Effects of different exercise modalities on blood pressure and endothelial function in prehypertension individuals: a systematic review and network meta-analysis by Yingtian Yang, Qianyu Lv, Xinzheng Hou, Yanfei Lv, Xirui Zhang, Qian Wu, Xuejiao Ye, Chenyan Yang, Mingyu Huang, Shihan Wang

“…ObjectiveTo evaluate the relative impacts of various exercise protocols on blood pressure (BP) and endothelial function in prehypertension individuals.MethodsIn this systematic review and network meta-analysis (NMA), PubMed, Cochrane Library, Web of Science, Embase, CINAHL, SPORTDiscus, and Rehabilitation & Sports Medicine databases were searched until September 12, 2024. …”
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Resting-State Brain Network Characteristics Related to Mild Cognitive Impairment: A Preliminary fNIRS Proof-of-Concept Study by Guohui Yang, Chenyu Fan, Haozheng Li, Yu Tong, Shuang Lin, Yashuo Feng, Fengzhi Liu, Chunrong Bao, Hongyu Xie, Yi Wu

“…Subject sRSFC strength and dRSFC variability coefficients were evaluated via fNIRS. The study evaluated the feasibility of using fNIRS to measure these connectivity metrics and compared resting-state brain network characteristics between the two groups. …”
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Neuroradiological Evaluation of Anatomo-Morphometric Arcuate Fascicle Modifications According to Different Brain Tumor Histotypes: An Italian Multicentric Study by Roberto Altieri, Andrea Bianconi, Stefano Caneva, Giovanni Cirillo, Fabio Cofano, Sergio Corvino, Oreste de Divitiis, Giuseppe Maria Della Pepa, Ciro De Luca, Pietro Fiaschi, Gianluca Galieri, Diego Garbossa, Giuseppe La Rocca, Salvatore Marino, Edoardo Mazzucchi, Grazia Menna, Antonio Mezzogiorno, Alberto Morello, Alessandro Olivi, Michele Papa, Daniela Pacella, Rosellina Russo, Giovanni Sabatino, Giovanna Sepe, Assunta Virtuoso, Giovanni Vitale, Rocco Vitale, Gianluigi Zona, Manlio Barbarisi

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Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack by Khaled Abud, Sergey Lavrushkin, Dmitry Vatolin

“…Neural-network-based models have made considerable progress in many computer vision areas over recent years. …”
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Logging Evaluation of Mixed-sediments Reservoirs of the Lower Cambrian Canglangpu Formation in Penglai Gas Area, Central Sichuan Basin by WANG Haiqing, WANG Zeyu, LAI Qiang, LI Shurong, WU Yuyu, ZHANG Mingyong, WANG Yaqing

“…From an overall perspective, the regularity of porosity to permeability is poor, the electrical differentiation of gas abundance is fuzzy, so the traditional parameter calculation model is not applicable, and it is urgent to study an effective logging evaluation method. Using the core analysis data such as cast thin section, XRD, physical property, logging data and gas testing data, the paper studied a systematic evaluation method for calculating the mineral component by dimensionality reduction, neural network analysis and element content to mineral content inversion method, calculating porosity by variable matrix parameters on the basis of tri-porosity logging curves, calculating permeability by lithologic subdivision model, and quantitative evaluation of reservoir property, connectivity and permeability by array acoustic wave combined with electrical imaging logging, then determined the lower limit of effective reservoir and the classification standard of reservoir. …”
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An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data by Huan Tao, Ziyang Li, Shengdong Nie, Hengkai Li, Dan Zhao

“…We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. …”
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A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk by Huong Nguyen Thi Cam, Aliza Sarlan, Noreen Izza Arshad

“…The potential of the DeepS3VM is evaluated with respect to various evaluation metrics and the results are compared with various existing models such as Random Forest (RF), decision tree (DT), XGBoost, artificial neural network (ANN) and convolutional neural network (CNN). …”
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Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods by Zuopeng Zhang, Zhe Li, Zhirong Li

