The role of correspondence analysis in medical research

Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret “bi–plots,” where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection

Data canyons, a machine learning approach for interpretable artificial intelligence

Z uporabo algoritmov strojnega učenja je mogoče izvesti zapletene analize in pridobiti globlje vpoglede na osnovi obsežnih količin podatkov, kar presega človeške zmožnosti. Navedena značilnost je ključni dejavnik, zaradi katerega je strojno učenje vpeljano v številne domene. Kljub številnim prednostim ni vedno možno integrirati strojnega učenja na določena področja, predvsem zaradi tega, ker se za naprednimi metodami pogosto skrivajo modeli tipa črne skrinje. Ti modeli uporabnikom ne omogočajo vpogleda v logiko njihovega odločanja, kar lahko predstavlja oviro v kontekstih, kjer so odločitve kritične in lahko napačna odločitev vodi v resne posledice. Z namenom ublažiti te problematike smo razvili metodo strojnega učenja, temelječo na naravnem pojavu rečnih kanjonov. Ta pojav lahko vizualiziramo v digitalni grafični obliki, kar omogoča intuitiven prikaz logike odločanja. Rezultat je model strojnega učenja, ki generira globinske slike gibanja podatkov za posamezen razred. V teh slikah je pripadnost posamezne instance kanjonu prikazana s pomočjo barvno kodiranih grafov. Podatkovni kanjoni se zaradi svojih lastnosti in metodologije lahko uporabljajo za potrebe razložljive umetne inteligence, bodisi samostojno ali kot dopolnilni mehanizem drugim pristopom strojnega učenja.With machine learning algorithms avast amounts of data can be analyzed to gain profound insights beyond human capabilities. This quality is a key reason for incorporating machine learning across numerous domains. Although machine learning has various benefits aincorporating it into certain areas can be difficult due to the blackbox nature of advanced methods. These models do not grant users insight into their decision-making logic aposing challenges in contexts where decisions are critical and an erroneous decision might result in severe consequences. To address these issues awe developed a machine-learning method based on the natural phenomenon of river canyons. This phenomenon can be visualized in a digital graphical format aoffering an intuitive representation of decision-making logic. The outcome is a machine learning model that produces depth maps of data movement for individual classes. Within these maps athe affiliation of a particular instance to a canyon is denoted by color-coded graphs

STORING OF PRIVATE AND PUBLIC DATA IN QUICK RESPONSE CODE

Hitro odzivne kode so vedno bolj priljubljene, saj s poplavo pametnih mobilnih naprav omogočajo enostaven prenos podatkov na le-te. Uporabljajo se na različnih področjih, kot je marketing, šolstvo, pošti in na ogromno področjih kjer je pomembna logistika. Uporabnost teh kod pa je omejena, saj so podatki, ki se skrivajo v njih javnega značaja in jih tako lahko prebere vsak, ki ima primerno mobilno napravo s primerno programsko opremo. Tako se zdi smiselno, dodati funkcionalnost, ki omogoča da lahko v hitro odzivno kodo shranjujem tudi podatke zasebnega značaja.Quick response codes is becoming increasingly popular because of the flood of smart mobile devices, that enable easy transfer of data onto themselves thru the it. They are used in various fields such as marketing, education, post management and in a lot of areas where logistic is important. The applicability of these codes is limited, as the data stored in them is public and can therefore be read by anyone who has the appropriate mobile device and the appropriate software. Therefore, it seems reasonable to add functionality to the quick response code that allows storage of private dat

CAMERA NAVI - NAVIGATION WITH LIVE STREAMING

Diplomsko delo obravnava izkoriščanje naprednih zmožnosti modernih mobilnih telefonov in združevanje teh naprednih zmožnosti za uporabo v preprosti navigacijski aplikaciji. Poznavanje posameznih lastnosti teh naprednih naprav, ki jih ponujajo moderni mobilni telefoni, nam omogoča, da te lastnosti uporabimo za pridobivanje nam relevantnih informacij, ki lahko obogatijo malone vsako aplikacijo, ki te informacije pravilno izkoristi. Tako v naši aplikaciji združujemo globalno določanje položaja, zaznavanje pospeškov in koristimo kamero telefona na način, ki omogoča preprosto in intuitivno navigacijo.This diploma discusses the exploitation of advance capabilities of modern mobile phones and the ability to combine these advance capabilities for use in a simple navigation application. Knowing the individual characteristics of advance devices offered by modern mobile phones allows us to use these properties to obtain information relevant for us, that can enrich almost any application that makes correct use of this information. Thus in our application we combine global positioning, acceleration sensing, and the phone\u27s camera, in a way that makes navigation simple and intuitive

Agile Machine Learning Model Development Using Data Canyons in Medicine

Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains