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Producción científica reciente
Cryptocurrency Market Trends: A Machine Learning-Driven Time Series Forecasting with Twitter Sentiment Integration
Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE, MAE, MAPE, R 2 , and Directional Accuracy (DA) across five random seeds, with paired Wilcoxon significance tests. XGBoost-LSTM achieves the best performance (RMSE = 81.547, R 2 = 0.9254, DA = 80.0%), outperforming all nine literature baselines. Removing Twitter sentiment degrades DA by 14.3 percentage points ( p < 0.01 ), confirming that social media signals carry independent predictive information. Hybrid architectures consistently outperform single-model baselines; XGBoost-LSTM offers the best accuracy-to-compute ratio. VADER-enriched Twitter sentiment is a significant predictor beyond price history. Limitations include reliance on a single sentiment platform and a training window that predates several structural market events
A Multilevel Deep Learning Model for Automated Brain Tumor Segmentation Using Magnetic Resonance Images
Background/Objectives: Brain tumor segmentation from magnetic resonance imaging (MRI) plays an important role in clinical assessment and treatment planning. However, accurate segmentation remains challenging because of the complex anatomical structure of the brain, variations in tumor size and shape, and the imbalance between tumor and non-tumor regions in MRI datasets. These challenges highlight the need for reliable automated segmentation methods. Methods: This study proposes a multilevel deep learning model for automated brain tumor segmentation using MRI images. The BraTS dataset was used for model development and evaluation. To address class imbalance, a modified Synthetic Minority Oversampling Technique (SMOTE) was incorporated during preprocessing. A Multilevel Architecture-Based Modified U-Net was then employed to learn multiscale spatial features and generate pixel-wise tumor segmentation. The proposed framework was evaluated using the Dice coefficient, Jaccard coefficient, Matthews Correlation Coefficient (MCC), and accuracy. Results: The experimental results demonstrate that the proposed model consistently outperformed the Berkeley Wavelet Transform (BWT)-based method and the conventional U-Net across different tumor grades. Higher Dice, Jaccard, and MCC values indicate improved agreement between the predicted segmentation and the expert-annotated ground truth masks, demonstrating more accurate and consistent tumor delineation. Conclusions: The proposed multilevel deep learning model provides an effective framework for automated brain tumor segmentation from MRI images. By combining imbalance-aware preprocessing with a lightweight Modified U-Net architecture, the proposed method improves segmentation performance while maintaining a relatively simple network design. Future work will focus on validating the proposed framework using external clinical datasets and comparing it with recent state-of-the-art segmentation models.
Oral Microbiota, the Oral–Brain Axis, and Neurodegeneration: Mechanisms and Dietary Modulation
The oral microbiota represents a complex and dynamic microbial ecosystem that plays a critical role in preserving both oral and systemic homeostasis. Emerging evidence suggests that alterations in oral microbial milieu (dysbiosis) may contribute to the pathogenesis of neurodegenerative disorders, especially Alzheimer’s disease (AD), through the oral–brain axis. This review synthesizes current evidence on the pathways linking oral microbiota to cognitive decline, integrating microbial, immunological, and vascular perspectives. Oral pathogens may access the central nervous system via hematogenous dissemination or neural routes, including the trigeminal nerve, while simultaneously promoting systemic inflammation, immune activation, and blood–brain barrier disruption. These processes converge on key neurodegenerative mechanisms, including chronic neuroinflammation, amyloid-β accumulation, and tau pathology. In parallel, alterations in oral microbial composition have been linked to disease severity, supporting a potential role of dysbiosis in both initiation and progression of cognitive impairment. Diet emerges as a critical modifiable determinant of oral microbial ecology. Diets rich in refined sugars may promote dysbiosis and inflammatory signaling, whereas (poly)phenols, probiotics, and prebiotics may support microbial eubiosis and exert neuroprotective effects through modulation of host–microbe interactions. Although current evidence remains largely observational and mechanistic, the diet–oral microbiota–brain axis represents a promising target for preventive and therapeutic strategies aimed at mitigating cognitive decline and promoting healthy aging. Future longitudinal and interventional studies are required to establish causality and translate these insights into clinical practice.
LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination
Internet connectivity has significantly enhanced the efficiency of daily operations, information retrieval, and global communication. However, this heightened reliance on technology has also exposed us to cybersecurity threats that are often beyond our control. Consequently, securing the data, privacy, and critical systems demands essential cybersecurity measures. This study focuses on the role of artificial intelligence in strengthening security systems to thwart network breaches. The study proposes a comprehensive three-part approach for software-defined networking (SDN) security. The first is that the concentration is on assuring data integrity and reliability for an SDN intrusion dataset. This involves critical steps such as data cleaning, preprocessing, and normalization. In the second step, six popular feature selection strategies are applied, which encompass recursive feature elimination (RFE), polynomial features, artificial neural networks, SelectKBest, least absolute shrinkage and selection operator (LASSO), and correlation-based features. These techniques help identify and incorporate significant and relevant features, thereby improving the overall model performance. The third part involves the creation of a lightweight hybrid model (LwHM) that leverages the strengths of k-nearest neighbors and decision tree models, utilizing a voting classifier. The LwHM surpasses the performance of the InSDN dataset, achieved an impressive accuracy score of 99.93% with RFE features, and enhance the SDN security efficiently.
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.