“…Three classification models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accuracy of land use classification. …”
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Evaluating the impact of crop waterlogging and flood disasters using multi-source data: a case study of the Sanjiang Plain by Peng Wei, Huichun Ye, Chaojia Nie, Minghao Qin, Yue Zhang, Hongye Wang, Shanyu Huang, Ronghao Liu

“…A waterlogging impact evaluation index system was constructed, and the weighted comprehensive evaluation method was used to evaluate the impact of crop waterlogging disasters from 2020 to 2022. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. …”
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Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models by Wang Yuxin

“…Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. …”
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CochleaSpecNet: An Attention-Based Dual Branch Hybrid CNN-GRU Network for Speech Emotion Recognition Using Cochleagram and Spectrogram by Atkia Anika Namey, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…This research introduces a novel SER approach that utilizes cochleagram and spectrogram features to capture relevant speech patterns for the classifier network. The network integrates a hybrid model that combines Convolutional Neural Networks (CNN) for feature extraction with Gated Recurrent Units (GRU) to handle temporal dependencies. …”
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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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Neural networking-based approach for examining heat transfer and bioconvection in Non-Newtonian fluid with chemical reaction over a stretching sheet by Sohaib Abdal, Talal Taha, Liaqat Ali, Rana Muhammad Zulqarnain, Se-Jin Yook

“…Afterward, an Artificial Neural Network (ANN) with Levenberg-Marquardt training evaluates the flow pattern. …”
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An Adaptive Intrusion Detection System for Evolving IoT Threats: An Autoencoder-FNN Fusion by J. Jasmine Shirley, M. Priya

“…The Autoencoder captures and reduces the dimensionality of high-dimensional IoT network traffic data, and the FNN distinguishes between normal network behaviour and various intrusion patterns by leveraging its ability to model nonlinear relationships. …”
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Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images by Tao Zhang, Furong Shi, Yuanping Zhu

“…Second, taking into account the height distribution priors, we propose a scalable bins module that can create fully adaptive bins within a flexible height range for each input image, leading a more accurate delineation of height distribution pattern. The proposed MPG-Net is comprehensively evaluated on two datasets of different scenes (i.e., ISPRS Vaihingen and Potsdam datasets). …”
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Enhanced residual attention-based subject-specific network (ErAS-Net): facial expression-based pain classification with multiple attention mechanisms by Mahdi Morsali, Aboozar Ghaffari

“…This research aims to solve this issue by presenting ErAS-Net, an Enhanced Residual Attention-Based Subject-Specific Network that employs various attention mechanisms. …”
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A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“…Anomalies are subsequently identified through significant reconstruction errors, which serve as indicators of deviations from typical patterns. Experimental evaluations on the real-world PeMSD7 dataset demonstrate that the proposed TGNN + Autoencoder method improves detection accuracy by 17.33% compared to traditional methods, reduces false positives by 4.71%, and achieves a 6.02% higher F1-score relative to using TGNN or autoencoder individually. …”
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Pathways between childhood trauma, clinical symptoms, and functioning in new-onset psychosis: novel insights from a network analysis approach by Dimitrios Kiakos, Luis Alameda, Ines Lepreux, Caroline Conchon, Livia Alerci, Marianna Gorgellino, Nadir Mebdouhi, Teya Petrova, Philippe Golay, Lilith Abrahamyan Empson, Philippe Conus, Sandra Vieira

“…The aim of this study was to identify patterns of relationships between these three domains in first-episode psychosis (FEP). …”
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An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification by Aaqib Raza, Mohd Zuki Yusoff

“…Moreover, advanced deep learning models often utilize rigid architectures with fixed spatial-temporal filters, restricting their adaptability to dynamic EEG patterns. To address these challenges, this paper proposes an Adaptive Convolutional Neural Network with Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP), which introduces a novel dynamic pathway mechanism and adaptive attention strategy for robust MI-EEG decoding. …”
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