Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN) by Muhammad Yuslan Abu Bakar, Adiwijaya Adiwijaya

“…This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). …”
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Dependable identity recognition and authorization based on visual information by Chunlei PENG, Xinbo GAO, Nannan WANG, Jie LI

“…Recently,deep learning has been widely applied to video and image based identity recognition and authorization tasks,including face recognition and person identification.However,machine learning models,especially deep learning models,can be easily fooled by adversarial attacks,which may cause the identity recognition systems to make a wrong decision.Therefore,dependable identity recognition and authorization has become one of the hot topics currently.Recent advances on dependable identity recognition and authorization from both information space and physical space were presented,where the development of the attack models on face detection,face recognition,person re-identification,and face anti-spoofing as well as printable adversarial patches were introduced.The algorithms of visual identity anonymization and privacy protection were further discussed.Finally,the datasets,experimental protocols and performance of dependable identity recognition methods were summarized,and the possible directions in the future research were presented.…”
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Perspectives on Image Aesthetic Evaluation Techniques by Yang Kaiwei

“…Although deep learning technology can improve the accuracy of evaluation and has achieved some results in aesthetic evaluation tasks, the technology still faces challenges such as high subjectivity and poor diversity, and it is believed that with the progress of science and technology, these problems will be solved. …”
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Analysis of Intelligent Translation Systems and Evaluation Systems for Business English by Jianhong Chen

“…In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. …”
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Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications by Mohammad Kamrul Hasan, Muhammad Shafiq, Shayla Islam, Bishwajeet Pandey, Yousef A. Baker El-Ebiary, Nazmus Shaker Nafi, R. Ciro Rodriguez, Doris Esenarro Vargas

“…For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.…”
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A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet by Muhammad Waqas Ahmed, Moneerah Alotaibi, Sultan Refa Alotaibi, Dina Abdulaziz Alhammadi, Asaad Algarni, Ahmad Jalal, Jeongho Cho

“…Leveraging the power of deep learning, a DenseNet architecture achieves superior classification accuracy based on the optimized feature set. …”
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Transformer-Based Ionospheric Prediction and Explainability Analysis for Enhanced GNSS Positioning by He-Sheng Wang, Dah-Jing Jwo, Yu-Hsuan Lee

“…This study aims to investigate the impact of ionospheric models on Global Navigation Satellite System (GNSS) positioning and proposes an ionospheric prediction method based on a Transformer deep learning model. We construct a Transformer-based deep learning model that utilizes global ionospheric maps as input to achieve spatiotemporal prediction of Total Electron Content (TEC). …”
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A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification by Qing Zhang, Jialu Chen, Baohua Yuan

“…In recent years, deep learning has witnessed astonishing success in the field of remote sensing in images. …”
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Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism by RuoXi Qin, Zhenzhen Wang, LingYun Jiang, Kai Qiao, Jinjin Hai, Jian Chen, Junling Xu, Dapeng Shi, Bin Yan

“…Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. …”
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Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System by Galib Muhammad Shahriar Himel, Md. Masudul Islam, Kh. Abdullah Al-Aff, Shams Ibne Karim, Md. Kabir Uddin Sikder

“…Skin cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success in various computer vision tasks, including image classification. …”
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Research and Analysis of Facial Recognition Based on FaceNet, DeepFace, and OpenFace by Li Minghan

“…This study provides a comprehensive review of recent advancements in face recognition technology, focusing on deep learning models such as FaceNet, DeepFace, and OpenFace. …”
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Research on the graphical convolution neural network based benefits recommendation system strategy by Tao TAO, Zhen LI, Jibin WANG, Haiyong XU, Yong JIANG, Zhuo CEHN, Runbo ZHANG, Qingyuan HU

“…The recommendation system is one of the important methods to realize the intelligent recommendation of massive Internet benefit products.In order to improve the accuracy of personalized benefits recommendation, a deep learning recommendation system based on graph computing method was proposed.Considering the heterogeneity of multi-source data, a graph representation technology based on deep learning was carried out to construct the multiple relationship graph between users and benefit products.The multiple relationship graph extracted the information of graph structure, and model the heterogeneous graphs for the multi-dimensional features of users and the multiple interaction modes between rights and interests products, which effectively aggregated various interactive information and the multiple feature.A heterogeneous graph convolutional neural network was built to learn the high-dimensional feature vectors for various nodes, and excavate users＇ latent preferences to provide a recommendation link with strong interpretability, which greatly improved the recommendation success rate and generating economic value.…”
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Inequality in breast cancer: Global statistics from 2022 to 2050 by Ling Liao

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Automatic classification of mobile apps to ensure safe usage for adolescents. by Hanadi Hakami

“…This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. …”
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Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models by Zeng Hongzhi

“…The vast application of artificial intelligence in numerous fields—image generation being one of them—has been made possible by the quick development of deep learning. Generative Adversarial Networks (GAN) can generate high-quality images through an adversarial training mechanism. …”
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Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends by Lina Elmoiz Alatabani, Elmustafa Sayed Ali, Rania A. Mokhtar, Rashid A. Saeed, Hesham Alhumyani, Mohammad Kamrul Hasan

“…In this paper, some concepts related to deep learning networks will be discussed as one of the uses of machine learning in IoV systems, in addition to studying the effect of neural networks (NNs) and their types, as well as deep learning mechanisms that help in processing large amounts of unclassified data. …”
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Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models by Mi Jin Noh, Yang Sok Kim

“…<b>Methods</b>: Kaggle’s dataset from the National Institute of Diabetes and Digestive and Kidney Diseases was analyzed using logistic regression, deep learning, gradient boosting, and decision trees. …”
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Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
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A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross by Jianzheng Lin

“…Automatic Modulation Classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. …”
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