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international conference on computer applications & Information Security (ICCAIS)

作者： Meshal Alshaye Faisal Alawwad Ibrahim Elshafiey National Center for Communications King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Electrical Engineering Department King Saud University Riyadh Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Hurricane Monitoring using GNSS-R is an emerging application in earth remote sensing. Practically, the hurricane is categorized by its maximum win speed which can be measured from the reflected GNSS signals. In order to track the hurricane movement efficiently, a large number of measurements is required. Alternatively, by using deep learning algorithms, useful information from much fewer measurements can be inferred. In this paper, a deep learning-based technique is proposed to track the core of a moving hurricane using limited measurements of a reflected GNSS signals. The proposed technique has achieved a very high accuracy of 96.6% using a CNN model.

关键词： Hurricanes Global navigation satellite system Wind speed machine learning Monitoring Extraterrestrial measurements Sea measurements

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A Review of Bigdata and machine learning Techniques in Healthcare

A Review of Bigdata and Machine Learning Techniques in Healt...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Sumati boyapati Srinivasa Rao Swarna Shweta Sharma Rashmi Agrawal IRIS Software INC Edison NJ Tata Consultancy Services Edision NJ MDSU Ajmer Ajmer India Faculty of Computer Application Manav Rachna International Institute of Research and Studies

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The study of big data by machine learning offers significant favorable circumstances for the assimilation and assessment of large amounts of complex medical services information. To adequately use machine learning devices in medical services, some constraints must be measured and key points must be measured, for example, its clinical use and ethics in the use of medical services. Favorable circumstances of machine learning incorporate adaptability and versatility contrasted and conventional biostatistical strategies, which makes it deployable for some errands, for example, hazard separation, finding and grouping, and endurance expectations. The health care services measure includes a lot of gigantic information that can be of various kinds and put in better places. The digitization of data, if appropriately dissected, prompts an expansion like cycles and administrations in the Healthcare area. In this specific situation, machine learning (ML) is the key empowering innovation to diminish the expenses of medical services extricating knowledge from information. The objective of ML is to improve a framework with no ceaseless human mediation. Medical Decision Support Systems (MDSS) are an additional incentive from one viewpoint by accelerating determination and care measures, then again decreasing the hour of clinical staff or concentrated learning.

关键词： Medical services machine learning Genomics Bioinformatics Big Data Medical diagnostic imaging Electronic medical records

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application of machine learning in Comprehensive Evaluation of Agricultural High-tech 19

Application of Machine Learning in Comprehensive Evaluation ...

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3rd international conference on computer Science and application Engineering (CSAE)

作者： Wang, Cui Liu, Shihong Zheng, Huoguo Hu, Haiyan Song, Linpeng Chinese Acad Agr Sci Agr Informat Inst Beijing Peoples R China Shandong Agr & Engn Univ Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781450362948

Under the background of the national policy of promoting agriculture through science and technology, agricultural high-tech are emerging continuously. How to evaluate these technologies objectively and efficiently to help enterprises make decisions is of great significance to promote the industrialization of technologies. To solve problems in traditional evaluation process,such as the lag of evaluation method, the subjective influence of experts, time consuming, labor consuming and low efficiency, an intelligent comprehensive evaluation method based on machine learning was proposed. To establish a hierarchical evaluation index system using Analytic Hierarchy Process (AHP), the index system is firstly quantified, and then 25 agricultural high-tech in different fields are selected and scored by experts;To build BP Neural Network model, the data obtained from experts' experience are selected as samples to train the model, achieve the intelligent comprehensive evaluation and calculation of agricultural high-tech. The experiment proves that compared with the Support Vector machine(SVM) model, the absolute error between the output value and the real value obtained by BP Neural Network(BP-NN) model is within the range of (-0.06 similar to 0.08), the average relative error is 0.0265%. It can be seen that BP Neural Network applied to the comprehensive evaluation of agricultural high-tech is with high accuracy and simple usage, and can meet the requirements of agricultural high-tech comprehensive evaluation.

关键词： Agricultural High-tech Intelligent and comprehensive evaluation BP Neural Network

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Search for a diffuse astrophysical neutrino flux with KM3NeT/ARCA using data of 2021-2022 38

Search for a diffuse astrophysical neutrino flux with KM3NeT...

