Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…Our solution leverages the decentralized and tamper-resistant nature of Blockchain technology to enable secure and efficient data collection and processing at the network edge. We evaluate multiple machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Transformer architectures, and LightGBM, using the CICDDoS2019 dataset. …”
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A Variational-Mode-Decomposition-Cascaded Long Short-Term Memory with Attention Model for VIX Prediction by Do-Hyeon Kim, Dong-Jun Kim, Sun-Yong Choi

“…Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces a novel forecasting framework that integrates Variational Mode Decomposition (VMD) with a Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention mechanism. …”
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A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence by Xinyi Yang, Juan Li

“…We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. …”
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Enhancing hand-drawn diagram recognition through the integration of machine learning and deep learning techniques by Vanita Agrawal, MVV Prasad Kantipudi, Jayant Jagtap

“…This research proposes improvised methods such as Fossum Soergel k-means clustering, morphological Canny Bessel radial basis contour shape factor, Fisher kernel k-nearest neighbor, sing-scurve fuzzy rule generation, and wide context faster regional convolutional neural network to enhance the system’s performance. The system’s performance is evaluated by experimentation with benchmark datasets of hand-drawn flowcharts, finite automata, and business process models. …”
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Evolution of public broadcasting: TeleJurnal’s viewership trends and strategic implications for business sustainability by Săseanu Ramona, Stanomir Ruxandra, Bădulescu Andreea-Alexandra

“…This research examines the viewership trends of TeleJurnal, the network’s main flagship news program, utilizing data from 2018 to 2022, and projects forecasts for 2023–2025. …”
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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…Feature extraction combined texture features from the Gray-Level Co-occurrence Matrix (GLCM) and spatial features extracted from a pretrained VGG-16 network. Fisher Mantis Optimization (FMO) was employed for optimal feature selection. …”
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Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods by Ruixiang Kan, Mei Wang, Tian Luo, Hongbing Qiu

“…Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. …”
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Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach by Hao-Ju Lo, Chun-Hao Tsai, Tsan-Wen Huang

“…Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…Further, the features are processed via a vision transformer (ViT) network. This method enables the model to learn more complex relationships and patterns within the data, thereby improving the classification performance. …”
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Morphological analysis and functional connectivity of the insular in patients with dysphagia after cerebral infarction based on resting-state fMRI by Ming Guo, Bingjie Li, Jun Zhao, Chen Bai, Weiyong Yu, Hongxia Zhang, Haoyuan Li, Yongxue Yuan, Qingsu Zhang, Tong Zhang

“…However, the mechanisms underlying its structural and functional network reorganization remain elusive. This study aims to systematically investigate the alterations in gray matter volume and functional connectivity patterns of the insula in patients with dysphagia after cerebral infarction using multimodal neuroimaging techniques, and to untangle their clinical associations with swallowing function impairments. …”
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Influence of insulin sensitivity on food cue evoked functional brain connectivity in children by Lorenzo Semeia, Ralf Veit, Sixiu Zhao, Shan Luo, Brendan Angelo, Andreas L. Birkenfeld, Hubert Preissl, Anny H. Xiang, Stephanie Kullmann, Kathleen A. Page

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Past, present and future of goal scoring analysis in professional soccer (Pasado, presente y futuro del análisis de goles en el fútbol profesional) by Joaquin Gonzalez Rodenas, Rodrigo Aranda Malaves, Andres Tudela Desantes, Enrique Sanz Ramirez, Josep Crespo Hervas, Rafael Aranda Malaves

“…Currently, the emergence of new research methods such as position data, network analysis, temporal patterns or mixed methods has broadened the possibilities for researchers to capture the complexity of soccer actions and interactions. …”
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Mamba time series forecasting with uncertainty quantification by Pedro Pessoa, Paul Campitelli, Douglas P Shepherd, S Banu Ozkan, Steve Pressé

“…To achieve this, we propose a dual-network framework based on the Mamba architecture for probabilistic forecasting, where one network generates point forecasts while the other estimates predictive uncertainty by modeling variance. …”
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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

“…The most precise forecasting model was chosen by evaluating the effectiveness of four machine learning models. …”
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‘Stupid Girls’ vs. ‘Real Men’: Identity Construction and Social Media Polarization over Revenge Porn by Marta Sánchez Cócera

“…Additionally, a more detailed analysis of evaluative resources and linguistic patterns is carried out. …”
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Quantitative Real-Time RT-PCR Verifying Gene Expression Profile of Cavitations Within Human Jaw Bone by Shahram Ghanaati, Eva Dohle, Fabian Schick, Johann Lechner

“…<b>Methods:</b> This study is designed to determine whether BMDJ/FDOJ samples are indicative of medullary bone pathology by evaluating FDOJ gene expression patterns using quantitative real-time PCR. …”
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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia, Alessandra Spennato

“…This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. …”
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Exploring Pre-Trained Models for Skin Cancer Classification by Abdelkader Alrabai, Amira Echtioui, Fathi Kallel

“…In this paper, the performance of four pre-trained models—two convolutional neural networks (ResNet50 and VGG19) and two vision transformers (ViT-b16 and ViT-b32)—is evaluated in distinguishing malignant from benign skin cancers using a publicly available dermoscopic dataset. …”
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Pore Structure and Its Controlling Factors of Cambrian Highly Over-Mature Marine Shales in the Upper Yangtze Block, SW China by Dadong Liu, Mingyang Xu, Hui Chen, Yi Chen, Xia Feng, Zhenxue Jiang, Qingqing Fan, Li Liu, Wei Du

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Comparative analysis of digital safety training programs for minors by Marcos Gómez Puerta, Esther Chiner, Andrea Oliver-Ridaura

“…The aim is to identify common patterns, divergences, and gaps that will help guide future actions in critical digital literacy and online risk prevention. …”
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