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Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review
Lucija Gosak, Adrijana Svenšek, Mateja Lorber, Gregor Štiglic, 2023, pregledni znanstveni članek

Opis: Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.
Ključne besede: artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review
Objavljeno v DKUM: 27.11.2023; Ogledov: 221; Prenosov: 10
.pdf Celotno besedilo (654,91 KB)
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3.
IoT and satellite sensor data integration for assessment of environmental variables: a case study on NO2
Jernej Cukjati, Domen Mongus, Krista Rizman Žalik, Borut Žalik, 2022, izvirni znanstveni članek

Opis: This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10−6 mol/m2.
Ključne besede: Internet of Things, IoT, remote sensing, sensor integration, machine learning, ensemble method
Objavljeno v DKUM: 22.09.2023; Ogledov: 277; Prenosov: 11
.pdf Celotno besedilo (3,72 MB)
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4.
Development of the web service for grocery store receipt recognition and subsequent analysis of the nutritional value of the products purchased
Pavel Nesterov, 2023, magistrsko delo

Opis: The objective of this study is to tackle the worldwide problem of cardiovascular diseases (CVDs). These diseases represent the foremost reason for mortality on a global scale, accounting for approximately 17.9 million deaths annually. Unhealthy eating habits are one of the most frequent causes for CVD in the world and are addressed in various ways, including digital technologies. The goal of this project i`s to develop a web service that enables a non-intrusive way of tracking food consumption and monitoring the dynamics of nutrient intake compared to the traditional "record-every-meal" type of tracking. We followed a design science research approach to develop the web service. The web service employs semantic similarity algorithms and enables human feedback to improve its performance. It will be developed using the Serverless framework and deployed on the AWS platform. Receipt recognition will be performed using AWS Textract, and the nutritional value of the products will be obtained from publicly available databases. This approach aims to create an innovative and user-friendly method for tracking food consumption and nutrient dynamics.
Ključne besede: Healthy eating, Grocery store receipt, Nutritional analysis, Daily intake monitoring, machine learning, chatbot
Objavljeno v DKUM: 14.09.2023; Ogledov: 306; Prenosov: 11
.pdf Celotno besedilo (1,28 MB)

5.
Analyzing EEG signal with Machine Learning in Python : graduation thesis
Evgenija Siljanovska, 2023, diplomsko delo

Opis: This thesis presents a comprehensive analysis of EEG data using Python libraries, MNE and machine learning techniques. The thesis focuses on utilizing these tools to extract valuable insights from EEG recordings. Our dataset consists of EEG data in the BrainVision format, acquired during a psychology experiment. The analysis involves preprocessing, filtering, segmentation, and visualization of the EEG data. Additionally, machine learning algorithms are employed to classify and predict patterns within the EEG signals. The findings showcase the effectiveness of Python, MNE, and machine learning in EEG analysis.
Ključne besede: EEG data, MNE, Machine learning, Analyzing
Objavljeno v DKUM: 17.08.2023; Ogledov: 383; Prenosov: 43
.pdf Celotno besedilo (2,66 MB)

6.
Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness
Ivona Colakovic, Sašo Karakatič, 2022, objavljeni znanstveni prispevek na konferenci

Opis: This paper deals with the group fairness issue that arises when classifying data, which contains socially induced biases for age and ethnicity. To tackle the unfair focus on certain age and ethnicity groups, we propose an adaptive boosting method that balances the fair treatment of all groups. The proposed approach builds upon the AdaBoost method but supplements it with the factor of fairness between the sensitive groups. The results show that the proposed method focuses more on the age and ethnicity groups, given less focus with traditional classification techniques. Thus the resulting classification model is more balanced, treating all of the sensitive groups more equally without sacrificing the overall quality of the classification.
Ključne besede: fairness, classification, boosting, machine learning
Objavljeno v DKUM: 02.08.2023; Ogledov: 307; Prenosov: 25
.pdf Celotno besedilo (884,95 KB)
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7.
Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718
Matija Hriberšek, Lucijano Berus, Franci Pušavec, Simon Klančnik, 2020, izvirni znanstveni članek

Opis: This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts.
Ključne besede: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Objavljeno v DKUM: 14.07.2023; Ogledov: 318; Prenosov: 14
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8.
Data-driven supply chain operations : the pilot case of postal logistics and the cross-border optimization potential
Tanja Zdolšek Draksler, Miha Cimperman, Matevž Obrecht, 2023, izvirni znanstveni članek

