In this paper, we try to understand the concept of ’Neural Collapse’ from a mathemati-cal point of view. The survey will be conducted based on [1]. The authors of [1] providea first global optimization landscape analysis of Neural Collapse. Mainly there are threeaspects the authors like to investigate. The first is to add the weight decay on classicalcross-entropy loss to show that the global minimizers are the simplex ETF based onanalysing the Hessian. Secondly, the ’Layer-peeled’ network still preserves the im-portant features of the full network. In other words even simplifying the loss functionthe network does not lose its explainability. Lastly, how the Layer-peeled network canreduce the memory costs and generalization is as good as the full network. Our studydelves into these details on, how the simplified network is defined? How this simplifiednetwork is different from the original network in terms of the loss function, and finallywe understand the theory behind these steps. We also conduct numerical analysis onspecific input, observe and analyze this phenomenon and finally report our results.

Networks have an increasing influence on our modern life, making Cybersecurity an important field of research. Cybersecurity techniques mainly focus on antivirus software, firewalls and intrusion detection systems (IDSs), etc. These techniques protect networks from both internal and external attacks. This research is composed of three different essays. It highlights and improves the applications of machine learning techniques in the Cybersecurity domain. Since the feature size and observations of the cyber incident data are increasing with the growth of internet usage, conventional defense strategies against cyberattacks are getting invalid most of the time. On the other hand, the applications of machine learning tasks are getting better consistently to prevent cyber risks in a timely manner. For the last decade, machine learning and Cybersecurity have converged to enhance risk elimination. Since the cyber domain knowledge and adopting machine learning techniques do not align on the same page in the case of deployment of data-driven intelligent systems, there are inconsistencies where it is needed to bridge the gap. We have studied the most recent research works in this field and documented the most common issues regarding the implementation of machine learning algorithms in Cybersecurity. According to these findings, we have conducted research and experiments to improve the quality of service and security strength by discovering new approaches.

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Over the years, cancer has increasingly become a global health problem [12]. For successful treatment, early detection and diagnosis is critical. Radiomics is the use of CT, PET, MRI or Ultrasound imaging as input data, extracting features from image-based data, and then using machine learning for quantitative analysis and disease prediction [23, 14, 19, 1]. Feature reduction is critical as most quantitative features can have unnecessary redundant characteristics. The objective of this research is to use machine learning techniques in reducing the number of dimensions, thereby rendering the data manageable. Radiomics steps include Imaging, segmentation, feature extraction, and analysis. For this research, a large-scale CT data for Lung cancer diagnosis collected by scholars from Medical University in China is used to illustrate the dimension reduction techniques via R, SAS, and Python softwares. The proposed reduction and analysis techniques were PCA, Clustering, and Manifold-based algorithms. The results indicated the texture-based features

A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling. We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system. We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.

No / The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment.

The recent abundance of data combined with the current digitalisation all over the globe has made organisations across various industries become more involved with data-driven processes. The power of data is harnessed through wrangling and analysis in order to not only create valuable insights to guide strategic decision-making but to also improve efficiency and productivity. These data-driven processes often involve combining statistical analysis with sophisticated software such as machine learning, and while it shares similarities to business intelligence or big data analytics, it truly belongs to Data Science. The field is young and ever growing with rapid developments in both the industry and in academia, but its lack of maturity has made it challenging to determine how it fares in the landscape of other fields. Academic contributions have been made towards the field's interdisciplinary nature and suggest that Data Scientists are able to extract knowledge and insights from data and turn it into action. However, the constituents of the field have seen less attention and it is still unclear what the title entails. In this thesis, the phenomenon of Data Science is explored by investigating the field's possible interdisciplinary nature and what its possible constituents might be. Further, this thesis investigated the practical responsibilities and duties of a Data Scientist. The thesis followed a qualitative approach that consisted of interviews with experts within Data Science, an extensive review of relevant literature, and an analysis of a current education in Data Science. The conclusions suggest that the practical responsibilities of a Data Scientist are best described according to the workflow that permeates Data Science projects. The claim of the field being of interdisciplinary nature is strengthened, and the results suggest that its main constituents are mathematics and practices related to computer science. It also includes elements from less technical domains. / Den senaste tidens överflöd av data tillsammans med den pågående digitaliseringen över hela världen har gjort att organisationer inom olika branscher blir mer involverade i datadrivna processer. Kraften i data utnyttjas genom bearbetning och analys för att inte bara skapa värdefulla insikter som vägleder strategiska beslut, utan också för att förbättra effektivitet och produktivitet. Dessa datadrivna processer innefattar ofta kombinationer av statistisk analys med sofistikerad programvara som maskininlärning, och även om det har likheter med affärsintelligens eller storskalig dataanalys, hör det verkligen hemma inom Data Science. Fältet är ungt och ständigt växande med snabba framsteg både inom branschen och inom akademin, men dess brist på mognad har gjort det utmanande att bedöma hur det står sig i förhållande till andra områden. Akademiska bidrag har gjorts för att belysa fältets tvärvetenskapliga natur och antyder att Data Scientists har förmågan att utvinna kunskap och insikter från data och omsätta det i handling. Dock har mindre uppmärksamhet ägnats åt fältets beståndsdelar, och det är fortfarande oklart vad titeln egentligen innebär. I detta examensarbete utforskas fenomenet Data Science genom att undersöka fältets tvärvetenskapliga natur och vilka dess möjliga beståndsdelar kan vara. Dessutom undersöker avhandlingen de praktiska ansvar och uppgifter som en Data Scientist har. Avhandlingen följde en kvalitativ metod som bestod av intervjuer med experter inom Data Science, en omfattande granskning av relevant litteratur och en analys av en aktuell utbildning inom Data Science. Slutsatserna tyder på att de praktiska ansvar som en Data Scientist har bäst beskrivs utifrån arbetsflödet som genomsyrar Data Scienceprojekt. Påståendet om att fältet är tvärvetenskapligt stärks och resultaten tyder på att dess huvudsakliga beståndsdelar är statistisk matematik och metoder relaterade till datavetenskap. Det inkluderar också element från mindre tekniska områden.

