Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense by Jiacheng Huang, Long Chen, Xiaoyin Yi, Ning Yu

“…Abstract Deep neural networks have a recognized susceptibility to diverse forms of adversarial attacks in the field of natural language processing and such a security issue poses substantial security risks and erodes trust in artificial intelligence applications among people who use them. …”
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Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT by Lal Khan, Atika Qazi, Hsien-Tsung Chang, Mousa Alhajlah, Awais Mahmood

“…Among the different deep neural networks utilized for SA globally, Bi-directional long short-term memory (Bi-LSTM), BERT, and CNN models have received much attention. …”
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Arabic Speech Recognition Based on Encoder-Decoder Architecture of Transformer by Mohanad Sameer, Ahmed Talib, Alla Hussein, Husniza Husni

“…Previous choices for the Arabic ASR architecture have been time-delay neural networks, recurrent neural networks (RNN), and long short-term memory (LSTM). …”
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Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends by Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke

“…Deep learning architectures, such as convolutional neural networks (CNNs), autoencoders, deep belief networks, generative adverserial networks (GANs), and recurrent neural networks are examined for their suitability in processing hyperspectral data. …”
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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

“…We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. Employing analysis and cross-validation, we found that XGBoost performed the best, followed by random forests and neural networks, and stepwise regression performed the worst. …”
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Human activity recognition: an approach 2D CNN-LSTM to sequential image representation and processing of inertial sensor data by Wallace Camacho Carlos, Alessandro Copetti, Luciano Bertini, Leonard Barreto Moreira, Otávio de Souza Martins Gomes

“…This approach facilitated the effective exploration of data input and neural network architectures. By utilizing the recent history of movements as input for the models, this study evaluated the impact of this methodology on HAR using two adapted architectures: 2D convolutional neural networks combined with long short-term memory layers (2D CNN-LSTM) and standalone 2D convolutional neural networks (2D CNN). …”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…The advent of machine learning, particularly deep learning, revolutionized HDCR with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing performance. …”
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Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection by Brendan Ubochi, Abayomi E. Olawumi, John Macaulay, Oyawoye I. Ayomide, Kayode F. Akingbade

“…Models that are based on the convolutional neural networks (CNNs) have shown promise in the early detection of glaucoma. …”
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Effect of Central Pattern Generators Depended Different Walking Strategies on Gait Ability of Patients after Stroke by Hua WU, Zaihua RU, Congying XU, Xudong GU, Jianming FU

“…This indicated that fMRI is valuable in exploring the mechanisms of CPG neural network, and possessing positive effects on the improvement of walking ability and the reshaping of neural networks.…”
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Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election by Rhoma Cahyanti, Desiana Nurul Maftuhah, Aris Budi Santoso, Indra Budi

“…The result of the study also discovered that random forest and neural networks have the best performance for sentiment analysis.…”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. …”
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A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons by K. M. R. Hunt, K. M. R. Hunt, S. P. Harrison

“…<p>We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of models: dense neural networks (“regional models”) and convolutional neural networks (CNNs). …”
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A Computational Approach to Understanding Agglutinative Structures in Urdu by Muhammad Shoaib Tahir, Mahnoor Amjad

“…These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. …”
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EEG Source Imaging Guided by Spatiotemporal Specific fMRI: Toward an Understanding of Dynamic Cognitive Processes by Thinh Nguyen, Thomas Potter, Trac Nguyen, Christof Karmonik, Robert Grossman, Yingchun Zhang

“…To characterize the dynamics of the neural networks and the underlying neuroplasticity of the central motor system, neuroimaging tools with high spatial and temporal accuracy are desirable. …”
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Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers by Chanthol Eang, Seungjae Lee

“…This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. …”
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Complex Risk Statistics with Scenario Analysis by Fei Sun, Yichuan Dong

“…Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representations for such risk.…”
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Dynamic Analysis of Nonlinear Impulsive Neutral Nonautonomous Differential Equations with Delays by Jinxian Li

“…A class of neural networks described by nonlinear impulsive neutral nonautonomous differential equations with delays is considered. …”
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Wavenumber-dependent transmission of subthreshold waves on electrical synapses network model of Caenorhabditis elegans by Iksoo Chang, Taegon Chung, Sangyeol Kim

“…Recent experimental studies showed that electrically coupled neural networks like in mammalian inferior olive nucleus generate synchronized rhythmic activity by the subthreshold sinusoidal-like oscillations of the membrane voltage. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Time Complexity of Training DNNs With Parallel Computing for Wireless Communications by Pengyu Cong, Chenyang Yang, Shengqian Han, Shuangfeng Han, Xiaoyun Wang

“…Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. …”
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