Using Deep Learning to Predict Sentiments: Case Study in Tourism by C. A. Martín, J. M. Torres, R. M. Aguilar, S. Diaz

“…We develop and compare various classifiers based on convolutional neural networks (CNN) and long short-term memory networks (LSTM). …”
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Scenario modeling of the drug prescription process for children: application of machine learning methods by А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova

“…Objective: determining the most appropriate machine learning method to solve the problem of drug prescribtion for children, evaluating its performance and potential for implementation into scenario modeling systems of the pharmaceutical care structure.Material and methods. …”
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Keywords, morpheme parsing and syntactic trees: features for text complexity assessment by Dmitry A. Morozov, Ivan A. Smal, Timur A. Garipov, Anna V. Glazkova

“…The RuTermExtract algorithm was utilized to generate keywords, a convolutional neural network model was used to generate morphemic parses, and the Stanza model, trained on the SynTagRus corpus, was used to generate syntax trees. …”
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Literature Review: A Comparative Study of Waste Classification using Deep Learning Algorithms by Ariza Ikhlas, Billy Hendrik

“…The main objectives are to identify the most appropriate algorithms for waste type classification, determine the most suitable model architectures, and examine the correlation between dataset size, number of classes, and classification accuracy. …”
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Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle by Xianghua Ma, Zhenkun Yang, Shining Chen

“…Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. …”
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Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery by George S. Liu, Soraya Fereydooni, Melissa Chaehyun Lee, Srinidhi Polkampally, Jeffrey Huynh, Sravya Kuchibhotla, Mihir M. Shah, Noel F. Ayoub, Robson Capasso, Michael T. Chang, Philip C. Doyle, F. Christopher Holsinger, Zara M. Patel, Jon-Paul Pepper, C. Kwang Sung, Francis X. Creighton, Nikolas H. Blevins, Konstantina M. Stankovic

“…Publications increased exponentially from 2012–2022 across 48 countries and were most concentrated in otology and neurotology (28%), most targeted extending health care provider capabilities (56%), and most used image input data (55%) and convolutional neural network models (63%). …”
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Bi-directional Pre-trained Network for Single-station Seismic Waveform Analysis by Yuqi CAI, Ziye YU, Weitao WANG, Yanru AN, Lu LI

“…This model uses three-component seismic waveform data as input and employs convolutional neural networks and bi-directional Transformer models for feature extraction and processing. …”
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Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN) by C. Selvan, Basha H. Anwar, Soumyalatha Naveen, Shaik Thasleem Bhanu

“…For real-time route optimization, a convolutional neural network (CNN)-based deep learning model is used to adjust ambulance routes based on current traffic and road conditions, achieving an accuracy of 99.15%. …”
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Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…This study aimed to classify tomato fruits based on shape, group, color, and defects using Convolutional Neural Networks (CNNs). The performance of five architectures - VGG16, InceptionV3, ResNet50, EfficientNetB3, and InceptionResNetV2 was evaluated to identify and determine the most efficient one for this classification. …”
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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review by Oussama Khouili, Mohamed Hanine, Mohamed Louzazni, Miguel Angel López Flores, Eduardo García Villena, Imran Ashraf

“…Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. …”
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Odor prediction of whiskies based on their molecular composition by Satnam Singh, Doris Schicker, Helen Haug, Tilman Sauerwald, Andreas T. Grasskamp

“…Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…We compared the performance of our method against that of a convolutional neural network (CNN). We visualized the high-dimensional outputs of the ViT-L/32 model to achieve the interpretability of ViT-L/32 using the t-distributed stochastic neighbor embedding (t-SNE) method. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional approaches. …”
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A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition by Zixiang Ma, Bang Wu, Stefan Poslad

“…WiFi received signal strength indicator seem to be the basis of the most widely used method for indoor positioning systems driven by the growth of deployed WiFi access points, especially within urban areas. …”
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Handwritten Geez Digit Recognition Using Deep Learning by Mukerem Ali Nur, Mesfin Abebe, Rajesh Sharma Rajendran

“…Amharic language is the second most spoken language in the Semitic family after Arabic. …”
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Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform by Duy Nguyen, Minh Tuan Nguyen, Kou Yamada

“…This paper proposes a novel algorithm for human emotion detection using a hybrid paradigm of convolutional neural networks and a boosting model. The proposed algorithm employs two subsets of 18 and 14 features extracted from four sub-bands using discrete wavelet transform. …”
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Multibranch semantic image segmentation model based on edge optimization and category perception. by Zhuolin Yang, Zhen Cao, Jianfang Cao, Zhiqiang Chen, Cunhe Peng

“…In semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. …”
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Integrating deep learning in stride-to-stride muscle activity estimation of young and old adults with wearable inertial measurement units by Min Khant, Darwin Gouwanda, Alpha A. Gopalai, Chee Choong Foong

“…This work overcomes these limitations by proposing a Convolutional Neural Network (CNN) that processes tokenized data measured by the wearable Inertial Measurement Units (IMUs) to estimate muscle activities during walking. …”
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