Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations by Kai-Cheng Chuang, Ping-Sung Cheng, Yu-Hung Tsai, Meng-Hsiun Tsai

“…In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data. …”
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Assessment of trade-off balance of maize stover use for bioenergy and soil erosion mitigation in Western Kenya by Keiji Jindo, Keiji Jindo, Golaleh Ghaffari, Manisha Lamichhane, Asher Lazarus, Yoshito Sawada, Hans Langeveld

“…A decision-tree machine learning model identified farm characteristics favorable for maize stover use in biogas production.ResultsLarger households were found to consume more energy per capita, while proximity to forests did not significantly influence firewood or charcoal consumption. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. …”
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Association of Premature Ventricular Contraction (PVC) with hematological parameters: a data mining approach by Nafiseh Hosseini, Sara Saffar Soflaei, Pooria Salehi-Sangani, Mahdiyeh Yaghooti-Khorasani, Bahram Shahri, Helia Rezaeifard, Habibollah Esmaily, Gordon A. Ferns, Mohsen Moohebati, Majid Ghayour-Mobarhan

“…The association of hematological factors with PVC was evaluated using different machine learning (ML) algorithms, including logistic regression (LR), C5.0, and boosting decision tree (DT). …”
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Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems by Chenfei Xie, Yue Xiu, Songjie Yang, Qilong Miao, Lu Chen, Yong Gao, Zhongpei Zhang

“…Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. …”
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A Reproducible Method for Donor Site Computed Tomography Measurements in Abdominally Based Autologous Breast Reconstruction by Damini Tandon, MD, Arthur Sletten, MD, PhD, Austin Ha, MD, Gary B. Skolnick, BA, MBA, Paul Commean, BEE, Terence Myckatyn, MD

“…Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.…”
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Unified Visual-Aware Representations for Data Analytics by Ladislav Peska, Ivana Sixtova, David Hoksza, David Bernhauer, Jakub Lokoc, Tomas Skopal

“…The visual representations serve for data analytics tasks performed by human users as well as serve for universal data representations used in machine learning models for automated tasks. We show in large study that visual representations of complex data are effective in a number of domains while we also propose a recommender to help with the parameterization of the entire pipeline for certain domains and use cases. …”
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

“…Most existing methods for predicting IL-6-induced peptides use traditional machine learning methods, whose feature selection is based on prior knowledge. …”
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Local Binary and Multiclass SVMs Trained on a Quantum Annealer by Enrico Zardini, Amer Delilbasic, Enrico Blanzieri, Gabriele Cavallaro, Davide Pastorello

“…Support vector machines (SVMs) are widely used machine learning models, with formulations for both classification and regression tasks. …”
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The potential for fuel reduction to reduce wildfire intensity in a warming California by Patrick T Brown, Scott J Strenfel, Richard B Bagley, Craig B Clements

“…Here, we quantify the potential for fuel reduction to reduce wildfire intensity using empirical relationships derived from historical observations with a novel combination of spatiotemporal resolution (0.375 km, instantaneous) and extent (48 million acres, 9 years). We use machine learning to quantify relationships between sixteen environmental conditions (including ten fuel characteristics and four temperature-affected aridity characteristics) and satellite-observed fire radiative power. …”
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Anthropometric Landmark Detection Network via Geodesic Heatmap on 3D Human Scan by Min Hee Cha, Jae Hyeon Park, Ji Sun Byun, Sangyeon Ahn, Gyoomin Lee, Seung Hyun Yoon, Sung In Cho

“…With advancement in computer vision and machine learning, researchers have increasingly focused on developing automated anthropometric data extraction technique. …”
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Impacts of IOD and ENSO on the phytoplankton’s vertical variability in the Northern Indian Ocean by Qiwei Hu, Xiaoyan Chen, Xianqiang He, Yan Bai, Tingchen Jiang, Yu Huan, Zhanlin Liang

“…Using the three-dimensional Chlorophyll a concentration dataset generated by a machine learning model, this study examines IOD- and ENSO-linked vertical phytoplankton anomalies over the entire euphotic layer (0–100 m) in the NIO during 2000–2019. …”
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Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks by Maryam M. Kheirollahpour, Mahmoud M. Danaee, Amir Faisal A. F. Merican, Asma Ahmad A. A. Shariff

