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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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Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models 3

Forecasting Air Pollution Particulate Matter (PM2.5) Using M...

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3rd international conference on computing and Network Communications, CoCoNet 2019

作者： Doreswamy Harishkumar, K.S. Km, Yogesh Gad, Ibrahim Department of Computer Science Mangalore University Mangalagangothri Karnataka574199 India

From the past few decades, it has been observed that the urbanization and industrialization are expanding in the developed nations and are confronting the overwhelming air contamination issue. The citizens and governments have experienced and expressed the increasingly concerned regarding the impact of air pollution affecting human health and proposed sustainable development for overriding air pollution issues across the worldwide. The outcome of modern industrialization contains the liquid droplets, solid particles and gas molecules and is spreading in the atmospheric air. The heavy concentration of particulate matter of size PM10 and PM2.5 is seriously caused adverse health effect. Through the determination of particulate matter concentration in atmospheric air for the betterment of human being well in primary importance. In this paper machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017. These models were compared with the existing traditional models and perform better in predictive performance. The performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2). © 2020 The Authors. Published by Elsevier B.V.

关键词： Mean square error

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Early Stage Detection of Psoriasis Using Artificial Intelligence and Image Processing 3rd

Early Stage Detection of Psoriasis Using Artificial Intellig...

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3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Kalbande, D.R. Khopkar, Uday Sharma, Avinash Daftary, Neil Kokate, Yash Dmello, Royston Department of Computer Engineering Sardar Patel Institute of Technology Andheri Mumbai India Department of Dermatology Seth GS Medical College & KEM Hospital Parel Mumbai India Department of Computer Science & Engineering Maharishi Markandeshwar Engineering College AmbalaHaryana India

ISBN: (纸本)9789811507502

A large part of the Indian population comes below the poverty line. There are thousands of people living in poor sanitation areas. Owing to this, a majority of ailments prevail in such areas. People do not have access to good medical facilities and at times may not even have the awareness to visit a doctor at the correct time. This has been observed especially in the case of skin diseases where failure to get the correct diagnosis and treatment in time often results in dire consequences. With the growing surge of Artificial Intelligence (AI), it has been realized that AI can be helpful in the medical arena as well. Thus, an AI-based approach is proposed for the classification of a skin disease Psoriasis by making use of a multi-class machine learning classification algorithm. Psoriasis is a noncontagious, chronic skin condition that results in the formation of patches of thickened, scaling skin. This algorithm uses uploaded images from the users’ smartphone to give a probability estimate for Psoriasis. Over 60 medically validated images of Psoriasis, acquired from Department of Skin and V.D of Seth G.S. Medical College and KEM Hospital, Parel, Mumbai, were used as training data. It uses a Neural Network algorithm for training the symptoms. Using this training data, an accuracy of above 85% was obtained. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Bayesian Spatial–Temporal Models for Drug Abuse 3rd

Bayesian Spatial–Temporal Models for Drug Abuse

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3rd international conference on Innovative computing, IC 2020

作者： Wang, Siyou Qin, Haofan Fang, Hui Wang, Xiufen Ma, Zhihong College of Computer and Information Engineering Tianjin Agricultural University Tianjin300384 China College of Basic Science Tianjin Agricultural University Tianjin300384 China

ISBN: (纸本)9789811559587

The drug abuse crisis has become a national emergency in the USA. Based on drug abuse data for Ohio, Kentucky, West Virginia, Virginia, and Pennsylvania in 2014–2017, this study used WinBUGS14 to create a Bayesian model. We added time and space effects and socioeconomic factors to correct the model. With the minimum DIC value as standard, we find out the optimal model to dynamically analyze the spatial–temporal trend of the relative risk of drug abuse and its relationship with socioeconomic factors. Besides, We used ArcGIS 10.6 to map drug abuse rates and relative risk in five states and found that the Bayesian space–time model with socioeconomic factors is the best (DIC = 336.282). This paper found that the relative risk of space–time interaction trends has experienced a process from convergence to divergence within four years. Ohio and Pennsylvania are high-risk states, while Virginia is a low-risk state. Although the rate of drug abuse is high, the relative risk is not necessarily high. Then, combined with socioeconomic factors, we found that the lower the GDP, the worse the educational background, the higher the unemployment rate and the greater the relative risk it is. The Bayesian spatial–temporal model is an effective method for analyzing the drug abuse data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bayesian networks

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Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model 20

Multi-data Fusion Based Marketing Prediction of Listed Enter...