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38th international Cosmic Ray conference, ICRC 2023

作者： Tsourapis, Vasileios Drakopoulou, Evangelia Markou, Christos Sinopoulou, Anna Tzamariudaki, Ekaterini Aiello, S. Albert, A. Alves Garre, S. Aly, Z. Ambrosone, A. Ameli, F. Andre, M. Androutsou, E. Anguita, M. Aphecetche, L. Ardid, M. Ardid, S. Atmani, H. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Basegmez du Pree, S. Becherini, Y. Bendahman, M. Benfenati, F. Benhassi, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chadolias, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Cruz, A. Cuttone, G. Dallier, R. Darras, Y. De Benedittis, A. De Martino, B. Decoene, V. Del Burgo, R. Di Cerbo, U.M. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diaz, C. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Dörr, M. Drakopoulou, E. Drouhin, D. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gabriel, J. Gagliardini, S. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geißelbrecht, N. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goupilliere, D. Gozzini, S.R. Gracia, R. Graf, K. Guidi, C. Guillon, B. Gutiérrez, M. van Haren, H. Heijboer, A. Hekalo, A. Hennig, L. Hernández-Rey, J.J. Huang, F. Idrissi Ibnsalih, W. Illuminati, G. James, C.W. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kalekin, O. Katz, U.F. Khan Chowdhury, N.R. Khatun, A. Kistauri, G. Kopper, C. Kouchner, A. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Larosa, G. NCSR "Demokritos" Institute of Nuclear and Particle Physics Ag. Paraskevi Attikis Athens15310 Greece National Technical University of Athens School of Applied Mathematical and Physical Sciences Zografou Campus 9 Iroon Polytechniou str Zografou Athens15780 Greece INFN Sezione di Catania Via Santa Sofia Catania64 - 95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain University of Granada Dept. of Computer Architecture and Technology/CITIC Granada18071 Spain Subatech IMT Atlantique IN2P3-CNRS Université de Nantes 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Nikhef National Institute for Subatomic Physics PO Box 41882 Amsterdam1

KM3NeT is a research infrastructure hosting two neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector focuses on the detection of high energy neutrinos (>TeV) from astrophysical sources. In this contribution, an analysis of the data obtained during 2021-2022, using machine learning techniques, is presented. The potential of KM3NeT/ARCA to detect a diffuse flux of astrophysical neutrinos at the early stages of the detector construction is reported. The results of this analysis are presented. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Astrophysics

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Energy Aware Parking Lot Availability Detection Using YOLO on TX2 3

Energy Aware Parking Lot Availability Detection Using YOLO o...

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3rd international conference on Informatics and Computational Sciences (ICICoS)

作者： Anggawijaya, Yohan Marvel Weng, Tien-Hsiung Herawati, Rosita Soegijapranata Catholic Univ Dept Informat & Engn Semarang Indonesia Providence Univ Dept Comp Sci & Informat Engn Taichung 43301 Taiwan

ISBN: (纸本)9781728146102

Finding a parking space is a tedious and time-consuming task in a metropolitan city. Due to this problem, many researchers proposed an automatic parking lot occupancy detection system using a camera with a deep learning method to provide useful information in the smart city system. Since object detection for the parking lot is performed in real-time by utilizing CPU and GPUs while parking detection is working 24 hours a day and 365 days a year, therefore power saving is important to reduce the electricity cost. However, the energy-aware is not considered in most related works. In this paper, we proposed an energy-saving algorithm for parking lot availability detection using YOLO running on the TX2 machine. We experiment using small parking lot prototype and remote control cars. In the experiment, we compare our algorithm with the direct application of original YOLO for parking lot detection, the results show that it reduces power by 97 percent when there is no moving object in the parking lot area and 71 percent when there are moving objects in the parking lot area.

关键词： deep learning YOLO parking lot detection computer vision

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2019

3rd International Workshop on Connectomics in NeuroImaging, ...

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2019

ISBN: (纸本)9783030323905

The proceedings contain 13 papers. The special focus in this conference is on international Workshop on Connectomics in NeuroImaging, held in conjunction with the international conference on Medical Image Computing and computer Assisted Intervention. The topics include: Constraining disease progression models using subject specific connectivity priors;hemodynamic matrix factorization for functional magnetic resonance imaging;network dependency index stratified subnetwork analysis of functional connectomes: An application to autism;A novel graph neural network to localize eloquent cortex in brain tumor patients from resting-state FMRI connectivity;graph morphology-based genetic algorithm for classifying late dementia states;covariance shrinkage for dynamic functional connectivity;rapid acceleration of the permutation test via transpositions;heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism;a mass multivariate edge-wise approach for combining multiple connectomes to improve the detection of group differences;adversarial connectome embedding for mild cognitive impairment identification using cortical morphological networks;a machine learning framework for accurate functional connectome fingerprinting and an application of a siamese network.

