Impact Localization System of CFRP Structure Based on EFPI Sensors by Junsong Yu, Zipeng Peng, Linghui Gan, Jun Liu, Yufang Bai, Shengpeng Wan

“…The attention mechanism is integrated into the CNN-BIGRU model to enhance the significance of the TDOA of impact signals detected by proximal EFPI sensors. The model is trained using the training set, with its parameters optimized using the sand cat swarm optimization algorithm and validation set. …”
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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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A triple pronged approach for ulcerative colitis severity classification using multimodal, meta, and transformer based learning by Md. Nasim Ahmed, Dipta Neogi, Muhammad Rafsan Kabir, Shafin Rahman, Sifat Momen, Nabeel Mohammed

“…In parallel, we leverage multimodal pre-trained frameworks (e.g., CLIP, BLIP, FLAVA) integrated with conventional machine learning algorithms, yielding an accuracy of 83%. …”
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Control-Oriented Real-Time Trajectory Planning for Heterogeneous UAV Formations by Weichen Qian, Wenjun Yi, Shusen Yuan, Jun Guan

“…Inspired by Model Predictive Control (MPC), in the trajectory planning stage, the method generates multi-step trajectory points using an improved artificial potential field (APF) method, estimates the actual formation trajectory using the prediction network, and optimizes the trajectory through a multi-objective particle swarm optimization (MOPSO) algorithm after evaluating the planning costs. During actual flight, the optimized parameters generate trajectory points for the formation to follow. …”
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An Enhanced Approach for Remaining Useful Life Prediction of Bearings Using Incomplete Lifecycle Data by Xunmeng An, Chao Zhang, Caiye Liu, Wentao Zhao

“…The proposed method is rigorously evaluated on both the IEEE PHM 2012 and XJTU-SY datasets and benchmarked against state-of-the-art approaches. …”
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Spectroscopic Quantification of Metallic Element Concentrations in Liquid-Propellant Rocket Exhaust Plumes by Siyang Tan, Song Yan, Xiang Li, Tong Su, Qingchun Lei, Wei Fan

“…The predictive accuracy of the method is rigorously evaluated through 32 independent experimental trials. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…Three different artificial neural network (ANN) configurations are trained and evaluated regarding the amount of training data required for accurate predictions and the computational costs. …”
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A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting by Maria Krivova, Michael J. Olsen, Ben A. Leshchinsky

“…Based on these trend rasters, forecast DEMs were created based on several different combinations of training and forecast epochs. The forecast DEMs were evaluated against the original lidar data, to calculate residuals to determine optimal model parameters. …”
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Circadian Regulator-Mediated Molecular Subtypes Depict the Features of Tumor Microenvironment and Indicate Prognosis in Head and Neck Squamous Cell Carcinoma by Ling Aye, Zhanying Wang, Fanghua Chen, Yujun Xiong, Jiaying Zhou, Feizhen Wu, Li Hu, Dehui Wang

“…The correlation of CRGs with microenvironment, pathway activities, and prognosis was determined by bioinformatic algorithms. A novel circadian score was introduced to evaluate the circadian modification pattern of each patient and further validated in an independent cohort from the Gene Expression Omnibus (GEO) dataset. …”
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Developing a Machine Vision System to Detect Weeds from Potato Plant by Yousef Abbaspour Gılandeh, Hossein Javadıkıa, Sajad Sabzı

“…Accordingly, in this study, a machine vision system with the hybrid artificial neural network-ant colony algorithm (ANN-ACO) classifier was developed for a site-specific spraying considering the weed type. …”
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Novel Cuproptosis-Related Gene Signature for Precise Identification of High-Risk Populations in Low-Grade Gliomas by Ping Chen, Hailing Han, Xuejie Wang, Bing Wang, Zhanfeng Wang

“…It performed well in training and all validation sets (AUC values: 0.915, 0.894, and 0.774). …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. …”
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Segmentation of airways and soft tissues on panoramic radiographs using artificial intelligence technology by Aslıhan Şahan Keskin, İlknur Eninanç

“…Of the radiographs, 72% were allocated for training, 18% for validation, and 10% for testing. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
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Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib, Francisco J. Rey-Martínez

“…Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. …”
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Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions by Rola Abdallah, Mohamed Atef, Nasir Saeed

“…Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.…”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…Bone age assessment (BAA) is a critical diagnostic tool for evaluating skeletal maturity and monitoring growth disorders. …”
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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features by Haoru Wang MD, Ling He MD, Xin Chen MD, Shuang Ding MD, Mingye Xie MD, Jinhua Cai MD

“…A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. …”
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Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies by Fatih Gurcan

“…Hyperparameter optimization for the classifiers was conducted using GridSearchCV, while feature selection was performed with the recursive feature elimination (RFE) algorithm. Model performance was evaluated using seven different metrics: Accuracy, Precision, Recall, F1-score, receiver operating characteristic area under the curve (ROC AUC), precision recall area under the curve (PR AUC), and Matthews correlation coefficient (MCC). …”
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Development of Cyber Security Platform for Experiential Learning by Abhishek Vaish, Ravindra Kumar, Samo Bobek, Simona Sternad

“…The present research proposes a comprehensive web-based platform that can be used to communicate, collaborate and practice various use cases in the domain of network intrusion detection tools using machine learning algorithms and to evaluate user experience. The proposed platform CySecLearn is a collection of various functionalities and features that ensures experiential learning, will help the learner to develop critical thinking and expertise in network security and promote digital literacy in the domain of cyber security.…”
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