Using Machine Learning and Nationwide Population-Based Data to Unravel Predictors of Treated Depression in Farmers by Pascal Petit, Vincent Bonneterre, Nicolas Vuillerme

“…To complement these traditional studies, big data and machine learning (ML) can advantageously be harnessed. While ML algorithms have shown high accuracy in identifying depression predictors in mental health research, no study has yet applied ML in farmers. …”
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A comparative ensemble approach to bedload prediction using metaheuristic machine learning by Ajaz Ahmad Mir, Mahesh Patel, Fahad Albalawi, Mohit Bajaj, Milkias Berhanu Tuka

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The Transformative Influence of Artificial Intelligence on Supply Chain Management by Tarihal Balakrishna, Kaliwal Rohit, Ammanagi Ashok

“…This paper aims to explore the effects of AI on supply chains with a particular emphasis on how machine learning and other forms of intelligent algorithms can improve decision-making, operational effectiveness and real-time responsiveness. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or traditional machine learning, which suffer from low classification efficiency and insufficient accuracy. …”
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Inconsistency evaluation of the curriculum logical structure by Yu. D. Ageev, S. V. Fedoseev, Yu. A. Kavin, S. G. Vorona, I. S. Pavlovskiy

“…Conclusion. As a result of the research, the methods were proposed that allow identifying possible contradictions in the structure of the curriculum and evaluating its inconsistency. …”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…This research presents an enhanced predictive model for green sand casting, designed to tackle the nonlinear complexities arising from interdependent process parameters. …”
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A review of deep-learning-based models for afaan oromo fake news detection on social media networks by Kedir Lemma Arega, Kula Kekeba Tune, Asrat Mulatu Beyene, Wegderes Tariku, Nurhussen Menza Bune

“…It compares machine learning and deep learning algorithms, proposes future studies using picture data, and encourages more effective methodologies. …”
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Digitalization of Spectral Measurements in the Fourier Basis – Development Trends and Problems by O. V. Ponomareva, A. V. Ponomarev, N. V. Smirnova

“…The purpose of the article is to analyze the development of digital technologies for measuring spectra, identifying the problems that arise in this case and formulating priority scientific and applied problems for their resolution.Theoretical and applied research has established that digital spectrum measurement technologies, in addition to significant advantages, have certain disadvantages. …”
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The role of mitochondria-related genes in hepatocellular carcinoma prognosis: construction of prognostic models based on machine learning by Fei Gao, Fei Teng, Yuxiang Wan, Qiaoli Zhang, Jinchang Huang

“…We evaluated 113 machine learning algorithms to develop mitochondrial gene-based prognostic models. …”
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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

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Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar by Mohammad Sadegh Barkhordari, Chongchong Qi

“…Nevertheless, consistent removal efficiency remains challenging to achieve due to the inherent variability of biochar and its interactions with complex environmental factors. This research introduces an advanced machine learning (ML) framework, utilizing deep forest (DF) algorithms, to predict and optimize the efficiency HM removal through biochar applications. …”
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The class project of cyber-driver by P. S. Sorokoumov

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From gene expression to causal associations: investigating the role of ferroptosis in cataract development by Chen Li, Xian-bing Yuan, Yi-cheng Lu, Zi-yue Song

“…Nine hub genes were identified using the PPI network and five algorithms in Cytoscape. The RT-PCR results validated TIGAR, IL6, ATF3, and TNFAIP3 as potential biomarkers. …”
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Reconstruction of Tokamak Plasma Emissivity Distribution by Approximation with Basis Functions by Tomasz Czarski, Maryna Chernyshova, Katarzyna Mikszuta-Michalik, Karol Malinowski

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Using weak signals to predict spontaneous breathing trial success: a machine learning approach by Romain Lombardi, Mathieu Jozwiak, Jean Dellamonica, Claude Pasquier

“…The study focuses on the development of ML algorithms to predict the success of the spontaneous breathing trial based on a combination of discrete variables and biosignals (time series) recorded during the 24 h prior to the SBT. …”
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Comparative analysis of high-resolution UAV photogrammetry and terrestrial laser scanning for detecting and quantifying urban vegetation changes by O. B. Shafaat, H. Kauhanen, A. Julin, M. Vaaja

“…This study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022. The research utilized terrestrial laser scanning (TLS) and UAV-photogrammetry datasets for change detection in urban vegetation and point cloud-based algorithms for seasonal variations such as C2C, C2M, and M3C2. …”
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Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data by Ailing Zhang, Haobo Zhang

“…Support vector machine and random forest algorithms were then used to construct prediction models. …”
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Implementation of personalized frameworks in computational thinking development: implications for teaching in software engineering by Josué Guevara-Reyes, Mariuxi Vinueza-Morales, Erick Ruano-Lara, Cristian Vidal-Silva

“…The development of computational thinking (CT) is crucial in software engineering education, as it enables students to analyze complex problems, design algorithmic solutions, and adapt to an evolving digital landscape. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

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Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy by Wayez Naqvi, Prekshi Garg, Prachi Srivastava

“…It aids clinicians in addressing patient parameters and translational research. Artificial neural networks (ANNs) are computer models that attempt to mimic the neurons present in the human brain. …”
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