Appropriate access management techniques can improve the safety and efficiency of arterial roads. In order to determine which roads can most benefit by the implementation of access management techniques, a prioritization process was developed to recommend various access management treatments such as limiting access points, installing raised medians, and ensuring adequate signal spacing along corridors. To serve as the basis for the performance index, a database was created including identifying features, characteristics, and crash history for 175 arterial road segments on Utah state routes. Stepwise linear regression was applied to the data collected to determine which characteristics of the roads were correlated with crash rate, crash severity, and specific collision types. Signal spacing, access density, and median type were all determined to be correlated with crash rates and crash severity. Specifically, signals per mile, access density, and two-way left-turn lanes were all positively correlated with crashes. Other characteristics such as adjacent land use and volume were also analyzed. Finally, recommendations for access management treatments were given in the form of a decision tree. The decision tree may be used to classify existing or future road segments into subcategories based on volume, signal spacing, land use, and other criteria, with recommendations provided for each subcategory.

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Yes / Enterprises and individual users heavily rely on the abilities of antiviruses and other security mechanisms. However, the methodologies used by such software are not enough to detect and prevent most of the malicious activities and also consume a huge amount of resources of the host machine for their regular oper- ations. In this paper, we propose a combination of machine learning techniques applied on a rich set of features extracted from a large dataset of benign and malicious les through a bespoke feature extraction tool. We extracted a rich set of features from each le and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate. We also introduce a cloud-based scalable architecture hosted on Amazon web services to cater the needs of detection methodology. We tested our methodology against di erent scenarios and generated high achieving results with lowest energy con- sumption of the host machine.

Yes / Social media provides a significant avenue for stakeholder engagement which is crucial to ensure loyalty and satisfaction of stakeholders who possess valuable resources that can influence the business outcomes. Container lines – imperative members of global supply chains and facilitators of international trade – utilize social media to engage their stakeholders due to environmental and commercial complexity of their business. However, not all social media posts generate the same amount of stakeholder engagement. This study aims to identify and examine the social media post characteristics that lead to higher stakeholder engagement in the container shipping market. The study applies Chi-Squared Automatic Interaction Detection method to categorize social media posts based on their engagement levels. The analysis is conducted on the tweets of four global container lines which are posted between 1 September 2018 and 31 January 2019. The results demonstrate that social media posts of container lines have varying effects on engagement level. We found that fluency of tweets, tangibility of company resources in the tweet, vividness level, content type, existence of a link, and existence of a call-to-action significantly influence the container lines’ stakeholder engagement rate. This study is the first that finds out social media post classes based on the interaction between their characteristics and engagement rates by employing a decision tree methodology. The results are expected to help container lines in their social media management and stakeholder engagement policies.

Yes / The purpose of this study is to investigate what type of Facebook posts help cruise lines build bridging and bonding social capital. The study applies the Chi-Square Automatic Interaction Detection (CHAID) method to identify which types of posts establish bridging and bonding social capital. The analysis is conducted on an international cruise line’s official Facebook posts posted between 1 January 2018 and 1 January 2020 before the Covid-19 pandemic. The results highlight that media type, embedding passenger motivation, and a ship image help establish both bridging and bonding social capital, while content type helps establish bridging social capital. The paper is original because it helps understand how cruise lines can improve bonding and bridging social capital via social media. The paper also enhances understanding of social capital theory in the travel industry by investigating the relationship between Facebook post types and social capital in cruise shipping.

Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation. / Master of Science / Battery powered sensor nodes have access to a limited amount of energy. However, many applications of sensor nodes such as animal monitoring require energy intensive, long range data transmissions. In this work, we used machine learning to process motion data within our sensor node to classify cattle behaviors. We estimate that transmitting processed data dissipates up to 50 times less power when compared to transmitting raw data. Due to the properties of our transmission protocol, we also observe that transmitting processed data increases the range of transmissions without impacting power dissipation.

This thesis investigates the pair production of Higgs bosons (di-Higgs events) at the ATLAS experiment in the Large Hadron Collider (LHC), focusing on the channel where one Higgs boson decays into two bottom quarks and the other decays into two tau leptons. The main objective was to determine whether introducing a split in the invariant mass of the decay products from the two Higgs bosons (the di-Higgs mass) and using this as an analysis variable improves the sensitivity of the Boosted Decision Tree (BDT) machine learning algorithm to the di-Higgs signal. A mass split was performed at 350 GeV, and the BDT algorithm was trained on both the split and un-split data sets, where the split data set included a high-mass region (di-Higgs mass above 350 GeV) using the Standard Model Higgs boson coupling constant of 1 and a low-mass region (di-Higgs mass below 350 GeV) using the enhanced coupling constant of 10 to create a low-mass region more sensitive to the signal.  The results showed that the BDT algorithm training performed on the split data set provided a 3.6% improvement in the exclusion limits, indicating an improvement in the algorithm's sensitivity to the di-Higgs signal compared to the training performed on the un-split data set. This finding suggests that the introduction of a split at 350 GeV can enhance the accuracy and efficiency of machine learning algorithms in detecting di-Higgs boson production at the LHC.  The improvement in sensitivity was attributed to the enhanced discrimination between signal and background events provided by the split in the di-Higgs mass analysis variable. The improved separation between the signal and background events lead to a higher signal-to-background ratio and a corresponding increase in the BDT algorithm's sensitivity to the di-Higgs signal.  In conclusion, this thesis provided evidence that introducing a split in the di-Higgs mass analysis variable can improve the sensitivity of machine learning algorithms to the di-Higgs signal in the channel where one Higgs boson decays into two bottom quarks and the other into two tau particles. This finding has important implications for future research on di-Higgs boson production at the LHC and could lead to more accurate and efficient detection of this rare and important process.

