Pub Date : 2021-01-01DOI: 10.5220/0010546101230131
Yassir Alharbi, Daniel Arribas-Bel, F. Coenen
A framework for UN Sustainability for Development Goal (SDG) attainment prediction is presented, the SDG Track, Trace & Forecast (SDG-TTF) framework. Unlike previous SDG attainment frameworks, SDGTTF takes into account the potential for causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity), and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. Six alternatives mechanisms are considered. The identified relationships are used to build multivariate time series prediction models which feed into a bottom-up SDG prediction taxonomy, which in turn is used to make SDG attainment predictions. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-TTF framework is able to produce better predictions than alternative models which do not take into consideration the potential for intra and intercausal relationships.
{"title":"Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis","authors":"Yassir Alharbi, Daniel Arribas-Bel, F. Coenen","doi":"10.5220/0010546101230131","DOIUrl":"https://doi.org/10.5220/0010546101230131","url":null,"abstract":"A framework for UN Sustainability for Development Goal (SDG) attainment prediction is presented, the SDG Track, Trace & Forecast (SDG-TTF) framework. Unlike previous SDG attainment frameworks, SDGTTF takes into account the potential for causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity), and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. Six alternatives mechanisms are considered. The identified relationships are used to build multivariate time series prediction models which feed into a bottom-up SDG prediction taxonomy, which in turn is used to make SDG attainment predictions. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-TTF framework is able to produce better predictions than alternative models which do not take into consideration the potential for intra and intercausal relationships.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"46 1","pages":"123-131"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82355683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009799100230029
A. Panahi, R. A. Moghadam, K. Madani
Eye diseases such as glaucoma, if undiagnosed in time, can have irreversible detrimental effects, which can lead to blindness. Early detection of this disease by screening programs and subsequent treatment can prevent blindness. Deep learning architectures have many applications in medicine, especially in medical image processing, that provides intelligent tools for the prevention and treatment of diseases. Optic disk segmentation is one of the ways to diagnose eye disease. This paper presents a new approach based on deep learning, which is accurate and fast in optic disc segmentation. By Comparison proposed method with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, the proposed algorithm is much faster, which can segment the optic disc in 0.008 second with outstanding performance concerning IOU and DICE scores. Therefore, this method can be used in ophthalmology clinics to segment the optic disc in retina images and videos as online medical assistive tool.
{"title":"Deep Learning Residual-like Convolutional Neural Networks for Optic Disc Segmentation in Medical Retinal Images","authors":"A. Panahi, R. A. Moghadam, K. Madani","doi":"10.5220/0009799100230029","DOIUrl":"https://doi.org/10.5220/0009799100230029","url":null,"abstract":"Eye diseases such as glaucoma, if undiagnosed in time, can have irreversible detrimental effects, which can lead to blindness. Early detection of this disease by screening programs and subsequent treatment can prevent blindness. Deep learning architectures have many applications in medicine, especially in medical image processing, that provides intelligent tools for the prevention and treatment of diseases. Optic disk segmentation is one of the ways to diagnose eye disease. This paper presents a new approach based on deep learning, which is accurate and fast in optic disc segmentation. By Comparison proposed method with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, the proposed algorithm is much faster, which can segment the optic disc in 0.008 second with outstanding performance concerning IOU and DICE scores. Therefore, this method can be used in ophthalmology clinics to segment the optic disc in retina images and videos as online medical assistive tool.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"25 1","pages":"23-29"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91359153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009832200300041
Sheela Raju Kurupathi, Pramod Murthy, D. Stricker
One of the main challenges of human-image generation is generating a person along with pose and clothing details. However, it is still a difficult task due to challenging backgrounds and appearance variance. Recently, various deep learning models like Stacked Hourglass networks, Variational Auto Encoders (VAE), and Generative Adversarial Networks (GANs) have been used to solve this problem. However, still, they do not generalize well to the real-world human-image generation task qualitatively. The main goal is to use the Spectral Normalization (SN) technique for training GAN to synthesize the human-image along with the perfect pose and appearance details of the person. In this paper, we have investigated how Conditional GANs, along with Spectral Normalization (SN), could synthesize the new image of the target person given the image of the person and the target (novel) pose desired. The model uses 2D keypoints to represent human poses. We also use adversarial hinge loss and present an ablation study. The proposed model variants have generated promising results on both the Market-1501 and DeepFashion Datasets. We supported our claims by benchmarking the proposed model with recent state-of-the-art models. Finally, we show how the Spectral Normalization (SN) technique influences the process of human-image synthesis.
