Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

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Deep learning model using continuous skin temperature data predicts labor onset by Chinmai Basavaraj, Azure D. Grant, Shravan G. Aras, Elise N. Erickson

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Deep Learning for the Early Diagnosis of Candidemia by Daniele Roberto Giacobbe, Sabrina Guastavino, Anna Razzetta, Cristina Marelli, Sara Mora, Chiara Russo, Giorgia Brucci, Alessandro Limongelli, Antonio Vena, Malgorzata Mikulska, Alessio Signori, Antonio Di Biagio, Anna Marchese, Ylenia Murgia, Marco Muccio, Nicola Rosso, Michele Piana, Mauro Giacomini, Cristina Campi, Matteo Bassetti

“…Conclusions A deep learning model trained on nonspecific laboratory features showed some discriminatory ability to differentiate candidemia from bacteremia, highlighting the ability of deep learning to exploit complex patterns within nonspecific laboratory data. However, the learned patterns did not improve the diagnostic performance of more specific markers. …”
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The AI Paradox: Balancing Free Speech with Online Safety in the Era of Automated Moderation by Cosmin PROȘCANU, Miruna PROȘCANU

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Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training by Joseph A. Kilgore, Jeffrey D. Kopsick, Giorgio A. Ascoli, Gina C. Adam

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Intelligent Functions Development on Autonomous Electric Vehicle Platform by Ruxin WANG, Raivo SELL, Anton RASSOLKIN, Tauno OTTO, Ehsan MALAYJERDI

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Artificial intelligence and robotics in education: Advances, challenges, and future perspectives by Javier Fombona, Jose-Manuel Sáez, Susana Sánchez

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Hybrid Android Malware Detection and Classification Using Deep Neural Networks by Muhammad Umar Rashid, Shahnawaz Qureshi, Abdullah Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh

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Mind wandering in sensory cortices by Shao-Min Hung, Po-Jang Hsieh

“…Unique patterns in classification were revealed in different individuals, pointing to individual variances in our phenomenal experiences.…”
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Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches by Juscimara G. Avelino, Bruna A. O. Brito, Thiago T. de Oliveira, Jean M. M. de Lima, Andre M. Gurgel, Ramon S. Malaquias, Itamir de M. B. Filho

“…Artificial Intelligence (AI) and Machine Learning (ML) are promising tools to anticipate and mitigate these threats, offering predictive capabilities based on historical data. …”
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Leveraging LSTM and ensemble classifiers for enhanced food waste classification by Khalaf Alsalem

“…The proposed feature extraction approach detects temporal patterns in feature data, improving the robustness of decisions and making this method suitable for waste classification applications. …”
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Identification and validation of glycosylation-related gene signatures for prognostic stratification in sepsis by Chunyang Li, Haiyan Xue, Haiyan Xue, Lihe Chen, Fengxue Zhu, Jie Li

“…Glycosylation is one of the key modes of protein modification, affecting protein folding, transportation, and localization. Glycosylation patterns are closely related to sepsis, but their specific impact still needs further investigation. …”
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Big Data in Neuroscience: Transforming Research and the Understanding of Brain Function by Adina DZIŢAC

“…Since the year 2000, artificial intelligence and machine learning have transformed neuroscience research and have revolutionized data analysis and made it possible for researchers to discover complex patterns in brain activity, relating this to neurological disorders and cognitive functions. …”
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Definition of innovation problems in organizations using data analysis tasks from hybrid sources: social networks and organizational databases by Ana Gutiérrez, Jose Aguilar, Ana Ortega, Edwin Montoya

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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

“…This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. …”
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Hadron Identification Prospects with Granular Calorimeters by Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore

“…The analysis focuses on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns and timing information. Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. …”
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Study of ESG transformation of the region by the artificial intelligence system by Nikolay I. Lomakin, Tatyana I. Kuzmina, Maxim S. Maramygin, Olga V. Yurova, Oksana A. Minaeva, Aleksey A. Polozhentsev, Tamila D. Eliseeva

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Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer by Xiao Guo, Chuanbo Feng, Jiaying Xing, Yuyan Cao, Tengda Liu, Wenchuang Yang, Runhong Mu, Tao Wang

“…Epigenetic changes play a critical role in breast cancer progression and therapy responses, providing a foundation for prognostic model development.MethodsWe developed the Machine Learning-derived Epigenetic Model (MLEM) to identify prognostic epigenetic gene patterns in breast cancer. …”
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Enhancing Cloud Job Failure Prediction With a Novel Multilayer Voting-Based Framework by Ahmed Elkaradawy, Ayman Elshenawy, Hany Harb

“…To address this challenge, researchers have progressively developed machine learning and deep learning techniques that examine cloud logs to identify patterns linked to such failures. …”
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Application of Discrete Element Method to Potato Harvesting Machinery: A Review by Yuanman Yue, Qian Zhang, Boyang Dong, Jin Li

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