Monte Carlo-Based Strategy for Assessing the Impact of EEG Data Uncertainty on Confidence in Convolutional Neural Network Classification by Pierre Sedi Nzakuna, Vincenzo Gallo, Vincenzo Paciello, Aime Lay-Ekuakille, Angelo Kuti Lusala

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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. …”
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Cross-species AI: shifting a convolutional neural network from pigs to lambs to detect pneumonia at slaughter by Anastasia Romano, Antonio De Camillis, Domenico Sciota, Simona Baghini, Andrea Di Provvido, Alfonso Rosamilia, Andrea Capobianco Dondona, Nicola Bernabò, Francesca Vaccarelli, Attilio Corradi, Giuseppe Marruchella

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Variable pulse width dexterous jamming based on chaotic sampling fusion by WANG Guoxuan, YE Zijie

“…Aiming at the problem of strong distribution regularity of false targets in traditional repetitive repeater deception jamming, this paper proposes a variable pulse width smart jamming method based on intermittent chaotic sampling from the perspective of changing the amplitude and position distribution of false targets. …”
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An Ultrafast Image Simulation Technique with Spatially Variable Point-spread Functions by Zeyu Bai, Peng Jia, Jiameng Lv, Xiang Zhang, Wennan Xiang, Lin Nie

“…During real observations, images obtained by optical telescopes are affected by spatially variable point-spread functions (PSFs), a crucial effect requiring accurate simulation. …”
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A Lightweight Rolling Bearing Fault Diagnosis Method Based on Multiscale Depth-Wise Separable Convolutions and Network Pruning by Qingming Hu, Xinjie Fu, Dandan Sun, Donghui Xu, Yanqi Guan

“…In this paper, we introduce a multiscale Depth-wise Separable Convolutions and network pruning (MS-DWSC-PN) approach for lightweight rolling bearing fault diagnosis. …”
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Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer by Mustafa Hatem Al Ghariri, Seyed Omid Shahdi

“…The research employs a new deep learning model named CNN-MRMR which combines features from the Minimum Redundancy Maximum Relevance (MRMR) feature selection approach and Convolutional Neural Network (CNN) technology. MRI images of human brains are initially processed by the convolutional network to extract age-related characteristics. …”
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Fault Detection and Diagnosis in Air-Handling Unit (AHU) Using Improved Hybrid 1D Convolutional Neural Network by Prince, Byungun Yoon, Prashant Kumar

“…While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. …”
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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection by Omneya Attallah

“…To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. …”
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CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting by Zhenkai Qin, Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Baozhong Wei, Yujia Zhai, Ziqian Lin, Xiaochuan Yu, Xiaochuan Yu, Jingxuan Jiang

“…The CMDMamba model employs a dual-layer Mamba structure that effectively captures price fluctuations at both the micro- and macrolevels in financial markets and integrates an innovative Dual Convolutional Feedforward Network (DconvFFN) module. …”
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Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression by Ying Jian, Yong Zheng, Gang Li, Siyang Yao, Tianfu Wen, Zhangjun Liu

“…This study introduces a novel multi-scale isometric convolution network (MICN) model to predict long-term lake WL for the first time. …”
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Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification by Irwan Budi Santoso, Shoffin Nahwa Utama, Supriyono

“…Convolutional Neural Networks (CNNs) are commonly used due to their proven performance, but their effectiveness diminishes with the high variability of tumour characteristics. …”
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Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga by Ricky Mardianto, Stefanie Quinevera, Siti Rochimah

“… Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. …”
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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…Existing FPGA-based OCR implementations have primarily focused on simplified datasets such as MNIST digits, limiting their generalizability to scripts like Urdu that exhibit extensive intra-class variability, contextual shaping, and diacritics. This study presents a hardware-accelerated Urdu OCR framework using a custom-designed Convolutional Neural Network (CNN) optimized for deployment on the Xilinx Zynq UltraScale+ MPSoC (ZCU104). …”
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Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence by Zhao Zhang, Wang Li, Yuyang Zhang

“…In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. …”
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Fitting the Distribution of Linear Combinations of t− Variables with more than 2 Degrees of Freedom by Onel L. Alcaraz López, Evelio M. Garcia Fernández, Matti Latva-aho

“…The linear combination of Student’s t random variables (RVs) appears in many statistical applications. …”
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Deep-Learning Techniques Applied for State-Variables Estimation of Two-Mass System by Grzegorz Kaczmarczyk, Radoslaw Stanislawski, Marcin Kaminski

“…The article is focused on the application of neural models for state-variables estimation. The estimators are applied in the control structure (with the state speed controller) of the electric drive with an elastic shaft. …”
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Prediction of Atmospheric Refractivity From Clutter Power Images Using a Convolutional Neural Network and a Trilinear Atmospheric Model by Taekyeong Jin, Doyoung Jang, Hosung Choo

“…In this paper, we propose a method for predicting atmospheric conditions from clutter power images using a convolutional neural network (CNN). The proposed refractivity from clutter (RFC) method employs a CNN model that utilizes clutter power images as input. …”
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Exploring the Latent Information in Spatial Transcriptomics Data via Multi‐View Graph Convolutional Network Based on Implicit Contrastive Learning by Sheng Ren, Xingyu Liao, Farong Liu, Jie Li, Xin Gao, Bin Yu

“…This study introduces STMIGCL, a novel framework that leverages a multi‐view graph convolutional network and implicit contrastive learning. …”
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Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation. by Van-Trang Nguyen, Quoc Bao Diep

“…This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. …”
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