State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. …”
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Using Deep Learning to Predict Sentiments: Case Study in Tourism by C. A. Martín, J. M. Torres, R. M. Aguilar, S. Diaz

“…In this paper, we propose using different deep-learning techniques and architectures to solve the problem of classifying the comments that tourists publish online and that new tourists use to decide how best to plan their trip. …”
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Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…The monitoring system detected three different emulated cyberattacks with high accuracy. …”
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Damage Detection Method for Road Ancillary Facilities Integrating Attention Mechanism by Shuang Yang, Huiqin Wang, Ke Wang, Nan Guo

“…The model first introduces the D-GhostNet V3Conv module, replacing the standard convolutional layers, significantly enhancing feature extraction capabilities while reducing computational costs. …”
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Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN by Nivea Kesav, M.G. Jibukumar

“…The field of image processing has experienced remarkable growth in the area of biomedical applications with the invention of different techniques in deep learning. Brain tumor classification and detection is a subject of prime importance where Convolutional Neural Networks (CNN) find application. …”
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Gait Phase Recognition in Multi-Task Scenarios Based on sEMG Signals by Xin Shi, Xiaheng Zhang, Pengjie Qin, Liangwen Huang, Yaqin Zhu, Zixiang Yang

“…The method proposed in this paper is significantly different compared to other methods (<i>p</i> < 0.001). …”
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Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs by Michal Maciusowicz, Grzegorz Psuj

“…Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. …”
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DECTNet: A detail enhanced CNN-Transformer network for single-image deraining by Liping Wang, Guangwei Gao

“…Recently, Convolutional Neural Networks (CNN) and Transformers have been widely adopted in image restoration tasks. …”
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An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images by Muhammad Zubair, Muhammad Owais, Taimur Hassan, Malika Bendechache, Muzammil Hussain, Irfan Hussain, Naoufel Werghi

“…The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. …”
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Advancements in detecting Deepfakes: AI algorithms and future prospects − a review by Laishram Hemanta Singh, Panem Charanarur, Naveen Kumar Chaudhary

“…The primary goal is to provide studies on protecting the integrity and authenticity of digital content using different algorithms and standard datasets, while also investigating potential future developments in the field. …”
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Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder by Abdelrahman Elfikky, Zouheir Rezki

“…Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes.…”
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Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

“…This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. …”
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Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography by Robert Banasiak, Mateusz Bujnowicz, Anna Fabijańska

“…However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. …”
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Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

“…The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. …”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…We analyse the effectiveness of different types of recall in mitigating forgetting and show that CR outperforms existing methods.…”
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Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
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Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

“…The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. …”
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How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area by Yuanyuan Zhang, Joseph Luttrell, IV, Chaoyang Zhang

“…To address this challenge, this study explores different deep learning-based solutions, including the aerial-view method (AVM) and street-view method (SVM), which are commonly used, and a combination of them, i.e., the dual-perspective method (DPM). …”
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Quantum Vision Theory in Deep Learning for Object Recognition by Cem Direkoglu, Melike Sah

“…Quantum-scale world looks different from our human-scale world. Attempts to relate the microscopic quantum world to our macroscopic world led to philosophical issues and questions. …”
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A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

“…Additionally, the VMD-TCN model maintains high predictive accuracy across different sensor placements and data collection periods, demonstrating strong generalization capabilities. …”
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