An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires.

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Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. The neural network is trained using the Levenberg-Marquardt algorithm and the back propagation method by comparing the estimated off-board magnetic field intensity to the true value. This research also investigates sensitivity, scalability, and implementation issues of the neural network for signature estimation in a practical environment. / Electrical and Computer Engineering

Polynomial neural networks (NNs-Hp) have recently demonstrated high expressivity and efficiency across several tasks. However, a theoretical explanation toward such success is still unclear, especially when compared to the classical neural networks. Neural tangent kernel (NTK) is a powerful tool to analyze the training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but is incomplete for NNs-Hp. In this work, we derive the finite-width NTK formulation for NNs-Hp, and prove their equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK. Based on our results, we elucidate the difference of NNs-Hp over standard neural networks with respect to extrapolation and spectral bias. Our two key insights are that when compared to standard neural networks, a) NNs-Hp are able to fit more complicated functions in the extrapolation region; and b) NNs-Hp admit a slower eigenvalue decay of the respective NTK. Our empirical results provide a good justification for a deeper understanding of NNs-Hp / Polynomiska neurala nätverk (NNs-Hp) har nyligen visat hög uttrycksförmåga och effektivitet över flera uppgifter. En teoretisk förklaring till sådan framgång är dock fortfarande oklar, särskilt jämfört med de klassiska neurala nätverken. Neurala tangentkärnor (NTK) är ett kraftfullt verktyg för att analysera träningsdynamiken i neurala nätverk och deras generaliseringsgränser. Studien om NTK har ägnats åt typiska neurala nätverksarkitekturer, men är ofullständig för NNs-Hp. I detta arbete härleder vi NTK-formuleringen med ändlig bredd för NNs-Hp och bevisar deras likvärdighet med kärnregressionsprediktorn med den associerade NTK, vilket utökar tillämpningsomfånget för NTK. Baserat på våra resultat belyser vi skillnaden mellan NNs-Hp jämfört med standardneurala nätverk med avseende på extrapolering och spektral bias. Våra två viktiga insikter är att jämfört med vanliga neurala nätverk, a) NNs-Hp kan passa mer komplicerade funktioner i extrapolationsregionen; och b) NNs-Hp medger en långsammare egenvärdesavklingning av respektive NTK. Våra empiriska resultat ger en bra motivering för en djupare förståelse av NNs-Hp.

Artificial intelligence is a swiftly growing field, and many are researching whether AI can serve as a diagnostic aid in the medical domain. However, the primary weakness of traditional machine learning for many applications is energy efficiency, and this may hamper its ability to be effectively utilized in medicine for portable or edge systems. In order to be more effective, new energy-efficient machine learning paradigms must be investigated for medical applications. In addition, smaller models with fewer parameters would be better suited to medical edge systems. By processing data as a series of "spikes" instead of continuous values, spiking neural networks (SNN) may be the right model architecture to address these concerns. This work investigates the proposed advantages of SNNs compared to more traditional architectures when tested on various medical datasets. We compare the energy efficiency of SNN and recurrent neural network (RNN) solutions by finding sizes of each architecture that achieve similar accuracy. The energy consumption of each comparable network is assessed using standard tools for such evaluation. On the SEED human emotion dataset, SNN architectures achieved up to 20x lower energy per inference than an RNN while maintaining similar classification accuracy. SNNs also achieved 30x lower energy consumption on the PTB-XL ECG dataset with similar classification accuracy. These results show that spiking neural networks are more energy efficient than traditional machine learning models at inference time while maintaining a similar level of accuracy for various medical classification tasks. With this superior energy efficiency, this makes it possible for medical SNNs to operate on edge and portable systems. / Master of Science / As artificial intelligence grows in popularity, especially with the rise of new large language models like Chat-GPT, a weakness in traditional architectures becomes more pronounced. These AI models require ever-increasing amounts of energy to operate. Thus, there is a need for more energy-efficient AI models, such as the spiking neural network (SNN). In SNNs, information is processed in a series of spiking signals, like the biological brain. This allows the resulting architecture to be highly energy efficient and adapted to processing time-series data. A domain that often encounters time-series data and would benefit from greater energy efficiency is medicine. This work seeks to investigate the proposed advantages of spiking neural networks when applied to the various classification tasks in the medical domain. Specifically, both an SNN and a traditional recurrent neural network (RNN) were trained on medical datasets for brain signal and heart signal classification. Sizes of each architecture were found that achieved similar classification accuracy and the energy consumption of each comparable network was assessed. For the SEED brain signal dataset, the SNN achieved similar classification accuracy to the RNN while consuming as little as 5% of the energy per inference. Similarly, the SNN consumed 30x less energy than the RNN while classifying the PTB-XL ECG dataset. These results show that the SNN architecture is a more energy efficient model than traditional RNNs for various medical tasks at inference time and may serve as the solution to the energy consumption problem of medical AI.

In May of 2006, the Hrubec group suddenly began to observe neural tube defects (NTDs) in embryos of untreated control mice. Unintentional exposure to a teratogenic agent in tap water was identified as the cause. We aimed to identify the contaminant, but first we demonstrated that the NTDs were pathological being present on both gestational day 9 and 10. We also found that a second species, rats, developed NTDs when exposed to tap waters. Disinfection by-products (DBPs) arise when natural organic matter in municipal water sources reacts with disinfectants used in the water treatment process. Purge and trap gas chromatography-mass spectrometry (PT GC-MS) and animal exposure studies were used to determine if the teratogenic contaminant was a DBP. Since the distribution pattern of DBPs did not match the distribution pattern of NTDs, we concluded that a DBP was not likely to be responsible for the observed malformations. Pharmaceuticals and personal care products have emerged as ubiquitous contaminants of ground and surface waters, and have been detected in drinking water. In order to analyze for these compounds, we submitted different water samples to a commercial water analysis lab (AXYS Analytical Services, Sidney, BC, Canada). Several pharmaceuticals were identified in a number of samples, including a known teratogenic drug used to treat mood disorders and seizures: carbamazepine. Further analysis for carbamazepine was conducted in-house. Carbamazepine was found in several ground, surface, and tap waters, at various concentrations. To establish whether or not carbamazepine was responsible for NTDs in our mice, we conducted 2 dosing studies. Carbamazepine was provided to mice at concentrations detected in tap water, as well as approximately 2 x and 1000 x that concentration. Both studies found no significant differences in NTD rates among the dose groups. As no dose effect was observed, we concluded that CBZ was not directly responsible for the malformations. The identity of the teratogenic contaminant is not known at this time, but is unlikely to be a DBP or low concentrations of the pharmaceutical carbamazepine. / Master of Science