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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ergash Yu. Akhmedov Dmitriy E. Palchunov Durdona Z. Khaitboeva Mukhiddin F. Ibragimov Otojon R. Sultanov Laylo S. Rakhimova IT Department Novosibirsk State University Novosibirsk Russia DSc. Academian of REA Leading researcher Sobolev Institute of Mathematics Novosibirsk Russia Software engineering Department Urgench branch of the Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Urgench Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

the study of user opinions about products and services expressed in the form of unstructured texts in the Uzbek language and accessible through social networks is an important area of research. User opinion surveys measure user satisfaction and feedback about products and services. Analysis of unstructured texts in Uzbek written by users on social networks can help in identifying the positive and negative aspects of products and services, as well as understanding the needs and preferences of users. To conduct research, it is necessary to use natural language processing and text analysis methods. this may include the use of machine learning algorithms, sentiment analysis, topic modeling and other techniques to extract information from unstructured text data. An important aspect of such research is taking into account the features of the Uzbek language, its vocabulary and grammar. It is necessary to take into account context, semantics and cultural characteristics when analyzing user opinions in the Uzbek language. To achieve this goal, it is necessary to conduct deep learning experiments on Uzbek language text classes using long short - term memory models, convolutional neural networks and transformer-based deep learning models such as the multilingual Bidirectional Encoder Representations from Transformers model.

关键词： Deep learning Sentiment analysis Analytical models Vocabulary Accuracy Social networking (online) Biological system modeling

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Investigating Principle Component Analysis Impact on the Performance of Machine learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. the dataset used in this study consists of thousands of images that represent four types of blood cells. these images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. the study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. the performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. the recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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City-Scale Localization with Telco Big data 16

City-Scale Localization with Telco Big Data

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Proceedings of the 25th ACM international on conference on Information and Knowledge Management

作者： Fangzhou Zhu Chen Luo Mingxuan Yuan Yijian Zhu Zhengqing Zhang Tao Gu Ke Deng Weixiong Rao Jia Zeng School of Computer Science and Technology Soochow University Suzhou China Huawei Noah's Ark Lab Hong Kong Hong Kong Hong Kong China United Network Communications Corporation Limited Shanghai Branch Shanghai China Computer Science RMIT University Melbourne Australia School of Software Engineering Tongji University China Shanghai China

ISBN: (纸本)9781450340731

It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MR-based positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 x 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. the proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of $80m$, outperforming the state-of-art range-based and fingerprinting localization methods.

关键词： regression models localization telco big data

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An Integrated Approach of GIS and Machine learning to Assess the Spatio-Temporal Earthquake Vulnerability in South Africa 4

An Integrated Approach of GIS and Machine Learning to Assess...

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4th IEEE international conference on Computing and Information Sciences, ICCIS 2021

作者： Atif, Iqra Cawood, Frederick thomas Mahboob, Muhammad Ahsan Ali, Sarfraz University of the Witwatersrand Wits School of Geography Archaeology and Environmental Studies Johennesburg South Africa University of the Witwatersrand Johennesburg South Africa

ISBN: (纸本)9781665494410

South Africa is experiencing a high frequency of seismic events which have catastrophic effects on individuals and infrastructure. this study aims to utilize the integrated approach of Geographical Information Systems (GIS) and Machine learning (ML) based algorithms to analyse the large amount of seismicity data to discover the meaningful patterns and assess the geo-vulnerability of earthquakes with good confidence level in South Africa. Several analytical and modelling techniques including Space-Time Pattern Mining, Artificial Neural Networks (ANN) based hot and cold spots were applied. the results of earthquake data from year 1973 to 2021 revealed that in total there were 1, 680 earthquake events that occurred with magnitude ranges from mild to moderate (2 ≤ M ≤ 5). Earthquakes with higher magnitude were concentrated notably in the Gauteng (48%) followed by North-West (31%) provinces of the South Africa. Also, 63% of the magnitude and depth of earthquakes are oriented from North-East to South-West direction. A significant increasing trend of earthquake was observed in some areas of Free State (p ≤ 0.1), Limpopo (p ≤ 0.1), Western Cape (p ≤ 0.5) and Gauteng (p ≤ 0.5) provinces. Whereas decreasing trend was found in areas of North-West (p ≤ 0.1) and Mpumalanga (p ≤ 0.5). the ANN based hot spot analysis predicted the cluster of high magnitude earthquakes (hot spots) in North-West province and low magnitude earthquakes (cold spots) in Gauteng province. Although the earthquake vulnerability is low in Gauteng province but these cold spots could be related to the deep mining activities in the region and have the potential to trigger the rock burst phenomena at the mines. the results can help the disaster management authorities for smart decision making, and urban and regional planning of future activities in the region. © 2021 IEEE.

关键词： Neural networks

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). the challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. the scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. this paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. the obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. these findings have the potential to enable new developments in diabetic retinopathy analysis. the challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, the Authors. All rights reserved.

关键词： Optical tomography

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Automatic Modulation Classification Using Convolutional Neural Network with Batch Normalization: A Novel Approach

Automatic Modulation Classification Using Convolutional Neur...

