The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

Preoperative breast volume estimation is very important for the success of the breast surgery. In the present study, two different breast volume determination methods, Cavalieri principle and 3D reconstruction were compared. Consecutive sections were taken in slice thickness of 5 mm. Every 2nd breast section in a set of consecutive sections was selected. We marked breast tissue with blue line on each selected section, and so prepared CT scans used for breast volume estimation. The volumes of the 60 breasts were estimated using the Cavalieri principle and 3D reconstruction. The mean breast volume value was established to be 467.79 ± 188.90 cm(3) with Cavalieri method and 465.91 ± 191.41 cm(3) with 3D reconstruction. The mean CE for the estimates in this study was calculated as 0.25%. Skin-sparing volume was about 91.64% of the whole breast volume. Both methods are very accurate and have a strong linear association. Our results suggest that the calculation of breast volume or its part in vivo from systematic series of CT scans using the Cavalieri principle or 3D breast reconstruction is accurate enough to have a significant clinical benefit in planning reconstructive breast surgery. These methods can help the surgeon guide the choice of the most appropriate implant or/and flap preoperatively.

Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

We introduce the term “prolonged emergency remote teaching” to accentuate the temporal aspect of the current educational crisis. In-depth lived experiences of students and teaching staff in one engineering education institution were examined, in order to depict what being involved in an exclusively online communication feels like from the personal perspective, thus examining e-learning sustainability during the COVID-19 pandemic. Interviews with samples of students and teaching staff were conducted, and then psychological phenomenology was employed to produce rich descriptions of their personal experiences and impressions. Both the students and the faculty employees observed e-learning during the Fall semester of 2020 as challenging and artificial. Although trying to function as everything is in order, as the semester unfolded, motivation deteriorated, communication turned more and more superficial, and limitations became more prominent. For both sides, although there were some conveniences, the educational process yielded sub-optimal results and was hard to sustain; intellectual and social capital of both sides appeared to be underutilized. We present conclusions and recommendations, suggesting that modern technologies should help both sides to adapt to the new reality, instead of feeling stuck in an online “limbo”.