Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. …”
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Advancements in the application of artificial intelligence in the field of colorectal cancer by Mengying Zhu, Mengying Zhu, Zhenzhu Zhai, Yue Wang, Fang Chen, Ruibin Liu, Ruibin Liu, Xiaoquan Yang, Guohua Zhao

“…This poses a significant threat to global public health. Early screening methods, such as fecal occult blood tests, colonoscopies, and imaging techniques, are crucial for detecting early lesions and enabling timely intervention before cancer becomes invasive. …”
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A large-scale dataset for training deep learning segmentation and tracking of extreme weather by Sol Kim, Andre Graubner, Lukas Kapp-Schwoerer, Karthik Kashinath, Konrad Schindler

“…The resulting annotations are demonstrated to have characteristics similar to other methods and those generated directly by domain experts.…”
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The implications of mutations in multiple genes associated with ethambutol resistance among multidrug-resistant tuberculosis isolates from China by Ma-chao Li, Wei Wang, Tong-yang Xiao, Hai-can Liu, Shi-qiang Lin, Hao Hang, Xiang-long Bo, Xiao-tian Nan, Cheng Qian, Xue-ting Fan, Xiu-qin Zhao, Gui-lian Li, Kang-Lin Wan, Li-li Zhao

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Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning by Yintao Liu, Chao Ren, Hongjuan Shao

“…Abstract This study introduces two innovative methods—Three-frequency pseudorange combination (TFPC) and Three-frequency carrier phase combination (TFCPC)—for estimating soil moisture using GNSS-IR technology. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…The ViT-ResNet152 model’s convolutional and transformer processing elements worked together to improve the accuracy of the diagnosis to 91.33% and make it better at finding different cases of autism spectrum disorder (ASD).The research outcomes demonstrate that AI tools show promise for delivering highly precise and standardized methods to detect ASD at an early stage. Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. …”
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Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification by Mingquan Hong, Hongcai Zhang, Lihua Wu, Jialiang Chen, Lijin Dai, Lujun Wang, Tengchao Dong, Jinling Yang, Lihua Fang

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Prediction on rock strength by mineral composition from machine learning of ECS logs by Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan

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Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode by Deo Arpit, Korde Manish, Tiwari Anant, Jain Anant, Choudhary Akash

“…The model incorporates both financial and non-financial characteristics, solving research deficiencies concerning cooperative societies, governance, market rivalry, and external economic factors. Data preprocessing methods, including outlier detection, feature selection, and dimensionality reduction, improve model accuracy. …”
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Tracking Biosecurity Through the Diversity and Network Structure of International Trade by Kong-Wah Sing, Rachel Peden, Damien Hicks

“…Traditional correlations to total number of interceptions remained intractable, but machine learning tools demonstrated predictive power to forecast these temporal patterns. Combined, these methods provide a novel approach for biosecurity monitoring in plant and animal trade across international borders. …”
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Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision by Ethan P. White, Lindsey Garner, Ben G. Weinstein, Henry Senyondo, Andrew Ortega, Ashley Steinkraus, Glenda M. Yenni, Peter Frederick, S. K. Morgan Ernest

“…Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. …”
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Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations by Sumit Singh Dhanda, Deepak Panwar, Chia-Chen Lin, Tarun Kumar Sharma, Deependra Rastogi, Shantanu Bindewari, Anand Singh, Yung-Hui Li, Neha Agarwal, Saurabh Agarwal

“…Mental health prediction systems based on NLP and wearable data delivered up to 91% accuracy in stress and depression detection, while hospital resource forecasting models using deep learning minimized errors in predicting emergency admissions. …”
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A new gridded offshore wind profile product for US coasts using machine learning and satellite observations by J. Frech, J. Frech, K. Saha, K. Saha, P. D. Lavin, P. D. Lavin, H.-M. Zhang, J. Reagan, B. Fung

“…<p>Offshore wind speed data around wind turbine hub heights are fairly limited, available through in situ observations from wind masts, sonic detection and ranging (sodar) instruments, or floating light detection and ranging (lidar) buoys at selected locations or as forecasting-model-based output from reanalysis products. …”
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Shifts in seasonal influenza patterns in Australia during and after COVID-19: A comprehensive analysis by Cynthia Lu, Ian G. Barr, Stephen Lambert, Kerrie Mengersen, Liping Wang, Weizhong Yang, Zhongjie Li, Sotiris Vardoulakis, Hilary Bambrick, Wenbiao Hu

“…Epidemic weeks were detected using a negative binomial threshold, and epidemic onset was estimated with a Bayesian Poisson count detection algorithm. …”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…Abstract Accurately forecasting carbon dioxide (CO2) adsorption in KOH-activated biochar is crucial for advancements in geoenergy engineering and environmental technology. …”
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MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation by Huantong Geng, Han Zhao, Zhanpeng Shi, Fangli Wu, Liangchao Geng, Kefei Ma

“…However, traditional echo extrapolation methods fail to fully utilize historical radar echo data, resulting in limited accuracy for future radar echo prediction. …”
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A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes by Cosmina-Mihaela Rosca, Adrian Stancu

“…The research notes that previous studies have focused on wind forecasting, fault detection, or turbine efficiency. …”
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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…By integrating RSCconv and ASCFF into other detection frameworks such as Faster R-CNN, RetinaNet, and Deformable DETR, we observed enhanced detection and counting accuracy, further validating the effectiveness of our proposed method. …”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…To solve these issues, Bille-Viper-Segmentation with the Tandem-MU-Net Model is suggested as a solution for tissue damage detection problems. This study improves blood flow detection in stroke images by introducing the Bille-Viper-Segmentation method to overcome difficulties in recognizing tissue injury. …”
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Improving the Accuracy of Neural Network Pattern Recognition by Fractional Gradient Descent by Ruslan I. Abdulkadirov, Pavel A. Lyakhov, Valentina A. Baboshina, Nikolay N. Nagornov

“…Further research of fractional calculus in modern neural network methodology can improve the quality of solving various challenges such as pattern recognition, time series forecasting, moving object detection, and data generation.…”
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