Network archaeology: uncovering ancient networks from present-day interactions. by Saket Navlakha, Carl Kingsford

“…To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. …”
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Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis by Alikhan Anuarbekov, Jiří Kléma

“…By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. …”
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Radiomics-based machine learning model for diagnosing internal abdominal hernias: a retrospective study by Zhong-Kai Ni, Tian-Han Zhou, Shu-Chao Kang, Ye-Hong Han, Hai-Min Jin, Shi-Fei Huang, Hai Huang

“…A predictive model for internal abdominal hernias was constructed based on a machine learning algorithm incorporating 7 different features. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. …”
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Empirical Reduced-Order Modeling for Boundary Feedback Flow Control by Seddik M. Djouadi, R. Chris Camphouse, James H. Myatt

“…This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. …”
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GenAI-Based Jamming and Spoofing Attacks on UAVs by Burcu Sonmez Sarikaya, Serif Bahtiyar

“…Creating effective intrusion detection systems against such attacks has been a significant challenge since there is a lack of sufficient attack data that can be used to design an intrusion detection system with advanced computing algorithms. In this research, we propose a novel framework to create attacks data for UAVs by using generative artificial intelligence algorithms. …”
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A clustering fractional-order grey model in short-term electrical load forecasting by Xiang Yu, Lihua Lu, Jianming Qi, Yuchen Qian, Lisen Zhao, Chang Tan, Yangquan Chen, Zhigang Han

“…Hyper parameters of these equations are then sent to a global optimization algorithm to reduce predictive errors. Simulation results on two electricity datasets demonstrated that our algorithm can learn from datasets hyper parameters inside equations and produce forecast values efficiently. …”
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Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici, Panagiotis Kyratsis

“…Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both <i>Ra</i> and <i>T</i> simultaneously. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…Among the tested models, the extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) of approximately 3%, indicating its high reliability for Rs prediction. …”
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Optimizing the Finishing Treatment of Potato Harvester Transmission Shafts by A. V. Nemenko, M. M. Nikitin

“…The paper provides the results of testing the proposed algorithm for predicting the yield of viable products using the proposed method based on interim measurements of defect characteristics during the treatment process. …”
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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping by Jonas Schmidinger, Sebastian Vogel, Viacheslav Barkov, Anh-Duy Pham, Robin Gebbers, Hamed Tavakoli, Jose Correa, Tiago R. Tavares, Patrick Filippi, Edward J. Jones, Vojtech Lukas, Eric Boenecke, Joerg Ruehlmann, Ingmar Schroeter, Eckart Kramer, Stefan Paetzold, Masakazu Kodaira, Alexandre M.J.-C. Wadoux, Luca Bragazza, Konrad Metzger, Jingyi Huang, Domingos S.M. Valente, Jose L. Safanelli, Eduardo L. Bottega, Ricardo S.D. Dalmolin, Csilla Farkas, Alexander Steiger, Taciara Z. Horst, Leonardo Ramirez-Lopez, Thomas Scholten, Felix Stumpf, Pablo Rosso, Marcelo M. Costa, Rodrigo S. Zandonadi, Johanna Wetterlind, Martin Atzmueller

“…We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. …”
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Dealing With Class Imbalance in Uplift Modeling-Efficient Data Preprocessing via Oversampling and Matching by Carla Vairetti, Maria Jose Marfan, Sebastian Maldonado

“…To ensure scalability and effectiveness, we adopt a distributed framework based on MapReduce and utilize a hybrid spill trees algorithm for efficient nearest neighbor search. Our experimental results demonstrate the advantages of the proposed method, achieving statistically superior predictive performance compared to other resampling approaches while maintaining efficiency in terms of overall running times.…”
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A review on artificial intelligence-enabled mechanical analysis of 3D printed and FEM-modelled auxetic metamaterials by Aman Garg, Anshu Sharma, Weiguang Zheng, Li Li

“…This review provides a comprehensive analysis of the use of ML algorithms in predicting the properties and performance of auxetic metamaterials. …”
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A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins by Harald H. Rietdijk, Patricia Conde-Cespedes, Talko B. Dijkhuis, Hilbrand K. E. Oldenhuis, Maria Trocan

“…Preliminary investigations revealed a significant difference in performance, as measured by prediction accuracy and F1-score, between the optimal choice of machine learning algorithms for generalized and personalized processing of the available data. …”
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MicroRNA-1912 regulates cholesterol homeostasis by targeting PCSK9 by Chan Joo Lee, Myeongjune Go, Sae-Bom Jeon, Sun-Ho Lee, Dongkook Min, So Hee Park, Seunghyun Lee, Sahng Wook Park

“…Specific interactions of miR-1912 and miR-224 with the 3′ untranslated region (UTR) of PCSK9 mRNA were confirmed by a luciferase reporter assay and site-directed mutagenesis analysis of the predicted seed region. In transgenic mice expressing liver-specific human PCSK9 with its 3ʹ UTR, administration of miR-1912 mimics resulted in a reduction of plasma total cholesterol levels as determined by fast protein liquid chromatography following hepatic delivery. …”
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Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review by Seun Orenuga, Philip Jordache, Daniel Mirzai, Tyler Monteros, Ernesto Gonzalez, Ahmed Madkoor, Rahim Hirani, Raj K. Tiwari, Mill Etienne

“…This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. …”
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Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review by Shahid Gulzar Padder, Jayesh Ambulkar, Atul Banotra, Sudhakar Modem, Sidharth Maheshwari, Kolleboyina Jayaramulu, Chinmoy Kundu

“…In this study, machine learning algorithms exhibited substantial enhancements in predicting and maintaining the lifespan of electric vehicle batteries. …”
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Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali, Marco Leo

“…In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. …”
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Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful? by Murat Kılınç, Can Aydın, Çiğdem Tarhan

“…Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms. …”
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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

“…RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.…”
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