Deep learning (Machine learning)

Epilepsy is a multifaceted neurological disorder characterized by superfluous and recurrent seizure activity. Electroencephalogram (EEG) signals are indispensable tools for epilepsy diagnosis that reflect real-time insights of brain activity. Recently, epilepsy researchers have increasingly utilized Deep Learning (DL) architectures for early and timely diagnosis. This research focuses on resolving the challenges, such as data diversity, scarcity, limited labels, and privacy, by proposing potential contributions for epilepsy detection, prediction, and forecasting tasks without impacting the accuracy of the outcome. The proposed design of diversity-enhanced data augmentation initially averts data scarcity and inter-patient variability constraints for multiclass epilepsy detection. The potential features are extracted using a graph theory-based approach by analyzing the inherently dynamic characteristics of augmented EEG data. It utilizes a novel temporal weight fluctuation method to recognize the drastic temporal fluctuations and data patterns realized in EEG signals. Designing the Siamese neural network-based few-shot learning strategy offers a robust framework for multiclass epilepsy detection.

Deep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep learning-based systems depend highly. Additionally, obtaining high-quality labeled data to train deep learning models is often expensive, requiring significant time and resources. Another significant challenge is ensuring the practicality, accessibility, and convenience of the monitoring systems.
This dissertation proposes an innovative deep learning framework to overcome these challenges and improve system generalization performance in classification and regression tasks, specifically monitoring patients with neurological disorders like Parkinson’s.

Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were used. Furthermore, two hybrid models were created by combining machine learning techniques: RF, XGBoost, AdaBoost, KNN, and SVM and these hybrid models were voting based hybrid classifier. Where one is for binary, and the other one is for multi-class classification. These models were tested using precision, recall, accuracy, and F1-score criteria and compared the performance of each model. This work thoroughly explains how hybrid, standalone ML and DL techniques could improve IDS (Intrusion Detection System) in terms of accuracy and scalability in IoT (Internet of Things).

The Internet of Things (IoT) has undergone remarkable expansion in recent years, leading to a proliferation of devices capable of connecting to the internet, collecting data, and sharing information. However, this rapid growth has also introduced a myriad of security challenges, resulting in an uptick in cyber-attacks targeting IoT infrastructures. To mitigate these threats and ensure the integrity of data, researchers have been actively engaged in the development of robust Intrusion Detection Systems (IDS) utilizing various machine learning (ML) techniques. This dissertation presents a comprehensive overview of three distinct approaches toward IoT intrusion detection, each leveraging ML methodologies to enhance security measures. The first approach focuses on a multi-class classification algorithm, integrating models such as random forest, logistic regression (LR), decision tree (DT), and Xgboost. Through meticulous evaluation utilizing evaluation metrics including F1 score, recall, and precision under the Receiver Operating Characteristics (ROC) curve, this approach demonstrates a remarkable 99 % accuracy in detecting IoT attacks. In the second approach, a deep ensemble model comprising Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) architectures is proposed for intrusion detection in IoT environments. Evaluation on the UNSW 2018 IoT Botnet dataset showcases the proficiency of this approach, achieving an accuracy of 98.4 % in identifying malicious activities. Lastly, the dissertation explores a real-time Intrusion Detection System (IDS) framework deployed within the Pyspark architecture, aimed at efficiently detecting IoT attacks while minimizing detection time.

Telemetry data has become a crucial resource for detecting abnormal driving behaviors, especially for elderly drivers with Mild Cognitive Impairment (MCI) or dementia. This thesis proposes a novel spatial deep learning method that combines traditional telematics features with Grid-Index Resolution (GIR) to enhance the detection of abnormal driving behavior. By utilizing grid-indexed spatial-temporal analysis, the approach aims to capture more intricate driving patterns, which are often missed by traditional methods that rely only on basic telematics data such as speed, direction, and distance.
The methodology integrates Simple Neural Networks (SNN) to process traditional telematics features and Convolutional Neural Networks (CNN) to handle spatial relationships through grid-based data. The fusion of these two feature sets into a combined model improves the model's ability to accurately classify normal and abnormal driving behaviors.
This thesis evaluates the proposed approach using a dataset collected over 3.5 years from elderly drivers, including those with MCI. Experimental results demonstrate that the combined model achieves a classification accuracy of 97%, outperforming existing methods. The findings suggest that integrating grid-based spatial-temporal analysis into deep learning models offers significant potential for improving road safety, insurance risk assessment, and targeted interventions for at-risk drivers.