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A major healthcare problem is the overcrowding of hospitals and emergency departments which leads to negative patient outcomes and increased costs. In a previous study, performed by Leiden University Medical Centre, a new and innovative prehospital triage method was developed where two nurse paramedics could consult a cardiologist for patients with cardiac symptoms, via a live connection on a digital triage platform. The developed triage method resulted in a recall = 0.995 and specificity = 0.0113. This study arise the following research question: ‘Would there be enough (good) information gathered on the prehospital scene to make a machine learning model able to predict myocardial infarction?’. By testing different pre-processing steps, several features (premade ones and self-made ones), multiple models (Support Vector Machine, K Nearest Neighbour, Logistic Regression and Random Forest), various outcome settings and hyperparameters, led to the final results: recall = 0.995 and specificity = 0.1101. This is gained through the feature selected by a cardiologist and the Support Vector Machine model. The outcomes are controlled by an extra explainability layer named Explain Like I’m Five. This outcome illustrates that the created machine learning model is trained mostly on the right words and characters.

Microbial natural products have served a key role in the development of clinically relevant drugs. Despite significant interest, traditional strategies in their characterization have lead to diminishing returns, leaving this field stagnant. Recently developed technologies such as low-cost, high-throughput genome sequencing and high-resolution mass spectrometry allow for a much richer experimental strategy, allowing us to gather data at an unprecedented scale. Naive efforts in analyzing genomic data have already revealed the wealth of natural products encoded within diverse bacterial phylogenies. Herein, I leverage these technologies through the development of specialized computational platforms cognizant of existing natural products and their biosynthesis in order to reinvigorate our drug discovery protocols. As a first, I present a strategy for the targeted isolation of novel and structurally divergent ribosomally synthesized and post-translationally modified peptides (RiPPs). Specifically, this software platform is able to directly compare genomically encoded RiPPs to previously characterized chemical scaffolds, allowing for the identification of bacterial strains producing these specialized, and previously unstudied metabolites. Further, using metabolomics data, I have developed a strategy that facilitates direct identification and targeted isolation of these uncharacterized RiPPs. Through these set of tools, we were able to successfully isolate a structurally unique lasso peptide from a previously unexplored \textit{Streptomyces} isolate. With the technological rise of genomic sequencing, it is now possible to survey polymicrobial environments with remarkable detail. Through the use of metagenomics, we can survey the presence and abundances of bacteria, and further metatranscriptomics is able to reveal the expression of their biosynthetic pathways. Here, I developed a platform which is able to identify microbial peptides exclusively found within the human microbiome, and further characterize their putative antimicrobial properties. Through this endeavour, we identified a bacterially encoded peptide that can effectively protect against pathogenic \textit{Clostridium difficile} infections. With the wealth of publicly available multi-omics datasets, these works in conjunction demonstrate the potential of informatics strategies in the advancement of natural product discovery. / Thesis / Master of Science (MSc) / Biochemistry is the study in which life is built upon a series of diverse chemistry and their interactions. Some of these chemicals are not essential for the maintaining basic metabolism, but are instead tailored for alternative functions best suited to their environment. Often, these molecules mediate biological warfare, allowing organisms to compete and establish dominance amongst their neighbours. Understanding this, several of these molecules have been exploited in our modern pharmaceutical regimen as effective antibiotics. Due to the ever rising reality of antibiotic resistance, we are in dire need of novel antibiotics. With this goal, I have developed several software tools that can both identify these molecules encoded within bacterial genomes, but also predict their effects on neighbouring bacteria. Through these computational tools, I provide an updated strategy for the discovery and characterization of these biologically derived chemicals.