Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
In real world applications, it is very common that the data skewness occurs among multiple classes. Several studies and various attempts were made in the past to overcome this imbalance problem which is a serious issue to the standard machine learning techniques especially classification and regression but, still there exists a need to handle the imbalance problem effectively. Datasets which are imbalanced generally include safe and unsafe minority samples. Our proposed approach is a classifier independent two tier iterative ensemble approach which focuses the rare minority sample's influence on learning from imbalanced datasets. Most of the informed oversampling techniques like SMOTE and its variants cannot be applied directly on rare class samples especially when the count of rare samples is too low. To alleviate this problem, in our proposed approach to learn from rare and outlying samples we proposed a hybrid oversampling technique used at different levels and make them balanced. The goal is to tone down the data imbalance at the data preprocessing stage itself by correcting or balancing the training data sets before moving to the learning part which makes the classifier to focus on its primary role and thereby it improves the learning process. The proposed two tier iterative ensemble approach shows a much significant improvement in the learning process among the multiclass imbalanced data which is clearly evident with the experimental results.
The evolution of communication and information systems has raised the volume of data distributed through the internet. As an effect, a majority of digital resources have been increased, so does the challenge of cybersecurity. Intrusion detection systems (IDSs) are closely connected to a holistic approach for preventing cyberattacks. Due to the high utilization of network traffic in the cyber world, conventional machine learning approaches used in intrusion detection systems are becomes ineffective. Recently evolved deep learning techniques are successfully applied in the detection and classification of threats at both the network and host levels, with a focus on deep learning. This study proposed an efficient IDS based on Recurrent Neural Network (RNN) via Bi-directional Long Short- Term Memory (RNN BiLSTM). The strategy uses a two-step mechanism to develop the expertise of the suggested solution to address network problems. This research aims to determine the algorithm's processing time and increase attack classification accuracy. The proposed model was evaluated on the CICIDS2017 intrusion detection dataset. The Random forest and Principal component analysis algorithms were used to detecting the valuable features and eliminating the unwanted features from the given dataset. The findings revealed that BLSTM outperform all other RNN architectures in terms of classification accura 98.48%.
Electroencephalography is a non-invasive technique used to monitor brain activity and make a variety of neurological problems diagnoses. The electrical activity of the brain is measured using an EEG instrument, which converts chemical variations in the brain into voltage. With either the intracranial EEG method or the Scalp EEG approach, several electrodes are implanted at the right location on the brain to measure EEG signals. Analysis of electroencephalograms, or EEGs, has grown to be crucial for identifying many human disorders. The most crucial and straightforward step in processing EEG readings is using temporal frequency analysis to make a potential diagnosis. EEG signals are the important data for any type of analysis of brain activity. In this study, we first analysis the EEG signal characteristics that have been used in the literature for a variety of activities, then we concentrate on looking at EEG feature applications, and finally we talk about the potential and unresolved issues with EEG feature extraction. Everyone should assess the wide variety of EEG signal properties and their effects on BCI interfaces in order to improve the accuracy of different brain activity detection in the future.
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