In medical diagnosis, high resolution (HR) images are indispensable for giving more correct decision. However, in order to obtain high resolution medical images, it is necessary to impose long-time, hence it leads to heavy burden to the patient. Therefore Super Resolution technique, which can generate high resolution images from low resolution images using machine learning techniques, attracts hot attention recently. Therein, face hallucination is one of widely used super-resolution methods in image restoration field. However, the conventional face hallucination generally cannot recover high frequency information. Therefore, this paper integrates a further learning step into the conventional method, and proposes a 2-step image hallucination, which is prospected to recover most high frequency information lost in the available low-resolution input. Furthermore, we apply the proposed strategy to generate the high-resolution Z-direction data using self-similarity among different direction for 3D medical MR images. Experimental results show that the proposed strategy can reconstruct promising HR coronal or sagittal plane by using available LR and HR data pairs in axial plane. Keywords-image restoration; super-resolution; medical volumetric image
{"title":"Two-Step Image Hallucination and Its Application to 3D Medical Image Super-resolution","authors":"Y. Kondo, X. Han, X. Wei, Yenwei Chen","doi":"10.2991/AIIE-15.2015.91","DOIUrl":"https://doi.org/10.2991/AIIE-15.2015.91","url":null,"abstract":"In medical diagnosis, high resolution (HR) images are indispensable for giving more correct decision. However, in order to obtain high resolution medical images, it is necessary to impose long-time, hence it leads to heavy burden to the patient. Therefore Super Resolution technique, which can generate high resolution images from low resolution images using machine learning techniques, attracts hot attention recently. Therein, face hallucination is one of widely used super-resolution methods in image restoration field. However, the conventional face hallucination generally cannot recover high frequency information. Therefore, this paper integrates a further learning step into the conventional method, and proposes a 2-step image hallucination, which is prospected to recover most high frequency information lost in the available low-resolution input. Furthermore, we apply the proposed strategy to generate the high-resolution Z-direction data using self-similarity among different direction for 3D medical MR images. Experimental results show that the proposed strategy can reconstruct promising HR coronal or sagittal plane by using available LR and HR data pairs in axial plane. Keywords-image restoration; super-resolution; medical volumetric image","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245809","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 : 2015-04-24DOI: 10.2991/AMCCE-15.2015.62
Li Dong, Binpeng Qu, Meng Zhang, Jing Yu
Before data transmission, in order to improve the utilization ratio of transmission channel, there is need to compress the transmitted data. Before data storage, in order to save storage space, there is need to compress the stored data. There are many methods for data compression and decompression, but the existing methods may be not effective for some particular applications. Therefore, for the special applications, special compression and decompression methods should be applied, which means to combine neural network and collaboration cognitive model. When finite multi-dimensional data needs to transmit with long distance or store data for a long time, the method combining neural network and collaborative computer should be applied to implement compression and decompression of finite multi-dimensional data.
{"title":"Application of Data Compression and Decompression Based on Artificial Neural Network and Collaborative Computer Network","authors":"Li Dong, Binpeng Qu, Meng Zhang, Jing Yu","doi":"10.2991/AMCCE-15.2015.62","DOIUrl":"https://doi.org/10.2991/AMCCE-15.2015.62","url":null,"abstract":"Before data transmission, in order to improve the utilization ratio of transmission channel, there is need to compress the transmitted data. Before data storage, in order to save storage space, there is need to compress the stored data. There are many methods for data compression and decompression, but the existing methods may be not effective for some particular applications. Therefore, for the special applications, special compression and decompression methods should be applied, which means to combine neural network and collaboration cognitive model. When finite multi-dimensional data needs to transmit with long distance or store data for a long time, the method combining neural network and collaborative computer should be applied to implement compression and decompression of finite multi-dimensional data.","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469841","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.033
Mouna Dhmiri, Yassine Manai, Tahar Ezzeddine
{"title":"A Novel Approach for Determination of Red Blood Cells Concentration based on Beer Lambert Law","authors":"Mouna Dhmiri, Yassine Manai, Tahar Ezzeddine","doi":"10.2991/aisr.k.220201.033","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.033","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125533412","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.036
Z. Machmuddah, Hayu Wikan Kinasih, E. Yuyetta, Abdul Rohman
{"title":"Stock Prices in Industrialized and Emerging Countries during Covid-19 Pandemic","authors":"Z. Machmuddah, Hayu Wikan Kinasih, E. Yuyetta, Abdul Rohman","doi":"10.2991/aisr.k.220201.036","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.036","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"24 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124106974","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.035
Kamel Abbassi, Kamel khedhiri, T. Ezzedine, A. Cherif
{"title":"Spatio-Temporal Data Model for Wireless Sensor Network","authors":"Kamel Abbassi, Kamel khedhiri, T. Ezzedine, A. Cherif","doi":"10.2991/aisr.k.220201.035","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.035","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116214660","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.037
M. Abdullahi, Sudeshna Chakraborty, P. Kaushik, Ben Slama Sami
{"title":"Detection of Dry and Wet Age-Related Macular Degeneration Using Deep Learning","authors":"M. Abdullahi, Sudeshna Chakraborty, P. Kaushik, Ben Slama Sami","doi":"10.2991/aisr.k.220201.037","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.037","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131103180","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 : 1900-01-01DOI: 10.2991/978-94-6463-174-6
{"title":"Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)","authors":"","doi":"10.2991/978-94-6463-174-6","DOIUrl":"https://doi.org/10.2991/978-94-6463-174-6","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116929949","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.008
Ohoud Almughram, B. Zafar, S. Slama
Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Home Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.
{"title":"Home Energy Management Machine Learning Prediction Algorithms: A Review","authors":"Ohoud Almughram, B. Zafar, S. Slama","doi":"10.2991/aisr.k.220201.008","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.008","url":null,"abstract":"Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Home Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128221292","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.030
Cherifa Nakkach, A. Zrelli, Tahar Ezzeddine
{"title":"Deep Learning Algorithms Enabling Event Detection: A Review","authors":"Cherifa Nakkach, A. Zrelli, Tahar Ezzeddine","doi":"10.2991/aisr.k.220201.030","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.030","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129549020","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 : 1900-01-01DOI: 10.2991/aisr.k.220201.010
S. Suhana, Anwar Mansyur, L. Rachmawati
{"title":"Efforts to Increase Student Academic Achievement Through Knowledge Sharing","authors":"S. Suhana, Anwar Mansyur, L. Rachmawati","doi":"10.2991/aisr.k.220201.010","DOIUrl":"https://doi.org/10.2991/aisr.k.220201.010","url":null,"abstract":"","PeriodicalId":127514,"journal":{"name":"Advances in Intelligent Systems Research","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126533403","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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