Prediksi Gagal Jantung Menggunakan Artificial Neural Network by Simeon Yuda Prasetyo

“…In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Networks by conducting several hyperparameter tests, namely the number of hidden layers and the number of neuron units in the hidden layer. …”
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GINSER: Geographic Information System Based Optimal Route Recommendation via Optimized Faster R-CNN by S. D. Anitha Selvasofia, B. SivaSankari, R. Dinesh, N. Muthukumaran

“…PSO is utilized for hyperparameter tuning achieving a high accuracy of 95.24%. …”
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Multiple objectives escaping bird search optimization and its application in stock market prediction based on transformer model by Dedai Wei, Zimo Wang, Minyu Qiu, Juntao Yu, Jiaquan Yu, Yurun Jin, Xinye Sha, Kaichen Ouyang

“…Despite the availability of various forecasting methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these models often faces limitations, particularly in single-objective optimization, where they can easily fall into local optima. …”
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Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study by Shailesh Appukuttan, Shailesh Appukuttan, Aude-Marie Grapperon, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson

“…This study provides a methodological framework to guide future research and emphasizes the need for dataset enrichment to improve clinical utility.…”
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Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example by Yahya Masri, Zifu Wang, Anusha Srirenganathan Malarvizhi, Samir Ahmed, Tayven Stover, David W. S. Wong, Yongyao Jiang, Yun Li, Qian Liu, Mathieu Bere, Daniel Rothbart, Dieter Pfoser, Chaowei Yang

“…Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. …”
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A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters by Enes Algül, Saadin Oyucu, Onur Polat, Hüseyin Çelik, Süleyman Ekşi, Faruk Kurker, Ahmet Aksoz

“…These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion.…”
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Artificial intelligence-driven sentiment analysis and optimization of movie scripts by Hong Zheng

“…Cross-validation and hyperparameter tuning guarantee the stability and generalizability of the model. …”
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A Differential Evolutionary-Based XGBoost for Solving Classification of Physical Fitness Test Data of College Students by Baoyue Liang, Weifu Qin, Zuowen Liao

“…To accurately and timely identify the physical health status of college students, a hybrid model of DE-XGBoost is proposed in this study: a discrete coding strategy is designed to solve the XGBoost hyperparameter optimization problem, and differential evolution (DE) is used to achieve global parameter optimization. …”
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Performance Analysis of Resampling Techniques for Overcoming Data Imbalance in Multiclass Classification by Anggit Larasati, Sugiyarto Surono, Aris Thobirin, Deshinta Arrova Dewi

“…The architecture uses MobileNetV2, which compares hyperparameter fine-tuning in three optimizers. This research shows that the performance of MobileNetV2, which implements the bootstrap-based random oversampling technique, has the highest accuracy compared to random undersampling and no resampling methods. …”
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Optimizing context-based location extraction by tuning open-source LLMs with RAG by Zifu Wang, Yahya Masri, Anusha Srirenganathan Malarvizhi, Tayven Stover, Samir Ahmed, David Wong, Yongyao Jiang, Yun Li, Mathieu Bere, Daniel Rothbart, Dieter Pfoser, David Marshall, Chaowei Yang

“…RAG-based tuning delivered the highest F1 score (>0.9) for extracting and associating location data with conflict incidents. This research highlights the benefits of using RAG for multi-incident context-based location extraction and provides insights into optimizing LLMs through prompt-tuning, hyperparameter adjustment, and model selection for location extraction tasks. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. …”
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Reservoir Neural Network Computing for Time Series Forecasting in Aerospace: Potential Applications to Predictive Maintenance by Juan Manuel Rodríguez Riesgo, Juan Luis Cabrera Fernández

“…This research is a starting point to develop methods that could inform accurately on the remaining useful life of a component in aerospace systems.…”
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Performance Analysis of SpectralNet Algorithm on Fashion MNIST and KMNIST Datasets: A Study on Clustering Efficiency and Resource Utilization by Mujeeb Ur Rehman, Zenab Bibi

“…Higher embedding dimension values lead to superior clustering precision, whereas it demands increased processor capacity. The research reveals how SpectralNet handles accuracy-efficiency relationships in dataset analysis to demonstrate its practical capabilities for complex information. …”
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Comparison of the Performance of Pegasos and Traditional Models in the Task of Sentiment Classification of Product Reviews by Zhu Di

“…However, most of the existing research literature only uses traditional models or does not have a scientific comparison between models, and only provides training steps for a model that can be used for text sentiment analysis. …”
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OncoFusion: Multi-Model Approach for Generalized and Ovarian Cancer Detection with Stacked Ensembles by Zaware Sarika, Neharkar Rutuja, Sayyad Ayesha, Patil Aarya, Mulla Salma

“…Cancer detection is a critical task in the medical field, where accurate and early diagnosis can save lives. However, current research faces challenges such as high false positive rates, difficulty in generalizing models across diverse datasets, and inconsistent accuracy in real- world scenarios. …”
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Comparison of EfficientNetB0 and EfficientNetB7 Models in Classifying Malaria Based on Blood Cells by Muhammad Rizkiansyah Maulana Kiki, Zamah Sari, Didih Rizki

“…The first scenario uses Hyperparameter Tuning and the EfficientNetB0 model with classification results of 95%. …”
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The BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification by Komang Ayu Triana Indah, I Ketut Gede Darma Putra, I Made Sudarma, Rukmi Sari Hartati, Minho Jo

“…This problem is overcome by classifying news based on its context to avoid material that has many interpretations. This research combines the Uncased model BiDirectional Encoder Representations from Transformer (BERT) with other models to create a text classification model. …”
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Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset by Cindy Dinda Sabella, Yoga Pristyanto

“…Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. …”
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Forecasting Inland Waterway Container on Barge Volume: A Machine Learning Approach Using Economic Features by Fan Bu, Heather Nachtmann

“…The results demonstrate the feasibility of accurately forecasting COB volume using economic features. This research develops an alternative method to forecast COB volume and provides a foundation for developing transfer learning models to predict COB volume in other emerging markets where historical COB volume data is limited. …”
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Non‐Autoregressive Translation Algorithm Based on LLM Knowledge Distillation in English Corpus by Fang Ju, Weihui Wang

“…We designed a dynamic temperature hyperparameter distillation strategy that effectively enhances the precision of translations. …”
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