Split Edge-Cloud Neural Networks for Better Adversarial Robustness by Salmane Douch, Mohamed Riduan Abid, Khalid Zine-Dine, Driss Bouzidi, Driss Benhaddou

“…Similarly, this paper study a promising approach for running deep learning models at the edge: split neural networks (SNN). SNNs feature a neural network architecture with multiple early exit points, allowing the model to make confident decisions at earlier layers without processing the entire network. …”
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Using artificial intelligence methods for the optimal synthesis of reversible networks by Taras Kyryliuk, Mykhailo Palahuta, Vitaly Deibuk

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Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia by Clara S. Vetter, Clara S. Vetter, Clara S. Vetter, Annika Bender, Dominic B. Dwyer, Dominic B. Dwyer, Dominic B. Dwyer, Max Montembeault, Anne Ruef, Katharine Chisholm, Lana Kambeitz-Ilankovic, Linda A. Antonucci, Stephan Ruhrmann, Joseph Kambeitz, Marlene Rosen, Theresa Lichtenstein, Anita Riecher-Rössler, Rachel Upthegrove, Raimo K. R. Salokangas, Jarmo Hietala, Christos Pantelis, Christos Pantelis, Rebekka Lencer, Rebekka Lencer, Eva Meisenzahl, Stephen J. Wood, Stephen J. Wood, Paolo Brambilla, Paolo Brambilla, Stefan Borgwardt, Peter Falkai, Peter Falkai, Alessandro Bertolino, Nikolaos Koutsouleris, Nikolaos Koutsouleris, Nikolaos Koutsouleris, PRONIA Consortium

“…Structural covariance networks (SCN) describe the shared variation in morphological properties emerging from coordinated neurodevelopmental processes, This study evaluates the potential of SCNs as diagnostic biomarker for schizophrenia.MethodsWe compared the diagnostic value of two SCN computation methods derived from regional gray matter volume (GMV) in 154 patients with a diagnosis of first episode psychosis or recurrent schizophrenia (PAT) and 366 healthy control individuals (HC). …”
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Radar-Based Hand Gesture Recognition With Feature Fusion Using Robust CNN-LSTM and Attention Architecture by Irshad Khan, Young-Woo Kwon

“…This article introduces a novel deep learning approach for hand gesture recognition, leveraging convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms. …”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa’s multi-output architecture. …”
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Hybrid face recognition under adverse conditions using appearance‐based and dynamic features of smile expression by Murat Taskiran, Nihan Kahraman, Cigdem Eroglu Erdem

“…We propose a novel hybrid face recognition, which uses appearance‐based features extracted using deep convolutional networks and statistical facial dynamics features extracted from facial landmark positions during smile expression. …”
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Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…Deep learning models, including custom artificial neural networks (ANN1) and convolutional neural networks (CNN1, CNN2), are introduced with hidden layers for improved performance. …”
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A Unified Framework for Fault and Performance Prediction Using Spatio-Temporal Geometric Features Based on STSFE by Dong-Hyun Kang, A-Youn Yang, Jong-Min Lee, Jong-Gu Lee

“…This paper proposes a unified deep-learning framework for fault and performance prediction in communication equipment by utilizing spatiotemporal geometric features. The core methodology, Spatio-Temporal Slope Feature Extraction (STSFE), transforms irregular time-series data into slope-, area-, and volume-based representations, capturing both temporal dynamics and spatial correlations. …”
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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. …”
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RUL Prediction of DC Contactor Using CNN-LSTM With Channel Attention and Fusion of Dual Aggregated Features by Sai Wang, Yuanfeng Zhang, Hao Huang, Yun Shi, Jianfei Si

“…Challenges arise due to high-dimensional operational data, difficulty fusing spatial-temporal features, and noisy environments. This paper proposes a novel deep learning model called DAF-CA-CNN-LSTM, which integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a Channel Attention (CA) mechanism, and a Dual Aggregated Features (DAF) strategy. …”
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Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). …”
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Hyperspectral imaging combined with residual-attention-net for spectral-spatial feature fusion in liver disease diagnosis by Yunze Li, Jingjing Wang, Miaoqing Zhao, Jinlin Deng, Chongxuan Tian, Qize Lv, Yifei Liu, Kun Ru, Wei Li

“…This study aims to propose a novel hyperspectral image (400–1000 nm) processing method based on 3D-Residual-attention networks (3D Ra-Net) to improve the accuracy of differentiation between the two.The study employs a 3D Ra-Net model that integrates spectral features with spatial information to enhance classification accuracy. …”
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Deep Learning-Based Feature Extraction Technique for Single Document Summarization Using Hybrid Optimization Technique by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak, Premananda Sahu, Kaushik Mishra

“…Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly challenging for individuals to retrieve relevant information efficiently. …”
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Evaluating the effects of volume censoring on fetal functional connectivity by Jung-Hoon Kim, Josepheen De Asis-Cruz, Kevin M. Cook, Catherine Limperopoulos

“…Resting-state functional magnetic resonance imaging (rs-fMRI) has provided critical insights into emerging brain networks in fetuses. However, acquiring high-quality fetal rs-fMRI remains challenging due to the unpredictable and unconstrained motion of the fetal head. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…To address these challenges, we propose a novel methodology that combines a genetic algorithm (GA) with Random Forest Cross-Validation (RF-CV) to evaluate input features and select the most relevant subset. …”
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Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning by Shiwen Zhang, Zhimei Huo, Yanjin Sun, Fujuan Li, Bo Jia

“…Quick access recorder (QAR) data have been used to evaluate pilot performance for decades. However, traditional evaluation methods suffer from the inability to consider multiple parameters simultaneously, and most of them need to select features manually in advance. …”
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Capacity Evaluation for IEEE 802.16e Mobile WiMAX by Chakchai So-In, Raj Jain, Abdel-Karim Tamimi

“…We present a simple analytical method for capacity evaluation of IEEE 802.16e Mobile WiMAX networks. Various overheads that impact the capacity are explained and methods to reduce these overheads are also presented. …”
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A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction by Pinzhang Zhao, Lihui Wang, Jian Wei, Yifan Wang, Haifeng Wu

“…Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. …”
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Preclinical Evaluation and Advancements in Vascularized Bone Tissue Engineering by Toshiyuki Kawai

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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…Transitioning to recurrent neural network architectures, mainly BIGRU, enabled the modeling of sequential dependence on emissions data. …”
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