Opis: According to the defined challenge of cross-border delivery, a pilot experiment based on the integration of new digital technologies to assess process optimization potential in the postal sector was designed. The specifics were investigated with events processing based on digital representation. Different events were simulated with scenario analysis with the integration of the Cognitive Advisor and supported by the monitoring of KPIs. The business environment is forcing logistics companies to optimize their delivery processes, integrate new technologies, improve their performance metrics, and move towards Logistics 4.0. Their main goals are to simultaneously reduce costs, environmental impact, delivery times, and route length, as well as to increase customer satisfaction. This pilot experiment demonstrates the integration of new digital technologies for process optimization in real time to manage intraday changes. Postal operators can increase flexibility, introduce new services, improve utilization by up to 50%, and reduce costs and route length by 12.21%. The Cognitive Advisor has shown great potential for the future of logistics by enabling a dynamic approach to managing supply chain disruptions using sophisticated data analytics for process optimization based on the existing delivery infrastructure and improving business processes. Research originality is identified with a novel approach of real-time simulation based on the integration of the Cognitive Advisor in postal delivery.
Ključne besede: cognitive logistics, machine learning, social IoT, last mile, digital twin, supply chain digitization
Objavljeno v DKUM: 24.02.2023; Ogledov: 495; Prenosov: 36
.pdf Celotno besedilo (1,70 MB)
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9.
Construction of deep neutral networks using swarm intelligence to detect anomalies : master's thesis
Sašo Pavlič, 2021, magistrsko delo

Opis: The design of neural network architecture is becoming more difficult as the complexity of the problems we tackle using machine learning increases. Many variables influence the performance of a neural model, and those variables are often limited by the researcher's prior knowledge and experience. In our master's thesis, we will focus on becoming familiar with evolutionary neural network design, anomaly detection techniques, and a deeper knowledge of autoencoders and their potential for application in unsupervised learning. Our practical objective will be to build a neural architecture search based on swarm intelligence, and construct an autoencoder architecture for anomaly detection in the MNIST dataset.
Ključne besede: neural architecture search, machine learning, swarm intelligence
Objavljeno v DKUM: 18.10.2021; Ogledov: 992; Prenosov: 93
.pdf Celotno besedilo (3,18 MB)
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10.
Implementation of a new reporting process in a group x
Sara Črešnik, 2021, magistrsko delo

Opis: Reporting is present in every company. Whether it is small or big, it cannot be avoided. It plays a crucial role in the process and progress of business. The quality of reporting affects the development of the work environment and the company. Since business report is a document that contains business information, which supports the decisions about the future-oriented business decisions, it is very important for it to be designed in such a way that it contains the key information for the recipient and provides support for business decisions. The reporting process can take place horizontally upwards or downwards. Content and structure vary depending on the recipient of the report. We live in an age when our every step is accompanied by digitization, computerization, artificial intelligence, mass data, the Internet of Things, machine learning, and robotics. These changes have also affected the reporting process as well as its processes. The processes of data acquisition, processing and sharing have changed. Furthermore, the data quantity has increased, whereas the speed of the time in which to prepare the reports has decreased. We can have data without information, but we cannot have information without data. There is never enough time, especially nowadays when we are used to having everything at our fingertips. These are two conflicting factors – having more data and less time to prepare quality reports. The systems are developed to optimize the process, increase efficiency and quality and, what is nowadays most important, they have been created to obtain mass data in the shortest possible time. Therefore, it is important to adapt and implement software that can help achieve our daily tasks. We must know how to process huge amounts of real-time data and deliver the information they contain. It is crucial for companies to keep up with the environment and implement changes and innovations into their business process. A company is like a living organism for it must constantly evolve and grow. As soon as it stops growing and evolving, it can fail because it starts lagging and is therefore no longer competitive to others. To deliver faster feedback, companies need data of better quality. There are tools that can improve the business process, better facilitating the capacity of the human agents. The goal is to harness the employees’ full potential and knowledge for important tasks, such as analyzing, reviewing, and understanding data and acting upon them, invoking information technology to automate repetitive processes and facilitate better communication. The focus in this master’s thesis is on the reporting process in Group X. Group X is one of the world leaders in the automotive industry, a multinational corporation based in Canada with subsidiaries around the world. The complexity of the business reporting that is implemented for the Headquarters in Canada has to address the complexity of the multinational corporation to support the decision process. The aim of the thesis is to propose a reporting process for preparing and producing reports with a huge amount of data in a very time-efficient manner. We start by examining the existing processes and upon that, identifying the processes required for the reports to reach the final recipients. Our goal is to identify the toolset, which would increase efficiency, accuracy, credibility, and reduce errors in the fastest possible time. We investigate a short-term and a long-term solution. By a short-term solution, we mean a system, program, or a tool that can help us increase our potential by using digital resources, which are already existing in the organization. By a long-term solution, we mean a solution, which requires employment of specialized future tools in the field of reporting and in repetitive processes, which we can identify with current knowledge and expectations for development. This includes machine learning, robotic process automatization, artificial intelligence.
Ključne besede: Consolidated reporting, reporting process, robotic process automatization, business intelligence, artificial intelligence, machine learning, SharePoint, Big Data, digital transformation, electronic data interchange.
Objavljeno v DKUM: 01.09.2021; Ogledov: 745; Prenosov: 3
.pdf Celotno besedilo (1,71 MB)

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