This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.

Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built with privacy constraints at the core of the design. WiFi sensing emerges as a promising solution. Modern mobile devices ubiquitously support the 802.11 standard and regularly emit WiFi probe requests for network discovery. We can passively monitor this traffic to estimate the levels of congestion in public spaces.
In this dissertation, we address three fundamental research problems pertaining to developing streetscape-scale mobility intelligence: scalable infrastructure for WiFi signal capture, passive device localization, and device re-identification.

In the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study validates the significant impacts of genetic interactions and mutations on the virus’s structural changes over time, offering insights into its evolutionary dynamics. Secondly, the dissertation explores medical diagnosis by implementing Convolutional Neural Networks to differentiate between lung CT-scans of COVID-19 and non-COVID patients. This portion of the research demonstrates the capability of deep learning to enhance diagnostic processes, thereby reducing time and increasing accuracy in clinical settings. Lastly, we delve into gravitational wave detection, an area of astrophysics requiring precise data analysis to identify signals from cosmic events such as black hole mergers. Our goal is to utilize Convolutional Neural Network models in hopes of improving the sensitivity and accuracy of detecting these difficult to catch signals, pushing the boundaries of what we can observe in the universe. The findings of this dissertation underscore the utility of combining statistical methods and machine learning models to solve problems that are not only varied but also highly impactful in their respective fields.

This dissertation explores one-class classification (OCC) in the context of big data and fraud detection, addressing challenges posed by imbalanced datasets. A detailed survey of OCC-related literature forms a core part of the study, categorizing works into outlier detection, novelty detection, and deep learning applications. This survey reveals a gap in the application of OCC to the inherent problems of big data, such as class rarity and noisy data. Building upon the foundational insights gained from the comprehensive literature review on OCC, the dissertation progresses to a detailed comparative analysis between OCC and binary classification methods. This comparison is pivotal in understanding their respective strengths and limitations across various applications, emphasizing their roles in addressing imbalanced datasets. The research then specifically evaluates binary and OCC using credit card fraud data. This practical application highlights the nuances and effectiveness of these classification methods in real-world scenarios, offering insights into their performance in detecting fraudulent activities. After the evaluation of binary and OCC using credit card fraud data, the dissertation extends this inquiry with a detailed investigation into the effectiveness of both methodologies in fraud detection. This extended analysis involves utilizing not only the Credit Card Fraud Detection Dataset but also the Medicare Part D dataset. The findings show the comparative performance and suitability of these classification methods in practical fraud detection scenarios. Finally, the dissertation examines the impact of training OCC algorithms on majority versus minority classes, using the two previously mentioned datasets in addition to Medicare Part B and Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) datasets. This exploration offers critical insights into model training strategies and their implications, suggesting that training on the majority class can often lead to more robust classification results. In summary, this dissertation provides a deep understanding of OCC, effectively bridging theoretical concepts with novel applications in big data and fraud detection. It contributes to the field by offering a comprehensive analysis of OCC methodologies, their practical implications, and their effectiveness in addressing class imbalance in big data.

The relentless expansion of space exploration necessitates the development of robust and dependable anomaly detection systems (ADS) to safeguard the safety and efficacy of space missions. Conventional anomaly detection methods often falter in the face of the intricate and nuanced dynamics of space systems, resulting in a proliferation of false positives and/or false negatives. In this study, we explore into cutting-edge techniques in deep learning (DL) to tackle the challenges inherent in ADS. This research offers an in-depth examination of recent breakthroughs and hurdles in deep learning-driven anomaly detection tailored specifically for space systems and operations. A key advantage of deep learning-based anomaly detection lies in its adaptability to the diverse data encountered in space missions. For instance, Convolutional Neural Networks (CNNs) excel at capturing spatial dependencies in high-dimensional data, rendering them well-suited for tasks such as satellite imagery analysis. Conversely, Recurrent Neural Networks (RNNs), with their temporal modeling prowess, excel in identifying anomalies in time-series data generated by spacecraft sensors. Despite the potential of deep learning, several challenges persist in its application to anomaly detection in space systems. The scarcity of labeled data presents a formidable hurdle, as acquiring labeled anomalies during space operations is often prohibitively expensive and impractical. Additionally, the interpretability of deep learning models remains a concern, particularly in mission-critical scenarios where human operators need to comprehend the rationale behind anomaly predictions.