Highest F1 Score Research Articles

Realtime Anomaly Detection in Healthcare IoT: A Machine Learning-Driven Security Framework

Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. Feature engineering is essential for accurate anomaly detection. Integrating HMM and SVM capabilities is the framework’s core. HMM accurately represent concealed states within divided data sequences and analyze patterns of change over time. After that, an independent-trained SVM examines each state. Using proximity to a decision hyperplane in feature space, this SVM can classify data points as normal or anomalous. This method augments HMM temporal capabilities with SVM classification efficiency, increasing sensitivity to anomalous patterns and reducing false positives. The framework’s exceptional performance is shown by extensive evaluations on the PhysioNet Challenge 2017 dataset, which includes diverse ECG recordings with labeled anomalies. HMM-SVM outperforms Naive Bayes, LSTM, and Random Forest with 98.66% accuracy. The framework also has high precision, recall, and F1-score, indicating a refined ability to detect real anomalies and reduce false alarms. The framework prioritizes real-time understanding and application alongside its remarkable precision. HMM-SVMs reveal hidden data state changes, revealing context and possible causes of anomalies. Modular design and efficient algorithms enable seamless integration of real-time functionality in low-resource IoT devices, enabling quick and effective security responses. To conclude, this study introduces an HMM-SVM framework for fast Healthcare IoT abnormality detection. The framework emphasizes comprehensibility and real-time applicability while achieving high accuracy. This framework can protect patient data, improve device security, and create a more reliable healthcare IoT ecosystem.

Sports Video Classification Method Based on Improved Deep Learning

Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.

Machine Learning for Predicting Neurodevelopmental Disorders in Children

Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.

Experimental investigation of machine-learning-based soft-failure management using the optical spectrum

The demand for high-speed data is exponentially growing. To conquer this, optical networks have undergone significant changes, getting more complex and versatile. The increasing complexity necessitates that the fault management be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning-based soft-failure management framework. It utilizes a variational autoencoder-based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identify unknown failure types. The investigations show that the VAE-GAN outperforms the other machine learning algorithms when up to 10% of the total training data are available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy

Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario. Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets. Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively. Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.

SAR marine oil spill detection based on an encoder-decoder network

ABSTRACT Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model’s generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%.

Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road’s structural features (lanes, turns, road speed, etc.). In this paper, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2 D . This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation.

Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.

Improved Tree Segmentation Algorithm Based on Backpack-LiDAR Point Cloud

For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R2s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection.

Hybrid grey wolf and whale optimization for enhanced Parkinson's prediction based on machine learning models using biomedical sound

BackgroundBiomedical voice measurements have been used by many physicians and scientists to distinguish Parkinson's patients from ordinary people. Measurements of biomedical voices involve many variables calculated from signal analysis of the voice. These variables can be used to distinguish Parkinson's patients from non-Parkinson's patients. Unfortunately, using all computed variables may be ineffective and inaccurate due to the complexity of establishing a relationship between all the input variables and Parkinson's states. MethodsThis paper describes the development of a hybrid optimizer by combining two optimization algorithms: the Grey Wolf Optimizer and the Whale Optimizer. The hybrid optimizer enhances feature selection to provide a fast and robust Parkinson's prediction model. Additionally, this research incorporates five other feature selection algorithms for comparison purposes: Ranker, Greedy, BestFirst, Hybrid Grey Wolf Optimization, and Whale Optimization. The outputs from these algorithms are fed into six prediction models to determine the most accurate combination. These models include the neural network, Quest, Chi-squared Automatic Interaction Detection, support vector machine, CR-tree, and logistic regression models. Subsequently, the developed models are compared to identify the most accurate model based on various performance metrics. ResultsThe combination of the hybrid grey wolf and whale models yielded the highest scores in most metrics, achieving a perfect recall and a high F1 score. All models generated similar output, with an accuracy greater than 0.89. Quest, CR-tree, and neural networks are the most reliable and accurate models. ConclusionsBiomedical sound measurements can be used to develop an accurate and cost-effective Parkinson's prediction model.

Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT

Objective Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to standardized medical vocabularies. Typically, natural language processing divides this task into named entity recognition (NER) and medical concept normalization (MCN). State-of-the-art results require supervised setups with abundant training data. However, the limited availability of annotated data due to sensitivity and time constraints poses challenges. This study addressed the need for unsupervised medical concept annotation (MCA) to overcome these limitations and support the creation of annotated datasets. Method We use an unsupervised SapBERT-based bi-encoder model to analyze n-grams from narrative text and measure their similarity to SNOMED CT concepts. At the end, we apply a syntactical re-ranker. For evaluation, we use the semantic tags of SNOMED CT candidates to assess the NER phase and their concept IDs to assess the MCN phase. The approach is evaluated with both English and German narratives. Result Without training data, our unsupervised approach achieves an F1 score of 0.765 in English and 0.557 in German for MCN. Evaluation at the semantic tag level reveals that “disorder” has the highest F1 scores, 0.871 and 0.648 on English and German datasets. Furthermore, the MCA approach on the semantic tag “disorder” shows F1 scores of 0.839 and 0.696 in English and 0.685 and 0.437 in German for NER and MCN, respectively. Conclusion This unsupervised approach demonstrates potential for initial annotation (pre-labeling) in manual annotation tasks. While promising for certain semantic tags, challenges remain, including false positives, contextual errors, and variability of clinical language, requiring further fine-tuning.

PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack

Federated Learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data. However, traditional FL systems are vulnerable to Free-Rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems. The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients. Moreover, they aimed for Anonymous FR (AFR) attacks and lost effectiveness in resisting Selfish FR (SFR) attacks. In this paper, we propose a Parameter Audit-based Secure and fair federated learning Scheme (PASS) against FR attack. PASS has the following key features: (a) prevent from privacy leakage with less accuracy loss; (b) be effective in countering both AFR and SFR attacks; (c) work well no matter whether AFR and SFR adversaries occupy the majority of clients or not. Extensive experimental results validate that PASS: (a) has the same level as the State-Of-The-Art method in mean square error against privacy leakage; (b) defends against AFR and SFR attacks in terms of a higher defense success rate, lower false positive rate, and higher F1-score; (c) is still effective where adversaries exceed 50%, with F1-score 89% against AFR attack and F1-score 87% against SFR attack. Note that PASS produces no negative effect on FL accuracy when there is no FR adversary.

Enhancing Diagnostic Accuracy with SE-Inception Model Integration in Pressure Ulcer Detection.

Pressure ulcers are a prevalent health concern, often leading to severe complications if not diagnosed and treated promptly. This study introduces the Squeeze-and-Excitation (SE)-Inception model, which integrates SE blocks into the Inception architecture, aiming to enhance classification performance in medical image analysis. The performance of the SE-Inception model was compared to the Xception and Inception v4 models. Key performance metrics such as accuracy, Area Under the Curve (AUC), recall, and Harmonic Mean of Precision and Recall (F1 score) were used to evaluate its efficacy. Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps were utilized to provide interpretable visual evidence consistent with expert annotations. The SE-Inception model demonstrated superior accuracy (93%) and AUC (94%), with high recall and F1 scores, indicating its efficacy in reducing false negatives and improving diagnostic reliability. Despite the promising outcomes, the study acknowledges the limitation of dataset homogeneity and suggests further validation with diverse datasets for enhanced scalability. The findings support the inclusion of the SE-Inception model in clinical settings to improve diagnostic precision and patient care, particularly in nursing practices for effective pressure ulcer management.

Intelligent IOT Based Audio Signal Processing for Healthcare Applications

This research introduces a novel approach to intelligent IoT-based audio signal processing for healthcare applications. Leveraging advanced feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCC) and Wavelet Transform, combined with sophisticated classification models like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), the proposed method demonstrates superior performance in accurately classifying healthcare data. Through extensive experimentation and analysis, the method achieves high accuracy, precision, recall, and F1 score, while exhibiting robustness in discriminating between different classes and maintaining precision in classification, as evidenced by its high AUC-ROC and AUC-PR values. The ablation study provides insights into the significance of key components and parameters, offering guidance for further refinement and optimization of the method. Overall, the proposed method holds promise for revolutionizing healthcare management through proactive monitoring and intervention, leading to improved patient outcomes and healthcare delivery.

