Identification of gene signatures and potential pharmaceutical candidates linked to COVID-19-related depression based on gene expression profiles by Shaojun Chen, Yiyuan Luo, Lihua Zhang

“…Subsequently, we employed two machine learning analyses—least absolute shrinkage and selection operator (LASSO) and random forest algorithms– to pinpoint shared hub gene between the two diseases. …”
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Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…We establish soil sampling sites in this region, employ Google Earth Engine algorithms to develop models for soil salinity inversion and land use classification, and analyze the change patterns of land salinization and land use types. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…The model demonstrates strong predictive performance, achieving high precision and recall values across all classes, with an overall classification accuracy of 98.5% for organophosphate pesticides and their mixtures. Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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Supply Chains Problem During Crises: A Data-Driven Approach by Farima Salamian, Amirmohammad Paksaz, Behrooz Khalil Loo, Mobina Mousapour Mamoudan, Mohammad Aghsami, Amir Aghsami

“…To enhance system robustness, probabilistic demand patterns and disruption risks are considered, ensuring supply chain reliability. …”
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A novel integrated TDLAVOA-XGBoost model for tool wear prediction in lathe and milling operations by Zhongyuan Che, Chong Peng, Chi Wang, Jikun Wang

“…Machine learning models, particularly eXtreme Gradient Boosting (XGBoost), demonstrate pattern recognition capabilities for such predictions. …”
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Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence by Hüseyin Abacı, Ahmet Çağdaş Seçkin

“…In addition, unlike the Global Navigation Satellite System (GNSS), Wi-Fi is more suitable for use indoors because signal blocking, attenuation, and reflection restrictions create a unique pattern in places with many Wi-Fi transmitters, and more precise positioning can be performed than GNSS. …”
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Identification of Anomalous Behavior Through the Observation of an Individual’s Emotional Variation: A Systematic Review by Monique S. L. S. Tomaz, Bruno J. T. Fernandes, Alessandra Sciutti

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Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence by Stefania Gentili, Piero Brondi, Giuliana Rossi, Monica Sugan, Giuseppe Petrillo, Jiancang Zhuang, Stefano Campanella

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Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…Passive data will be collected through sensors on the wearable-device, while EMA data will be collected four times a day through a smartphone app. A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. …”
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Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks by Atefeh Pour Shafei, J.Fernando A. Silva, J. Monteiro

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Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications by Felix Wang, Julian Wolf, Mazda Farshad, Mirko Meboldt, Quentin Lohmeyer

“…The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. …”
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Detection and prevalence of avian influenza epidemic in the southwest of Poyang Lake and analysis of the influence of meteorological factors by Kang Fang, Xiansheng Ni, Xi Wang, Wentao Song, Zhiqiang Deng, Zeyu Zhao, Wei Hua, Zhizhong Zeng, Wei Wang, Qianqian Si, Jiang Wu, Bo Zhang, Ping Zhang, Hui Li, Tianmu Chen

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Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation by Isabell Pappert, Stefan Klir, Luca Jokic, Celine Ühlein, Khanh Tran Quoc, Ralf Kaldenhoff

“…To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. …”
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Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…Using artificial intelligence-powered machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals in this way to enable timely medical intervention. …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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A comprehensive review of flood damage in mountainous regions: challenges, solutions, and advanced management technologies by Mohammad Roohi, Jila Dehghani, Maryam Irani, Paramiss Mina

“…This work employs techniques such as LiDAR for precise topographic models, integrating remote sensing with hydrological/hydraulic models, and analyzing satellite imagery to study flood patterns and land cover changes. Furthermore, a precise and useful understanding of the effects of floods in these areas has been made possible by the use of machine learning algorithms to forecast flood episodes and evaluate damage, in conjunction with field research and community participation, such as citizen science initiatives for data collection and local knowledge integration. …”
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WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer by Jing Lv, Jing Lv, Yuhua Zhou, Yuhua Zhou, Shengkai Jin, Shengkai Jin, Chaowei Fu, Chaowei Fu, Yang Shen, Yang Shen, Bo Liu, Bo Liu, Menglu Li, Yuwei Zhang, Yuwei Zhang, Ninghan Feng, Ninghan Feng, Ninghan Feng

“…The intersection of differentially expressed genes and driver genes was taken, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed. Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. …”
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Theoretical approaches to detecting anomalies in meter readings in scientific literature by D.V. Furikhata, T.A. Vakalyuk

“…Particular attention is paid to analysing the effectiveness of various machine learning algorithms for anomaly detection. …”
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AI generations: from AI 1.0 to AI 4.0 by Jiahao Wu, Hengxu You, Jing Du

“…Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. …”
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