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Energy efficient system for detection of elephants with Machine Learning
Marko Sagadin, 2020, master's thesis

Abstract: Human-Elephant Conflicts are a major problem in terms of elephant conservation. According to WILDLABS, an average of 400 people and 100 elephants are killed every year in India alone because of them. Early warning systems replace the role of human watchers and warn local communities of nearby, potentially life threatening, elephants, thus minimising the Human-Elephant Conflicts. In this Master's thesis we present the structure of an early warning system, which consists of several low-power embedded systems equipped with thermal cameras and a single gateway. To detect elephants from captured thermal images we used Machine Learning methods, specifically Convolutional Neural Networks. The main focus of this thesis was the design, implementation and evaluation of Machine Learning models running on microcontrollers under low-power conditions. We designed and trained several accurate image classification models, optimised them for on-device deployment and compared them against models trained with commercial software in terms of accuracy, inference speed and size. While writing firmware, we ported a part of the TensorFlow library and created our own build system, suitable for the libopencm3 platform. We also implemented reporting of inference results over the LoRaWAN network and described a possible server-size solution. We finally a constructed fully functional embedded system from various development and evaluation boards, and evaluated its performance in terms of power consumption. We show that embedded systems with Machine Learning capabilities are a viable solution to many real life problems.
Keywords: machine learning, microcontroller, on-device inference, thermal camera, low-power system
Published: 06.01.2021; Views: 118; Downloads: 33
.pdf Full text (13,35 MB)

Development of a Model for Predicting Brake Torque Using LSTM and TCN Models
Tomaž Roškar, 2020, master's thesis

Abstract: The main purpose of this thesis is to compare two state-of-the-art machine learning models, LSTM (Long Short-Term Memory) and TCN (Temporal Convolutional Network), on an AVL List GmbH case use, where the goal is to predict vehicle brake torque. Dataset used for model testing consists of multiple features which are preprocessed using several preprocessing methods. For model implementation Python’s libraries Keras and TensorFlow are used. Results from this thesis show that TCN is able to outperform LSTM. TCN achieves lower RMSE on the test dataset and is significantly faster in training and evaluation.
Keywords: brake torque, machine learning, neural network, LSTM, TCN, RNN, CNN
Published: 24.09.2020; Views: 118; Downloads: 0

The Digital Pig: Automatic Systems for Behavior Detection in Weaned Pigs
Anja Žnidar, 2020, undergraduate thesis

Abstract: In this bachelor's thesis, we used machine learning techniques to detect pigs in group pens, which would help to improve the welfare and comfort of pigs. Mask-RCNN was used for object segmentation. The implementation was based on Resnet101. The goal was to achieve the highest possible precision in detection of the pig's body, head, and tail. We predicted that the accuracy will be the highest for body detection and lower for head and tail detection. We also concluded that the difference in precision and recall will be less than 10% between hand-labeled bounding boxes and the predicted bounding boxes from our model. As predicted, body detection represented the highest results, as the accuracy of head and tail detection was lower. The difference between precision and recall was 10% for body detection and higher than 10% for head and tail detection. Precision of the body detection was 96%, as the whole body is easier to detect. The head detection precision score was 66%. Tail detection precision was 77%, which is a large difference compared to the percentage of head detection. The use of machine learning in livestock farming could be a potentially useful tool for detecting welfare in pigs, as it would reduce the frequency of aggressive behaviors and the number of injuries. In the future, we want to refine our model to achieve higher precision for head and tail detection. Once the algorithm has clearly detected all the pigs in the image, we will try to refine the model to detect different forms of behavior. This technology would help us to evaluate welfare, which would be improved if necessary.
Keywords: pig, pig annotation, behavior, welfare, machine learning
Published: 08.09.2020; Views: 202; Downloads: 67
.pdf Full text (1,40 MB)

Nature-inspired algorithms for hyperparameter optimization
Filip Glojnarić, 2019, master's thesis

