Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media by Irwan Budiman, Mohammad Reza Faisal, Astina Faridhah, Andi Farmadi, Muhammad Itqan Mazdadi, Triando Hamonangan Saragih, Friska Abadi

“…The classification of social media messages can be achieved through the application of classification algorithms. Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. …”
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Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings by Biplov Paneru, Bipul Thapa, Bishwash Paneru

“…In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). …”
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Deep attributes and decisions fusion for no-reference video quality analysis by Adil Baig

“…This study describes a novel, deep learning-based strategy for NR-VQA that uses several pre-trained deep neural networks to characterize probable image and video distortions across parallel. …”
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Open-world disaster information identification from multimodal social media by Chen Yu, Bin Hu, Zhiguo Wang

“…Abstract The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. …”
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Gradient pooling distillation network for lightweight single image super-resolution reconstruction by Zhiyong Hong, GuanJie Liang, Liping Xiong

“…In recent years, significant progress about SISR has been achieved through the utilization of deep learning technology. However, these deep methods often exhibit large-scale networks architectures, which are computationally intensive and hardware-demanding, and this limits their practical application in some scenarios (e.g., autonomous driving, streaming media) requiring stable and efficient image transmission with high-definition picture quality. …”
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Multiscale characterization of medical devices and pharmaceutical formulations with 3D X-ray microscopy and computed tomography by Herminso Villarraga-Gómez, Ria L. Mitchell

“…This paper discusses advancements in 3D X-ray imaging workflows for characterizing medical devices and pharmaceutical formulations. It also covers deep-learning (DL) data reconstruction techniques that enhance 3D X-ray microscopy (XRM) and computed tomography (CT) applications in drug delivery and the pharmaceutical industry. …”
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Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network by Pan Xu, Dongbao Gao, Shui Yu, Guangming Li, Yun Zhao, Guojun Xu

“…The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. …”
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Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection. by Guofeng Qin, Qinkai Zou, Mengyan Li, Yi Deng, Peiwen Mi, Yongjian Zhu, Hao Liu

“…Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. …”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Advancements in medical diagnosis using artificial intelligence have popularised transfer learning among researchers in machine learning and deep learning. This scoping review focuses on the application of ECG-based transfer learning for CVD identification, examining the most common types of CVD studied, the input formats used, frequently referenced databases, and the extent of transfer learning's application in diagnosing CVD through ECG analysis. …”
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AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer by Jiawei Zhang, Feng Ding, Yitian Guo, Xiaoying Wei, Jibo Jing, Feng Xu, Huixing Chen, Zhongying Guo, Zonghao You, Baotai Liang, Ming Chen, Dongfang Jiang, Xiaobing Niu, Xiangxue Wang, Yifeng Xue

“…This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). …”
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Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data by Jaeseok Yoo, Young-jin Oh, Nam-hyun Kim, Soo-ill Lee, Jaepil Ko

“…In this study, we explored how to effectively handle the long-term dependence problem and various data characteristics to increase the forecasting accuracy of transmission power in NPPs by introducing a Seq2Seq model with an encoder-decoder structure and an attention mechanism, beyond traditional time series deep learning models, especially LSTM. This approach will improve the accuracy of transmission power forecasting and contribute to a stable power supply. …”
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A systematic review of progress test as longitudinal assessment in Saudi Arabia by Naseel Ahmed Moursy, Khaled Hamsho, Arwa Mohammad Gaber, Muhammad Faisal Ikram, Muhammad Raihan Sajid

“…Contrasting traditional assessments, PT promotes deep learning strategies and meaning-oriented approaches, fostering holistic understanding rather than rote memorization. …”
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Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture by Simon Prananta Barus, Jeriko Ichtus Seo

“…Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.…”
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Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network by Yi Ren, Runfei Yi, Zhaoxin Lian, Quan Xia, Dezhen Yang, Bo Sun, Qiang Feng

“…These results indicate that integrating physical information into deep learning methods is a promising approach by which to assess BLDC motor stator health.…”
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A Preliminary Study on 2D Convolutional Neural Network-Based Discontinuous Rail Position Classification for Detection on Rail Breaks Using Distributed Acoustic Sensing Data by Hye-Yeun Chun, Jungtai Kim, Dongkue Kim, Ilmu Byun, Kyeongjun Ko

“…In this research, as a preliminary study on rail break detection system, a deep learning-based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. …”
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Multi-modal framework for battery state of health evaluation using open-source electric vehicle data by Hongao Liu, Chang Li, Xiaosong Hu, Jinwen Li, Kai Zhang, Yang Xie, Ranglei Wu, Ziyou Song

“…Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. …”
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Edge and texture aware image denoising using median noise residue U-net with hand-crafted features by Soniya S., Sriharipriya K. C.

“…Although fully convolution neural networks (CNN) are capable of removing the noise using kernel filters and automatic extraction of features, it has failed to reconstruct the images for higher values of noise standard deviation. Additionally, deep learning models require a huge database to learn better from the inputs. …”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The unified representation of features is another challenging task in spam detection. Various deep learning approaches have been proposed for spam detection and classification but these methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features but there is a lack of comprehensive models that integrate linguistic and behavioral features to improve the accuracy of spam detection. …”
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A Next-Generation Codebook Evolution Strategy for Massive Arrays Using Deep Neurals Networks by Minwoo Choi, Wonjin Sung

“…Precoder matrix indicator (PMI) and channel quality indicator (CQI) reports from the users have become the sources for the generation of a new set of codevectors, which are autonomously determined by the deep learning (DL) module at the base station (BS). The process is operated in an iterative fashion to produce updated versions of the codebook with the reduced return of the loss function at the deep neural network (DNN). …”
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Assessing bias and computational efficiency in vision transformers using early exits by Seth Nixon, Pietro Ruiu, Marinella Cadoni, Andrea Lagorio, Massimo Tistarelli

“…Abstract Face recognition with deep learning is generally approached as a problem of capacity. …”
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