Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution by Muhammad Imaduddin Abdur Rohim, Auliati Nisa, Muhammad Nurkhoiri Hindratno, Radhiyatul Fajri, Gembong Satrio Wibowanto, Nova Hadi Lestriandoko, Pesigrihastamadya Normakristagaluh

“…Kami menginvestigasi penggunaan metode super-resolution (SR) berbasis deep learning, termasuk DFDNet, LapSRN, GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN, dan FaceSPARNet. …”
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Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data by Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu, Hongfei Jin

“…Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. …”
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hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses by Dohyeon Lee, Sunyong Yoo

“…Scientific contribution: hERGAT is a deep learning model for predicting hERG blockers by combining GAT and GRU, enabling it to capture complex interactions at atomic and molecular levels. …”
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Micro-Expression Recognition Using Convolutional Variational Attention Transformer (ConVAT) With Multihead Attention Mechanism by Hafiz Khizer Bin Talib, Kaiwei Xu, Yanlong Cao, Yuan Ping Xu, Zhijie Xu, Muhammad Zaman, Adnan Akhunzada

“…Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. …”
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Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield by Nisha P. Shetty, Balachandra Muniyal, Ketavarapu Sriyans, Kunyalik Garg, Shiv Pratap, Aman Priyanshu, Dhruthi Kumar

“…The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. …”
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EPRNG: Effective Pseudo-Random Number Generator on the Internet of Vehicles Using Deep Convolution Generative Adversarial Network by Chenyang Fei, Xiaomei Zhang, Dayu Wang, Haomin Hu, Rong Huang, Zejie Wang

“…Before generating the encryption keys, a random number generator (RNG) plays an important component in cybersecurity. Several deep learning-based RNGs have been deployed to train the initial value and generate pseudo-random numbers. …”
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Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning by Ning Liu, Yu Shen, Shuangcheng Zhang, Xuejian Zhu

“…For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. …”
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Precision Imaging for Early Detection of Esophageal Cancer by Po-Chun Yang, Chien-Wei Huang, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Chu-Kuang Chou, Kai-Yao Yang, Hsiang-Chen Wang

“…This study underscores the potential of deep learning identification models to aid in medical detection research. …”
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Semantic Tokenization-Based Mamba for Hyperspectral Image Classification by Ri Ming, Na Chen, Jiangtao Peng, Weiwei Sun, Zhijing Ye

“…Experimental results on three HSI datasets demonstrate that the proposed STMamba outperforms existing state-of-the-art deep learning and transformer-based methods.…”
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An advanced 3D lymphatic system for assaying human cutaneous lymphangiogenesis in a microfluidic platform by Minseop Kim, Sieun Choi, Dong-Hee Choi, Jinchul Ahn, Dain Lee, Euijeong Song, Hyun Soo Kim, Mijin Kim, Sowoong Choi, Soojung Oh, Minsuh Kim, Seok Chung, Phil June Park

“…In addition, we rapidly analyzed prolymphangiogenic effects using methods that incorporate a high-speed image processing system and a deep learning-based vascular network analysis algorithm by 12 indices. …”
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A Survey on Event Tracking in Social Media Data Streams by Zixuan Han, Leilei Shi, Lu Liu, Liang Jiang, Jiawei Fang, Fanyuan Lin, Jinjuan Zhang, John Panneerselvam, Nick Antonopoulos

“…Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. …”
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Transfer Learning-Based Health Monitoring of Robotic Rotate Vector Reducer Under Variable Working Conditions by Muhammad Umar Elahi, Izaz Raouf, Salman Khalid, Faraz Ahmad, Heung Soo Kim

“…Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. …”
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Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems by Sebastian Pecolt, Andrzej Błażejewski, Tomasz Królikowski, Igor Maciejewski, Kacper Gierula, Sebastian Glowinski

“…It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. …”
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Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening by Shenming Hu, Hong Wu, Xinze Luan, Zhuoshi Wang, Mary Adu, Xiaoting Wang, Chunhong Yan, Bo Li, Kewang Li, Ying Zou, Xiaoya Yu, Xiangdong He, Wei He

“…Furthermore, the images collected by the smartphone are uploaded to the deep learning cataract screening system, which can achieve real-time and effective screening of cataract. …”
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Моделі самоорганізації колективу однорідних безпілотних літальних апаратів при рішенні слабоформалізованих завдань... by А.В. Тристан, Д.І. Жуков

“…The practicality of this method lies in the fact that the artificial intelligence of the UAV will constantly self-learn and improve through the use of machine and deep learning. Thus, the results and time required to complete missions will improve significantly, and the number of control operators will decrease. …”
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Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation by Hua Xie, Minghua Zhang, Jiaming Ge, Xinfang Dong, Haiyan Chen

“…Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. …”
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Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR by Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Andreas Dengel

“…Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. …”
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MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks by Jianwei Li, Xukun Zhang, Bing Li, Ziyu Li, Zhenzhen Chen

“…Results In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…Many machine learning (ML) and deep learning (DL) methods have been proposed to facilitate automated early diagnosis of this cancer. …”
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MM-HiFuse: multi-modal multi-task hierarchical feature fusion for esophagus cancer staging and differentiation classification by Xiangzuo Huo, Shengwei Tian, Long Yu, Wendong Zhang, Aolun Li, Qimeng Yang, Jinmiao Song

“…However, some previous studies have employed deep learning-based methods for esophageal cancer analysis, which are limited to single-modal features, resulting in inadequate classification results. …”
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