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Inflammatory Bowel Disease Classification Improvement with Metagenomic Data Binning Using Mean-Shift Clustering 7th

Inflammatory Bowel Disease Classification Improvement with M...

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7th international conference on Future Data and Security Engineering, FDSE 2020

作者： Phan, Nhi Yen Kim Nguyen, Hai thanh College of Information and Communication Technology Can Tho University Can Tho Viet Nam

ISBN: (纸本)9789813343696

In the human body, where the greatest concentration of bacteria is the gastrointestinal tract, it is considered to be a diverse and complex microbial population, involving many different diseases. the development of metagenomics has many achievements in evolution and biodiversity. the application of machine learning algorithms to solve metagenomics problems has helped researchers make new advances in the field of personalized medicine, especially the diagnosis and improvement of human health people. In this study, we propose an unattended binning approach combined with Mean-shift algorithm to improve predictive performance. We performed on the Inflammatory Bowel Disease (IDB) dataset with 6 subclasses. this clustering method has improved results when applying deep learning techniques and shows the promising potential of data preprocessing methods when applied on different datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Multi-UAV Cooperative Path Planning via Mutant Pigeon Inspired Optimization with Group Learning Strategy 12th

Multi-UAV Cooperative Path Planning via Mutant Pigeon Inspir...

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12th international conference on advances in Swarm Intelligence, ICSI 2021

作者： Yu, Yueping Deng, Yimin Duan, Haibin School of Automation Science and Electrical Engineering Beihang University Beijing100083 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9783030788100

this paper proposes a mutant pigeon-inspired optimization algorithm with group learning strategy (MGLPIO), for multi-unmanned aerial vehicle(UAV) cooperative path planning. the group learning strategy is introduced in map and compass operator to reduce computation complexity and enhance the global search ability. At the same time, the triple mutations strategy is employed in landmark operator to enhance swarm diversity. What’s more, in order to synchronize multi-UAV, the time stamp segmentation technique is designed to prove waypoints, which can simplify the cost function by reducing the number of independent variables. Besides, we geometric the threat sources to quantify their dangerous level. the coordination costs can guarantee collision-free flight and real-time communication. Finally, the proposed method is applied to path planning in set scenarios. the simulation results indicate that our model is feasible and effective, and the MGLPIO algorithm can have a good balance between exploration and exploitation by comparing with other four algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Free flight

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Deep Convolutional Neural Networks for Recognition of Historical Handwritten Kannada Characters 7th

Deep Convolutional Neural Networks for Recognition of Histor...

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7th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA 2018

作者： Chandrakala, H.T. thippeswamy, G. Department of Computer Science and Engineering Visveswaraya Technological University Belagavi India Department of Computer Science and Engineering BMS Institute of Technology & Management Bengaluru India

ISBN: (纸本)9789811399190

Handwritten character recognition is an important step toward automatic transliteration of the valuable historical documents archived in digital libraries. this is a challenging task as it requires a labor-intensive handcrafting of features from a huge set of character classes. Moreover, the intra-class variability of handwritten characters is high causing a major bottleneck for recognition accuracy. A deep convolutional neural networks (DCNN) approach for character recognition of handwritten historical Kannada manuscripts is presented in this paper. DCNN is a model that unifies feature extraction and classification. It inherently learns the most discriminative features from the given data, thus subverting the usage of handcrafted features. In this work, the features extracted from the characters using DCNN are fed to SGDM and SVM classification algorithms for recognition. this approach is experimented on the digitized estampages of historical Kannada stone inscriptions belonging to eleventh century and promising results are observed. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Feature extraction

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Punctuation Prediction in Vietnamese ASRs Using Transformer-Based Models 18th

Punctuation Prediction in Vietnamese ASRs Using Transformer-...

