A hierarchical reinforcement learning approach for energy‐aware service function chain dynamic deployment in IoT by Shuyi Wang, Haotong Cao, Longxiang Yang

“…Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. …”
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Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization by Noah S. Diffenbaugh, Elizabeth A. Barnes

“…Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. …”
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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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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

“…For the example that generated the most complex mapping, the variational autoencoder produces outputs that have less than 10% error for more than 90% of inputs across our test data.…”
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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

“…Abstract In the User and Entity Behaviour Analytics (UEBA), unknown malicious behaviours are often difficult to be automatically detected due to the lack of labelled data. Most of the existing methods also fail to take full advantage of the threat intelligence and incorporate the impact of the behaviour patterns of the benign users. …”
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Mexican dataset of digital mammograms (MEXBreast) with suspicious clusters of microcalcificationsMendeley Data by Ricardo Salvador Luna Lozoya, Karina Núnez Barragán, Humberto de Jesús Ochoa Domínguez, Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas

“…Breast cancer is one of the most prevalent cancers affecting women worldwide. …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…Abstract The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. …”
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Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion by Abdolraheem Khader, Jingxiang Yang, Sara Abdelwahab Ghorashi, Ali Ahmed, Zeinab Dehghan, Liang Xiao

“…Hyperspectral (HS) and multispectral (MS) image fusion is the most favorable way to obtain a hyperspectral image that has high resolution in terms of spatial and spectral information. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. Deep learning is useful when the most relevant features are not known in advance, while feature-based…”
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Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). …”
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Indoor Positioning Systems in Logistics: A Review by Laura Vaccari, Antonio Maria Coruzzolo, Francesco Lolli, Miguel Afonso Sellitto

“…Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. …”
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TSDCA-BA: An Ultra-Lightweight Speech Enhancement Model for Real-Time Hearing Aids with Multi-Scale STFT Fusion by Zujie Fan, Zikun Guo, Yanxing Lai, Jaesoo Kim

“…Lightweight speech denoising models have made remarkable progress in improving both speech quality and computational efficiency. However, most models rely on long temporal windows as input, limiting their applicability in low-latency, real-time scenarios on edge devices. …”
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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…We identified Accident Analysis and Prevention as the most important journal, Fred Mannering as the main author, and The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives as the most cited publication. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…Fingerprint recognition is one of the most effective and widely adopted methods for person identification. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. …”
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An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning by E. O. Yilmaz, T. Kavzoglu

“…The SHAP analysis indicated that the HSI transform was the most influential factor in the XGBoost algorithm’s decision-making process whereas the average DN values of the green band were the most effective feature for the CNN model. …”
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