Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…Machine learning methods can be further broken down into two categories: traditional machine learning and deep learning approaches. The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. by Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahmad

“…We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). …”
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Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study by Grayson R. Morgan, Danny Zlotnick, Luke North, Cade Smith, Lane Stevenson

“…Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. …”
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Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification by Tsaqif Muhammad Arkan, Aris Sugiharto, Helmie Arif Wibawa

“…The modified model integrates tunable parameters such as dropout rates, convolutional filters, and dense units, while maintaining the foundational structure of VGG16. …”
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Study of the Current–Voltage Characteristics of Membrane Systems Using Neural Networks by Evgenia Kirillova, Anna Kovalenko, Makhamet Urtenov

“…The best predictive results on test samples are given by the neural network consisting of convolutional and LSTM (Long Short-Term Memory) layers.…”
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Quantifying the non-isomorphism of global urban road networks using GNNs and graph kernels by Linfang Tian, Weixiong Rao, Kai Zhao, Huy T. Vo

“…This finding challenges the claim that “GNNs are at most as powerful as the WL test in distinguishing graph structures.” …”
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A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification by Fadilah Karamun Nisaa Nadiyah, Nayla Nur Alifah, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

“…The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN). …”
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A semantic‐based method for analysing unknown malicious behaviours via hyper‐spherical variational auto‐encoders by Yi‐feng Wang, Yuan‐bo Guo, Chen Fang

“…The authors further use a Graph Convolutional Network (GCN) to reduce the impact of different user behaviour patterns before projection. …”
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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction by Mutiara Khairunisa, Desak Made Sidantya Amanda Putri, I Gusti Ngurah Lanang Wijayakusuma

“…This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. …”
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Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST by Abdullah Hulusi Kökçam, Gültekin Çağıl, Şevval Toprak

“…Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. …”
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Measuring Comparative Statistical Effectiveness of Cancer Subtype Categorization Using Gene Expression Data by Avila Clemenshia P., Deepa C.

“…When dealing with limited samples and high-dimensional biological data, most classifiers may suffer from overfitting and lower precision. …”
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BRAIN TUMOR DIAGNOSIS BASED ON MEDICAL IMAGES USING VISION TRANSFORMER by Masuma Mammadova, Fargana Abdullayeva

“… Brain tumor is one of the most common causes of death in modern times. Early and accurate detection of this disease can save the lives of a large part of the world’s population. …”
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A hierarchical reinforcement learning approach for energy‐aware service function chain dynamic deployment in IoT by Shuyi Wang, Haotong Cao, Longxiang Yang

“…In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. …”
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4D hypercomplex-valued neural network in multivariate time series forecasting by Radosław Kycia, Agnieszka Niemczynowicz

“…We evaluate different architectures, varying the input layers to include convolutional, Long Short-Term Memory (LSTM), or dense hypercomplex layers for 4D algebras. …”
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SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION by Maysaa R. Naeemah, Mohammed Y. Kamil

“…Skin cancer, one of the most common and potentially fatal cancers, requires early and correct diagnosis to improve patient outcomes. …”
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BN-SNN: Spiking neural networks with bistable neurons for object detection. by Siddiqui Muhammad Yasir, Hyun Kim

“…Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN conversion process. …”
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Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization by Noah S. Diffenbaugh, Elizabeth A. Barnes

“…We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO2 emissions. …”
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Input-output optics as a causal time series mapping: A generative machine learning solution by Abhijit Sen, Bikram Keshari Parida, Kurt Jacobs, Denys I. Bondar

“…Using both the transverse and nonintegrable Ising models as examples, we show that not only can temporal convolutional networks capture the input/output mapping generated by the system but can also be used to characterize the complexity of the mapping. …”
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DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization by Hardi Sabah Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh

“…Ant Colony Optimization (ACO) is integrated with machine learning algorithms such as XGBoost, K-Nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Networks (CNNs) using CIRA-CIC-DoHBrw-2020 as the benchmark dataset. …”
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Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic–Ischemic Encephalopathy Injury by Leah Twomey, Sergi Gomez, Emanuel Popovici, Andriy Temko

“…The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. …”
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