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Proceedings of the 2020 3rd international conference on Algorithms, computing and Artificial Intelligence

作者： Ziyang Pan Zhishan Huang Xiaowen Lin Songxia Li Huanze Zeng Daifeng Li Sun Yat-Sen University China

ISBN: (纸本)9781450388115

The intelligent analysis and marketing prediction of high-tech enterprises based on artificial intelligence is a hot topic in the field. Most of the existing researches mainly focus on taking the internal structural features of the enterprise as the starting point to study the influencing factors of enterprise marketing trends. Different with previous studies, This research attempts to simulate the analyzing and decision processes of domain experts towards issues of enterprise operations by using artificial intelligent. One main challenge of the research is to simulate domain experts’ behaviors of analyzing multi-data including both structured and unstructured data, especially how to extract knowledge, patterns and import factors from unstructured data to support enterprise decisions. In order to solve the challenge mentioned above, an intelligent analysis framework MS-LSTM based on business management theory is proposed. Firstly, MS-LSTM collects, processes and analyzes multi-data by using an encoder strategy module, which contains more than 10 strategy models such as normalization, one-hot, distribution fitting, time series completion, semantic encoding, Bert, etc., providing high quality input for downstream tasks. Finally, a LSTM based time series processing model is proposed to make marketing prediction based on upstream processed multi-source data. Extensive experiments are conducted to verify the proposed model. Compared with traditional benchmark model, the proposed MS-LSTM could efficiently extract meaningful knowledge and patterns, which could be explained to a certain extent by business management theory, from multi-source data. The model has improved the accuracy of enterprise trend prediction by 19.3 times compared with state-of-art baselines, which further verify the application values of the research.

关键词： Bert

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Analysis of Adequate Bandwidths to Guarantee an Electoral Process in Ecuador 3rd

Analysis of Adequate Bandwidths to Guarantee an Electoral Pr...

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3rd international conference on Smart Trends for Information Technology and Computer Communications, SmartCom 2019

作者： Toapanta, Segundo Moisés Toapanta Piguave, Johan Eduardo Aguilar Gallegos, Luis Enrique Mafla Department of Computer Science Universidad Politécnica Salesiana Del Ecuador Robles 107 Chambers GuayaquilGuayas Ecuador School of Systems Engineering Escuela Politécnica Nacional Ladrón de Guevara E11-253 QuitoPichincha Ecuador

ISBN: (纸本)9789811500763

The analysis of the appropriate bandwidths was made based on the electoral processes in countries with the availability of electronic voting and these have been adapted to the situation in the electoral processes of Ecuador. The problem is the low importance given and the recidivism in the scarce bandwidth used in the current electoral processes. The objective reflects the problems caused by a mediocre bandwidth for events of such magnitude and importance in any country and creates awareness of the appropriate conditions to guarantee all aspects of the electronic process. The method used is deductive to analyze the data that were used as parameters to calculate the bandwidth in the electoral process in Nigeria. The analysis shows that the voting processes up to the 2017 period have not been optimal, but they are sustainable and acceptable despite the fact that there were setbacks when issuing the results of the votes. It is concluded that there must be simulations to avoid failures at the time of the actual electoral process and improve resources for the implementation of the electoral processes. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bandwidth

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An Efficient Density-Based Local Outlier Detection Approach for Scattered data

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IEEE ACCESS 2019年 7卷 1006-1020页

作者： Su, Shubin Xiao, Limin Ruan, Li Gu, Fei Li, Shupan Wang, Zhaokai Xu, Rongbin Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

关键词： Outlier detection local outlier factor neighborhood variance rough clustering scattered dataset

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A Multi-Temporal Landsat data Analysis for Land-use/Land-cover Change in Haridwar Region using Remote Sensing Techniques 3

A Multi-Temporal Landsat Data Analysis for Land-use/Land-cov...

