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Browsing by Author "Ara, Lena"
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Item Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies(Elsevier, 2021) Luo, Xiao; Ara, Lena; Ding, Haoran; Rollins, David; Motaganahalli, Raghu; Sawchuk, Alan P.; Engineering Technology, School of Engineering and TechnologyBackground: Lower extremity arterial Doppler (LEAD) and duplex carotid ultrasound studies are used for the initial evaluation of peripheral arterial disease and carotid stenosis. However, intra- and inter-laboratory variability exists between interpreters, and other interpreter responsibilities can delay the timeliness of the report. To address these deficits, we examined whether machine learning algorithms could be used to classify these Doppler ultrasound studies. Methods: We developed a hierarchical deep learning model to classify aortoiliac, femoropopliteal, and trifurcation disease in LEAD ultrasound studies and a random forest machine learning algorithm to classify the amount of carotid stenosis from duplex carotid ultrasound studies using experienced physician interpretation in an active, credentialed vascular laboratory as the reference standard. Waveforms, pressures, flow velocities, and the presence of plaque were input into a hierarchal neural network. Artificial intelligence was developed to automate the interpretation of these LEAD and carotid duplex ultrasound studies. Statistical analysis was performed using the confusion matrix. Results: We extracted 5761 LEAD ultrasound studies from 2015 to 2017 and 18,650 duplex carotid ultrasound studies from 2016 to 2018 from the Indiana University Health system. The results showed the ability of artificial intelligence algorithms and method, with 97.0% accuracy for predicting normal cases, 88.2% accuracy for aortoiliac disease, 90.1% accuracy for femoropopliteal disease, and 90.5% accuracy for trifurcation disease. For internal carotid artery stenosis, the accuracy was 99.2% for predicting 0% to 49% stenosis, 100% for predicting 50% to 69% stenosis, 100% for predicting >70% stenosis, and 100% for predicting occlusion. For common carotid artery stenosis, the accuracy was 99.9% for predicting 0% to 49% stenosis, 100% for predicting 50% to 99% stenosis, and 100% for predicting occlusion. Conclusions: The machine learning models using LEAD data, with the collected blood pressure and waveform data, and duplex carotid ultrasound data with the flow velocities and the presence of plaque, showed that novel machine learning models are reliable in differentiating normal from diseased arterial systems and accurate in classifying the extent of vascular disease.Item Integrate Model and Instance Based Machine Learning for Network Intrusion Detection(2018-12) Ara, Lena; Luo, Xiao; King, Brian; El-Sharkawy, MohamedIn computer networks, the convenient internet access facilitates internet services, but at the same time also augments the spread of malicious software which could represent an attack or unauthorized access. Thereby, making the intrusion detection an important area to explore for detecting these unwanted activities. This thesis concentrates on combining the Model and Instance Based Machine Learning for detecting intrusions through a series of algorithms starting from clustering the similar hosts. Similar hosts have been found based on the supervised machine learning techniques like Support Vector Machines, Decision Trees and K Nearest Neighbors using our proposed Data Fusion algorithm. Maximal cliques of Graph Theory has been explored to find the clusters. A recursive way is proposed to merge the decision areas of best features. The idea is to implement a combination of model and instance based machine learning and analyze how it performs as compared to a conventional machine learning algorithm like Random Forest for intrusion detection. The system has been evaluated on three datasets by CTU-13. The results show that our proposed method gives better detection rate as compared to traditional methods which might overfit the data. The research work done in model merging, instance based learning, random forests, data mining and ensemble learning with regards to intrusion detection have been studied and taken as reference.