False data injection attack sample generation using an adversarial attention-diffusion model in smart grids by Kunzhan Li, Fengyong Li, Baonan Wang, Meijing Shan

“…Considering the scarcity of FDIA attack samples, the traditional FDIA detection models based on neural networks are always limited in their detection capabilities due to imbalanced training samples. …”
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Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction by Yuehjen E. Shao, Jun-Ting Dai

“…Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). …”
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Machine-Vision-Based Intelligent Manufacturing by Fine-Grained Point Cloud Identification by Yinhai Li, Fei Wang, Yang Jiang

“…Dense learned grids were analyzed, and network parameter tuning and management were done; then comparisons with voxel-representation-enabled three-dimensional convolutional neural networks (3D-CNN) and existing methods revealed detailed literature for fabricating networks with better robustness and lower algorithm cycle complexity; finally, the actual completion of the network is verified through the example section, and the manufacturability of the cave shape is analyzed to identify the unmanufacturable overall form and explain its considerations. …”
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Automated CATS system for distance learning by Yu. B. Popova

“…As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. …”
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An Overview of Pavement Degradation Prediction Models by Amir Shtayat, Sara Moridpour, Berthold Best, Shahriar Rumi

“…The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.…”
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Das Kind mit APD und CAPD: Untersuchung zentraler auditiver Verarbeitungsstörungen und der Wirksamkeit des Hörtrainings by Agnieszka Kuniczuk

“…The child’s condition was evaluated using an interview sheet, tests examining Central Auditory Processing Disorders, and methods and techniques aimed at neural networks to enhance brain neuroplasticity and improve Central Auditory Processing Disorders. …”
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Prediction of Quality Food Sale in Mart Using the AI-Based TOR Method by Daniyal Irfan, Xuan Tang, Vipul Narayan, Pawan Kumar Mall, Swapnita Srivastava, V. Saravanan

“…This paper uses artificial intelligence, business intelligence, and neural networks to forecast the food sale prediction by evaluating the data of each individual client. …”
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Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning by Zheng-Meng Zhai, Mohammadamin Moradi, Ying-Cheng Lai

“…Further justification for using the LSTM is provided by our comparing its performance with that of alternative machine-learning architectures such as feedforward neural networks and random forest. Despite the gigantic existing literature on applications of LSTM to power grids, to our knowledge, the problem of locating an attack and estimating the state from limited observations had not been addressed before our work. …”
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Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP. by Sajid Mehmood, Rashid Amin, Jamal Mustafa, Mudassar Hussain, Faisal S Alsubaei, Muhammad D Zakaria

“…We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. …”
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Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation by Zhizhe Liu, Luo Sun

“…To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. …”
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Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models by Martin Brablc, Jan Žegklitz, Robert Grepl, Robert Babuška

“…Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. …”
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Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice by Ryszard Gomolka

“…Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. …”
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Deep learning-based prediction of autoimmune diseases by Donghong Yang, Xin Peng, Senlin Zheng, Shenglan Peng

“…This study focuses on the prediction of several autoimmune diseases mediated by T cells, and proposes two models: one is the AutoY model based on convolutional neural networks, and the other is the LSTMY model, a bidirectional LSTM network model that integrates the attention mechanism. …”
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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…Furthermore, the utilization of an attention mechanism serves the purpose of dynamically selecting the significant segments within the sequence, thereby improving the model’s comprehension of context and enhancing the efficacy of deep neural networks (DNNs) in the domain of human activity recognition (HAR). …”
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Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods by Omar Hazim Hameed Hameed, Uğurhan Kutbay, Javad Rahebi, Fırat Hardalaç, Ibrahim Mahariq

“…Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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Avances en el aprovechamiento de biopolímeros y productos peruanos by Erika Del Milagro Lozano-Flores

“…Asimismo, el análisis de palabras clave destaca la relevancia de técnicas como "machine learning", "deep learning" y "neural networks". Los mapas de colaboración reflejan que Estados Unidos y China son líderes en producción y coautoría. …”
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Data-Driven Approach to Evaluate the Level of Service (LOS) of Demand-Responsive Transport for the Disabled (DRTD) with an ANFIS Algorithm by Seohyeon Park, Sooyeon Park, Hosik Choi, Do-Gyeong Kim

“…The model was estimated using an Adaptive Neuro-Fuzzy Inference System (ANFIS), which is known to have an excellent predictive performance by combining the advantages of both artificial neural networks and fuzzy inference systems. Four variables, including the number of calls (or requests), the number of vacant vehicles, Medical Infrastructure Concentration Index (MICI), and Disabled Population Concentration Index (DPCI), were used as input variables for the ANFIS-based model. …”
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Comparison of deep transfer learning models for classification of cervical cancer from pap smear images by Harmanpreet Kaur, Reecha Sharma, Jagroop Kaur

“…In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. …”
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Edge and texture aware image denoising using median noise residue U-net with hand-crafted features by Soniya S., Sriharipriya K. C.

“…Although fully convolution neural networks (CNN) are capable of removing the noise using kernel filters and automatic extraction of features, it has failed to reconstruct the images for higher values of noise standard deviation. …”
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