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  1. Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities. 
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    Free, publicly-accessible full text available January 1, 2025
  2. Free, publicly-accessible full text available June 4, 2024
  3. Analyzing network traffic activities is imperative in network security to detect attack patterns. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identifying the patterns is one of the challenging research topics. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. For extracting features, various sliding windows and step sizes are tested. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. From the study, we generated multiple visualizations associated with various window and step sizes to emphasize the effectiveness of the proposed approach in differentiating normal and attack events by forming distinctive clusters. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters. 
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  4. Network traffic data analysis is important for securing our computing environment and data. However, analyzing network traffic data requires tremendous effort because of the complexity of continuously changing network traffic patterns. To assist the user in better understanding and analyzing the network traffic data, an interactive web-based visualization system is designed using multiple coordinated views, supporting a rich set of user interactions. For advancing the capability of analyzing network traffic data, feature extraction is considered along with uncertainty quantification to help the user make precise analyses. The system allows the user to perform a continuous visual analysis by requesting incrementally new subsets of data with updated visual representation. Case studies have been performed to determine the effectiveness of the system. The results from the case studies support that the system is well designed to understand network traffic data by identifying abnormal network traffic patterns. 
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