Training-Free VLM-Based Pseudo Label Generation for Video Anomaly Detection by Moshira Abdalla, Sajid Javed

“…The framework adopts a triple-branch architecture: the first branch generates pseudo-labels, while the second and third perform coarse-grained binary and fine-grained categorical classification. Temporal modeling is enhanced through the integration of transformers and Graph Convolutional Networks (GCNs) to capture both short- and long-range dependencies. …”
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Detecting shallow subsurface anomalies with airborne and spaceborne remote sensing: A review by Adam M. Morley, Tamsin A. Mather, David M. Pyle, J-Michael Kendall

“…To close, we take a brief look at future research opportunities with very high resolution (VHR) datasets, multi-branch convolutional neural networks (CNNs) and active remote sensing in variable potential fields.…”
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Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer by Joseph Finkelstein, Aref Smiley, Christina Echeverria, Kathi Mooney

“…To address class imbalance—where 84% of cases showed no escalation—symptoms were grouped into intervals of 3 to 7 days. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were trained on 80% of the data and evaluated on the remaining 20%. …”
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Use of information-fusion deep-learning techniques to detect possible electricity theft: A proposed method by Maria Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…This study tested an NTL detection method that transformed electricity consumption (EC) profiles into two-dimensional (2D) and one-dimensional (1D) representations, and utilised deep-learning techniques, specifically convolutional neural networks (CNN) and multi-layer perceptron (MLP), to extract features indicating NTLs. …”
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Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study by Simon Stankoski, Ivana Kiprijanovska, Martin Gjoreski, Filip Panchevski, Borjan Sazdov, Bojan Sofronievski, Andrew Cleal, Mohsen Fatoorechi, Charles Nduka, Hristijan Gjoreski

“…These results demonstrate the sensitivity of the sensor data. Furthermore, the convolutional long short-term memory model, which is a combination of convolutional and long short-term memory neural networks, emerged as the best-performing DL model for chewing detection. …”
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Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short‐Term Load Forecasting in Smart Grid by Syed Muhammad Hasanat, Muhammad Haris, Kaleem Ullah, Syed Zarak Shah, Usama Abid, Zahid Ullah

“…The proposed model leverages Convolution Neural Networks (CNNs) to extract spatial patterns, LSTMs to capture temporal dependencies, and attention mechanisms to prioritize important information, enhancing predictive performance. …”
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Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback by Hafiz Muhammad Qadir, M. Taseer Suleman, Rafaqat Alam Khan, Muhammad Sohaib, Md Junayed Hasan, Syed Abid Hussain

“…In this paper, we implement several ensemble models-AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost-and deep learning architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). …”
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Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants by Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu, Yingying Xu, Chengshuai Liu, Lu Tian, Chen Shi, Biao Cao, Tianning Xie, Jianghui Zhang, Caihong Hu

“…In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. …”
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Inertial sensor-based heel strike and energy expenditure prediction using a hybrid machine learning approach by Kethohalli R Vidyarani, Viswanath Talasila, Raafay Umar, Venkatesan Prem, Ravi Prasad K Jagannath, Gurusiddappa R Prashanth

“…This study introduces a hybrid machine learning approach integrating convolutional neural networks (CNNs), long-short-term memory (LSTM) networks, and transfer learning (TL) to estimate volume of oxygen (VO 2 ) and detect heel strikes (HS) using data from a single 9-axis inertial measurement unit (IMU). …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…A diverse set of techniques was implemented to tackle this challenge, encompassing the utilisation of the prophet, random forest, and three different deep learning architectures: long short-term memory networks, convolutional neural networks, and multilayer perceptrons. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting by Mustafa M. Kara, H. Irem Turkmen, M. Amac Guvensan

“…We evaluated these strategies using four popular model types: long short-term memory (LSTM), gated recurrent unit networks (GRUs), bi-directional LSTM, and convolutional neural networks (CNNs). …”
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AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management by Joseph Finkelstein, Aref Smiley, Christina Echeverria, Kathi Mooney

“…This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). …”
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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

“…The model integrates convolutional neural networks (CNN) for feature extraction and an attention mechanism to capture spatio-temporal patterns, significantly improving classification performance of fetal movements. …”
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Optimizing battery health monitoring in electric vehicles using interpretable CART–GX model by Mohnish Karthikeyan B, Anirudh N, Navaneetha Krishnan S, Christopher Columbus C, Aravind C. K

“…The proposed model combines Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), attention mechanisms, residual connections, and transformers to extract spatial and temporal features, prioritize critical information, and model long-range dependencies. …”
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Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field by Changyu Su, Bishnupriya Sahoo, Miaohua Mao, Meng Xia

“…Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. …”
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A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring by Demetris Christofi, Christodoulos Mettas, Evagoras Evagorou, Neophytos Stylianou, Marinos Eliades, Christos Theocharidis, Antonis Chatzipavlis, Thomas Hasiotis, Diofantos Hadjimitsis

“…Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. …”
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