Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…Sarcasm and irony accounted for 22% of the classification errors, while mixed sentiment accounted for 18%, and implicit accounted for 15%. To sum up, this research has shown the efficiency of LSTM models on e-commerce user review sentiment analysis, especially bidirectional LSTM. …”
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Embedded feature selection using dual-network architecture by Abderrahim Abbassi, Arved Dörpinghaus, Niklas Römgens, Tanja Grießmann, Raimund Rolfes

“…However, existing methods often face challenges due to the complexity of feature interdependencies, uncertainty regarding the exact number of relevant features, and the need for hyperparameter optimization, which increases methodological complexity.This research proposes a novel dual-network architecture for feature selection that addresses these issues. …”
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Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture by Elias Tabane, Ernest Mnkandla, Zenghui Wang

“…The findings underscore the robustness of hybrid structures in genomic classification tasks and warrant future research on encoding strategy, model and parameter tuning, and hyperparameter tuning to further improve accuracy and generalization in DNA sequence analysis.…”
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An Empirical Comparison of Urban Road Travel Time Prediction Methods—Deep Learning, Ensemble Strategies and Performance Evaluation by Yizhe Wang, Yangdong Liu, Xiaoguang Yang

“…To address the randomness issue in deep learning models, we adopt a strategy of conducting five independent training runs for each hyperparameter configuration and using statistical measures to obtain stable performance evaluations. …”
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A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems by Christian Perez, Carlos March, Miguel A. Salido

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Ransomware detection and family classification using fine-tuned BERT and RoBERTa models by Amjad Hussain, Ayesha Saadia, Faeiz M. Alserhani

“…The lack of standardization across IoT devices creates interoperability issues and complicates data transfer between medical devices and healthcare systems. This research explores these challenges and proposes a novel approach using hyperparameter-optimized transfer learning-based models, Bidirectional Encoder Representations from Transformers (BERT), and a Robustly Optimized BERT Approach (RoBERTa), to not only detect but also classify ransomware targeting IoT devices by analyzing dynamically executed API call sequences in a sandbox environment. …”
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Klasifikasi Aktivitas Manusia Menggunakan Algoritme Computed Input Weight Extreme Learning Machine dengan Reduksi Dimensi Principal Component Analysis by M. Sofyan Irwanto, Fitra A. Bachtiar, Novanto Yudistira

“…Pada penelitian ini juga dilakukan pemilihan hyperparameter terbaik pada masing-masing metode menggunakan metode Grid Search Cross Validation. …”
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APD-BayNet: Jakarta Air Quality Index Prediction Using Bayesian Optimized Tabnet by Raey Faldo, Satria Mandala, Rina Pudji Astuti, Ary Setijadi Prihatmanto, Mohd Soperi Mohd Zahid

“…These findings highlight the effectiveness of APD-BayNet in providing a robust and scalable solution for air quality monitoring. Future research could explore its adaptability to other geographical regions, enhancing its applicability on a global scale.…”
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Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti by Fathi Farah Fadoul, Abdoulaziz Ahmed Hassan, Ramazan Çağlar

“…At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. …”
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Mining autonomous student patterns score on LMS within online higher education by Ricardo Ordoñez-Avila, Jaime Meza, Sebastian Ventura

“…The variables analyzed focused on download rate, homework submission rate, test performance rate, median daily accesses, median days of access per month, observation of comments on teacher-reviewed assignments, length of final exam, and not requiring the supplemental exam. Hyperparameter adjustment improved the performance of the models after applying RFEcv. …”
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Imaging Estimation for Liver Damage Using Automated Approach Based on Genetic Programming by David Herrera-Sánchez, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, Socorro Herrera-Meza, Eduardo Rivadeneyra-Domínguez, Isaac Zamora-Bello, María Fernanda Almanza-Domínguez

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De novo design of polymer electrolytes using GPT-based and diffusion-based generative models by Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh

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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…To overcome these limitations, this study applies advanced machine learning (ML) and deep learning (DL) techniques with systematic hyperparameter tuning to enhance predictive performance. …”
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Bio-Inspired Metaheuristics in Deep Learning for Brain Tumor Segmentation: A Decade of Advances and Future Directions by Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana, Wahyu Caesarendra, Nurul Huda

“…Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. …”
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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…Additionally, the customized ABC algorithm specifically optimizes hyperparameters to increase the adaptability and performance of the model under varying market conditions. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

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Kombinasi Intent Classification dan Named Entity Recognition pada Data Berbahasa Indonesia dengan Metode Dual Intent and Entity Transformer by Zahra Asma Annisa, Rizal Setya Perdana, Putra Pandu Adikara

“…The best hyperparameter combination obtained is a warm-up step of 70, early stopping patience of 15, weight decay of 0.01, NER loss weight of 0.6, and intent classification loss weight of 0.4. …”
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Development and evaluation of deep learning models for cardiotocography interpretation by Nicole Chiou, Nichole Young-Lin, Christopher Kelly, Julie Cattiau, Tiya Tiyasirichokchai, Abdoulaye Diack, Sanmi Koyejo, Katherine Heller, Mercy Asiedu

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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and... by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). …”
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Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation by Penni Wintasari Simarmata, Putri Taqwa Prasetyaningrum

“…The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. …”
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