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2021 International Conference on Automatics and Energy, ICAE 2021

作者： Lednov, A.V. Logunova, O.S. Khudyakov, P.Y. Kukhta, Y.B. Department of Informatics and Computer Engineering Nosov Magnitogorsk State Technical University Lenina 38 Magnitogorsk455000 Russia Department of Mechanics and Automation of Technological Processes and Production Non-State Higher Educational Establishment UMMC Technical University Uspensky 3 Verkhnyaya Pyshma624090 Russia

The aim of the study is to scientifically substantiate the need to use the axiomatic approach when choosing and studying the processes of centralized dispatching control of technological parameters for a complex of flotation machines. During the research, the au-thors performed: comparison of a real object and its properties with the classical control axioms of system analysis;substantiated the use of a hierarchical structure of interrelated technological parameters and their application for situational management. Methods of classical control axioms of system analysis were used for the research. The authors formu-lated the criteria for situational management, the concept of visualization in the dispatching system. The results obtained allow us to assert that the implementation of the Ross-Ashby principle significantly increases the controllability of technological processes with a large number of units and measured parameters. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Systems analysis

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Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences

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Computational Biology and Chemistry 2022年第0期98卷 107638-107638页

作者： Sattar, Mohsin Majid, Abdul Kausar, Nabeela Bilal, Muhammad Kashif, Muhammad Biomedical Informatics Research Lab Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan MIS Division Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan Department of Computing and Technology Iqra University Islamabad Pakistan

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches. © 2022 Elsevier Ltd

关键词： Proteins

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Computational systems overview and Random Process with Theoretical analysis 7

Computational systems overview and Random Process with Theor...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Singh, Balbir Jalil, Nasir Abdul Sharma, D.K. Steffi, R. Kumar, Kailash Jebakumar Immanuel, D. Administrative Staff College of India India Sunway University Business School Sunway University Department of Business Analytics Malaysia Jaypee University of Engineering and Technology M.P India Vins Christian Women's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu India Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia Sns College of Engineering Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665405201

There are some convenient statistical properties available in Random dissemination. It is total and additive;it has ample statistics. Its exponential structure allows for 'exact' conditional tests to be constructed. In implementing effective statistical techniques, this paper explores why these properties can be used. A Random Method is a model for a detached subset sequence in which the mean number between subsets is established, but arbitrary is the precise sequence of subsets. A subset's appearance is unrestrained of the previous subset. First of all, the basics of Random dissemination is given. A probabilistic action that models several real-world phenomena is Random action. We present the Random action description and address some information and also some associated disseminations of probabilities. Subset ally, we deliver some new action applications with special emphasis on Random action. All the approaches are explained with realistic examples. © 2021 IEEE.

关键词： Random processes

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How we can use text classification in the Back-Office environment of a bank as ‘business as usual’ solution

How we can use text classification in the Back-Office enviro...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Zsolt Krutilla Attila Kovari Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty Óbuda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary Department of Natural Science Institute of Engineering University of Dunaújváros Dunaújváros Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a ‘business as usual’ (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models.

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Convolutional Neural Networks and YOLOv5 for the Detection of License Plates in Digital Photographic Images

Convolutional Neural Networks and YOLOv5 for the Detection o...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Guillermo Enrique Medina Zegarra Ciro Rodriguez Rodriguez Edgar Lobaton Roger Chipa Sierra Department of Systems and Informatics Engineering Graduate School Universidad Nacional Mayor de San Marcos Lima Peru School of System Engineering and Informatic Universidad Nacional Mayor de San Marcos Lima Peru School of Electrical and Computer Engineering North Caroline State University North Caroline USA School of Economic Engineering and Statistics Universidad Nacional de Ingenierías Lima Peru

In this research, a dataset of two thousand images was obtained, which were taken through a speed camera to vehicles in the city of Lima, Peru, with the purpose of detecting license plates. Therefore, a distribution of this dataset was made: 70% for training (1400 images), 20% for validation (400 images), and 10% for testing (200 images). To visualize and analyze the behavior and convergence of the loss function, a distribution was made using the cross-validation method with the following three folds: 1400, 840, and 280, with two optimization methods (stochastic gradient descent with momentum and Adam), both optimization techniques were used for training from scratch and transfer learning. The results obtained demonstrated that the loss function converges better with the first optimization method. We used precision metric, too.

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FastAGEDs: Fast Approximate Graph Entity Dependency Discovery 24th

FastAGEDs: Fast Approximate Graph Entity Dependency Discover...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Zhou, Guangtong Kwashie, Selasi Zhang, Yidi Bewong, Michael Nofong, Vincent M. Hu, Junwei Cheng, Debo He, Keqing Liu, Shanmei Feng, Zaiwen College of Informatics Huazhong Agricultural University Hubei Wuhan China AI & Cyber Futures Institute Charles Sturt University Bathurst Australia School of Computing Mathematics & Engineering Charles Sturt University Wagga Wagga Australia Department of Computer Science & Engineering University of Mines & Technology Tarkwa Ghana STEM University of South Australia Adelaide Australia School of Computing Science Wuhan University Hubei Wuhan China Hubei Hongshan Laboratory Huazhong Agricultural University Hubei Wuhan China Macro Agricultural Research Institute Huazhong Agricultural University Hubei Wuhan China

