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PurposeDecision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.Design/methodology/approachThe present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.FindingsThe result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.Originality/valueThe study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

The Norwegian national curricula for primary and secondary education are currently undergoing reforms, which will be implemented from 2020. The underlying documents regarding the reform are studied in this article, and conclude that learners should be provided with beneficial conditions to develop values and knowledge to manage their lives well. An essential notion is 'deep learning' whose implication seems to be acquisition of more in-depth knowledge and understanding of subject areas. The present article argues that learning is about making connections, whether it be noticing connections between isolated subject areas, or, perhaps, between theoretical concepts and practical tasks. Further, the article argues that interdisciplinary approaches extend knowledge as they typically work across subject areas and support learners in discovering disciplinary connections. Claims are made that interdisciplinary strategies have the capacities to facilitate various learning styles, motivation and variation. Such facilitations are required in education that is democratic and which targets all learners. Hence, the article argues that interdisciplinarity and deep learning are concepts that work together, and in which critical thinking and creativity are core elements. The argumentation is based on the four official documents NOU 2014:7, NOU 2015:8, Report to the Storting 28, The Standing Committee on Education, Research and Church Affairs' recommendation to the Storting 19 S, as well as, data findings discussed in a master study which discusses teachers' perceptions of interdisciplinarity and learning.

The Norwegian national curricula for primary and secondary education are currently undergoing reforms, which will be implemented from 2020. The underlying documents regarding the reform are studied in this article, and conclude that learners should be provided with beneficial conditions to develop values and knowledge to manage their lives well. An essential notion is 'deep learning' whose implication seems to be acquisition of more in-depth knowledge and understanding of subject areas. The present article argues that learning is about making connections, whether it be noticing connections between isolated subject areas, or, perhaps, between theoretical concepts and practical tasks. Further, the article argues that interdisciplinary approaches extend knowledge as they typically work across subject areas and support learners in discovering disciplinary connections. Claims are made that interdisciplinary strategies have the capacities to facilitate various learning styles, motivation and variation. Such facilitations are required in education that is democratic and which targets all learners. Hence, the article argues that interdisciplinarity and deep learning are concepts that work together, and in which critical thinking and creativity are core elements. The argumentation is based on the four official documents NOU 2014:7, NOU 2015:8, Report to the Storting 28, The Standing Committee on Education, Research and Church Affairs' recommendation to the Storting 19 S, as well as, data findings discussed in a master study which discusses teachers' perceptions of interdisciplinarity and learning.

El glaucoma es una de las principales causas de ceguera. Cambia la morfología del nervio óptico. La copa es la región central del disco óptico. El CDR (relación del diámetro de la copa y el disco) es un indicador del estado de la enfermedad. Un CDR normal se encuentra entre 0.3 y 0.4. A mayor CDR Glaucoma más avanzado. En este trabajo se pretende demostrar la viabilidad de los algoritmos en los que la forma del disco y la copa son aproximadamente círculos. En estos casos el número de parámetros es mucho menor y, al menos, en nuestras pruebas iniciales la aproximación es más realista que con las formas originales para su implementación en aprendizaje profundo. ; Glaucoma is one of the main causes of blindness. It changes the morphology of the optic nerve. The cup is the central region of the optical disc. The CDR (ratio of the diameter of the cup and disc) is an indicator of the state of the disease. A normal CDR is between 0.3 and 0.4. A higher CDR more advanced glaucoma. In this work we try to demonstrate the viability of algorithms in which the shape of the disc and the cup are approximately circles. In these cases, the number of parameters is much lower and, at least, in our initial tests the approach is sometimes more realistic than with the original forms for a Deep learning implementation. ; NPP project funded by SAIT (2015-2018) ; Spanish government grant European Regional Development Fund COFNET TEC2016-77785-P

Hyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data augmentation methods to enhance the dataset photographs, and balance the dataset (which was quite uneven), to avoid overfitting. By using Transfer learning we save training time by using a previously learned deep CNN and transfer learning weights. In this research, EfficientNetB1 is compared against Xception, InceptionV3, MobileNet, and ResNet50 as a deep transfer learning model. The proposed model's accuracy, precision, recall, and f1-score are all examined. The EfficientNetB1 model outperforms all others in terms of overall testing accuracy (86.1%), sensitivity (87.24%), precision (97.6%), and F1-Score (89.32 percent). This approach might help physicians diagnose Diabetic Retinopathy earlier.

Intro -- Deep Learning for Sustainable Agriculture -- Copyright -- Contents -- Contributors -- Chapter 1: Smart agriculture: Technological advancements on agriculture-A systematical review -- 1. Introduction -- 2. Methodology -- 3. Role of image processing in agriculture -- 3.1. Plant disease identification -- 3.2. Fruit sorting and classification -- 3.3. Plant species identification -- 3.4. Precision farming -- 3.5. Fruit quality analysis -- 3.6. Crop and land assessment -- 3.7. Weed recognition -- 4. Role of Machine Learning in Agriculture -- 4.1. Yield prediction -- 4.2. Disease detection -- 4.3. Weed recognition -- 4.4. Crop quality -- 4.5. Species recognition -- 4.6. Soil management -- 5. Role of deep learning in agriculture -- 5.1. Leaf disease detection -- 5.2. Plant disease detection -- 5.3. Land cover classification -- 5.4. Crop type classification -- 5.5. Plant recognition -- 5.6. Segmentation of root and soil -- 5.7. Crop yield estimation -- 5.8. Fruit counting -- 5.9. Obstacle detection -- 5.10. Identification of weeds -- 5.11. Prediction of soil moisture -- 5.12. Cattle race classification -- 6. Role of IoT in agriculture -- 6.1. Climate condition monitoring -- 6.2. Crop yield -- 6.3. Soil patter -- 6.4. Pest and crop disease monitoring -- 6.5. Irrigation monitoring system -- 6.6. Optimum time for plant and harvesting -- 6.7. Tracking and tracing -- 6.8. Farm management system -- 6.9. Agricultural drone -- 7. Role of wireless sensor networks in agriculture -- 7.1. Irrigation management -- 7.2. Soil moisture prediction -- 7.3. Precision farming -- 7.4. Climate condition monitoring -- 8. Role of data mining in agriculture -- 8.1. Irrigation management -- 8.2. Prediction and detection of plant diseases -- 8.3. Pest monitoring -- 8.4. Optimum management of inputs (fertilizer and pesticides) -- 8.5. Crop yield prediction.

This presentation was aimed to explore the growing opportunities to merge two booming fields: deep learning and autonomous vehicles, from a technical point of view. It addressed some Intelligent Systems Laboratory (Universidad Carlos III de Madrid, Spain) developments in this line of research, such as an obstacle detection framework using convolutional neural networks (CNNs). Furthermore, it presented a large number of challenging driving-related tasks that were expected to become tractable through this new approach, with the focus on the strong requirements posed by the upcoming self-driving systems. This presentation was part of the 6th LSI Ph.D. Meeting, which was held on 14 Jun 2016 at the Escuela Politécnica Superior of the Universidad Carlos III de Madrid. It was published on Zenodo as an exercise within the THOR Bootcamp on Open Data, organized on 16 Nov 2016. ; Research supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R), and the Comunidad de Madrid through SEGVAUTO-TRIES (S2013/MIT-2713). The Tesla K40 used for this research was donated by the NVIDIA Corporation.