Investigating Transfer Learning in Noisy Environments: A Study of Predecessor and Successor Features in Spatial Learning Using a T-Maze by Incheol Seo, Hyunsu Lee

“…This research advances our understanding of the functionality of PF and SF algorithms, particularly in navigating the inherent uncertainty of transfer learning tasks. …”
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Spiking Neural Networks Optimized by Improved Cuckoo Search Algorithm: A Model for Financial Time Series Forecasting by Panke Qin, Yongjie Ding, Ya Li, Bo Ye, Zhenlun Gao, Yaxing Liu, Zhongqi Cai, Haoran Qi

“…The proposed method not only reduces manual intervention in hyperparameter tuning but also offers a scalable solution for high-frequency trading and multi-modal data fusion in future research.…”
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Comparative Analysis of Transformers to Support Fine-Grained Emotion Detection in Short-Text Data by Robert H. Frye, David C. Wilson

“…To understand the applicability and tradeoffs among common transformers within such contexts, our research investigates accuracy and efficiency considerations in fine-tuning transformers for granular emotion detection in short-text data. …”
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Unveiling the secrets of neural network scaling for ECG classification by Byeong Tak Lee, Joon-myoung Kwon, Yong-Yeon Jo

“…Finally, we explore why scaling hyperparameters affects ECG and computer vision differently. …”
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Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos

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A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity by Zhenbo Chang, Zhilin Guo, Kewei Chen, Zibo Wang, Yang Zhan, Wenxi Lu, Chunmiao Zheng

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Hybrid Optimization based on Deep Learning Approach for Short-Term Load Forecast of Electricity Demand in Buildings by Charan Sekhar Makula, Ratna Dahiya

“…This research proposes a hybrid optimization-based deep learning (DL) approach to increase the accuracy of short-term forecasts. …”
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Performance evaluation of enhanced deep learning classifiers for person identification and gender classification by Vasu Krishna Suravarapu, Hemprasad Yashwant Patil

“…Abstract Person authentication using periocular images is a prominent research domain. Although the biometric identification systems have advanced, the existing approaches still struggle with accuracy, overfitting issues and computational efficiency, especially when utilizing periocular images for person identification and gender classification. …”
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Optimizing Deep Learning Models with Improved BWO for TEC Prediction by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang

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A guideline study for optimal machine learning approaches to predict the off-design performance of sCO2-PCHEs by Xin Sui, Wenqi Wang, Chunyang Liu, Peixin Dong

“…Then, the sensitivity of ML models to hyperparameter, split ratio, and data distribution is evaluated. …”
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A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) by Liu Yang, Jiang Guofan, Zhang Yixin, Wei Qianze, Zhang Jian, Roohallah Alizadehsani, Pawel Plawiak

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An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. …”
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Glioma Grade Classification: Hypertuned CNN With Saliency-Based Insight From MRI by Khondakar A. Shahriar, Enamul H. Bhuiyan, Md. Forhad Hossain, Rakibur Rahman, Muhammad E. H. Chowdhury

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A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction by Rakibul Islam, Azrin Sultana, MD. Nuruzzaman Tuhin

“…This study proposes a system for predicting the LD by combining the techniques of Machine Learning (ML) algorithms that include the Decision Tree, Random Forest, Extra Tree Classifier (ETC), LightGBM, and Adaboost, with the Tree-Structured Parzen Estimator (TPE) method for hyperparameter tuning. No previous literature research has utilized ML algorithms with TPE to predict LD. …”
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Carbon Dioxide Emission Forecasting Using BiLSTM Network Based on Variational Mode Decomposition and Improved Black-Winged Kite Algorithm by Yueqiao Yang, Shichuang Li, Haijun Liu, Jidong Guo

“…This framework aims to address the challenges associated with predicting non-stationary data and optimizing model hyperparameters. Initially, experiments were conducted on 29 benchmark functions using the IBKA algorithm, demonstrating its superior performance in highly nonlinear and complex environments. …”
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Intelligence modeling of nanomedicine manufacture by supercritical processing in estimation of solubility of drug in supercritical CO2 by Shuhui Wu, Ting Zhang, Yunxia Tao, Lina Fu, Ying Chen, Weidong Qiang, Enzhong Li

“…Abstract The primary goal of this research is to apply bagging-based regression techniques to forecast the solubility of raloxifene and the density of carbon dioxide (CO₂). …”
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Genetic algorithm optimization of ensemble learning approach for improved land cover and land use mapping: Application to Talassemtane National Park by Ali Azedou, Aouatif Amine, Isaya Kisekka, Said Lahssini

“…The GA optimization framework was implemented to tune the ensemble classifier’s hyperparameters, outperforming the GS method. The GA-optimized ensemble classifier achieved an overall accuracy of 96.2%, significantly higher than all individual classifiers, whose accuracies ranged from 77.5% to 85.8%. …”
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Egg freshness during storage: the effect of laying hen age and shelf life prediction using a novel hybrid modeling method by Yifeng Lu, Jing Li, Zihao He, Linyun Chen, Huixin Tian, Chen Xu, Xinglian Xu, Minyi Han

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Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions by Leonardo Buscarini, Paola Romano, Elena Sofia Cocco, Carlo Damiani, Sanaz Pournajaf, Marco Franceschini, Francesco Infarinato

“…Abstract In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. …”
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Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Kashish Ara Shakil, Mudasir Ahmad Wani, Muhammad Asim

“…On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. …”
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