A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing by Larissa Silva Piauilino, Vanderlei Coelho Oliveira Junior, Valeria Cristina Ferreira Barbosa

“…We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. …”
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Generative Artificial Intelligence for Hyperspectral Sensor Data: A Review by Diaa Addeen Abuhani, Imran Zualkernan, Raghad Aldamani, Mohamed Alshafai

“…Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. …”
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Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters by Yong Qing, Ke Li, Peng-Fei Zhou, Shi-Ju Ran

“…The complexity of NNs, if unbounded or unconstrained, might unpredictably cause severe issues including overfitting, loss of generalization power, and excessive cost of hardware. In this study, we propose a general compression scheme that considerably reduces the variational parameters of NNs, regardless of their specific types (linear, convolutional, etc.), by encoding them into deep automatically differentiable tensor networks (ADTNs) that contain exponentially fewer free parameters. …”
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A Scalable All-Digital Near-Memory Computing Architecture for Edge AIoT Applications by Masoud Nouripayam, Arturo Prieto, Joachim Rodrigues

“…This work introduces a platform-agnostic NMC architecture tailored for convolutional neural network (CNN) workloads, integrated into the shared cache memory subsystem of a microcontroller unit (MCU). …”
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Time-series visual representations for sleep stages classification. by Rebeca Padovani Ederli, Didier A Vega-Oliveros, Aurea Soriano-Vargas, Anderson Rocha, Zanoni Dias

“…Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. …”
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Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. …”
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Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion by Bohao Shen, Jianzhi Li, Zhe Ji

“…Based on these theoretical analyses, this paper proposes a specklegram traversal occlusion data augmentation with a shallow convolutional neural network (CNN) model to mitigate overfitting in specklegram datasets. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…Additionally, we construct a hybrid attention enhancement module, integrating convolutional operations with a self-attention mechanism to improve feature discrimination without compromising computational efficiency. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. …”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…Subsequently, the complexity of each sub-component is evaluated using the permutation entropy (PE), and similar low-frequency components are clustered based on the entropy value to reduce the computational cost. Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. …”
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Automated assessment of simulated laparoscopic surgical skill performance using deep learning by David Power, Cathy Burke, Michael G. Madden, Ihsan Ullah

“…We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. …”
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Efficient Sedimentary Facies Recognition Using Vision Transformer and Weakly Supervised Deep Multi-View Clustering by Hao Wu, Yu-Jie Dai, Xin-Yu Liu

“…Additionally, we apply weak supervision, combining a small amount of labeled data with a large amount of unlabeled data, and use strategies like pseudo-labeling to enhance the model’s generalization ability and reduce the cost of data labeling. Experimental results show that this method achieves higher accuracy and robustness across multiple sedimentary facies datasets, significantly outperforming traditional Convolutional Neural Networks methods in recognition performance. …”
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Multi-Scale Contextual Coding for Human-Machine Vision of Volumetric Medical Images by Jietao Chen, Weijie Chen, Qianjian Xing, Feng Yu

“…Different from the existing 3D convolutional compression algorithms oriented only for human vision, this paper proposes a Multi-scale Contextual Autoencoder (MCAE) architecture that recurrently incorporates anatomical inter-slice context to optimize the compression of the current slice for both human and machine vision. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. …”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…To achieve rapid, low-cost, and intelligent oil production prediction, we propose a multi-input deep neural network model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with an attention mechanism. …”
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Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features by Chunmei Duan, Taochuan Zhang, Lei Han, Huilin Tan

“…The original image and feature map of the PET preform are respectively input into the deep convolutional neural network for feature extraction and fusion. …”
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Forecasting formation density from well logging data based on machine learning model by Xiankang Cheng, Haoyu Zhang, Haoyu Zhang

“…A system structure integrating Convolutional neural network (CNN) and Transformer is suggested to accomplish the goal of automatic formation density prediction and solve the problem of insufficient model feature extraction ability under multiple logging data conditions. …”
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YOLOv8-BaitScan: A Lightweight and Robust Framework for Accurate Bait Detection and Counting in Aquaculture by Jian Li, Zehao Zhang, Yanan Wei, Tan Wang

“…The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. …”
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A VHDL Code for Offset Pulse Position Modulation Working with Reed Solomon System by Using ModelSim by Ahmed H. Albatoosh, Mohamed Ibrahim Shuja'a, Basman M. Al-Nedawe

“…There are numerous variations of ECC, including linear block, convolutional, and turbo codes, among others. The results of a simulation of a linear block reed Solomon, for example, with offset pulse position modulation have been presented in this study. …”
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