Fish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network by Gil Gabornes Dialogo, Larmie Santos Feliscuzo, Elmer Asilo Maravillas

“…The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. …”
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U-GAN Model for Infrared and Visible Images Fusion

“…Then the U-net extracted feature is confronted with the discriminator containing infrared image, and the generator model is obtained. The experimental results show that the present algorithm can obtain the fusion image with clear outline, prominent texture and obvious target. …”
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Data‐driven image captioning via salient region discovery by Mert Kilickaya, Burak Kerim Akkus, Ruket Cakici, Aykut Erdem, Erkut Erdem, Nazli Ikizler‐Cinbis

“…Moreover, they present a novel phrase selection paradigm and a sentence generation model which depends on a joint analysis of salient regions in the input and retrieved images within a clustering framework. …”
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Flexible and Robust Real-Time Intrusion Detection Systems to Network Dynamics by Kicho Yu, Khanh Nguyen, Younghee Park

“…The proposed system adaptively and intelligently adjusts the generated model with given system parameters that can be used as security parameters to defend against the attacker’s obfuscation techniques in real time. …”
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Verification Method of Safety Properties of Embedded Assembly Program by Combining SMT-Based Bounded Model Checking and Reduction of Interrupt Handler Executions by Satoshi Yamane, Junpei Kobashi, Kosuke Uemura

“…Our proposed method generates models of assembly codes in detail with the fixed-sized bit-vectors theory. …”
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A method for generating tire defect images based on improved DCGAN by Chunhua LI, Ruizhi FU, Yukun LIU, Yulin WANG

“…An improved DCGAN tire defect image generation model was proposed to solve the problems of poor image quality, unstable model frame and slow model convergence in the data expansion method of deep convolutional generative adversarial network. …”
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Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck by Zhelun Zhang, Yi-Zhuang You

“…Our approach opens up a new avenue for using the big data generated on noisy intermediate-scale quantum devices to train generative models for representation learning of quantum operators, which might be a step toward our ultimate goal of creating an artificial intelligence quantum physicist.…”
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Machine Learning-Based Automatic Utterance Collection Model for Language Development Screening of Children by Jeong-Myeong Choi, Yoon-Kyoung Lee, Jong-Dae Kim, Chan-Young Park, Yu-Seop Kim

“…To acquire speech data, we present a machine learning-based phrase generating model. It has the benefit of being able to cope with several children, which reduces costs and allows for the collection of objectified utterance data through consistent conversation settings. …”
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Design and development of a novel tunable electrorheological fluid (ERF) damper-foundation to attenuate residual vibrations in machine tools by Jadhav Abhijitkumar A., Zope Sanjay B., Malagi Ravindra R., Suryawanshi Deepali A.

“…This paper provides a first-generation model of a semiactive-viscous damper (ERF damper-foundation) with tunable damping facilitating machine installation. …”
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A Heterogeneous IoT Data Analysis Framework with Collaboration of Edge-Cloud Computing: Focusing on Indoor PM10 and PM2.5 Status Prediction by Jaewon Moon, Seungwoo Kum, Sangwon Lee

“…While typical edges do not have enough processing power to train machine learning models in real time, it is common to generate models in the cloud for use on the edge. The pattern of heterogeneous Internet of Things (IoT) data is dependent on individual circumstances. …”
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Minimum Description Length Codes Are Critical by Ryan John Cubero, Matteo Marsili, Yasser Roudi

“…First, when they are taken as generative models of samples, they generate samples with broad empirical distributions and with a high value of the relevance, defined as the entropy of the empirical frequencies. …”
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Keypoint Transfer Segmentation by Toews, M., Wachinger, Christian, Langs, Georg, Wells, William M, Golland, Polina

“…Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. …”
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An integrative computational architecture for object-driven cortex by Yildirim, Ilker, Wu, Jiajun, Kanwisher, Nancy, Tenenbaum, Joshua B

“…Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations. …”
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Coarse-graining auto-encoders for molecular dynamics by Wang, Wujie, Gomez-Bombarelli, Rafael

“…Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates. We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse-grained force fields. …”
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On the relevance of turbulent structures resolution for cross-flow in a helical-coil tube bundle by Feng, Jinyong, Acton, Michael, Baglietto, Emilio, Kraus, Adam R, Merzari, Elia

“…In this work, the recently proposed hybrid second-generation model STRUCT is compared to classic URANS turbulence formulations against high-fidelity LES simulations. …”
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Modeling applied to problem solving by Pawl, Andrew, Barrantes, Analia, Pritchard, David E.

“…Instead, students classify problems under the appropriate instructor‐generated Model by selecting a system to consider and describing the interactions that are relevant to that system. …”
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Generation of mouse models of myeloid malignancy with combinatorial genetic lesions using CRISPR-Cas9 genome editing by Heckl, Dirk, Kowalczyk, Monika S., Yudovich, David, Belizaire, Roger, Puram, Rishi V., McConkey, Marie, Thielke, Anne, Aster, Jon C., Regev, Aviv, Ebert, Benjamin L.

“…By delivering combinations of small guide RNAs (sgRNAs) and Cas9 with a lentiviral vector, we modified up to five genes in a single mouse hematopoietic stem cell (HSC), leading to clonal outgrowth and myeloid malignancy. We thereby generated models of acute myeloid leukemia (AML) with cooperating mutations in genes encoding epigenetic modifiers, transcription factors and mediators of cytokine signaling, recapitulating the combinations of mutations observed in patients. …”
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Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network by Li, Kexin, Gan, Vincent J. L., Li, Mingkai, Gao, Maggie Y., Tiong, Robert Lee Kong, Yang, Yaowen

“…Following this, a graph-constrained layout generator is developed, with a generative modelling algorithm and graph convolutional neural network, to extract pairwise spatial-geometric features for generating the optimal precast layout. …”
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Standardization of Malaysian adult female nasal cavity by Lee, Chih Fang, Abdullah, Mohd Zulkifly, Ahmad, Kamarul Arifin, Shuaib, Ibrahim Lutfi

“…This evaluation shows that the generated model represents an averaged and standardized model of adult Malaysian females.…”
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Automatic model learning and its applications in malware detection by Hao, Xiao

“…Automatic learning of behavior models of programs can benefit many applications such as software verification by generating the models of the target program for verification, software testing by generating models of standard libraries for test generation, security analysis by generating the attack models of malicious software (malware) for malware detection, and software maintenance by generating models of legacy programs for code comprehension. …”
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