Abstract: This master thesis is focusing on the utilization of nature-inspired algorithms for hyperparameter optimization, how they work and how to use them. We present some existing methods for hyperparameter optimization as well as propose a novel method that is based on six different nature-inspired algorithms: Firefly algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Genetic algorithm, Differential Evolution, and Hybrid Bat algorithm. We also show the optimization results (set of hyperparameters) for each algorithm and we present the plots of the accuracy for each combination and handpicked one. In discussion of the results, we provide the answers on our research questions as well as propose ideas for future work.
Keywords: artificial intelligence, artificial neural networks, machine learning, nature-inspired algorithms, evolutionary algorithms
Published: 09.12.2019; Views: 535; Downloads: 61
.pdf Full text (969,13 KB)

Evaluation of Machine Learning Algorithms for Predicting the Processing Time of Order Picking in a Warehouse
Tilen Škrinjar, 2019, master's thesis

Abstract: Optimization of warehouse processes increases efficiency and lowers the cost of managing a warehouse. The most expensive and time-consuming activity is picking. Knowing picking process time is an important factor for proper organization of material and information flow. Orders delivered to a packing station too early or too late can cause delays in a warehouse. The purpose of this study is to evaluate machine learning pipeline for processing time prediction of order picking. This includes data gathering, data preprocessing and the evaluation of machine learning algorithms, which are the most important aspects of this research.
Keywords: warehouse, order picking, machine learning, regression analysis
Published: 25.02.2019; Views: 558; Downloads: 0

Classification of perimetric data for supporting glaucoma diagnosis
Janja Belinc, 2018, master's thesis

Abstract: The aim of the study: Glaucoma is a chronic, progressive and asymptomatic retinal disease which results in an irreversible visual field loss. The main objective of this Master’s thesis work was to study the applicability of classification techniques for supporting glaucoma diagnosis. Research Methodology: In this study perimetric data was obtained by SPARK strategy implemented in Oculus perimeters and provided by medical experts from the Hospital Universitario de Canarias (HUC). This data was used for constructing the feature vectors for the classification problem. Feature vectors of 66 values and feature vectors of 6 values were tested in the experiments. The proposed classification study attempted to: a) demonstrate that the studied classifiers were able to distinguish between “healthy” and “glaucomatous” eyes using only perimetric data, and b) analyse which feature vector design was the most suitable to accomplish this task. Results: The classification results showed that classifiers performed better on 6 than on 66 perimetry values, which demonstrated the suitability of the 6 points selected by the SPARK strategy and supported its use in medical field. Conclusion: In this study two remarkable findings for pattern recognition in perimetric data were obtained. Firstly, that reducing the dataset improved the efficiency of the studied classifier, and secondly, that simple pattern recognition models types were more efficient than complex ones.
Keywords: Eye disease, visual field, SPARK perimetry, pattern recognition, machine learning, supervised learning, ROC analysis
Published: 27.08.2018; Views: 460; Downloads: 53
.pdf Full text (4,25 MB)

Organizational learning supported by machine learning models coupled with general explanation methods
Marko Bohanec, Marko Robnik Šikonja, Mirjana Kljajić Borštnar, 2017, original scientific article

Abstract: Background and Purpose: The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML) models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting. Design/Methodology/Approach: Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilizes R software environment. Results: ML model was developed in several design cycles involving users. It was evaluated in the company for several months. Results suggest that based on the explanations of the ML model predictions the users’ forecasts improved. Furthermore, when the users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model. Conclusions: The proposed model promotes understanding, foster debate and validation of existing beliefs, and thus contributes to single and double-loop learning. Active participation of the users in the process of development, validation, and implementation has shown to be beneficial in creating trust and promotes acceptance in practice.
Keywords: decision support, organizational learning, machine learning, explanations
Published: 01.09.2017; Views: 815; Downloads: 122
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Link prediction in multiplex online social networks
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, Matjaž Perc, 2017, original scientific article

Abstract: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Keywords: social networks, complex networks, signed networks, link prediction, machine learning
Published: 08.08.2017; Views: 623; Downloads: 329
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A biometric authentication model using hand gesture images
Simon Fong, Yan Zhuang, Iztok Fister, Iztok Fister, 2013, original scientific article

Abstract: A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information,associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i', 'l', 'o', 'v', 'e', and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. Itis believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy ofthis novel biometric authentication model which shows up to 93.75% recognition accuracy.
Keywords: biometric authentication, hand gesture, hand sign recognition, machine learning
Published: 28.06.2017; Views: 746; Downloads: 367
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