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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Reihaneh Hassanzadeh Anees Abrol Hamid Reza Hassanzadeh Vince D. Calhoun School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA Courtesy Faculty Appointment College of Pharmacy University of Florida

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and their ability to preserve diagnostic patterns remain relatively unexplored. Hence, in this study, we investigated the use of a generative adversarial network (GAN) in the context of Alzheimer’s disease (AD) to generate functional network connectivity (FNC) and T1-weighted structural magnetic resonance imaging data from each other. We employed a cycle-GAN to synthesize data in an unpaired data transition and enhanced the transition by integrating weak supervision in cases where paired data were available. Our findings revealed that our model could offer remarkable capability, achieving a structural similarity index measure (SSIM) of 0.89 ± 0.003 for T1s and a correlation of 0.71 ± 0.004 for FNCs. Moreover, our qualitative analysis revealed similar patterns between generated and actual data when comparing AD to cognitively normal (CN) individuals. In particular, we observed significantly increased functional connectivity in cerebellar-sensory motor and cerebellar-visual networks and reduced connectivity in cerebellar-subcortical, auditory-sensory motor, sensory motor-visual, and cerebellar-cognitive control networks. Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer’s patients.

关键词： Neuroimaging Biological system modeling Magnetic resonance imaging Generative adversarial networks Motors Particle measurements data models Indexes Alzheimer's disease Engineering in medicine and biology

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Improvement of Flexibility in the Integrated Energy Systems 15

Improvement of Flexibility in the Integrated Energy Systems

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2021

作者： Bayasgalan, Zagdkhorol Bayasgalan, Tsetsaee Hardt, Wolfram Reva, Ivan L. Power Engineering School Mongolian University of Science and Technology Dept. of Electrotechnics Ulaanbaatar Mongolia School of Business Administration and Humanities Mongolian University of Science and Technology Dept. of Technology Management Ulaanbaatar Mongolia Chemnitz University of Technology Computer Engineering Professorship Faculty of Computer Science Chemnitz Germany Novosibirsk State Technical University Dept. of Data Security Novosibirsk Russia

ISBN: (纸本)9781665434089

This paper presents possibilities to improve flexibility for integrated energy systems using optimization techniques in a capacity market. Long-Term planning of the energy system is considered. Meeting the rapidly growing demand for electricity requires a complete integrated power system solution that must support consumers, stakeholders, and regional operations. The rapid change in the global economy is creating an energy transition towards the strategic future of power systems and energy markets-a new era of flexible grids, or the era of energy flexibility. The proposed capacity market model improves the competitiveness of the energy market system in accordance with the assessment and flexibility of electricity security. It also presents recommendations for evaluating the dimensions of the integrated energy system. All participants in the energy market can accept it as a useful tool to support decision-making and increase the sustainability of the energy system. The smart grids provide the reliability of power supply in an increasingly complex operating environment, achieved through observability, digitalization, and an increase in the number of measurements. Power supply and grid companies remain the main drivers of change. © 2021 IEEE.

关键词： Power markets

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Z-Number-Valued Rule-Based Classification System

SSRN

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SSRN 2022年

作者： Li, Yangxue Herrera-Viedma, Enrique Pérez, Ignacio Javier Barragán-Guzmán, Mónica Morente-Molinera, Juan Antonio Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

The fuzzy rule-based classification system (FRBCS) is a popular tool in classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to its high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Using the second fuzzy number to adjust the first fuzzy number to fit the unevenly distributed data. Then we create a Z-number-valued if-then rule by extending the fuzzy if-then rule. In addition, a Z-number-valued rule-based classification system (ZRBCS) is developed, including two main processes: rules generation and new pattern classification. Compared to the classic fuzzy rule-based system, the proposed system can cover more information that can improve classification results. We evaluated the proposed method on 20 datasets to compare its learning and predictive abilities against the classic fuzzy rule-based system. The results demonstrate the Z-number-valued rule-based classification system is superior to the fuzzy rule-based classification system. © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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AWT - Clustering Meteorological Time Series Using an Aggregated Wavelet Tree

AWT - Clustering Meteorological Time Series Using an Aggrega...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Christina Pacher Irene Schicker Rosmarie DeWit Kateřina Hlaváčková-Schindler Claudia Plant Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Zentralanstalt für Meteorologie und Geodynamik Vienna Austria

ISBN: (纸本)9781665473316

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.

关键词： Temperature distribution Analytical models Time series analysis Urban areas Clustering algorithms Wavelet analysis data models

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Analysis of Prediction data for the Third Wave of COVID-19 in Bogor Regency

Analysis of Prediction Data for the Third Wave of COVID-19 i...

