Hyperparameter Optimization of Neural Networks Using Grid Search for Predicting HVAC Heating Coil Performance by Yosef Jaber, Pasidu Dharmasena, Adam Nassif, Nabil Nassif

“…The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10⁻⁵, and Adam as the optimizer. …”
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Multi-objective predictive control of cascaded H-bridge multilevel inverter based grid integrated PV based distributed generation system with improved power quality features by Avismit Dutta, Roshan Pradhan, Aurobinda Panda

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Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Tree-based models, despite their high performance metrics, demonstrated poor convergence, raising concerns about their reliability on unseen data. …”
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Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables by Feng Gao, Yage Xing, Jialong Li, Lin Guo, Yiye Sun, Wen Shi, Leiming Yuan

“…This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. …”
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Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis by Ana María Cabanas, Nicolás Sáez, Patricio O. Collao-Caiconte, Pilar Martín-Escudero, Josué Pagán, Elena Jiménez-Herranz, José L. Ayala

“…The review examined AI models, key features, oximeters used, datasets, tested saturation intervals, and performance metrics while also assessing bias through the QUADAS-2 criteria. …”
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Effects of Irregular Bathymetry on the Performance and Wake Characteristics of Tidal Stream Turbines: A Case Study of a Tidal Power Site by Ngome Mwero, Song Fu, Takafumi Inamitsu, Stephanie Ordonez-Sanchez, Benson Mwangi, Patxi Garcia-Novo, Cameron Johnstone, Daisaku Sakaguchi

“…This paper investigates the impact of irregular-bathymetry seabed elements near a tidal turbine location on the turbine’s performance and wake. A high-resolution three-dimensional bathymetry model was created, and full-scale unsteady simulations were performed using the ANSYS-Fluent computational fluid dynamics tool and the Shear Stress Turbulence (SST) model for two cases: one with the site bathymetry and one with a flat seabed. …”
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A novel deep learning model for extracting arable land from high-resolution remote sensing images in hilly areas: a case study in the Sichuan Basin of Southwest China by Yanxi Chen, Xingzhu Xiao, Yongle Zhang, Min Huang, Ziyi Tang, Hao Li

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A Fuzzy Neural Network-Based Intelligent Prediction Model for Useful Lifespan of Lithium-Ion Batteries by Yaowei Xu, Zhenxing Hao, Wei Li, Angang Cao

“…By comparing the predictive performance of these models, a new hybrid neural network model was proposed, which combines the advantages of convolutional neural networks (CNN) in feature extraction and long short-term memory networks (LSTM) in processing time-series data, while introducing the advantages of other neural network structures to improve the predictive ability of the model. …”
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SAM2Former: Segment Anything Model 2 Assisting UNet-Like Transformer for Remote Sensing Image Semantic Segmentation by Xuewen Li, Xiaomin Tian, Zihong Wang, Feng Zhang, Yanting Zhang, Na Yang, Chuanzhao Tian

“…Firstly, we incorporate the lightweight Adapter to perform parameter-efficient fine-tuning on SAM2 and design a multi-scale information aggregation module (MIAM) to connect the dual-encoder, which weighted multi-scale features layer by layer of SAM2 Block and CNN, preserving key information in the image. …”
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Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence by Yasin Görmez, Fatma Hilal Yagin, Burak Yagin, Yalin Aygun, Hulusi Boke, Georgian Badicu, Matheus Santos De Sousa Fernandes, Abedalrhman Alkhateeb, Mahmood Basil A. Al-Rawi, Mohammadreza Aghaei, Mohammadreza Aghaei

“…SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.ResultsThe CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. …”
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CNN-BiLSTM and DC-IGN fusion model and piecewise exponential attenuation optimization: an innovative approach to improve EEG emotion recognition performance by Shaohua Zhang, Yan Feng, Ruzhen Chen, Song Huang, Qianchu Wang

“…These results not only improve the performance of EEG emotion recognition, but also provide new ideas and methods for research in related fields, and prove the significant advantages of our model in capturing complex features and improving classification accuracy.…”
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Real Estate Appraisal Performance Improvement by Adapting a Hybrid Model: Geographically Weighted Regression and Extreme Gradient Boosting in Al Bireh, Palestine by Jamal A.A. Numan, Izham Mohamad Yusoff

“…This aim is achieved by identifying features influencing real estate appraisal, particularly apartments within residential buildings, in the context of Al Bireh city, Palestine, designing the hybrid GWR XGBoost model, and evaluating its performance. …”
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Label dependency modeling in Multi-Label Naïve Bayes through input space expansion by PKA Chitra, Saravana Balaji Balasubramanian, Omar Khattab, Mhd Omar Al-Kadri

“…Multi-label techniques often employ a similar feature space to build classification models for every label. …”
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Evaluating machine leaning algorithms for accuracy, stability, and among-predictors discriminability in modeling species-richness across ten datasets by Yong Cao, Tyler E. Schartel, David C. Houghton, Jared Ross, Dana M. Infante

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Development and validation of a probability-assessment nomogram for non-suicidal self-injury in hospitalized adolescents and young adults with mental disorders by Qi Liu, Yanjuan Ren, Yalin Huang, Ruyi Xie, Yinyuan Li, Zaiquan Dong, Xiaoxia Zhang

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The Diagnostic Performance of Large Language Models and Oral Medicine Consultants for Identifying Oral Lesions in Text-Based Clinical Scenarios: Prospective Comparative Study by Sarah AlFarabi Ali, Hebah AlDehlawi, Ahoud Jazzar, Heba Ashi, Nihal Esam Abuzinadah, Mohammad AlOtaibi, Abdulrahman Algarni, Hazzaa Alqahtani, Sara Akeel, Soulafa Almazrooa

“…There were no significant differences in the accuracy of providing the correct differential diagnoses between AI models and oral medicine consultants. ChatGPT was as accurate as consultants in making the final diagnoses, but Copilot was significantly less accurate than ChatGPT (PP ConclusionsChatGPT and Copilot show promising performance for diagnosing oral medicine pathology in clinical case scenarios to assist dental practitioners. …”
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American Sign Language Recognition Model Using Complex Zernike Moments and Complex-Valued Deep Neural Networks by Selda Bayrak, Vasif Nabiyev, Celal Atalar

“…In the developed model, complex Zernike moments are used to obtain the feature vector of character images. …”
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Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf by Weirong Qin, Mohd Hasmadi Ismail, Yangyang Luo, Yifeng Yuan, Junlin Deng, Mohammad Firuz Ramli, Ning Wu

“…This study introduces SwinNet, a semantic segmentation model leveraging multi-scale feature fusion to enhance the extraction of aquaculture areas, particularly in the Maowei Sea of the Beibu Gulf, China. …”
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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and Machine Learning Models by A. Aylin Tokuc, Tamer Dag

“…Notably, the hybrid representation with LightGBM achieved superior predictive performance, significantly outperforming alternative methods. …”
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Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms by Manal Abdullah Alohali, Sultan Alahmari, Mohammed Aljebreen, Mashael M. Asiri, Achraf Ben Miled, Sami Saad Albouq, Othman Alrusaini, Ali Alqazzaz

“…Besides, the proposed MDMIoV-DLXAI model utilizes the reptile search algorithm (RSA) model for feature selection. …”
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