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1. Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice

   https://doi.org/10.3390/s20072027  

   Vishwanath, Manoj ; Jafarlou, Salar ; Shin, Ikhwan ; Lim, Miranda M. ; Dutt, Nikil ; Rahmani, Amir M. ; Cao, Hung ( April 2020 , Sensors)

   null (Ed.)

   Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios. 

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2. Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data

   https://doi.org/10.1145/3307339.3343482  

   Eslami, Taban ; Saeed, Fahad ( January 2019 , Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)

   Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS). 

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3. Detecting Malicious Browser Extensions by Combining Machine Learning and Feature Engineering

   https://doi.org/10.1007/978-3-031-28332-1_13  

   Rydecki, J. ; Tong, J. ; Zheng, J. ( May 2023 , Advances in intelligent systems and computing)

   Latifi, S. (Ed.)

   As the popularity of the internet continues to grow, along with the use of web browsers and browser extensions, the threat of malicious browser extensions has increased and therefore demands an effective way to detect and in turn prevent the installation of these malicious extensions. These extensions compromise private user information (including usernames and passwords) and are also able to compromise the user’s computer in the form of Trojans and other malicious software. This paper presents a method which combines machine learning and feature engineering to detect malicious browser extensions. By analyzing the static code of browser extensions and looking for features in the static code, the method predicts whether a browser extension is malicious or benign with a machine learning algorithm. Four machine learning algorithms (SVM, RF, KNN, and XGBoost) were tested with a dataset collected by ourselves in this study. Their detection performance in terms of different performance metrics are discussed. 

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4. Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses

   https://doi.org/10.1029/2023WR036690  

   Heydari, Zahra ; Stillwell, Ashlynn S ( June 2024 , Water Resources Research)

   Water sustainability in the built environment requires an accurate estimation of residential water end uses (e.g., showers, toilets, faucets, etc.). In this study, we evaluate the performance of four models (Random Forest, RF; Support Vector Machines, SVM; Logistic Regression, Log‐reg; and Neural Networks, NN) for residential water end‐use classification using actual (measured) and synthetic labeled data sets. We generated synthetic labeled data using Conditional Tabular Generative Adversarial Networks. We then utilized grid search to train each model on their respective optimized hyperparameters. The RF model exhibited the best model performance overall, while the Log‐reg model had the shortest execution times under different balanced and imbalanced (based on number of events per class) synthetic data scenarios, demonstrating a computationally efficient alternative for RF for specific end uses. The NN model exhibited high performance with the tradeoff of longer execution times compared to the other classification models. In the balanced data set scenario, all models achieved closely aligned F1‐scores, ranging from 0.83 to 0.90. However, when faced with imbalanced data reflective of actual conditions, both the SVM and Log‐reg models showed inferior performance compared to the RF and NN models. Overall, we concluded that decision tree‐based models emerge as the optimal choice for classification tasks in the context of water end‐use data. Our study advances residential smart water metering systems through creating synthetic labeled end‐use data and providing insight into the strengths and weaknesses of various supervised machine learning classifiers for end‐use identification. 

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5. Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks

   https://doi.org/10.3390/fi14080230  

   Arzo, Sisay Tadesse ; Akhavan, Zeinab ; Esmaeili, Mona ; Devetsikiotis, Michael ; Granelli, Fabrizio ( August 2022 , Future Internet)

   Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best. 

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