{"title":"Generation of Human Images with Clothing using Advanced Conditional Generative Adversarial Networks","authors":"Sheela Raju Kurupathi, Pramod Murthy, D. Stricker","doi":"10.5220/0009832200300041","DOIUrl":"https://doi.org/10.5220/0009832200300041","url":null,"abstract":"One of the main challenges of human-image generation is generating a person along with pose and clothing details. However, it is still a difficult task due to challenging backgrounds and appearance variance. Recently, various deep learning models like Stacked Hourglass networks, Variational Auto Encoders (VAE), and Generative Adversarial Networks (GANs) have been used to solve this problem. However, still, they do not generalize well to the real-world human-image generation task qualitatively. The main goal is to use the Spectral Normalization (SN) technique for training GAN to synthesize the human-image along with the perfect pose and appearance details of the person. In this paper, we have investigated how Conditional GANs, along with Spectral Normalization (SN), could synthesize the new image of the target person given the image of the person and the target (novel) pose desired. The model uses 2D keypoints to represent human poses. We also use adversarial hinge loss and present an ablation study. The proposed model variants have generated promising results on both the Market-1501 and DeepFashion Datasets. We supported our claims by benchmarking the proposed model with recent state-of-the-art models. Finally, we show how the Spectral Normalization (SN) technique influences the process of human-image synthesis.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"40 1","pages":"30-41"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90856551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009834600760083
B. Ferreira, B. Lima, Tiago F. Vieira
: Even though Deep Learning models are presenting increasing popularity in a variety of scenarios, there are many demands to which they can be specifically tuned to. We present a real-time, embedded system capable of performing the visual inspection of Collective Protection Equipment conditions such as fire extinguishers (presence of rust or disconnected hose), emergency lamp (disconnected energy cable) and horizontal and vertical signalization, among others. This demand was raised by a glass-manufacturing company which provides devices for optical-fiber solutions. To tackle this specific necessity, we collected and annotated a database with hundreds of in-factory images and assessed three different Deep Learning models aiming at evaluating the trade-off between performance and processing time. A real-world application was developed with potential to reduce time and costs of periodic inspections of the company’s security installations.
{"title":"Visual Inspection of Collective Protection Equipment Conditions with Mobile Deep Learning Models","authors":"B. Ferreira, B. Lima, Tiago F. Vieira","doi":"10.5220/0009834600760083","DOIUrl":"https://doi.org/10.5220/0009834600760083","url":null,"abstract":": Even though Deep Learning models are presenting increasing popularity in a variety of scenarios, there are many demands to which they can be specifically tuned to. We present a real-time, embedded system capable of performing the visual inspection of Collective Protection Equipment conditions such as fire extinguishers (presence of rust or disconnected hose), emergency lamp (disconnected energy cable) and horizontal and vertical signalization, among others. This demand was raised by a glass-manufacturing company which provides devices for optical-fiber solutions. To tackle this specific necessity, we collected and annotated a database with hundreds of in-factory images and assessed three different Deep Learning models aiming at evaluating the trade-off between performance and processing time. A real-world application was developed with potential to reduce time and costs of periodic inspections of the company’s security installations.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"7 1","pages":"76-83"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83977092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009970501110118
Boriharn Kumnunt, O. Sornil
: Depression problems can severely affect not only personal health, but also society. There is evidence that shows people who suffer from depression problems tend to express their feelings and seek help via online posts on online platforms. This study is conducted to apply Natural Language Processing (NLP) with messages associated with depression problems. Feature extractions, machine learning, and neural network models are applied to carry out the detection. The CNN-LSTM model, a unified model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), is used sequentially and in parallel as branches to compare the outcomes with baseline models. In addition, different types of activation functions are applied in the CNN layer to compare the results. In this study, the CNNLSTM models show improvement over the classical machine learning method. However, there is a slight improvement among the CNN-LSTM models. The three-branch CNN-LSTM model with the Rectified Linear Unit (ReLU) activation function is capable of achieving the F1-score of 83.1%.
