An intelligent simplification method for river networks with an unsupervised variational autoencoder by Di Wang, Haizhong Qian, Xiao Wang, Limin Xie, Yue Chen, Linghui Kong

“…Intelligent simplification of river networks is an important part in map generalisation. Traditional rule-based methods often have limitations, such as relying on the determination of parameters and thresholds. …”
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Wind Turbine Fault Detection Through Autoencoder-Based Neural Network and FMSA by Welker Facchini Nogueira, Arthur Henrique de Andrade Melani, Gilberto Francisco Martha de Souza

“…The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. …”
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Automated building typology clustering and identification using a variational autoencoder on digital land cadastres by Jaime de-Miguel-Rodriguez, M.V. Requena-Garcia-Cruz, E. Romero-Sánchez, A. Morales-Esteban

“…This study introduces a novel, automated methodology for extracting urban building typologies from digital land cadastres using a Variational Autoencoder (VAE). Unlike traditional shape clustering approaches, that depend on predefined rules or manual labelling, the method employs unsupervised learning to identify building typologies, based solely on geometric features, derived from roof-print shapes. …”
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Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition by Xiaodong Sun, Jie Zhu, Bing Tang, Zhaohui Jiang

“…Monitoring the blast furnace shaft static pressure is crucial for maintaining a stable ironmaking process. Traditional rule-based methods and manual inspections suffer from high labor costs and inconsistent standards. …”
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Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security by Yakub HOSSAIN, Zannatul FERDOUS, Tanzillah WAHID, Md. Torikur RAHMAN, Uttam Kumar DEY, Mohammad Amanul ISLAM

“…Conventional IDS methods, which often rely on signatures or rules it will struggle to keep up with its complex attacks and evolution. …”
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Flexible and objective diagnosis of type II diabetes by using a fuzzy deep learning ensemble approach by Min-Chi Chiu, Tin-Chih Toly Chen, Yu-Cheng Wang

“…The different diagnostic rules created in the FDLE approach complement each other and facilitate an accurate diagnosis.…”
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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.…”
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Targeted molecular generation with latent reinforcement learning by Ragy Haddad, Eleni E. Litsa, Zhen Liu, Xin Yu, Daniel Burkhardt, Govinda Bhisetti

“…We have paired our optimization framework with the latent spaces of two different architectures of autoencoder models showing that the method is agnostic to the underlying architecture. …”
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Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) by Isra Mahmoudi, Djallel Eddine Boubiche, Samir Athmani, Homero Toral-Cruz, Freddy I. Chan-Puc

“…Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. …”
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Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments by Nithesh Naik, Neha Surendranath, Sai Annamaiah Basava Raju, Chennaiah Madduri, Nagaraju Dasari, Vinod Kumar Shukla, Vathsala Patil

“…Comparative results against rule-based and statistical baselines showed a 12–18% improvement in detection sensitivity. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Molecular generation technology employs deep learning techniques for automatically generating new molecular structures, often based on generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These technologies can learn the rules of molecular generation from existing corrosion inhibitor molecule data and generate new molecules with specific properties. …”
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