When banks lend money to another party they face a risk that the borrower will not fulfill its obligation towards the bank. This risk is called credit risk and it’s the largest risk banks faces. According to the Basel accord banks need to have a certain amount of capital requirements to protect themselves towards future financial crisis. This amount is calculated for each loan with an attached risk-weighted asset, RWA. The main parameters in RWA is probability of default and loss given default. Banks are today allowed to use their own internal models to calculate these parameters. Thus hold capital with no gained interest is a great cost, banks seek to find tools to better predict probability of default to lower the capital requirement. Machine learning and supervised algorithms such as Logistic regression, Neural network, Decision tree and Random Forest can be used to decide credit risk. By training algorithms on historical data with known results the parameter probability of default (PD) can be determined with a higher certainty degree compared to traditional models, leading to a lower capital requirement. On the given data set in this article Logistic regression seems to be the algorithm with highest accuracy of classifying customer into right category. However, it classifies a lot of people as false positive meaning the model thinks a customer will honour its obligation but in fact the customer defaults. Doing this comes with a great cost for the banks. Through implementing a cost function to minimize this error, we found that the Neural network has the lowest false positive rate and will therefore be the model that is best suited for this specific classification task. / När banker lånar ut pengar till en annan part uppstår en risk i att låntagaren inte uppfyller sitt antagande mot banken. Denna risk kallas för kredit risk och är den största risken en bank står inför. Enligt Basel föreskrifterna måste en bank avsätta en viss summa kapital för varje lån de ger ut för att på så sätt skydda sig emot framtida finansiella kriser. Denna summa beräknas fram utifrån varje enskilt lån med tillhörande risk-vikt, RWA. De huvudsakliga parametrarna i RWA är sannolikheten att en kund ej kan betala tillbaka lånet samt summan som banken då förlorar. Idag kan banker använda sig av interna modeller för att estimera dessa parametrar. Då bundet kapital medför stora kostnader för banker, försöker de sträva efter att hitta bättre verktyg för att uppskatta sannolikheten att en kund fallerar för att på så sätt minska deras kapitalkrav. Därför har nu banker börjat titta på möjligheten att använda sig av maskininlärningsalgoritmer för att estimera dessa parametrar. Maskininlärningsalgoritmer såsom Logistisk regression, Neurala nätverk, Beslutsträd och Random forest, kan användas för att bestämma kreditrisk. Genom att träna algoritmer på historisk data med kända resultat kan parametern, chansen att en kund ej betalar tillbaka lånet (PD), bestämmas med en högre säkerhet än traditionella metoder. På den givna datan som denna uppsats bygger på visar det sig att Logistisk regression är den algoritm med högst träffsäkerhet att klassificera en kund till rätt kategori. Däremot klassifiserar denna algoritm många kunder som falsk positiv vilket betyder att den predikterar att många kunder kommer betala tillbaka sina lån men i själva verket inte betalar tillbaka lånet. Att göra detta medför en stor kostnad för bankerna. Genom att istället utvärdera modellerna med hjälp av att införa en kostnadsfunktion för att minska detta fel finner vi att Neurala nätverk har den lägsta falsk positiv ration och kommer därmed vara den model som är bäst lämpad att utföra just denna specifika klassifierings uppgift.

Train delays occur on a daily basis in the commuter rail of Stockholm. This means that the travellers might become delayed themselves for their particular destination. To find the most accurate method for predicting train delays, the machine learning methods decision tree with and without AdaBoost and neural network were compared with different settings. Neural network achieved the best result when used with 3 layers and 22 neurons in each layer. Its delay predictions had an average error of 122 seconds, compared to the actual delay. It might therefore be the best method for predicting train delays. However the study was very limited in time and more train departure data would need to be collected. / Tågförseningar inträffar dagligen i Stockholms pendeltågstrafik. Det orsakar att resenärerna själva kan bli försenade till deras destinationer. För att hitta den mest träffsäkra metoden för att förutspå tågförseningar jämfördes maskininlärningsmetoderna beslutsträd, med och utan AdaBoost, och artificiella neuronnät med olika inställningar. Det artificiella neuronnätet gav det bästa resultatet när det användes med 3 lager och 22 neuroner i varje lager. Dess förseningsförutsägelse hade ett genomsnittligt fel på 122 sekunder jämfört med den verkliga förseningen. Det kan därför vara den bästa metoden för att förutspå tågförseningar. Den här studien hade dock väldigt begränsat med tid och mer information om tågavgångar hade behövts samlas in.

Mastitis is one of the most costly diseases to dairy producers around the world with milk yield loss being the biggest contributor to economic losses. The objective of first study of this thesis was to determine the impacts of high somatic cell counts on milk yield loss. To accomplish this, over one million cow data records were collected from Southeastern US dairy herds. The objective of the second study was to determine optimum treatment cost of clinical mastitis by combining two economic modeling approaches used in animal health economics. The last objective of this thesis was to determine how much Southeastern US dairy producers are spending to control milk quality on farm and determine if they understand how milk quality affects them economically. This was accomplished through a collaborative project within the Southeast Quality Milk Initiative.