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2021 International Conference on Artificial Intelligence and Big data Analytics, ICAIBDA 2021

作者： Labib, Alfian Fadhil Maulana, Difa Bagasputra Mustopa, Sina Yulita, Intan Nurma Ardisasmita, Mulya Nurmansyah Agustian, Dwi Universitas Padjadjaran Department of Computer Science Indonesia Universitas Padjadjaran Research Center for Artificial Intelligence and Big Data Sumedang Indonesia Universitas Padjadjaran Faculty of Medicine Department of Public Health Sumedang Indonesia

ISBN: (纸本)9781665408905

The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.

关键词： Forecasting

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AWT - Clustering Meteorological Time Series Using an Aggregated Wavelet Tree

arXiv

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arXiv 2022年

作者： Pacher, Christina Schicker, Irene DeWit, Rosmarie Hlaváčková-Schindler, Kateřina Plant, Claudia Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Zentralanstalt für Meteorologie und Geodynamik Vienna Austria

关键词： Anomaly detection

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GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning

arXiv

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arXiv 2022年

作者： Kamar, Doğay Üre, Nazım Kemal Ünal, Gözde Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Artificial Intelligence and Data Science Research Center Istanbul Technical University Istanbul Turkey

In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approaches do not work well in environments with sparse or no rewards. We propose Generative Adversarial Network-based Intrinsic Reward Module that learns the distribution of the observed states and sends an intrinsic reward that is computed as high for states that are out of distribution, in order to lead agent to unexplored states. We evaluate our approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge for a sparse reward setting and show that our approach is indeed capable of exploring efficiently. We discuss a few weaknesses and conclude by discussing future *** Codes 68T20 (Primary) 68T05, 68T07 (Secondary) © 2022, CC BY.

关键词： Generative adversarial networks

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

Attention Swin U-Net: Cross-Contextual Attention Mechanism f...

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IEEE International Symposium on Biomedical Imaging

作者： Ehsan Khodapanah Aghdam Reza Azad Maral Zarvani Dorit Merhof Department of Electrical Engineering Shahid Beheshti University Tehran Iran Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Computer Engineering Alzahra University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Bayern Germany

Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range dependency, which is crucial for the segmentation task in medical images. To address this limitation, recently a Transformer based U-Net architecture that replaces the CNN blocks with the Swin Transformer module has been proposed to capture both local and global representation. In this paper, we propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. In our design, we seek to enhance the feature re-usability of the network by carefully designing the skip connection path. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism. By performing a comprehensive ablation study on several skin lesion segmentation datasets, we demonstrate the effectiveness of our proposed attention mechanism.

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Mahmood Fazlali Amirhossein Alihoseini Ebrahim Zarei Myasar Tabani Seyedali Pourmoafi Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfiled UK Dept. of data and Computer Sciensce Faculty of Mathematical sciences Shahid Beheshti University Tehran Iran Dep. of Computer Science Khansar Campus University Of Isfahan Isfahan Iran

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm.

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A Survey on Machine Learning Solutions for Graph Pattern Extraction

arXiv

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arXiv 2022年

作者： Yow, Kai Siong Liao, Ningyi Luo, Siqiang Cheng, Reynold Ma, Chenhao Han, Xiaolin School of Computer Science and Engineering College of Engineering Nanyang Technological University Singapore The Department of Mathematics and Statistics Faculty of Science Universiti Putra Malaysia UPM Serdang Selangor43400 Malaysia Department of Computer Science University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen China

A subgraph is constructed by using a subset of vertices and edges of a given graph. There exist many graph properties that are hereditary for subgraphs. Hence, researchers from different communities have paid a great deal of attention in studying numerous subgraph problems, on top of the ordinary graph problems. Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph. Due to the complex structures of certain types of graphs and to improve overall performances of the existing frameworks, machine learning techniques have recently been employed in dealing with various subgraph problems. In this article, we present a comprehensive review on five well known subgraph problems that have been tackled by using machine learning methods. They are subgraph isomorphism (both counting and matching), maximum common subgraph, community detection and community search problems. We provide an outline of each proposed method, and examine its designs and performances. We also explore non-learning-based algorithms for each problem and a brief discussion is given. We then suggest some promising research directions in this area, hoping that relevant subgraph problems can be tackled by using a similar strategy. Since there is a huge growth in employing machine learning techniques in recent years, we believe that this survey will serve as a good reference point to relevant research *** Codes 05C90, 68M07, 68R10 © 2022, CC BY.

关键词： Population dynamics

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