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iSeePlus : A cost effective smart assistance archetype based on deep learning model for visually impaired

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2020年第7期41卷 1741-1756页

作者： Virmani, Deepali Gupta, Charu Bamdev, Pakhi Jain, Prateek Guru Gobind Singh Indraprastha Univ Dept Comp Sci & Engn Bhagwan Parshuram Inst Technol Sect 17 New Delhi 110089 India

In this paper, a unique smart assistance aid, iSeePlus, for visually impaired people is presented. iSeePlus, as a prototype product, is equipped with smart glasses (iSee Glasses), a smart stick (iStick) and an android application which will be initially used to set up the device. The smart glasses are trained on proposed deep learning models to provide features like face detection and recognition, emotion detection and image captioning with a click of a specifically placed buttons on the device. The proposed prototype product also supports reading texts from images via Google Cloud Vision API and allows the user to send alert texts with his current location to the saved emergency contacts. The iStick is an integrated accessory that comes with iSeePlus which creates vibrations to alert the user of any incoming obstacle within the range of 25 cm. The android application will be used to set up the device at initial stages as it helps to add a database of known people and the emergency contacts. Presently, iSeePlus, has an accuracy of 98.03% on face detection and recognition model, FDREnet, 68.5% on emotion detection with ExpressNet and 65.7% on image captioning with CaptionMeNet BLUE-1: 65.7). With the help of iSeePlus, people suffering from visual impairment will now be able to do their day-to-day chores independently up to a significant degree.

关键词： Deep learning computer vision machine learning Android application Raspberry Pi3 Visual impairment Assistive technology Convolution neural network

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An FFM-based Model for Students' In-class learning Postures Estimation

An FFM-based Model for Students' In-class Learning Postures ...

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international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Ziyun Zhao Shifeng Zhang Kangying Hu Zhan Chen Junqi Guo School of Artificial Intelligence Beijing Normal University Beijing China Hangzhou Hikvision Digital Technology Co. Ltd. Hangzhou China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

In the recent years, education quality monitoring has been attached great importance by Chinese government, and with the promulgation of the development plan for the new generation of artificial intelligence, the application of this technology in education quality monitoring fields has attracted wide attention of scholars. How to apply relevant algorithms of machine vision to classroom quality assessment is the research focus of smart education. In this paper, considering the timeliness of feedback, the stability of algorithms, the accuracy rate and speed in classroom quality assessment, we propose a model called Face Features Matching (FFM), based on face detection networks and feature points matching methods, which innovatively combines machine learning with parameter estimation and numerical methods. Our model is divided into two sub-modules: face feature extraction and optimal parameter estimation. It can be applied successfully to classroom scenarios and become an important part of automated classroom quality assessment.

关键词： Education Feature extraction Numerical models Quality assessment Mathematical model Monitoring Facial features

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Classification of Service Robot Environments Using Multimodal Sequence Data 3rd

Classification of Service Robot Environments Using Multimoda...

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3rd international conference on Emerging Research in Electronics, computer Science and Technology (ICERECT)

作者： Saleena, P. Radhakrishnan, Gopalapillai Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Engn Bangalore Karnataka India

ISBN: (纸本)9789811358029;9789811358012

The usage of autonomous robots is getting increased day by day. Most of the applications are moving toward automation with the help of robots. This paper mainly focuses on service robots and understanding their working environments. A few robotic scenarios are created using Webots tool, and the data from there are collected as a sequence of images and lidar sensor values. The lidar values are collected with both single layer and multilayer. The environments are analyzed with the help of the collected data. The collected multimodal data are preprocessed in order to reduce the number of features. After that, the collected data are sorted out to suitably characterize each environment, and the machine learning techniques are applied to classify the environments. Different machine learning algorithms like Naive Bayes classifier, support vector machine, decision-tree-like random forest tree, and simple logistic regression are used for the classification, and results are compared with each other.

关键词： Robotic application Webots Naive Bayes Support vector machine Decision tree

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Forecasting Corporate Revenue by Using Deep-learning Methodologies 3

Forecasting Corporate Revenue by Using Deep-Learning Methodo...

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3rd international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2019

作者： Mishev, Kostadin Gjorgjevikj, Ana Vodenska, Irena Chitkushev, Ljubomir Souma, Wataru Trajanov, Dimitar Faculty of Computer Science and Engineering Ss. Cyril and Methodius Skopje Macedonia Boston University Metropolitan College Boston University United States College of Science and Technology Nihon University Chiba Japan

ISBN: (纸本)9781728135724

In the past few years, deep learning evolved into a powerful machine learning technique, which uses multiple layers for feature representation to learn specific attitudes of the raw input data, in order to produce state of the art prediction results. Deep learning has become popular in many application domains which use rich variety of data. Large volumes of online business news provide an opportunity to explore various aspects of companies. Sentiment analysis of text establishes a new viewpoint of large scale data identifying, among other features, the tone of the author towards the subject of the text. Hence, the sentiment of news articles offers an insight into the internal state of the company, potential for revenue growth, and it can be useful for corporate decision making of the company. In this paper, we demonstrate a deep convolution LSTM neural network that uses a fusion of data including company stock price and sentiment of company-related news articles as time-series, in order to predict the revenue growth or decline of the companies belonging to the Dow Jones Industrial Average. Additionally, we present a method based on transfer learning for sentiment analysis of news articles related to finances, and compare this method with standard statistical sentiment analysis approaches. © 2019 IEEE.

关键词： Sentiment analysis

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