{"title":"Detection of Depression in Thai Social Media Messages using Deep Learning","authors":"Boriharn Kumnunt, O. Sornil","doi":"10.5220/0009970501110118","DOIUrl":"https://doi.org/10.5220/0009970501110118","url":null,"abstract":": Depression problems can severely affect not only personal health, but also society. There is evidence that shows people who suffer from depression problems tend to express their feelings and seek help via online posts on online platforms. This study is conducted to apply Natural Language Processing (NLP) with messages associated with depression problems. Feature extractions, machine learning, and neural network models are applied to carry out the detection. The CNN-LSTM model, a unified model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), is used sequentially and in parallel as branches to compare the outcomes with baseline models. In addition, different types of activation functions are applied in the CNN layer to compare the results. In this study, the CNNLSTM models show improvement over the classical machine learning method. However, there is a slight improvement among the CNN-LSTM models. The three-branch CNN-LSTM model with the Rectified Linear Unit (ReLU) activation function is capable of achieving the F1-score of 83.1%.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"307 1","pages":"111-118"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77447725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009874400840088
Constantin Rieder, M. Germann, Samuel Mezger, K. Scherer
: In many intelligent technical assistance systems (especially diagnostics), the sound classification is a significant and useful input for intelligent diagnostics. A high performance classification of the heterogeneous sounds of any mechanical components can support the diagnostic experts with a lot of information. Classical pattern recognition methods fail because of the complex features and the heterogeneous state noise. Because of no explicit human knowledge about the characteristic representation of the classes, classical feature generation is impossible. A new approach by generation of a concept for neural networks and realization by especially convolutional networks shows the power of technical sound classification methods. After the concept finding a parametrized network model is devised and realized. First results show the power of the RNNs and CNNs. Dependent on the parametrized configuration of the net architecture and the training sets an enhancement of the sound event classification is possible.
{"title":"Technical Sound Event Classification Applying Recurrent and Convolutional Neural Networks","authors":"Constantin Rieder, M. Germann, Samuel Mezger, K. Scherer","doi":"10.5220/0009874400840088","DOIUrl":"https://doi.org/10.5220/0009874400840088","url":null,"abstract":": In many intelligent technical assistance systems (especially diagnostics), the sound classification is a significant and useful input for intelligent diagnostics. A high performance classification of the heterogeneous sounds of any mechanical components can support the diagnostic experts with a lot of information. Classical pattern recognition methods fail because of the complex features and the heterogeneous state noise. Because of no explicit human knowledge about the characteristic representation of the classes, classical feature generation is impossible. A new approach by generation of a concept for neural networks and realization by especially convolutional networks shows the power of technical sound classification methods. After the concept finding a parametrized network model is devised and realized. First results show the power of the RNNs and CNNs. Dependent on the parametrized configuration of the net architecture and the training sets an enhancement of the sound event classification is possible.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"49 1","pages":"84-88"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72709939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009776600130022
Jason Uhlemann, Oisín Cawley, T. Kakouli-Duarte
: Nematodes are microscopic, worm-like organisms with applications in monitoring the environment for potential ecosystem damage or recovery. Nematodes are an extremely abundant and diverse organism, with millions of different species estimated to exist. This trait leads to the task of identifying nematodes, at a species level, being complicated and time-consuming. Their morphological identification process is fundamentally one of pattern matching, using sketches in a standard taxonomic key as a comparison to the nematode image under a microscope. As Deep Learning has shown vast improvements, in particular, for image classification, we explore the effectiveness of Nematode Identification using Convolutional Neural Networks. We also seek to discover the optimal training process and hyper-parameters for our specific context.
