Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Technological solutions for older people with Alzheimer's disease(Bentham Science Publishers, 2018-09-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, End-users’ AAL and ELE service scenarios in smart personal environments(2017-11-24) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CoviHealth: Novel approach of a mobile application for nutrition and physical activity management for teenagers(2019-09-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A short review of the environmental impact of automated weed control(2017); ; ; Agricultural food production is in constant struggle to meet the market demands. Weed control is used to increase the per land unit production from agricultural field. The process of weed removal is usually performed manually and is a time-consuming and labor demanding task. Since mechanical removal is a difficult process, the plantations use herbicides to remove unwanted plants. Herbicides are applied in large quantities, thus often have a degenerative effect on the land. Sometimes, they even endanger the health of the workers who apply them and the end users which consume the harvested product. We review the technologies used for automated weed control and its environmental impact, specifically on the pollution reduction. We also review the herbicides reduction reported in implemented and tested approaches for precision agriculture with emphasis on the weed control environmental impact. Based on the reviewed papers, we conclude that automated weed detection can identify unwanted plants with decent accuracy. Consequently, this can facilitate building autonomous spraying systems that can significantly reduce the quantity of applied herbicides by precisely applying the chemicals only on the plants or mechanically removing unwanted plants. We also review the challenges that need to be overcome, such as precise weed plant type detection, speed of the process and some security considerations that arise from the involvement of information and communication technologies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cloud based Data Acquisition and Annotation Architecture for Weed Control(2018-04); ; ; In this paper we present a short evaluation of a cloud based architecture for data acquisition and annotation. We evaluate the implemented system for annotation and give initial results on the ability of the system to produce accurate labels on the data. The used data is consisted of plant field images. The users partially annotate the data and we use segmentation algorithms for enriching the annotation of the images. We compare three different segmentation algorithms used for the annotation. The results show that Grabcut algorithm is better than Watershed and nearest-neighbor approaches, but there is still room for improvement. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th–7th February 2018(2018-04-19); Pfeil, JulianeOn 6th and 7th February 2018 a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The CHARME" Advanced Big Data Training School for Life Sciences": an example of good practices for training on current bioinformatics challenges(2019-02-05) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Skin lesion segmentation with deep learning(IEEE, 2019-07-01); ; Skin lesion segmentation is an important process in skin diagnostics because it improves manual and computeraided diagnostics by focusing the medical personnel on specific parts of the skin. Image segmentation is a common task in computer vision that partitions a digital image into multiple segments, for which deep neural networks have been proven to be reliable. In this paper, we investigate the applicability of deep learning methods for skin lesion segmentation evaluating three architectures: a pre-trained VGG16 encoder combined with SegNet decoder, TernausNet, and DeepLabV3+. The data set consists of images with RGB skin lesions and the ground truth of their segmentation. All the image sizes vary from hundreds to thousands of pixels per dimension. We evaluated the approaches with the Jaccard index and the computational efficiency of the training. The results show that the three deep neural network architectures achieve Jaccard Index scores of above 0.82, while the DeeplabV3+ outperforms the other approaches with a score of 0.876. The results are encouraging and can lead to fully-fledged automated approaches for skin lesion segmentation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scalable Cloud-based ETL for Self-serving Analytics(2019); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Music Generation With Deep Learning Algorithms(2018); ; Computer music generation has application in many areas, including computer aided music composition, on demand music generation for video games, sport events, multi-media experiences, creating music in the style of passed away artists, etc. In this work we describe our approach towards music generation. We trained a deep learning model on a corpus of works of several authors. By priming the model with a snippet of an authors work we used it to create new music in their style. The dataset consists of music for guitar in midi format, containing only 1 part/instrument. We gathered more than 2000 files, of which we used from 5 to 300 per experiment. The data for the deep learning model is represented in piano roll format, a binary matrix where one axis represents the time and the other axis represents midi notes. Two deep learning architectures were evaluated, a 2-layer recurrent neural network of LSTM (Long Short Term Memory) cells and an Encoder-Decoder (Auto-Encoder) architecture for sequence learning, where both the encoder and decoder are built as recurrent layers of LSTM cells. The models were implemented in the Keras deep-learning library. The results were evaluated on a subjective basis, and with the evaluated datasets both architectures produced results of limited quality.