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arXiv 2024年

作者： Cassells, Duncan Costantini, Lorenzo Ashery, Ariel Flint Gadge, Shreyas Pires, Diogo L. Sánchez-Cortés, Miguel Á. Santoro, Arnaldo Omodei, Elisa LIP6 Sorbonne Université médialab Sciences Po LPI Université Paris Cité Paris FR France CENTAI Institute IT Turin Italy Department of Mathematics City University of London London United Kingdom Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Netherlands Faculty of Information Engineering Computer Science and Statistics Sapienza University of Rome Rome IT Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University IT Venezia Italy Department of Network and Data Science Central European University AT Vienna Austria

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges of the 2030 Agenda for Sustainable Development. Food insecurity is a complex phenomenon, resulting from the interplay of environmental, socio-demographic, and political events. Previous work has investigated the nexus between climate change, conflict, migration and food security at the household level, however these relations are still largely unexplored at national scales. In this context, during the Complexity72h workshop, held at the Universidad Carlos III de Madrid in June 2024, we explored the co-evolution of international migration flows and food insecurity at the national scale, accounting for remittances, as well as for changes in the economic, conflict, and climate situation. To this aim, we gathered data from several publicly available sources (Food and Agriculture Organization, World Bank, and UN Department of Economic and Social Affairs) and analyzed the association between food insecurity and migration, migration and remittances, and remittances and food insecurity. We then propose a framework linking together these associations to model the co-evolution of food insecurity and international migrations. © 2024, CC BY.

关键词： Climate change

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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Sintering of alumina nanoparticles: Comparison of interatomic potentials, molecular dynamics simulations, and data analysis

arXiv

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arXiv 2022年

作者： Roy, Shyamal Prakash, Arun Sandfeld, Stefan Forschungszentrum Juelich GmbH Juelich52425 Germany Institute of Mechanics and Fluid Dynamics TU Bergakademie Freiberg Freiberg09599 Germany JARA Forschungszentrum Juelich GmbH Juelich52425 Germany Materials Data Science and Materials Informatics Faculty 5 RWTH Aachen University Aachen52056 Germany

Sintering of alumina nanoparticles is of interest both from the view of fundamental research as well as for industrial applications. Atomistic simulations are tailor-made for understanding and predicting the time- and temperature-dependent sintering behaviour. However, the quality and predictability of such analysis is strongly dependent on the performance of the underlying interatomic potentials. In this work, we investigate and benchmark four empirical interatomic potentials and discuss the resulting properties and drawbacks based on experimental and density functional theory data from the literature. The potentials, which have different origins and formulations, are then used in molecular dynamics simulations to perform a systematic study of the sintering process. To analyse the results, we develop a number of tailored data analysis approaches that are able to characterise and quantify the sintering process. Subsequently, the disparities in the sintering behaviour predicted by the potentials are critically discussed. Finally, we conclude by providing explanations for the differences in performance of the potentials, together with recommendations for molecular dynamics sintering simulations of alumina. Copyright © 2022, The Authors. All rights reserved.

关键词： Sintering

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security Information technology

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Assistive Technical Communication of Montenegrin eServices: a Case Study

Assistive Technical Communication of Montenegrin eServices: ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up Computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper.

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Modeling of the galactic cosmic-ray anisotropy at TeV energies using an intensity-mapping method in an MHD model heliosphere 38

Modeling of the galactic cosmic-ray anisotropy at TeV energi...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Sako, T.K. Pogorelov, N.V. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Department of Space Science Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville 320 Sparkman Drive HuntsvilleAL35899 United States Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan

It has been established by high-statistics cosmic-ray experiments that the distribution of arrival directions of TeV cosmic rays have small anisotropic features with amplitudes of ∼0.1%. In this presentation, we use the intensity-mapping method based on Liouville’s theorem for the modeling of the TeV cosmic-ray anisotropy. Our results derived from the data of the Tibet ASγ experiment and the calculation of cosmic-ray trajectories in an MHD model heliosphere indicate that the relative intensity distibution of cosmic rays at the outer boundary of the heliosphere contains small-scale ansiotropic features with angular scales of smaller than ∼10◦, which does not seem realistic. A possible problem would be that the MHD model heliosphere used in this presentation is a single snapshot of the positive polarity phase of a solar cycle, while the experimental data covers ten years during the negative polarity phase of the 23rd solar cycle. In order to improve our intensity-mapping method, it would be necessary to have multiple snapshots of the MHD model heliosphere corresponding to the negative polarity phase, perform the intensity-mapping for each of the snapshots and take the average of the results. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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