CSI-based symmetric encryption end-to-end communication system by Yongli AN, Zongrui LI, Haifei BAI, Xueyu MI

“…The proposed system employed convolutional neural networks to construct the transmitter, receiver, and key generator, optimizing the encoding and decoding process in an end-to-end manner. …”
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Hybrid deep learning framework for robust time-series classification: Integrating inception modules with residual networks by Duong Thi Kim Chi, Nguyen Thi Mai Trang, Tran Ba Minh Son, Nguyen Ngoc Thao, Thanh Q. Nguyen

“…To overcome these issues, convolutional neural networks (CNNs), particularly the Inception architecture, have emerged as powerful alternatives due to their ability to capture multiscale local patterns efficiently. …”
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DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale by Wenxuan Guo, Zhaoping Hu, Ling Jin, Yanyan Xu, Marta C Gonzalez

“…DeepAir integrates a pre-trained convolutional neural network with the LightGBM method. …”
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Investigation into the prognostic factors of early recurrence and progression in previously untreated diffuse large B-cell lymphoma and a statistical prediction model for POD12 by Ke Lian, Wenyao Zhu, Zhihui Hu, Fang Su, CaiXia Xu, Hui Wang

“…A prediction method for the characteristic variables of POD12 risk is proposed using the CNN-LSTM deep learning model based on chaotic time series. …”
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EvapoDeep: A Dual Deep Learning Framework Utilizing GNSS Data for Evapotranspiration Modeling and Predictive Analysis by Saeed Ebrahimi, Saeid Haji-Aghajany, Yazdan Amerian, Melika Tasan

“…This DET₀ is then modeled using an advanced Convolutional Neural Network-based deep learning method. …”
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Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights by Huiling Miao, Rui Zhang, Zhenghua Song, Qingrui Chang

“…Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. …”
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Toward the validation of crowdsourced experiments for lightness perception. by Emily N Stark, Terece L Turton, Jonah Miller, Elan Barenholtz, Sang Hong, Roxana Bujack

“…Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. …”
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Bureaucratic Behavior and Utilization of Online Single Submission (OSS) Technology by Nur Mulyani Sari, Bachtari Alam Hidayat, Rika Destiny Sinaga

“… Bureaucratic behavior in Indonesia is often criticized for being slow, convoluted, and lacking transparency, ultimately reducing investor interest at the regional level. …”
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Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients by Yinan Wang, Yinan Wang, Lizhou Gong, Yang Zhao, Yewei Yu, Hanxu Liu, Xiao Yang

“…However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. …”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…But since cardiomyocytes are objects with a high level of complexity and have significant morphological variability, automatic classification is complicated by the lack of implemented methods. …”
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Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest by Gulfem Ozlu Ucan, Omar Abboosh Hussein Gwassi, Burak Kerem Apaydin, Bahadir Ucan

“…However, its effectiveness is challenged by methodological variability and biological differences between individuals. …”
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Comparative Study of the Performance of Two Treatment Planning Systems Using Tests from the MPPG 5b Guidelines, TECDOC 1583 and Venselaar's Confidence Limits by Carlos Vega D'Espaux, Federico Lorenzo, Franco La Paz Mastandrea, Nicolás Larragueta

“… This work presents a comprehensive comparative study of the performance of two Treatment Planning Systems (TPS) in radiotherapy: the Eclipse™ TPS by Varian Medical Systems, which utilizes the Analytical Anisotropic Algorithm (AAA), and the MIRS TPS by Nuclemed, which uses the Convolution and Superposition (CS) Algorithm. The evaluation of both systems was conducted following different methods, including point measurements, dose profiles, and measurements on phantoms with heterogeneities. …”
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Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis by Saeed Reza Motamedian, Negin Cheraghi, Sadra Mohaghegh, Elham Babadi Oregani, Mahrsa Amjadi, Parnian Shobeiri, Niusha Solouki, Nikoo Ahmadi, Yassine Bouchareb, Arman Rahmim

“…Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88% and 67% sensitivity for mortality, severity assessment and need for ventilation, respectively. …”
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Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea by Hyeongmok Lee, Go-Eun Kim, Woo-Jin Shin, Yuyoung Lee, Sanghee Park, Kwang-Sik Lee, Jina Jeong, Seung-Ik Park, Sungwook Choung

“…., lithology, tectonic settings) and geochemical compositions, are used as input variables for training a feedforward deep neural network. …”
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Advanced attention-driven deep learning architectures for multi-depth soil temperature prediction by Safwan Mohammed, Sana Arshad, Akasairi Ocwa, Main Al-Dalahmeh, Ashraf ALDabbas, Muhammad Manhal Alzoubi, Attila Vad, Endre Harsányi

“…This research aimed to analyze and predict the dynamic relationship of multi depth soil temperature (SDT) at (5 cm, 10 cm, 20 cm, and 50 cm) with meteorological variables using Bi-wavelet coherence and deep learning models. …”
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A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers by Yadong Yao, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Miao Song, Qiang Li, Jie Li

“…A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Given the complex clinical characteristics of T2DM-FT patients, traditional statistical methods are often insufficient to effectively analyze nonlinear relationships among multiple variables. Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests.…”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. …”
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