Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices by Antonio Robles-Guerrero, Salvador Gómez-Jiménez, Tonatiuh Saucedo-Anaya, Daniela López-Betancur, David Navarro-Solís, Carlos Guerrero-Méndez

“…Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. …”
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Prediction of loess collapsibility coefficient using bayesian optimized random forest model by Wan Zhang, Jiangtao Guo, Zhaopeng Li, Ruifang Cheng, Cuiping Ning, Hongfeng Niu, Ze Liu

“…However, the process of hyperparameter optimization in previous studies was not sufficiently comprehensive, leading to suboptimal model performance. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

“…Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. …”
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SAR remote sensing for monitoring harmful algal blooms using deep learning models by Kritnipit Phetanan, Do Hyuck Kwon, Jinmyeong Lee, Heewon Jeong, Gibeom Nam, Euiho Hwang, JongCheol Pyo, Kyung Hwa Cho

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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…Conventional leukemia diagnostic methods are time-consuming, manually intensive, and inefficient. This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. …”
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Robust self-supervised denoising of voltage imaging data using CellMincer by Brice Wang, Tianle Ma, Theresa Chen, Trinh Nguyen, Ethan Crouse, Stephen J. Fleming, Alison S. Walker, Vera Valakh, Ralda Nehme, Evan W. Miller, Samouil L. Farhi, Mehrtash Babadi

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Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms by Mónica Mora, Lucile Riaboff, Ingrid David, Juan Pablo Sánchez, Miriam Piles

“…Using wearable accelerometers is gaining traction in research and animal production management for monitoring animal behaviour. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…Through training and meticulous hyperparameter tuning, this study compares 7 different AI models fed with experimental data from 21 thrusters and their different configurations, reaching a total of 58 thruster designs, spanning decades of thruster research and development work. …”
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Estimation and trend analysis of grassland aboveground biomass on the Qinghai-Xizang Plateau based on machine learning by Ruoqi Zhang, Qisheng Feng, Yonghui Zhang, Jingjing Mai, Tiangang Liang

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A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents by Ricardo Scheufen Tieghi, José Teófilo Moreira-Filho, Holli-Joi Martin, James Wellnitz, Miguel Canamary Otoch, Marielle Rath, Alexander Tropsha, Eugene N. Muratov, Nicole Kleinstreuer

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Uncertainty and driving factor analysis of streamflow forecasting for closed-basin and interval-basin: Based on a probabilistic and interpretable deep learning model by Chaowei Xu, Yasong Chen, Dianchang Wang, Yunpeng Zhao, Yukun Hou, Yating Zhu, Qiushi Shen

“…However, challenges persist, including hyperparameter optimization, lack of interpretability, and uncertainty quantification, with most studies focusing on closed-basins and limited research on human-influenced interval-basins. …”
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Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults by Xuecheng Liu, Peixiao Fan, Jun Yang, Song Ke, Binyu Ma, Yangzhou Pei, Jian Xu

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Sine Cosine Optimization With Deep Learning–Driven Object Detector for Visually Impaired Persons by Hadeel Alsolai, Fahd N. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar

“…Both exploit typical machine learning (ML) approaches and utilize deep learning (DL) models to execute object detection methods. Recently, several researchers have leveraged DL techniques such as convolutional neural networks (CNNs) for solving object detection problems and presented various recent object detection approaches. …”
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Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning by Arman Ahmadi, Andre Daccache, Minxue He, Peyman Namadi, Alireza Ghaderi Bafti, Prabhjot Sandhu, Zhaojun Bai, Richard L. Snyder, Tariq Kadir

“…Study region: This research focuses on the Central Valley of California, a climatically homogeneous region known for its significant agricultural productivity and reliance on extensive irrigation. …”
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An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values by Kumarmangal Roy, Muneer Ahmad, Kinza Waqar, Kirthanaah Priyaah, Jamel Nebhen, Sultan S Alshamrani, Muhammad Ahsan Raza, Ihsan Ali

“…There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. …”
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Quantitative Analysis of 3-Monochloropropane-1,2-diol in Fried Oil Using Convolutional Neural Networks Optimizing with a Stepwise Hybrid Preprocessing Strategy Based on Fourier Tra... by Xi Wang, Siyi Wang, Shibing Zhang, Jiping Yin, Qi Zhao

“…As one kind of ‘probable human carcinogen’ (Group 2B) compound classified by the International Agency for Research on Cancer, 3-MCPD is mainly formed during the thermal processing of food. …”
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SICGNN: structurally informed convolutional graph neural networks for protein classification by YongHyun Lee, Eunchan Kim, Jiwoong Choi, Changhyun Lee

“…In bioinformatics and chemical engineering, considerable research is being actively conducted to represent molecules or proteins on graphs by conceptualizing atoms or amino acids as nodes and the relationships between nodes as edges. …”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChOA) for hyperparameter tuning and feature refinement. The proposed method is evaluated using three recent and comprehensive benchmark datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, widely recognized as standard testbeds for IIoT intrusion detection research. …”
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Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

“…(1) Background: Survival analysis models in clinical research must effectively handle censored data, where complete survival times are unknown for some subjects. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…By applying the hyperparameter optimization process, the accuracy is estimated. …”
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