Solar Wind—Magnetosphere Coupling Functions: Pitfalls, Limitations, and Applications by Mike Lockwood

“…As they become increasingly subsumed into systems analysis and machine‐learning studies of the magnetosphere, it is timely to establish best practice in their derivation and study their limitations. …”
Get full text

Adversarial Drift Detection in Intrusion Detection System by Yaguan Qian, Xiaohui Guan

“…The recent intrusion detection systems based on machine learning generally assume that the intrusion traffic always satisfies stationary of statistics.However,this assumption is not always held when adversaries arbitrarily alter the distribution of traffic data,or develop new attack techniques,which may reduce the detection rate.To overcome this adversarial drift,a novel drift detection approach based on weighted Rényi distance was suggested.The experiment on KDD Cup99 shows that the weighted Rényi distance is able to perfectly detect the adversarial drift,and improve the intrusion detection rate by retraining the model.…”
Get full text

Passive forensics for video object removal tampering by Ye YAO, Wei-tong HU, Yi-zhi REN

“…Then, several current passive forensics algorithms for object removal tampering were selected and divided into two categories that based on correlation analysis and based on machine learning algorithm according to development trends. …”
Get full text

The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma by Wan-Bin He, Chuan Zhou, Zhi-Jun Yang, Yun-Feng Zhang, Wen-Bo Zhang, Han He, Jia Wang, Feng-Hai Zhou

“…The results of the best machine learning model were then fused with those of the deep learning model to create a combined model. …”
Get full text

Identification of mitophagy-related key genes and their correlation with immune cell infiltration in acute myocardial infarction via bioinformatics analysis by Zulong Sheng, Rui Zhang, Zhenjun Ji, Zhuyuan Liu, Yaqing Zhou

“…Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. …”
Get full text

Causal Discovery of Flight Service Process Based on Event Sequence by Qian Luo, Lin Zhang, Zhiwei Xing, Huan Xia, Zhao-Xin Chen

“…Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. …”
Get full text

Energy-based approach to the assessment of traffic flow by Volodymyr Polishuk, Stanislav Popov, Inna Vyhovska, Serhii Yanishevskiy, Liudmyla Nahrebelna

“…One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. …”
Get full text

Research on the Spatial Network Connection Characteristics and Influencing Factors of Chengdu–Chongqing Urban Agglomeration from the Perspective of Flow Space by Yangguang Hao, Zhongwei Shen, Jiexi Ma, Jiawei Li, Mengqian Yang

“…Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. …”
Get full text

Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation by Mayr W, Triantafyllopoulos A, Batliner A, Schuller BW, Berghaus TM

“…We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. …”
Get full text

Comparative study of IoT- and AI-based computing disease detection approaches by Wasiur Rhmann, Jalaluddin Khan, Ghufran Ahmad Khan, Zubair Ashraf, Babita Pandey, Mohammad Ahmar Khan, Ashraf Ali, Amaan Ishrat, Abdulrahman Abdullah Alghamdi, Bilal Ahamad, Mohammad Khaja Shaik

“…Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. …”
Get full text

A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone by Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo

“…These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.…”
Get full text

Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

“…Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. …”
Get full text

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid S... by Jing Wu, Ana-Maria Philip, Dominika Podkowinski, Bianca S. Gerendas, Georg Langs, Christian Simader, Sebastian M. Waldstein, Ursula M. Schmidt-Erfurth

“…Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. …”
Get full text

Explainable surrogate modeling for predicting temperature separation performance of the vortex tube by Hyo Beom Heo, Jun Ho Lee, Jeong Won Yoon, Sangseok Yu, Byoung Jae Kim, Seokyeon Im, Seung Hwan Park

“…Therefore, this study aims to develop a general surrogate model for predicting the performance of the vortex tube using symbolic regression, a representative white-box machine learning model. A white-box machine learning model is one that allows users to understand how it was able to produce its output. …”
Get full text

Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield by Nisha P. Shetty, Balachandra Muniyal, Ketavarapu Sriyans, Kunyalik Garg, Shiv Pratap, Aman Priyanshu, Dhruthi Kumar

“…The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. …”
Get full text

An emerging view of neural geometry in motor cortex supports high-performance decoding by Sean M Perkins, Elom A Amematsro, John Cunningham, Qi Wang, Mark M Churchland

“…Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. …”
Get full text

Vitamin A family suppresses periodontitis by restoring mitochondrial metabolic reprogramming in macrophages through JAK-STAT pathway by Zishuo Cheng, Shun Huang, Qiya Tang, Danlan Zhang, Lan Huang

“…Two ATG-guided phenotypes were identified, differing in gene expression and immune activation. WGCNA and machine learning models identified 145 markers and five key genes associated with periodontitis. …”
Get full text

AI enabled, mobile soil pH classification with colorimetric paper sensors for sustainable agriculture. by Ademir Ferreira da Silva, Ricardo Luis Ohta, Jaione Tirapu Azpiroz, Matheus Esteves Ferreira, Daniel Vitor Marçal, André Botelho, Tulio Coppola, Allysson Flavio Melo de Oliveira, Murilo Bettarello, Lauren Schneider, Rodrigo Vilaça, Noorunisha Abdool, Vanderlei Junior, Wellington Furlaneti, Pedro Augusto Malanga, Mathias Steiner

“…The classification task is performed by a machine-learning model which was trained on the colorimetric pH indicators deployed on the paper sensor. …”
Get full text

Direct conversion of peptides into diverse peptidomimetics using a transformer-based chemical language model by Atsushi Yoshimori, Jürgen Bajorath

Get full text

An optimized data analytics pipeline for improving healthcare diagnosis using ensemble learning by Lomat Haider Chowdhury, Shaira Tabassum, Swakkhar Shatabda, Ashir Ahmed

“…Later, five state-of-the-art ensemble models for healthcare diagnosis were implemented along with a proposed ensemble machine learning model, KNN-XGBoost-SVM-Random Forest (KNN-X-SVM-R). …”
Get full text