Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…Through a comparative analysis of different algorithms, the study seeks to identify the model with the best performance, which could serve as a foundation for efficient IDS solutions tailored to the specific characteristics of IoT networks. …”
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Lung volume assessment for mean dark-field coefficient calculation using different determination methods by Florian T. Gassert, Jule Heuchert, Rafael Schick, Henriette Bast, Theresa Urban, Tina Dorosti, Gregor S. Zimmermann, Sebastian Ziegelmayer, Alexander W. Marka, Markus Graf, Marcus R. Makowski, Daniela Pfeiffer, Franz Pfeiffer

“…Abstract Background Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation. …”
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Effects of different wearable sensors and locomotion tasks on machine learning-based joint moment prediction by Jonas Weber, Bernd J. Stetter

“…Simultaneously acquired motion capture and wearable sensor data (unilaterally positioned on the right foot, thigh, shank, as well as on the torso) from 21 participants performing stair ascent and descent, ramp ascent and descent, and treadmill walking were used (Camargo et al., 2021). Convolutional neural networks (CNNs) were trained on three different inputs combining all locomotion tasks: 1. a dataset from four IMUs, 2. a dataset from eleven EMG sensors, and 3. a dataset from both sensors (the IMUs and EMG sensors). …”
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Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks by Ran Wang, Ruyu Shi, Xiong Hu, Changqing Shen

“…Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. …”
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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…[Purposes] Two-stream convolutional networks primarily achieve high recognition accuracy by fusing spatial and temporal features of videos. …”
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<italic>DynaTrack</italic>: Low-Power Channel-Aware Dynamic Smartphone Tracking Using UWB DL-TDOA by Junyoung Choi, Sagnik Bhattacharya, Joohyun Lee

“…Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. …”
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Computing nasalance with MFCCs and Convolutional Neural Networks. by Andrés Lozano, Enrique Nava, María Dolores García Méndez, Ignacio Moreno-Torres

“…A new approach is proposed to compute nasalance using Convolutional Neural Networks (CNNs) trained with Mel-Frequency Cepstrum Coefficients (mfccNasalance). mfccNasalance is evaluated by examining its accuracy: 1) when the train and test data are from the same or from different dialects; 2) with test data that differs in dynamicity (e.g. rapidly produced diadochokinetic syllables versus short words); and 3) using multiple CNN configurations (i.e. kernel shape and use of 1 × 1 pointwise convolution). …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. …”
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Multi-Semantic Alignment Graph Convolutional Network by Jisheng Qin, Xiaoqin Zeng, Shengli Wu, Yang Zou

“…Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. …”
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Multiscale Convolutional Neural Networks for Hand Detection by Shiyang Yan, Yizhang Xia, Jeremy S. Smith, Wenjin Lu, Bailing Zhang

“…The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. …”
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Water Classification Using Convolutional Neural Network by Saira Asghar, Ghulam Gilanie, Mubbashar Saddique, Hafeez Ullah, Heba G. Mohamed, Irshad Ahmed Abbasi, Mohamed Abbas

“…The classification of water sources is a challenging task due to the low contrast texture features, the visual similarities between them, and the causes posed by image acquisition with different camera angles and placements. The various image enhancement techniques, i.e., Unsharp Masking (UM), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Contrast Stretching, were used to highlight the contrast and texture features of water images. …”
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Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images by Siribang-on Piboonniyom Khovidhunkit, Kunchidsong Phosri, Bhornsawan Thanathornwong, Dulyapong Rungraungrayabkul, Suvit Poomrittigul, Treesukon Treebupachatsakul

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CBAM-DeepConvNet: Convolutional Block Attention Module-Deep Convolutional Neural Network for asymmetric visual evoked potentials recognition by Zhouyu Ji, Shuran Li, Hongfei Zhang, Chuangquan Chen, Qian Xu, Junhua Li, Hongtao Wang

“…Purpose: This study aimed to improve the accuracy and the ITR in the stimulative paradigm of character spelling systems based on asymmetric Visual Evoked Potentials (aVEPs) by utilizing EEG signal and an improved Convolutional Block Attention Module-Deep Convolutional Neural Network. …”
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Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets by Xingjian Zhang, Gewen He, Tingyu Shang, Fangfang Fan

“…While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. <b>Methods:</b> In this study, four public data sets—Sleep-SC, APPLES, SHHS1, and MrOS1—are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging. …”
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CCD-Net: Color-Correction Network Based on Dual-Branch Fusion of Different Color Spaces for Image Dehazing by Dongyu Chen, Haitao Zhao

“…To overcome this, we propose a Color-Correction Network (CCD-Net) based on dual-branch fusion of different color spaces for image dehazing, that simultaneously handles image dehazing and color correction. …”
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Muscle Fat and Volume Differences in People With Hip‐Related Pain Compared With Controls: A Machine Learning Approach by Chris Stewart, Evert O. Wesselink, Zuzana Perraton, Kenneth A. Weber II, Matthew G. King, Joanne L. Kemp, Benjamin F. Mentiplay, Kay M. Crossley, James M. Elliott, Joshua J. Heerey, Mark J. Scholes, Peter R. Lawrenson, Chris Calabrese, Adam I. Semciw

“…Results When considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). …”
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Enhancing the trustworthiness of chaos and synchronization of chaotic satellite model: a practice of discrete fractional-order approaches by Saima Rashid, Sher Zaman Hamidi, Saima Akram, Moataz Alosaimi, Yu-Ming Chu

“…For achieving the intended formation, a framework of a discrete fractional difference satellite model is constructed by the use of commensurate and non-commensurate orders for the control and synchronization of fractional-order chaotic satellite system. …”
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Estimates for convolutions in the anisotropic Nikol'skiĭ-Besov spaces by V. I. Burenkov, G. E. García Almeida

“…We obtain various estimates for convolutions in the anisotropic Nikol'skiĭ-Besov spaces of functions of several real variables possessing some common smoothness of, in general, fractional order which may be different with respect to different variables.…”
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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). …”
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