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18th Pacific Rim international conference on Artificial Intelligence, PRICAI 2021

作者： Bui, Viet the Tran, Oanh thi FPT School of Business and Technology FPT University Hanoi Viet Nam International School Vietnam National University Hanoi Hanoi Viet Nam

ISBN: (纸本)9783030893620

Punctuation prediction is the task of predicting and inserting punctuation like periods, commas, exclamation marks, etc. into the appropriate positions in transcribed texts in ASR systems. this helps to improve user readability and the performance of many downstream tasks. While most related studies have been performed for popular languages like English and Chinese, there is very little work done for low-resource languages. In order to stimulate the research on these languages, in this paper, we target to improve the quality of punctuation prediction for Vietnamese ASRs. Specifically, we propose a method based on recent advances on pre-trained language models (LMs) for general purposes such as BERT and ELECTRA. the benefit of using these models is that they can be effectively fine-tuned on this punctuation prediction task where only a small amount of training data is available. To further enhance the performance, a simple yet effective technique to provide more context information in predicting punctuation marks for the very left and right words in each segment is also proposed. the experimental results of the proposed model on public benchmark datasets are quite promising. Overall, the proposed architecture substantially enhanced the prediction performance by a large margin and yielded a new state-of-the-art result on these datasets. Specifically, we achieved the F1 scores of 71.49% and 80.38% on the Novel and Newspaper public datasets, respectively. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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the Significance of Entrainment on Debris Flow Modelling: the Case of Hunnedalen, Norway 16th

The Significance of Entrainment on Debris Flow Modelling: Th...

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16th international conference of the international Association for computer Methods and advances in Geomechanics, IACMAG 2021

作者： Vicari, Hervé Nordal, Steinar thakur, Vikas Department of Civil and Environmental Engineering Norwegian University of Science and Technology Høgskoleringen 7A Trondheim7034 Norway

ISBN: (纸本)9783030645175

this paper studies the influence of entrainment on the runout behavior of a documented debris flow, in Hunnedalen (Norway), happened on 2nd June 2016. the steepness of the channel and the availability of sediments along the flow path caused the debris flow to grow from approximately 2000 m3 to approximately 19000 m3. this paper aims to back calculate this event, using a Voellmy rheology implemented in RAMMS:DF and including a debris flow entrainment model. Voellmy rheological parameters (friction coefficient μ and turbulence coefficient ξ) are back-calculated using the default parameters of the entrainment model in RAMMS:DF. the back-calculation aims to replicate available field data such as: the deposit shape and the total entrained volume. It is also shown how the total eroded volume is strongly affected by the choice of μ and ξ. Finally, the entrainment parameters are varied from their default value to understand their influence on entrainment magnitude. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Debris

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Optimal Path Selection in Wireless Sensor Networks Using Enhanced Spatial Reusability 7th

Optimal Path Selection in Wireless Sensor Networks Using Enh...

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7th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA 2018

作者： Patil, Santosh Rakesh, Mothe Rahul, D. Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad HyderabadTelangana500043 India Department of Information and Technology Institute of Aeronautical Engineering Hyderabad HyderabadTelangana500043 India

ISBN: (纸本)9789813291850

Wireless sensor network has facilitate the research organization to inquire into the study of geometric reusability concepts. It is very important to select optimal path for packet transmission because of restriction in wireless links. the optimal route can be selection is observed by large end-to-end throughput rate. the process of distinguishing mobile unintentional networks or cellular networks is performed by routing in WNS. In proposed work, we’ve got planned Associate in Nursing improved special reusability method victimization single-path and anypath routing protocols (SSAAR). the task of path routing is to help in-between nodes who accepts the packet to participate in packet forwarding, in wireless links the task of single routing is to reduce the cost of the nodes. Here we experimentally showed that the efficiency of proposed work is improved that is cost is minimized. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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Text-Line Extraction from Historical Kannada Document 7th

Text-Line Extraction from Historical Kannada Document

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7th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA 2018

作者： Ravi, P. Naveena, C. Sharath Kumar, Y.H. Manjunath Aradhya, V.N. Department of Computer Science and Engineering VTU-RRC Belagavi India Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Information Science & Engineering Maharaja Institute of Technology Mysuru India Department of MCA JSS Science and Technology University Mysuru India

