Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…Feature selection algorithms reduce data dimensionality, followed by a deep learning classification approach. …”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…This challenge highlights the need for models capable of learning from limited data, a scenario known as the few-shot learning problem. …”
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Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care. by Youli Jiang, Ao Li, Zhihuan Li, Yanfeng Li, Rong Li, Qingshi Zhao, Guisu Li

“…<h4>Methods</h4>We developed a machine learning model using clinical data from patients with acute ischemic stroke (AIS) admitted between December 2021 and December 2023. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. …”
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Facial Beauty Prediction Combining Dual-Branch Feature Fusion With a Stacked Broad Learning System by Junying Gan, Hantian Chen, Wenchao Xu, Huicong Li, Zhenxin Zhuang, Zhen Chen

“…Facial beauty prediction (FBP) is a key computer vision task that uses algorithms to assess facial attractiveness. Current models rely on single feature extraction, such as using a single convolutional neural network to extract local feature, failing to capture other potentially more important information contained within facial data and limiting feature diversity. …”
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Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning by Jianhui Chen, Qun Li, Xiaofang Liu, Fang Lin, Yaling Jing, Jiayan Yang, Lianfang Zhao

“…Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. …”
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Analysis of digital intelligent financial audit system based on improved BiLSTM neural network by Zhu Xincai

“…Traditional auditing methods have difficulties in detecting various financial issues hidden in massive amounts of data. …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. …”
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Detailed Time-resolved Spectral and Temporal Investigations of SGR J1550–5418 Bursts Detected with Fermi Gamma-Ray Burst Monitor by Mustafa Demirer, Ersin Göğüş, Yuki Kaneko, Özge Keskin, Sinem Şaşmaz, Shotaro Yamasaki

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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…Least absolute shrinkage and selection operator (LASSO) regression with 5-fold cross-validation was used to select the most predictive features. Twelve machine learning algorithms were independently trained. Their performances were evaluated by receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity. …”
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Unveiling the Shadows—A Framework for APT’s Defense AI and Game Theory Strategy by Pedro Brandão, Carla Silva

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Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma by Chunhong Li, Jiahua Hu, Mengqin Li, Yiming Mao, Yuhua Mao

“…In this study, we utilized a computational framework to integrate multi-omics data from HCC patients using the latest 10 different clustering algorithms, which were then employed a diverse set of 101 combinations derived from 10 different machine learning algorithms to develop a consensus machine learning-based signature (CMLBS). …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The performances of SVM and LR recorded higher percent accuracy of 99.474 each while the DT and RF recorded 99.123 percent accuracy each in detecting the DDoS-IoT data traffic from the normal data. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Finally, the leverage method demonstrated that only 2.14% of the data were identified as suspected, with no out-of-leverage points detected, underscoring the reliability of the CatBoost model and the gathered experimental data. …”
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A reliable score-based routing protocol using a fog-assisted intrusion detection system in vehicular ad-hoc networks by Samira Tahajomi Banafshehvaragh, Mani Zarei, Amir Masoud Rahmani

“…The IDS is trained using three machine learning-based algorithms and a voting technique to reduce false detection. …”
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Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects by Muhammad Abdullah Hanif, Nandish Chattopadhyay, Bassem Ouni, Muhammad Shafique

“…Deep Neural Networks (DNNs) have emerged as a prominent set of algorithms for complex real-world applications. However, state-of-the-art DNNs require a significant amount of data and computational resources to train and generalize well for real-world scenarios. …”
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A Machine Learning Aided Reference-Tone-Based Phase Noise Correction Framework for Fiber-Wireless Systems by Guo Hao Thng, Said Mikki

“…To evaluate the feasibility of the proposed machine learning based phase noise correction approach, software simulations were conducted to collect data needed for machine leanring algorithm training. …”
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Determination of Spatiotemporal Gait Parameters Using a Smartphone’s IMU in the Pocket: Threshold-Based and Deep Learning Approaches by Seunghee Lee, Changeon Park, Eunho Ha, Jiseon Hong, Sung Hoon Kim, Youngho Kim

“…This study proposes a hybrid approach combining threshold-based algorithm and deep learning to detect four major gait events—initial contact (IC), toe-off (TO), opposite initial contact (OIC), and opposite toe-off (OTO)—using only a smartphone’s built-in inertial sensor placed in the user’s pocket. …”
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Enhanced Fishing Monitoring in the Central-Eastern North Pacific Using Deep Learning with Nightly Remote Sensing by Jiajun Li, Jinyou Li, Kui Zhang, Xi Li, Zuozhi Chen

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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…Since density functional theory (DFT) is one of the first principles, DFT calculations were conducted to derive essential multi-physical parameters, including bandgap, density of states (DOS), Fermi level, adsorption energy, and structural modifications resulting from adsorption. The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. …”
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