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86-97% and an F1 (harmonic mean of precision and recall) score of 0.83-0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling small data sets in the prediction, which is critical given the challenge of obtaining large datasets for biomarker research. The combination of self-reported logs and the GCN algorithm is a promising approach for predicting depression and warrants further investigation.

Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models

In this study, the authors present a novel methodology adept at decoding multilingual topic dynamics and identifying communication trends during crises. We focus on dialogues within Tunisian social networks during the coronavirus pandemic and other notable themes like sports and politics. We start by aggregating a varied multilingual corpus of comments relevant to these subjects. This dataset undergoes rigorous refinement during data preprocessing. We then introduce our No‐English‐to‐English Machine Translation approach to handle linguistic differences. Empirical tests of this method show high accuracy and F1 scores, highlighting its suitability for linguistically coherent tasks. Delving deeper, advanced modeling techniques, specifically LDA and HDP models, are employed to extract pertinent topics from the translated content. This leads to applying ARIMA time series analysis to decode evolving topic trends. Applying our method to a multilingual Tunisian dataset, we effectively identify key topics mirroring public sentiment. Such insights prove vital for organizations and governments striving to understand public perspectives during crises. Compared to standard approaches, our model outperforms, as confirmed by metrics like coherence score, U‐mass, and topic coherence. Additionally, an in‐depth assessment of the identified topics reveals notable thematic shifts in discussions, with the proposed trends’ identification indicating impressive accuracy, backed by RMSE‐based analysis.

Improvement of Brain Tumor Categorization using Deep Learning: A Comprehensive Investigation and Comparative Analysis

A brain tumor is a critically severe health disorder that requires an accurate and timely diagnosis for effective treatment. Advances in medical imaging and deep learning methods have shown potential for enhancing the identification and categorization of brain cancers throughout the years. In the present research, our study compares the accuracy of eight different deep learning models in the classification of brain tumors employing brain MRI data that involve Densenet121, EfficientNet B7, InceptionResNetV2, Inception_V3, RestNet50V2, VGG16, VGG19, and Xception. To further improve performance, we propose integrating a hybrid deep learning technique. Efficient and timely diagnosis of brain tumors is critical for the treatment of patients, and our study aims to achieve high recall, accuracy, and F1-score in this context. With a precision of 96.63%, our innovative convolutional neural network (CNN) technique achieved outstanding results in brain tumor diagnosis. Also, our study investigates the unique capabilities of certain models, such as VGG19 and InceptionResNetV2, and their possibilities for better glioma tumor detection efficiency. Our results, in particular, provide insight into possible uses of deep learning frameworks, including the integration of hybrid techniques, in medical imaging, offering an innovative approach for increased brain tumor detection and identification.

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer's disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

Machine Learning-Based Intrusion Detection on Multi-Class Imbalanced Dataset Using SMOTE

The rapid development of information technology has brought numerous benefits to society, but it has also led to increased security vulnerabilities in network systems. Intrusion detection systems (IDS) play a crucial role in identifying malicious activities, but they face challenges due to imbalanced datasets where the number of attack samples outweighs normal activities. This paper explores the performance of an IDS using SMOTE (Synthetic Minority Over-sampling Technique) and various classification algorithms to address imbalanced datasets and enhance detection of multi-class intrusions. Related works in the field of intrusion detection are reviewed, highlighting the effectiveness of different algorithms and techniques. The proposed work presents a model that combines SMOTE with log normalization and feature selection to improve IDS performance. Experiments are conducted on the NSL-KDD and CIC-IDS2017 datasets, evaluating different oversampling configurations and machine learning models. The results show that applying SMOTE improves overall performance, with high accuracy, precision, recall, and F1-score. Feature selection has minimal impact on model performance, suggesting the presence of redundant features. The study concludes that SMOTE effectively addresses class imbalance and enhances IDS performance, emphasizing the importance of incorporating oversampling techniques in intrusion detection systems.