“…Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.…”
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Research on multi-label recognition of tongue features in stroke patients based on deep learning by Honghua Liu, Peiqin Zhang, Yini Huang, Shanshan Zuo, Lu Li, Chang She, Mailan Liu

“…To address this issue, this paper proposes a deep learning-based automatic recognition approach for the tongue images of stroke patients, aiming to improve the accuracy of automatic extraction and recognition of stroke-related tongue features through image processing and machine learning techniques. First, this study performs image cropping and data augmentation on tongue images. …”
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SpikeDroidDB: AN INFORMATION SYSTEM FOR ANNOTATION OF MORPHOMETRIC CHARACTERISTICS OF WHEAT SPIKE by M. A. Genaev, E. G. Komyshev, Fu Hao, V. S. Koval, N. P. Goncharov, D. A. Afonnikov

“…The effectiveness of ears’ phenotyping can be improved by the introduction of an automated image processing technology, storage of information in databases, use of machine learning algorithms to analyze this information. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

“…The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. …”
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Chronic lung lesions in COVID-19 survivors: predictive clinical model by Paulo A Lotufo, Juliana C Ferreira, Eloisa Bonfa, Anna S Levin, Rodrigo Caruso Chate, Marta Imamura, Esper G Kallas, Roger Chammas, Thais Mauad, Izabel Marcilio, Nelson Gouveia, Ricardo Nitrini, José Eduardo Krieger, Marcio Valente Yamada Sawamura, Michelle Louvaes Garcia, Cristiano Gomes, Guilherme Fonseca, Jorge Hallak, Luis Yu, Marcio Mancini, Maria Elizabeth Rossi, Thiago Avelino-Silva, Edivaldo M Utiyama, Aluisio C Segurado, Beatriz Perondi, Anna Miethke-Morais, Amanda C Montal, Leila Harima, Marjorie F Silva, Marcelo C Rocha, Maria Amélia de Jesus, Carolina Carmo, Clarice Tanaka, Julio F M Marchini, Thaís Guimarães, Ester Sabino, Carlos Roberto Ribeiro Carvalho, Celina Almeida Lamas, Diego Armando Cardona Cardenas, Daniel Mario Lima, Paula Gobi Scudeller, João Marcos Salge, Cesar Higa Nomura, Marco Antonio Gutierrez, Adriana L Araújo, Bruno F Guedes, Carolina S Lázari, Cassiano C Antonio, Claudia C Leite, Emmanuel A Burdmann, Euripedes C Miguel, Fabio R Pinna, Fabiane Y O Kawano, Geraldo F Busatto, Giovanni G Cerri, Heraldo P Souza, Izabel C Rios, Larissa S Oliveira, Linamara R Batisttella, Luiz Henrique M Castro, Marcello M C Magri, Maria Cassia J M Corrêa, Maria Cristina P B Francisco, Maura S Oliveira, Orestes V Forlenza, Ricardo F Bento, Rodolfo F Damiano, Rossana P Francisco, Solange R G Fusco, Tarcisio E P Barros-Filho, Wilson J Filho

“…Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.Setting A tertiary hospital in Sao Paulo, Brazil.Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years.Primary outcome measure A predictive clinical model for lung lesion detection on chest CT.Results There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. …”
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Performance evaluation and prediction of optimal operational conditions for a compact date seeds milling unit using feedforward neural networks by Khaled Abdeen Mousa Ali, Changyou Li, Wang Han, Sali Issa, Mohamed Hamdy Eid, Samy F. Mahmoud, Marwa Abd-Elnaby Mohammed

“…This research pioneers the application of machine learning in optimizing date seed processing, potentially revolutionizing agricultural waste valorization and opening new avenues for sustainable resource utilization.…”
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RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis by S. M. Nuruzzaman Nobel, S M Masfequier Rahman Swapno, Md. Ashraful Hossain, Mejdl Safran, Sultan Alfarhood, Md. Mohsin Kabir, M. F. Mridha

“…This study highlights how machine learning can revolutionize the medical field’s ability to classify ovarian cancer subtypes and identify outliers, giving doctors a valuable tool to lessen the severe effects of the disease. …”
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Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios by Angel Luis Perales Gomez, Lorenzo Fernandez Maimo, Alberto Huertas Celdran, Felix J. Garcia Clemente

“…The adversarial training technique has been shown to improve the robustness of Machine Learning and Deep Learning models to adversarial attacks in the Computer Vision field. …”
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