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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Compare machine learning Models in Text Classification Using Steam User Reviews 2021

Compare Machine Learning Models in Text Classification Using...

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2021 3rd international conference on Software Engineering and Development (ICSED)

作者： Youchen Miao Zeyu Jin Yumeng Zhang Yuchen Chen Junren Lai David R. Cheriton School of Computer Science University of Waterloo Canada The Fu Foundation School of Engineering and Applied Science Columbia University USA University of North Carolina at Chapel Hill USA Michigan State University USA James Cook University Singapore

ISBN: (纸本)9781450385213

Text Classification and Sentiment Analysis of game reviews are viewed as important parts in not only academic fields but also in game studies. In this paper, with more than 400 thousand game reviews on Steam platform, we preprocess the data using different libraries (sklearn, nltk, and spaCy) and use them as inputs to build three sentiment classification models based on different algorithms (Naive Bayes, SVM, and Random Forest). In contrast to previous studies that only focus on different sentiment analysis models, our paper also highlights the use of different APIs to preprocess the data and their corresponding model performance. The results show that no matter which API we choose, Random Forest models always perform the best. However, in terms of training time, Naive Bayes is the fastest. This work can be used to apply grid search for researchers to automatically find the optimum API before conducting sentiment analysis in the future.

关键词： text mining sentiment classification models sentiment analysis sklearn nltk spaCy

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Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet 2021

Epidemic Prevention System Based on Voice Recognition Combin...

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2021 3rd international conference on Video, Signal and Image Processing

作者： Huaming Zhou Aili Wang Meixin Li Yinghui Zhao Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Computer Science Chubu University Japan

ISBN: (纸本)9781450385886

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2℃, which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work.

关键词： mask recognition infrared temperature measurement YOLOv3 helmet recognition voice call elevator

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A Survey on Edge Detection based recent Marine Horizon Line Detection Methods and their applications

A Survey on Edge Detection based recent Marine Horizon Line ...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Manzoor Ahmed Hashmani Muhammad Umair Syed Sajjad Hussain Rizvi Abdul Rehman Gilal High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Sea and sky boundary identification (i.e. marine horizon line detection) from a marine image is a problem of great interest for reasons such as, unmanned surface or aerial vehicle navigation, surveillance by object detection and tracking, and determining the spatial orientation of the ship. Due to the complexity of the marine environment, the problem poses its own unique challenges. In recent years, different methods have been proposed by the researchers to solve the problem. Those methods can be grouped into two categories; (i) edge detection based horizon detection, and (ii) machine learning-based horizon detection. In this paper, we present a survey on edge detection based recent marine horizon line detection methods and their applications. We have selected studies from the previous three years and discussed each study's approach to marine horizon line detection issue, the datasets used for testing purposes and its results. The authors' observations for each study are presented with a recommendation for their suitability for a specific application in the marine environment. Findings of the survey and future research directions for the researchers are also identified and presented. We hope that this survey paper provides a comprehensive overview of edge detection based recent marine horizon line detection methods and help the researchers in exploring new solutions to this challenging problem.

关键词： Image edge detection Transforms Gray-scale Feature extraction Real-time systems Computational efficiency Image color analysis

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Expression of Concern for: Prediction of Coronary Artery Disease by Adapting Hybrid Approach of machine learning Methods

Expression of Concern for: Prediction of Coronary Artery Dis...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Vijay Parthasarthy L. Pallavi Harishchander Anandaram M. Praveen S. Arun R. Krishnamoorthy Chemical Engineering - Energy Cluster School of Engineering University of Petroleum and Energy Studies Dehradun Department of CSE B V Raju institute of technology Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Department of ECE Saveetha Engineering college Chennai India Department of ECE Jerusalem College of Engineering Chennai India Centre for Nonlinear Systems Chennai Institute of Technology Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497657

Recently, there have been many implementations of AI and its application tactics in the field of medicine, and it is commonly mentioned as a substantial rich data. One of the major causes of death from one end of the world to the other is coronary artery disease, which can be prevented with early diagnosis. The goal of this work is to use reliable clinical data to predict coronary course infection. Expecting Coronary Artery Disease (CAD) is a very challenging and challenging undertaking in the clinical profession. One of the virtuosi in the clinical field is the early forecast, especially in the cardiovascular region.. The earlier studies on the creation of the early forecast model encouraged an understanding of the new approaches to find the variation in clinical imaging. An eating plan graph prepared by the concerned doctor following early anticipation might satisfy the cardiovascular counteraction. Our exam paper includes a forecast based on a suggested computation created using a pooling region bend AI technology. This data-based ID is a crucial element for accurate expectation. Despite the weak pixels around it, this extensive methodology has a respectable impact on deciding variety in clinical images. With the help of vein halting and vein plaque, this pooling region development in our AI calculation is storing contracting veins and tissues. The new flexible picture-based grouping strategies are presented in this investigation piece, which also contrasts the current characterization techniques with anticipated CAD previous for a higher exact worth. This suggested method uses any prior cardiac ailment as evidence to draw a conclusion. In our suggested calculation, the decision-production of grouped yield yields more precise results.

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application Analysis of Artificial Intelligence Target Controlled Infusion in Clinical Anesthesia Operation 2021

Application Analysis of Artificial Intelligence Target Contr...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Li Yuan Guo-li Li Zhi-heng Teng Jin-liang Teng The First Hospital of Hebei North University China The first clinical college of Hebei North University China

ISBN: (纸本)9781450385046

Medical data are too scattered, complex and ethically difficult. Its development in the field of surgery is still in the initial stage of exploration. But with the development of machine deep learning, it is necessary for surgeons and data experts to strengthen interdisciplinary cross-disciplinary cooperation in order to promote the progress of AI in the field of surgical operation, and finally realize AI driven automatic robot for surgical operation. This process is long and hard, which requires a long time of effort from surgeons and data experts, and steadily promote the development of artificial intelligence, so as to achieve achievements. Many clinical operations require anesthesia, local anesthesia or general anesthesia. Artificial anesthesia can not control the dose and concentration of drugs accurately, which leads to the prolonged recovery time of patients and affects physiological function. In addition, the related research confirmed that target controlled intravenous anesthesia can effectively maintain the plasma drug concentration during operation, achieve the ideal anesthesia depth and reduce the stress response of operation. This paper discusses the application of artificial intelligence technology and target controlled infusion in clinical anesthesia. With the aid of the big data learning function of artificial intelligence, the paper analyzes the combination of body indexes of each patient and data of database, and summarizes the anesthesia infusion scheme for each patient. 84 patients who underwent laparoscopic appendicitis in a third class hospital from June 2018 to June 2019 were randomly divided into two groups, 42 patients in each group. The ratio of the number of men and women in the control group was 21:21, the age was between 18 and 55 years old; the ratio of the number of men and women in the control group was 21:21, and the age was between 18 and 55 years. There was no significant difference between the two groups (P > O.05), which was in l

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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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