ISBN: (纸本)9789811399190

In this work, we propose identification of text line from a historical Kannada document. the proposed method consists of three stages: initially, preprocess the image by using Sauvola’s method and then apply the connected component and projection profile method to detect the text position of the text line. Finally, each text line is segmented based on projection points. the propose method is evaluated on Kannada historical document. Experimentation is carried out on the seventeen Kannada historical documents, in which the total number of lines together in all documents is 217 lines. We have tried few trail-and-error methods to identify the lines in the historical document image. Using the first method, we have detected 140 lines, but multiple lines were seen between each text line;the accuracy using this method was 64.51%. In the second method, we could detect 107 and the accuracy achieved was 49.30%. By using the third method, we could clearly detect 178, with reduced number of lines in between the text lines, and the accuracy in this case is 82.02%. Hence, we can conclude that using the third method most of the lines were precisely detected and obtained encouraging the result. © Springer Nature Singapore Pte Ltd. 2020.

关键词： History

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Artificial Topographic Structure of CycleGAN for Stroke Patients Motor Imagery Recognition 10th

Artificial Topographic Structure of CycleGAN for Stroke Pati...

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10th international conference on computer Engineering and Networks, CENet 2020

作者： Rong, Fenqi Sun, Tao Xu, Fangzhou Zhang, Yang Lin, Fei Wang, Xiaofang Wang, Lei JinanShandong250353 China JinanShandong250353 China JinanShandong250353 China Department of Physical Medicine and Rehabilitation Qilu Hospital Cheeloo College of Medicine Shandong University JinanShandong250012 China

ISBN: (纸本)9789811584619

Motor imagery-based Brain-computer Interface (MI-BCI) has already become one of the hottest research fields and has made great achievement in stroke rehabilitation. Because it is difficult for stroke patients to complete a series of motor imagery tasks, the phenomenon of insufficient data in signal processing and application is often faced. At present, the most effective way to overcome the challenge is to expand the amount of data for analysis. In this paper, we used CycleGAN for data augmentation on brain activities. First, electroencephalogram data is transformed into brain topographic map through EEG2Image. After that, we train a CycleGAN neural network. the topographic maps of healthy people and stroke patients are put into the CycleGAN model for training at the same time. In the process of training, let healthy people learn the features of the stroke patients topographic maps and generate the artificial stroke patients topographic maps. the generated topographic map can be very intuitive to see the similarity with the original topographic map. Experiments demonstrate that the method effectively generate artificial topographic maps to expand stroke dataset. And these topographic maps can be applied to data processing and analysis of MI-BCI. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain computer interface

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A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works

A Recent Research on Malware Detection Using Machine Learnin...

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7th international conference on advances in Visual Informatics, IVIC 2021

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia Faculty of Informatics and Management Universiti Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9783030902346

Each year, malware issues remain one of the cybersecurity concerns since malware’s complexity is constantly changing as the innovation rapidly grows. As a result, malware attacks have affected everyday life from various mediums and ways. therefore, a machine learning algorithm is one of the essential solutions in the security of computer systems to detect malware regarding the ability of machine learning algorithms to keep up with the evolution of malware. this paper is devoted to reviewing the most up-to-date research works from 2017 to 2021 on malware detection where machine learning algorithm including K-Means, Decision Tree, Meta-Heuristic, Naïve Bayes, Neuro-fuzzy, Bayesian, Gaussian, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and n-Grams was discovered using a systematic literature review. this paper aims at the following: (1) it describes each machine learning algorithm, (2) for each algorithm;it shows the performance of malware detection, and (3) we present the challenges and limitations of the algorithm during research processes. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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the State of Reproducible Research in computer science 17th

The State of Reproducible Research in Computer Science

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17th international conference on Information technology: New Generations, ITNG 2020

作者： Cacho, Jorge Ramón Fonseca Taghva, Kazem Department of Computer Science University of Nevada Las Vegas Las VegasNV United States

ISBN: (纸本)9783030430191

Reproducible research is the cornerstone of cumulative science and yet is one of the most serious crisis that we face today in all fields. this paper aims to describe the ongoing reproducible research crisis along with counter-arguments of whether it really is a crisis, suggest solutions to problems limiting reproducible research along with the tools to implement such solutions by covering the latest publications involving reproducible research. © Springer Nature Switzerland AG 2020.

关键词： computers

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