{"title":"Nematode Identification using Artificial Neural Networks","authors":"Jason Uhlemann, Oisín Cawley, T. Kakouli-Duarte","doi":"10.5220/0009776600130022","DOIUrl":"https://doi.org/10.5220/0009776600130022","url":null,"abstract":": Nematodes are microscopic, worm-like organisms with applications in monitoring the environment for potential ecosystem damage or recovery. Nematodes are an extremely abundant and diverse organism, with millions of different species estimated to exist. This trait leads to the task of identifying nematodes, at a species level, being complicated and time-consuming. Their morphological identification process is fundamentally one of pattern matching, using sketches in a standard taxonomic key as a comparison to the nematode image under a microscope. As Deep Learning has shown vast improvements, in particular, for image classification, we explore the effectiveness of Nematode Identification using Convolutional Neural Networks. We also seek to discover the optimal training process and hyper-parameters for our specific context.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"22 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90071422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009826700680075
N. Ayoub, Peter Schneider-Kamp
The inspection of power line components is periodically conducted by specialized companies to identify possible faults and assess the state of the critical infrastructure. UAV-systems represent an emerging technological alternative in this field, with the promise of safer, more efficient, and less costly inspections. In the Drones4Energy project, we work toward a vision-based beyond-visual-line-of-sight (BVLOS) power line inspection architecture for automatically and autonomously detecting components and faults in real-time on board of the UAV. In this paper, we present the first step towards the vision system of this architecture. We train Deep Neural Networks (DNNs) and tune them for reliability under different conditions such as variations in camera used, lighting, angles, and background. For the purpose of real-time on-board implementation of the architecture, experimental evaluations and comparisons are performed on different hardware such as Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The use of such Single Board Devices (SBDs) is an integral part of the design of the proposed power line inspection architecture. Our experimental results demonstrate that the proposed approach can be effective and efficient for fully-automatic real-time on-board visual power line inspection.
{"title":"Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection","authors":"N. Ayoub, Peter Schneider-Kamp","doi":"10.5220/0009826700680075","DOIUrl":"https://doi.org/10.5220/0009826700680075","url":null,"abstract":"The inspection of power line components is periodically conducted by specialized companies to identify possible faults and assess the state of the critical infrastructure. UAV-systems represent an emerging technological alternative in this field, with the promise of safer, more efficient, and less costly inspections. In the Drones4Energy project, we work toward a vision-based beyond-visual-line-of-sight (BVLOS) power line inspection architecture for automatically and autonomously detecting components and faults in real-time on board of the UAV. In this paper, we present the first step towards the vision system of this architecture. We train Deep Neural Networks (DNNs) and tune them for reliability under different conditions such as variations in camera used, lighting, angles, and background. For the purpose of real-time on-board implementation of the architecture, experimental evaluations and comparisons are performed on different hardware such as Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The use of such Single Board Devices (SBDs) is an integral part of the design of the proposed power line inspection architecture. Our experimental results demonstrate that the proposed approach can be effective and efficient for fully-automatic real-time on-board visual power line inspection.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"115 1","pages":"68-75"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88080736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5220/0009638100530058
H. N. Shirvan, R. A. Moghadam, K. Madani
: Deep learning architectures have been proposed in some neural networks like convolutional neural networks (CNN), recurrent neural networks and deep belief neural networks. Among them, CNNs have been applied in image processing tasks frequently. An important section in intelligent image processing is medical image processing which provides intelligent tools and software for medical applications. Analysis of blood vessels in retinal images would help the physicians to detect some retina diseases like glaucoma or even diabetes. In this paper a new neural network structure is proposed which can process the retinal images and detect vessels apart from retinal background. This neural network consists of convolutional layers, concatenate layers and transpose convolutional layers. The results for DRIVE dataset show acceptable performance regarding to accuracy, recall and F-measure criteria.
{"title":"Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks","authors":"H. N. Shirvan, R. A. Moghadam, K. Madani","doi":"10.5220/0009638100530058","DOIUrl":"https://doi.org/10.5220/0009638100530058","url":null,"abstract":": Deep learning architectures have been proposed in some neural networks like convolutional neural networks (CNN), recurrent neural networks and deep belief neural networks. Among them, CNNs have been applied in image processing tasks frequently. An important section in intelligent image processing is medical image processing which provides intelligent tools and software for medical applications. Analysis of blood vessels in retinal images would help the physicians to detect some retina diseases like glaucoma or even diabetes. In this paper a new neural network structure is proposed which can process the retinal images and detect vessels apart from retinal background. This neural network consists of convolutional layers, concatenate layers and transpose convolutional layers. The results for DRIVE dataset show acceptable performance regarding to accuracy, recall and F-measure criteria.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"20 1","pages":"53-58"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86254713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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