No abstract

No abstract

No abstract

Abstract-This paper is inspired by the project proposal ID 7807232014 submitted for the EU Horizon 2020 topic ICT-11-2017 in April 25, 2017. It aims at applying state-of-the-art ICT technologies, systems and functions such as Cloud/Fog and IoT to enable food supply chain. A new approach will lead to trusted cooperative applications and services within the agro-food chains. Blockchain technologies will enhance the transparency, information flow and management capacity allowing better interactions of farmers with other part of supply chain, especially the consumer. Our research will provide better performing value chains by proposing new food-on-demand business model, based on new Quality of Experience (QoE) food metrics, bridging the gap between subjective experience and objective matrics based on quality standards. Finally, we provided an awareness qustionaire for fresh food products (FFP) and survay for a group of 30 students from the University of Skopje. This study showed that the majority of students are aware and focused just on few common FFP aspects without deeper knowledge of FFP quality.

Recently, deep learning is used with convolutional Neural Networks for image classification and figure recognition. In our research, we used Computed Tomography (CT) scans to train a double convolutional Deep Neural Network (CDNN) and a regular CDNN. These topologies were tested against lung cancer images to determine the Tx cancer stage in which these topologies can detect the possibility of lung cancer. The first step was to pre-classify the CT images from the initial dataset so that the training of the CDNN could be focused. Next, we built the double Convolution deep Neural Network with max pooling to perform a more thorough search. Finally, we used CT scans of different Tx cancer stages of lung cancer to determine the Tx stage in which the CDNN would detect possibility of lung cancer. We tested the regular CDNN against our double CDNN. Using this algorithm, doctors will have additional help in early lung cancer detection and early treatment. After extensive training with 100 epochs, we obtained the highest accuracy of 0.9962, whereas the regular CDNN obtained only 0.876 accuracy.