Memory-augment graph transformer based unsupervised detection model for identifying performance anomalies in highly-dynamic cloud environments by Huangyining Gao, Ruyue Xin, Peng Chen, Xi Li, Ning Lu, Peng You

“…Additionally, we introduce a novel dynamic gated memory module to guide the Transformer encoder in extracting hidden features, thereby enhancing the model’s robustness to varying data patterns in dynamic cloud environments. …”
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Emotion on the edge: An evaluation of feature representations and machine learning models by James Thomas Black, Muhammad Zeeshan Shakir

“…The study underscores the significance of combinations of models and features in machine learning, detailing how these choices affect model performance when low computation power needs to be considered. …”
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Feature Extraction Model of SE-CMT Semantic Information Supplement by DU Ruishan, ZHOU Changkun, XIE Hongtao, LI Hongjie

“…The model uses SE-CNN (Squeeze-and-Excitation Networks-CNN) to extract low-level features, enhance localization, and combine with Transformer to establish long-range dependencies to improve feature extraction performance by fusing SE-CNN and Transformer structures. …”
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Impact of Data Balancing and Feature Engineering on Accident Severity Models by Fayez ALANAZI, Aminu SULEIMAN

“…This study investigates the impacts of feature engineering techniques, including Clustering, Target Encoding and Anomaly Detection, in conjunction with data balancing methods, on the efficacy of machine learning models for predicting road accident severity. …”
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Feature-Separated Lightweight Model for Road Extraction in Wild Environments by Che Shi, Yuefeng Cen, Gang Cen

“…This design enhances prediction performance without significantly increasing parameters, reducing computational demands and making the model more adaptable for large-scale remote sensing tasks. …”
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Hybridization of Swarm for Features Selection to Modeling Heart Attack Data by Omar Shakir, Ibrahim Saleh

“…The study found that the proposed method of hybridization of the results of the (PSO,  BAT, and BCS) algorithms in selecting features is a promising solution in the field of selecting features and increases the accuracy of the system, and that traditional machine learning models are biased in the case of unbalanced data sets and that selecting the important features according to the target class has an impact on the performance of the models, In addition, the definition of hyperparameters reduces the bias of the selected model. …”
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Fulminant necrotizing enterocolitis: clinical features and a predictive model by Xiaoyu Chen, Yuqiao Li, Yuhan Liu, Tianjing Liu, Yongyan Shi

“…Abstract Background To develop and validate a nomogram model for predicting the risk of fulminant necrotizing enterocolitis (fNEC) in infants with NEC and to summarize the clinical features of fNEC. …”
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FeatureForest: the power of foundation models, the usability of random forests by Mehdi Seifi, Damian Dalle Nogare, Juan Manuel Battagliotti, Vera Galinova, Ananya Kedige Rao, Pierre-Henri Jouneau, Anwai Archit, AI4Life Horizon Europe Programme Consortium, Constantin Pape, Johan Decelle, Florian Jug, Joran Deschamps

“…We demonstrate the improvement in performance over a variety of datasets and provide an open-source implementation in napari that can be extended to new models.…”
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Features of the Local Dynamics of the Opto-Electronic Oscillator Model with Delay by Elena V. Grigorieva, Sergey A. Kashchenko, Dmitry V. Glazkov

“…The essential feature of this model is a small parameter in front of a derivative that allows us to draw a conclusion about the action of processes with different order velocities. …”
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Multitask Features Mapping Network Model for Cross-domain Recommendation by AN Qiuyu, YU Yan, XIONG Xi

“…The model first introduces a user features mapping network, which can map the user features from source domain to target domain. …”
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Features of ionic transport processes in a model of arterial hypertension by A. V. Nosarev, Yu. G. Birulina, I. V. Petrova, I. V. Kovalev, S. V. Gusakova, L. V. Smagliy, E. S. Teslya, A. Yu. Shamanaev, E. V. Pushkina, M. A. Medvedev

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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning by Mohamed Kamel Elshaarawy, Martina Zeleňáková, Asaad M. Armanuos

“…Hyperparameter optimization was conducted using Bayesian Optimization (BO) coupled with five-fold cross-validation to enhance model performance. Results showed that the CatBoost model demonstrated superior performance over other models, consistently yielding high R2 values, specifically surpassing 0.95, 0.93, and 0.97 for U/U o , i R /i Ro , and q/q o , respectively, along with low RMSE scores below 0.022, 0.089, and 0.019 for the same variables. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  …”
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Understanding Video Transformers: A Review on Key Strategies for Feature Learning and Performance Optimization by Nan Chen, Tie Xu, Mingrui Sun, Chenggui Yao, Dongping Yang

“…It then explores performance enhancement strategies and video feature learning methods for the video transformer, considering 4 key dimensions: input module optimization, internal structure innovation, overall framework design, and hybrid model construction. …”
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CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis by Mohammad Shariful Islam, Mohammad Abu Tareq Rony

“…We applied several ML and DL approaches to the newly created feature set to evaluate performance. The CDK ensemble model outperformed state-of-the-art studies with a high-performance score of 99.72% accuracy. …”
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Performance evaluations of AI-based obfuscated and encrypted malicious script detection with feature optimization by Kookjin Kim, Jisoo Shin, Jong-Geun Park, Jung-Tae Kim

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Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…Hyperparameter tuning is performed using a grid search technique to determine the best combination of hyperparameter values that can improve model accuracy. …”
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A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model by Akram Seifi, Sharareh Pourebrahim, Mohammad Ehteram, Hanieh Shabanian

“…The key points of the paper are the successful effectiveness of MVMD in processing non-stationary time series and improving the predictive performance of the models, as well as the high ability of the GRU model in data feature extraction. …”
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A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators by Jiahui Long, Xiang Jia, Bingyi Li, Lin Zhu, Miao Wang

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Multiscale Modeling of Silicon Carbide Cladding for Nuclear Applications: Thermal Performance Modeling by Gyanender Singh, Jianguo Yu, Fei Xu, Tiankai Yao, Peng Xu

“…The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. …”
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