Bitter-RF: A random forest machine model for recognizing bitter peptides by Yu-Fei Zhang, Yu-Hao Wang, Zhi-Feng Gu, Xian-Run Pan, Jian Li, Hui Ding, Yang Zhang, Ke-Jun Deng

“…Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set.ResultsThe proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides.DiscussionWe hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.…”
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Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise by Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur

“…We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. …”
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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder by Lin Wang, Haishu Tan, Fuqiang Zhou, Wangxia Zuo, Pengfei Sun

“…Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. …”
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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset by Youngghyu Sun, Jiyoung Lee, Soohyun Kim, Joonho Seon, Seongwoo Lee, Chanuk Kyeong, Jinyoung Kim

“…The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. …”
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Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods by Evan N. Crothers, Nathalie Japkowicz, Herna L. Viktor

“…Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. …”
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Detailed Reaction Mechanism for 350–400 °C Pyrolysis of an Alkane, Aromatic, and Long-Chain Alkylaromatic Mixture by Payne, A. Mark, Spiekermann, Kevin A., Green, William H.

“…To showcase the advancements that have been made in automatic generation of large mechanisms, we constructed such a model for a three-component mixture containing species with up to 18 carbon atoms. The generated model is able to predict many of the major and minor products with relatively high accuracy against gold-tube batch pyrolysis data collected for this system. …”
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Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning by Damewood, James, Schwalbe-Koda, Daniel, Gómez-Bombarelli, Rafael

“…To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. …”
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DLVGen: a dual latent variable approach to personalized dialogue generation by Lee, Jing Yang, Lee, Kong Aik, Gan, Woon-Seng

“…Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. …”
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Building a large-scale dataset for audio-conditioned dance motion synthesis by Wu, Jinyi

“…Generative models for audio-conditioned dance motion synthesis map music features to dance movements. …”
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Optimization of protein extraction from freeze dried fish waste using response surface methodology (RSM) by Husin, Nurdiyana, Mustapa Kamal, Siti Mazlina, M., Siti Nor Fadhilah

“…Response surface methodology (RSM) was used to study the effect of independent variables, namely time (30-60 minutes), pH (7-11), rotation speed (100-300 rpm), and NaOH: substrate ratio (1-3) on protein extraction from FD-FW. From RSM-generated model, the optimum conditions for extraction of protein from FD-FW were identified to be at pH 10.56 in 48.61 minutes reaction time, with rotation speed of 104.77 rpm and NaOH: substrate ratio of 1.54. …”
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Dual phase role of composite adsorbents made from cockleshell and natural zeolite in treating river water by Moideen, S. N .F., Din, M. F. M., Rezania, S., Ponraj, M., Rahman, A. A., Pei, L. W., Ismail, Z., Taib, S. M., Li, Y. Y., Komori, D.

“…Based on the results, the composite adsorbent was fitted better with Yoon-Nelson and Thomas model rather than Adam-Bohart model. The generated models were able to characterize the adsorption process using composite adsorbent in the ECPS column system.…”
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Data-mining natural language materials syntheses by Kim, Edward Soo.

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Towards efficient and robust reinforcement learning via synthetic environments and offline data by Lu, C

“…Leveraging recent advances in diffusion generative models, our approach outperforms and is composable with standard data augmentation, and is particularly effective in low-data regimes. …”

Motion control algorithm development and testing for a free-piston engine generator by Hanipah, M. Razali

“…Concurrently, a multi-domain free-piston engine generator model was developed for the project. Preliminary results of the model were used for technical specifications requirement for developing the linear actuator test rig. …”
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The Effect of Enhancing Unemployment Benefits in Korea: Wage Replacement Rate vs. Maximum Benefit Duration† by KIM, JIWOON

“…To this end, I build and calibrate an overlapping generation model which reflects the heterogeneity of the unemployed and the specificity of the unemployment insurance (UI) system in Korea. …”
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Expressibility and trainability of parametrized analog quantum systems for machine learning applications by Jirawat Tangpanitanon, Supanut Thanasilp, Ninnat Dangniam, Marc-Antoine Lemonde, Dimitris G. Angelakis

“…We conclude our work with an example application in generative modeling employing a well studied analog many-body model of a driven Ising spin chain. …”
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Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images by Sagar Kora Venu, Sridhar Ravula

“…In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). …”
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Machine learning for data integration in human gut microbiome by Peishun Li, Hao Luo, Boyang Ji, Jens Nielsen

“…For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. …”
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Generating Music with Data: Application of Deep Learning Models for Symbolic Music Composition by Pedro Ferreira, Ricardo Limongi, Luiz Paulo Fávero

“…However, the evaluation of symbolic musical generation models is mostly based on low-level mathematical metrics (e.g., the result of the loss function) due to the inherent difficulty in measuring the musical quality of a given performance due to the subjective nature of music. …”
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MSGeN: Multimodal Selective Generation Network for Grounded Explanations by Dingbang Li, Wenzhou Chen, Xin Lin

“…Our extensive experimentation demonstrates that MSGeN surpasses existing multimodal explanation generation models across various metrics, including BLEU, METEOR, ROUGE, CIDEr, SPICE, and Grounding. …”
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