Hardware Implementation of Next Generation Reservoir Computing with RRAM‐Based Hybrid Digital‐Analog System by Danian Dong, Woyu Zhang, Yuanlu Xie, Jinshan Yue, Kuan Ren, Hongjian Huang, Xu Zheng, Wen Xuan Sun, Jin Ru Lai, Shaoyang Fan, Hongzhou Wang, Zhaoan Yu, Zhihong Yao, Xiaoxin Xu, Dashan Shang, Ming Liu

“…Reservoir computing (RC) possesses a simple architecture and high energy efficiency for time‐series data analysis through machine learning algorithms. To date, RC has evolved into several innovative variants. …”
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Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review by Yuling Guo, Ghulam Qanmber, Zhao Liu, Han Yang, Jun Li, Zhikun Yang, Zhongxian Li, Linlin Yang, Zuoren Yang

“…These include the need for more accurate models and advanced algorithms to predict hormone-receptor interactions and metabolic pathways. …”
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Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification by Zhan Wang, Zhaokai Zhou, Shuai Yang, Zhengrui Li, Run Shi, Ruizhi Wang, Kui Liu, Xiaojuan Tang, Qi Li

“…Lastly, various machine learning algorithms were applied to identify novel potential targets of BLCA, following which their pro-tumorigenic effects were experimentally verified. …”
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Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography by Yujie Ma, Maged Ali Al-Aroomi, Yutian Zheng, Wenjie Ren, Peixuan Liu, Qing Wu, Ye Liang, Canhua Jiang

“…Abstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. …”
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Development and Validation of Early Alert Model for Diabetes Mellitus–Tuberculosis Comorbidity by Zhaoyang Ye, Guangliang Bai, Ling Yang, Li Zhuang, Linsheng Li, Yufeng Li, Ruizi Ni, Yajing An, Liang Wang, Wenping Gong

“…However, early risk prediction methods for DM patients complicated with TB (DM–TB) are lacking. …”
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Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas by Ao-Yu Li, Jie Bu, Hui-Ni Xiao, Zi-Yue Zhao, Jia-Lin Zhang, Bin Yu, Hui Li, Jin-Ping Li, Tao Xiao

“…We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). …”
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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…This research proposes an advanced fire prediction approach aiming to enhance decision-making accuracy with uncertain or incomplete fire sensor data in an edge computing IoT complex industrial environment that integrates multiple supervised machine learning algorithms for each sensor types and Dempster-Shafer theory (DST) with multi-sensor fusion. …”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…This approach has become feasible with the advent of machine learning, which can uncover hidden patterns in human microbiome data and enable disease prediction. Accordingly, the aim of our study was to test the hypothesis that machine learning algorithms can distinguish stool microbiota patterns—and their responses to fiber—across diseases with previously reported overlapping dysbiotic microbiota profiles. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). …”
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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…A radiomic model was developed using the LASSO algorithm for feature selection and regularization from 107 extracted radiomic features. …”
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Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar by Naseer Muhammad Khan, Liqiang Ma, Waleed Bin Inqiad, Muhammad Saud Khan, Imtiaz Iqbal, Muhammad Zaka Emad, Saad S. Alarifi

“…Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. …”
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Multi-UAV DMPC Cooperative Guidance with Constraints of Terminal Angle and Obstacle Avoidance by Zijie Jiang, Xiuxia Yang, Cong Wang, Yi Zhang, Hao Yu

“…This paper studies the salvo attack problem for multiple unmanned aerial vehicles (UAVs) against a maneuvering target, and a guidance scheme based on distributed model predictive control (DMPC) is presented to achieve cooperative interception with constraints of terminal impact angle and no-fly zone (or obstacle) avoidance. …”
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Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture by Minwoo Jung, Dae-Young Kim

“…This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. …”
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Performance Evaluation of Data Compression Systems Applied to Satellite Imagery by Lilian N. Faria, Leila M. G. Fonseca, Max H. M. Costa

“…This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Machine Learning algorithms are widely used in various fields due to their strong capability to extract and display high-level features of training samples. …”
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Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks by Mahdi Abdollahi, Nooshin Torabi, Sadegh Raeisi, Sohrab Rahvar

“…There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years, neural networks as classifiers have come to notice because of their lower computational cost compared to traditional algorithms. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. …”
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On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed by Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander

“…Abstract Model instability remains a core challenge for data‐driven parameterizations, especially those developed with supervised algorithms, and rigorous methods to address it are lacking. …”
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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

“…The research infers that ANNs outperform traditional machine learning algorithms in carrying out attacks on Arbiter PUFs. …”
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Soft Schemes for Earthquake-Geotechnical Dilemmas by Silvia García

“…Models make it possible to predict or simulate a system’s behavior; in earthquake geotechnical engineering, they are required for the design of new constructions and for the analysis of those that exist. …”
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