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international conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Pritam Sadhukhan Jaydeb Bhaumik Electronics and Telecommunication Engineering Department Jadavpur University Kolkata India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

In recent times, Deep Neural Network (DNN) has drawn a lot of interest due to its exceptional performance in identifying complex structured data. In this paper, the automatic classification of modulation types of analog and digital modulated signals is examined using the DNN method. Due to its crucial function in dynamic spectrum access, which can support fifth generation (5G) wireless communications, automatic modulation classification (AMC) is an unavoidable component of different intelligent communication systems. the AMC has been investigated for more than 25 years, but it has proven challenging to create a classifier that is effective in a variety of multipath fading scenarios and other limitations. AMC systems have recently embraced DNN or Deep learning (DL) based approaches, and significant advancements have been noted. this paper suggests AMC approaches based on Convolutional Neural Network (CNN) with Batch Normalization layers, added after each convolutional layer and first dense layer. the RadioML2016.10a dataset has been used in this investigation which comprises of synthetic signals with 11 modulation types: AM-DSB, AM-SSB, WBFM, GFSK, CPFSK, PAM-4, BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM. Google Colaboratory has been used as the foundation for all the simulations. Batch Normalization layers have significantly improved the accuracy of the model. An accuracy of 68.54% is achieved at high SNR for the CNN based AMC model with Batch Normalization which shows 9.275% better performance than the model without Batch Normalization.

关键词： Wireless communication Fading channels Accuracy Modulation Dynamic spectrum access Internet Convolutional neural networks Information technology Batch normalization Signal to noise ratio

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Extraction of Volumetric Indices from Echocardiography: Which Deep learning Solution for Clinical Use? 12th

Extraction of Volumetric Indices from Echocardiography: Wh...

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Functional Imaging and Modeling of the Heart - 12th international conference, FIMH 2023, Proceedings

作者： Ling, Hang Jung Painchaud, Nathan Courand, Pierre-Yves Jodoin, Pierre-Marc Garcia, Damien Bernard, Olivier Univ Lyon INSA-Lyon Université Claude Bernard Lyon 1 UJM-Saint Etienne CNRS Inserm CREATIS UMR 5220 U1294 Lyon69621 France Department of Computer Science University of Sherbrooke SherbrookeQC Canada Cardiology Department Hôpital Croix-Rousse Hospices Civils de Lyon Lyon France Cardiology Department Hôpital Lyon Sud Hospices Civils de Lyon Lyon France

ISBN: (纸本)9783031353017

Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresolved issues concerning i) the temporal consistency of their predictions, and ii) their ability to generalize across datasets. In this context, we propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects. We introduce a new private dataset, named CARDINAL, of apical two-chamber and apical four-chamber sequences, with reference segmentation over the full cardiac cycle. We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. We also report that the best models trained on CARDINAL, when tested on CAMUS without any fine-tuning, still manage to perform competitively with respect to prior methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to finally meet the standards of an everyday clinical device. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Echocardiography

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PREFACE

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Journal of Physics: conference Series 2021年第1期2115卷 011001-011001页

作者： Doss, Arockia Selvakumar Arockia Short, Michael Márquez, Fausto Pedro García Design and Automation Research Group SMEC Vellore Institute of Technology Tamil Nadu Chennai India School of Computing Engineering and Digital Technologies Teesside University United Kingdom Castilla-La Mancha University Spain

this proceedings consists of the peer-reviewed papers from the 2nd international conference on Robotics, intelligent Automation and Control Technologies (RIACT 2021), which was organized by the School of Mechanical engineering at Vellore Institute of Technology, Chennai, India on September from 23rd to 25th, 2021. However, due to the COVID 19 pandemic the conference was held Online. the main objective of RIACT 2021 is to provide a platform for researchers and practitioners from both academic institutions and industries to meet and share cutting-edge developments in the areas of Robot Design, Development and Control, Rehabilitation Robots and Devices, intelligent Automation and Fault Diagnosis, Agricultural Robots, Underwater Robots, Development of Drones, Control and Supervision Systems, Vision Systems, Actuators and Sensors, Mechatronics Systems, System Interface and Control, Sustainable Technologies and associated disciplines. this conference also provided an opportunity to exchange research evidence and innovative ideas and also the issues related to Robotics and Automation. this proceeding will include presentations on the latest research areas such as Agriculture Drones, Bio Mechanisms for rehabilitation applications and Deep learning in robotics. this conference had gathered an excellent group of keynote speakers from around the globe on different topics such as engineering Challenges of the AI-Enabled Automation and Process Optimisation for Healthcare and Business Domain, Upcoming future of robotics in protected horticulture, intelligent Systems: From Smart Manufacturing and Network Perspectives, High-performance surgical and wearable robots for translational medicine, Challenges in Rover Mission, Two-Degree-of-Freedom Control of Flexible Manipulator, Manipulator Rule Generation from Table data Sets and Its Application, Enduruns: Long-endurance sea surveying autonomous unmanned vehicle with gliding capability powered by hydrogen fuel cell, Bio-Inspired Swarm

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