ISBN: (纸本)9789819972531

This paper studies the discovery of approximate rules in property graphs. First, we propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) that almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. Second, we present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate GEDs efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FastAGEDs, with three real-world graphs. The results show FastAGEDs is effective and efficient for mining approximate GEDs in noisy and erroneous real-world graphs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： approximate dependency efficient algorithm graph constraints graph entity dependency

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A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention

arXiv

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arXiv 2023年

作者： Wankhede, Payal Das, Manisha Gupta, Deep Radeva, Petia Bakde, Ashwini M. The Department of Electronics and Comm. Engineering Visvesvaraya National Institute of Technology Nagpur40010 India The Department de Mathematics and Informatics Universitat de Barcelona Barcelona08007 Spain The Computer Vision Center Cerdanyola08193 Spain The Department of Radio-Diagnosis All India Institute of Medical Sciences Nagpur441108 India

Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients’ disorders and provide guidance during preoperative and intra-operative procedures. Deep learning (DL) models have proved to achieve end-to-end image fusion with high robust and accurate fusion performance. However, most of the DL-based fusion models perform down-sampling on the input images to minimize the number of learnable parameters and computations. During this process, salient features of the source images become irretrievable leading to the loss of crucial diagnostic edge details and contrast of various brain tissues. In this paper, we propose a new multimodal medical image fusion model is proposed that is based on integrated Laplacian-Gaussian concatenation with attention pooling (LGCA). We prove that our model preserves effectively complementary information and important tissue structures. Extensive experimental results demonstrate a notable improvement in the fusion performance of the proposed method compared to the recently developed state-of-the-art fusion approaches on 4 different image modalities using 6 different statistics metrics. © 2023, CC BY.

关键词： Image fusion

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GTrans: Spatiotemporal autoregressive transformer with graph embeddings for nowcasting extreme events

arXiv

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arXiv 2022年

作者： Feng, Bo Fox, Geoffrey Luddy School Of Informatics Computing And Engineering Indiana University Bloomington BloomingtonIN United States Biocomplexity Institute Initiative And Computer Science Department University Of Virginia CharlottesvilleVA United States

Spatiotemporal time series nowcasting should preserve temporal and spatial dynamics in the sense that generated new sequences from models respect the covariance relationship from history. Conventional feature extractors are built with deep convolutional neural networks (CNN). However, CNN models have limits to image-like applications where data can be formed with high-dimensional arrays. In contrast, applications in social networks, road traffic, physics, and chemical property prediction where data features can be organized with nodes and edges of graphs. Transformer architecture is an emerging method for predictive models, bringing high accuracy and efficiency due to attention mechanism design. This paper proposes a spatiotemporal model, namely GTrans, that transforms data features into graph embeddings and predicts temporal dynamics with a transformer model. According to our experiments, we demonstrate that GTrans can model spatial and temporal dynamics and nowcasts extreme events for datasets. Furthermore, in all the experiments, GTrans can achieve the highest F1and F2scores in binary-class prediction tests than the baseline models. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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DYNAMIC INDEPENDENT COMPONENT EXTRACTION WITH BLENDING MIXING VECTOR: LOWER BOUND ON MEAN INTERFERENCE-TO-SIGNAL RATIO

arXiv

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arXiv 2022年

作者： Čmejla, Jaroslav Koldovský, Zbyněk Kautský, Václav Adali, Tülay Faculty of Mechatronics Informatics Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian. Copyright © 2022, The Authors. All rights reserved.

关键词： Blind source separation

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Demonstration of Low-Complexity D-band Extension of Fiber X-haul for 5G and Beyond Infrastructure

Demonstration of Low-Complexity D-band Extension of Fiber X-...

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European Conference on Antennas and Propagation, EuCAP

作者： Ronis Maximidis Christos Vagionas Sotiris Soukaras Zhongxia Simon He Konstantina Kanta Panagiotis Toumasis Giannis Giannoulis Dimitris Apostolopoulos Georgios Kalfas Agapi Mesοdiakaki Marios Gatzianas Hercules Avramopoulos Amalia Miliou Nikos Pleros Department of Informatics Center for Interdisciplinary Research & Innovation Aristotle University of Thessaloniki Thessaloniki Greece Sinowave AB Hovås Sweden Department of Microtechnology and Nanoscience Microwave Electronics Laboratory Chalmers University of Technology Sweden School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

This work presents a seamless integration of a wireless D-band transmission link to a fiber network. The D-band wireless transmission is performed by utilizing recently developed direct up-/down- conversion and high gain flat EBG antennas, thus resolving the issue of high complexity and cost of the optical heterodyne mixing adopted until now for the generation of the D-band signals in fiber-wireless implementations. More specifically the transmission of a baseband signal up to 12.5GB/s as well the QAM modulated IF signal up to 256QAM 900Mbaud is demonstrated successfully validating the potential of D-band link to become the next "last mile" wireless extension of mobile networks.

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