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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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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Explainable Multi-Scale CAM Attention for Interpretable Cloud Segmentation in Astro-Meteorological Applications by Qing Xu, Zichen Zhang, Guanfang Wang, Yunjie Chen

“…Accurate cloud segmentation is critical for astronomical observations and solar forecasting. However, traditional threshold- and texture-based methods suffer from limited accuracy (65–80%) under complex conditions such as thin cirrus or twilight transitions. …”
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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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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…Beta was at 0.00, meaning no bias within the forecast. Gamma was also at 0.00, resulting in a very high level of precision within the forecast. …”
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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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An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction by Mohamed Ahwiadi, Wilson Wang

“…The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. …”
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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

“…There are increasing demands for analysts to use quantitative data to monitor this system, with the goals of early detection and forecasting. However, standard approaches often struggle with the incomplete and complex nature of trade data, which tends to include non-normality, temporal and spatial autocorrelation, and limited observations. …”
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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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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

“…Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. …”
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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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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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Possibilities of a biobank for serological monitoring of infectious morbidity by O. A. Svitich, V. V. Zverev, T. V. Solomay, T. A. Semenenko, A. V. Nozdracheva, T. P. Gotvyanskaya

“…Currently, monitoring infectious morbidity, control and forecasting the epidemic process is a topical health problem.Aim. …”
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Characterization of the avian influenza viruses distribution in the environment of live poultry market in China, 2019–2023 by Hong Bo, Ye Zhang, Jie Dong, Xiyan Li, Xiang Zhao, Hejiang Wei, Zi Li, Dayan Wang

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THEORY AND PRACTICE OF ASSESSING THE LEVEL OF INSTABILITY OF THE EXTERNAL ENVIRONMENT OF THE ENTERPRISE IN A HYBRID WAR by I. Zapukhliak, A. Shegda, О. Mykytiuk, T. Onysenko, K. Petrenko

“…Current trends in the globalization economy, especially in the context of economic crises caused by hybrid war, COVID-19, lead to a constant search for new methods of enterprise management that could ensure its effective functioning. …”
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Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review by Umile Giuseppe Longo, Martina Marino, Guido Nicodemi, Matteo Giuseppe Pisani, Jacob F. Oeding, Christophe Ley, Rocco Papalia, Kristian Samuelsson

“…Seven studies analysed the detection of rotator cuff tears (RCTs) in magnetic resonance imaging and found area under the curve (AUC) values ranged from 0.812 to 0.94 for the detection of RCTs. …”
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