Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…Progressions in disease forecasts aid farmers in making informed decisions, minimizing crop losses, and reducing pesticide use through targeted application of agrochemicals with the use of AI-driven variable rate sprayers. This leads to healthier crops, market stability, and a more sustainable farming environment.…”
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Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…The highest area under the curve (=1) was reported in the preoperative planning outcome variable and utilized CNN. All 20 studies demonstrated a high level of quality and low risk of bias, with a modified MINORS score of at least 7/8 (88%). …”
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Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…By comparing various machine learning and linear regression models, it is found that CNN and quomial regression perform relatively well when the regional average surface rainfall is taken as the statistical variable, with the determination coefficient of CNN being 0.516 and RMSE being 1.097 mm. …”
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Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network by Ahmad Taufiq Akbar, Shoffan Saifullah, Hari Prapcoyo

“…Abstract Facial expression recognition is a significant challenge in image processing and human-computer interaction due to its inherent complexity and variability. This study proposes a simple Convolutional Neural Network (CNN) architecture to enhance the efficiency of emotion classification on small datasets. …”
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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

“…Previous studies have demonstrated that calibrating the TH model with additional variables can improve its accuracy; however, these efforts have been largely preliminary. …”
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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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KERNEL DETERMINATION PROBLEM FOR ONE PARABOLIC EQUATION WITH MEMORY by Durdimurod K. Durdiev, Javlon Z. Nuriddinov

“…This paper studies the inverse problem of determining a multidimensional kernel function of an integral term which depends on the time variable \(t\) and \((n-1)\)-dimensional space variable \(x'= \left(x_1,\ldots, x_ {n-1}\right)\) in the \(n\)-dimensional diffusion equation with a time-variable coefficient at the Laplacian of a direct problem solution. …”
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Bureaucratic Behavior and Utilization of Online Single Submission (OSS) Technology by Nur Mulyani Sari, Bachtari Alam Hidayat, Rika Destiny Sinaga

“…This study employed a descriptive quantitative approach. The independent variables were bureaucratic behavior (X1) and business actor behavior (X2), while the dependent variable was investment acceleration (Y). …”
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CSI-based symmetric encryption end-to-end communication system by Yongli AN, Zongrui LI, Haifei BAI, Xueyu MI

“…At the same time, it leveraged the reciprocity, random time-variability, and spatial uniqueness of wireless channels to measure the CSI and generate keys from legitimate users, encrypting the original information. …”
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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

“…Accurate time-series classification (TSC) remains a fundamental challenge in deep learning due to the complexity and variability of temporal patterns. While recurrent neural networks (RNNs) such as LSTM and GRU have shown promise in modeling sequential dependencies, they often suffer from limitations like vanishing gradients and high computational cost when handling long sequences. …”
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ON PRESENTATION OF LINEAR OPERATORS COMMUTING WITH DIFFERENTIATION IN SIMPLY-CONNECTED DOMAIN by A. V. Bratishchev

“…It is known that a linear complex convolution operator is generated by a one - variable analytic function, a multivalued one in general. …”
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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

“…The specificity of 69%, 89% and 89% were reported for the aforementioned variables. Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. …”
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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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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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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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