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3rd international conference on computing and Network Communications, CoCoNet 2019

作者： Kumar, Saurabh Shwetank Jain, Kamal DCS Gurukula Kangri Vishwavidyalaya Haridwar Uttarakhand249404 India Department of Civil Engineering IIT Roorkee Uttarakhand India

LU/LC (land-use and land-cover) change detection information is essential for monitoring, planning, decision making, and socio-economic development. In the current study, multispectral Landsat-8 OLI, Landsat-7 ETM+, and Landsat-5 TM satellite imagery data has used in the Haridwar region for investigating the utility and possibility of remote sensing (RS) in LU/LC change assessment. After ensuring a ground survey with GPS, discussions with the individuals, and satisfactory literature-review, it has been found that RS can play a vital character in assessing the timely temporary changes in LU/LC of the Haridwar region. The satellite data of the different years 1996, 2003, 2010 and 2017 downloaded from USGS Earth Explorer. The satellite images of the selected study areas are categorized into seven different land use classes, viz. orchards, vegetation, agricultural land, rangeland, urban land, water bodies, and watershed. The pixel-based supervised classification method has performed 81.55%, 86.86%, 80.67%, and 93.00% accuracy using the Maximum Likelihood Classifier (MLC). The result of change detection of the LU/LC proved that the orchards area has been decreased very rapidly by 11806.65 ha (9.82%) and converted permanently in urban and agricultural land due to urbanization and industrialization in Haridwar region from 1996 to 2017 year. While the remaining area of land use such as urban land, rangeland, and watershed have increased up to 3022.11 ha (2.51%), 10088.82 ha (8.39%), and 5191.47 ha (4.32%). The vegetation land and water body have been decreasing since 1996 up to 12856.41 ha (10.70%) and 802.63 ha (0.66%) respectively in 21 years (1996-2017). The overall results presented by this research may be useful for orchard monitoring and socio-economic development of the Haridwar region. © 2020 The Authors. Published by Elsevier B.V.

关键词： Remote sensing

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Identify-Based Outsourcing data Auditing Scheme with Lattice 3rd

Identify-Based Outsourcing Data Auditing Scheme with Lattice

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3rd international conference on Frontiers in Cyber Security, FCS 2020

作者： Wang, Han Wang, Xu An Liu, Jiasen Lin, Chuan Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou550025 China Key Laboratory of Network and Information Security under PAP Engineering University of PAP Xi’an710086 China

ISBN: (纸本)9789811597381

To reduce the burden and overhead of local storage, more and more organizations and individuals chosen to outsourcing data to cloud servers. Cloud users can access cloud data through any network. At the same time, as users lose actual control over the data, the data security in the cloud server is exposed to great risks. Therefore, how to ensure the integrity and security of cloud data has become an open issue. Around this problem, researchers have put forward many plans. However, these schemes are based on traditional cryptography, they cannot resist quantum attacks and have high computational complexity. It cannot meet the needs of interconnection of everything in the future. To solve this problem, we propose an identity-based outsourcing data integrity auditing scheme with lattice, which has forward security. Our proposed scheme is based on lattice cryptosystem, which not only has higher security and computational efficiency, but also has forward security. In the proposed scheme, the data owner can designate a proxy to outsource the data, thus reducing the local computing burden. In addition, we analyze the security of the scheme through the assumption of ISIS problem, which shows the theoretical security of the scheme. Theoretical analysis and comparison indicate that our proposed scheme is more secure and practical. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Cloud storage

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Feature Selection in Cancer Genetics using Hybrid soft computing 3

Feature Selection in Cancer Genetics using Hybrid Soft Compu...

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3rd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2019

作者： Thangavelu, S. Akshaya, S. Naetra, K.C. Krishna Sathya, A.C. Lasya, Vuyyuru Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781728143651

Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results. © 2019 IEEE.

关键词： soft computing

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