representations for such risk.…”
Get full text

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation by Mingxin Gan, Hang Zhang

“…To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. …”
Get full text

Classification of earth surface image segmentation methods by D. V. Kypriyanava, D. Y. Pertsau, M. M. Tatur

“…Such approaches as template matching, machine learning and deep neural networks, as well as application of knowledge about analyzed objects are considered. …”
Get full text

Traditional guidance mechanism based deep robust watermarking by Xuejing GUO, Yixiang FANG, Yi ZHAO, Tianzhu ZHANG, Wenchao ZENG, Junxiang WANG

“…With the development of network and multimedia technology, multimedia data has gradually become a key source of information for people, making digital media the primary battlefield for copyright protection and anti-counterfeit traceability.Digital watermarking techniques have been widely studied and recognized as important tools for copyright protection.However, the robustness of conventional digital watermarking methods is limited as sensitive digital media can easily be affected by noise and external interference during transmission.Then the existing powerful digital watermarking technology’s comprehensive resistance to all forms of attacks must be enhanced.Moreover, the conventional robust digital watermarking algorithm’s generalizability across a variety of image types is limited due to its embedding method.Deep learning has been widely used in the development of robust digital watermarking systems due to its self-learning abilities.However, current initialization techniques based on deep neural networks rely on random parameters and features, resulting in low-quality model generation, lengthy training times, and potential convergence issues.To address these challenges, a deep robust digital watermarking algorithm based on a traditional bootstrapping mechanism was proposed.It combined the benefits of both traditional digital watermarking techniques and deep neural networks, taking into account their learning abilities and robust characteristics.The algorithm used the classic robust digital watermarking algorithm to make watermarked photos, and the constructed feature guaranteed the resilience of traditional watermarked images.The final dense image was produced by fusing the conventionally watermarked image with the deep network using the U-Net structure.The testing results demonstrate that the technique can increase the stego image’s resistance to various attacks and provide superior visual quality compared to the conventional algorithm.…”
Get full text

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT by Lal Khan, Atika Qazi, Hsien-Tsung Chang, Mousa Alhajlah, Awais Mahmood

“…Abstract Sentiment analysis (SA) as a research field has gained popularity among the researcher throughout the globe over the past 10 years. Deep neural networks (DNN) and word vector models are employed nowadays and perform well in sentiment analysis. …”
Get full text

De-anonymiation method for networks based on DeepLink by Pei WANG, Yan JIA, Aiping LI, Qianyue JIANG

“…Existing de-anonymization technologies are mainly based on the network structure.To learn and express network structure is the key step of de-anonymization.The purpose of the user identity linkage is to detect the same user from different social networking platforms.DeepLink is a cross-social network user alignment technology.It learns the structural of the social networks and align anchor nodes through deep neural networks.DeepLink was used to identify de-anonymization social networks,and the results outperforms the traditional methods.…”
Get full text

A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. …”
Get full text

Adversarial patch defense algorithm based on PatchTracker by Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG

“…The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant threat to target detection systems based on vision techniques.To tackle this issue, an adversarial patch defense algorithm based on PatchTracker was proposed, leveraging the semantic differences between adversarial patches and image backgrounds.This algorithm comprised an upstream patch detector and a downstream data enhancement module.The upstream patch detector employed a YOLOV5 (you only look once-v5) model with attention mechanism to determine the locations of adversarial patches, thereby improving the detection accuracy of small-scale adversarial patches.Subsequently, the detected regions were covered with appropriate pixel values to remove the adversarial patches.This module effectively reduced the impact of adversarial examples without relying on extensive training data.The downstream data enhancement module enhanced the robustness of the target detector by modifying the model training paradigm.Finally, the image with removed patches was input into the downstream YOLOV5 target detection model, which had been enhanced through data augmentation.Cross-validation was performed on the public TT100K traffic sign dataset.Experimental results demonstrated that the proposed algorithm effectively defended against various types of generic adversarial patch attacks when compared to situations without defense measures.The algorithm improves the mean average precision (mAP) by approximately 65% when detecting adversarial patch images, effectively reducing the false negative rate of small-scale adversarial patches.Moreover, compared to existing algorithms, this approach significantly enhances the accuracy of neural networks in detecting adversarial samples.Additionally, the method exhibited excellent compatibility as it does not require modification of the downstream model structure.…”
Get full text

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments by Shixi Tang, Jinan Gu, Keming Tang, Wei Ding, Zhengyang Shang

“…The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. …”
Get full text

Part of Speech Tagging: Shallow or Deep Learning? by Robert Östling

“… Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. …”
Get full text

Adversarial attacks and defenses in deep learning by Ximeng LIU, Lehui XIE, Yaopeng WANG, Xuru LI

“…The adversarial example is a modified image that is added imperceptible perturbations,which can make deep neural networks decide wrongly.The adversarial examples seriously threaten the availability of the system and bring great security risks to the system.Therefore,the representative adversarial attack methods were analyzed,including white-box attacks and black-box attacks.According to the development status of adversarial attacks and defenses,the relevant domestic and foreign defense strategies in recent years were described,including pre-processing,improving model robustness,malicious detection.Finally,future research directions in the field of adversarial attacks and adversarial defenses were given.…”
Get full text

Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid by N. A. Serebryakov

“…The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. …”
Get full text

Partial Attention in Global Context and Local Interaction for Addressing Noisy Labels and Weighted Redundancies on Medical Images by Minh Tai Pham Nguyen, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran, Quoc Duy Nam Nguyen

“…Recently, the application of deep neural networks to detect anomalies on medical images has been facing the appearance of noisy labels, including overlapping objects and similar classes. …”
Get full text

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach by Cheng Cheng, Elayaraja Aruchunan, Muhamad Hifzhudin Noor Aziz

“…This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. …”
Get full text