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GEOMETRIC MODELLING OF TRACKS AND FLOWS

UPB Scientific Bulletin, Series A: applied mathematics and Physics 2023年第3期85卷 87-92页

作者： Levterov, Andrii Pliekhova, Hanna Kostikova, Maryna Okun, Anton Dept. of Informatics and Applied Mathematics Kharkiv National Automobile and Highway University Ukraine Dept. of Computer Modeling and Integrated Forming Technologies National Technical University "Kharkiv Polytechnic Institute" Ukraine

Mathematical models to solve optimization connection problems in non-simply connected regions under typical technological restrictions on geometric and topological parameters of routes are investigated and developed. The issue is investigated as the development of the construction of the structures of models and the methodology of their use that could enable the constructive and effective modelling and examination of various models and algorithms that preserve the geometricity and invariance of the models, which are necessary for their specific use in the conditions of the acceptability of the use of various output data structures. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Topology

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An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions. 2

An Efficient Framework for classifying Cancer diseases using...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Metipatil, Prabhuraj Bhuvaneshwari, P. Basha, Syed Muzamil Patil, Siddanagouda S REVA University School of CSE Bengaluru India Dept. of Computer Science & Applied Mathematics UAS Bengaluru Bengaluru India

ISBN: (纸本)9798350320923

In recent years, a significant number of deaths worldwide have been due to cancer diseases. Analysis of Microarray gene expressions and protein interaction data facilitates early cancer identification. The accurate prediction of information for thousands of genes is made possible by using DNA microarray technology. Protein-Protein Interactions (PPIs) are the crucial protein activities involved in the cell cycle that replicates the DNA and cellular signaling. Determining whether a pair of proteins interacts is crucial for diagnosing an illness in molecular biology is therefore important. In existing machine learning classifiers have two-class problem that is limited and only be used to solve binary class problems, additionally, they can be prone to overfitting, as the classification framework may also become too specialized to the training data and not generalized to the varied data. To overcome this problem, this paper proposes an ensemble machine learning technique;ensembling combines the strengths of both classifiers that allow more robust and accurate framework. The better combination of both Support Vector machine and Naïve Bayes ensemble provides better performance in terms of various performance parameters. The proposed SVM-NB Ensemble classifier outperforms the existing classifiers by 15-20% over various performance parameters like classification accuracy, time taken for classification, precision, recall, and F-measure. The results were drawn by comparing the proposed ensemble (SVM+NB) classifier with the existing most applied classifiers like Logistic Regression (LR), Support Vector Machine and Naive Bayes techniques. © 2023 IEEE.

关键词： Support vector machines

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Density of Ham- and Lee- non-isometric k-ary Words 24

Density of Ham- and Lee- non-isometric k-ary Words

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24th Italian Conference on Theoretical computer science, ICTCS 2023

作者： Anselmo, Marcella Flores, Manuela Madonia, Maria Dept.of Computer Science University of Salerno Italy Dept. of Mathematics and Computer Science University of Palermo Italy Dept.of Mathematics and Computer Science University of Catania Italy

Isometric k-ary words have been defined referring to the Hamming and the Lee distances. A word is non-isometric if and only if it has a prefix at distance 2 from the suffix of same length;such a prefix is called 2-error overlap. The limit density of isometric binary words based on the Hamming distance has been evaluated by Klavžar and Shpectorov, obtaining that about 8% of all binary words are isometric. In this paper, the issue is addressed for k-ary words and referring to the Hamming and the Lee distances. Actually, the only meaningful case of Lee-isometric k-ary words is when k = 4. It is proved that, when the length of words increases, the limit density of quaternary Ham-isometric words is around 17%, while the limit density of quaternary Lee-isometric words is even bigger, it is about 30%. The results are obtained using combinatorial methods and algorithms for counting the number of k-ary isometric words. © 2023 CEUR-WS. All rights reserved.

关键词： Hamming distance

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Characterizing Evolutionary Trends in Temporal Knowledge Graphs with Linear Temporal Logic

Characterizing Evolutionary Trends in Temporal Knowledge Gra...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Fionda, Valeria Pirro, Giuseppe University of Calabria Dept. of Mathematics and Computer Science Rende Italy

ISBN: (纸本)9798350324457

Temporal changes in data set the need for the development of knowledge representation techniques able to capture these dynamics. Temporal Knowledge Graphs (TKGs) have emerged as a reference tool to represent dynamic, structured and machine-interpretable knowledge that evolves over time. However, characterizing the evolution of TKGs in a meaningful and human-understandable way remains a significant challenge. In this short paper, we argue that Linear Temporal Logic (LTL), with its expressive temporal constructs, is a promising approach for capturing a wide range of temporal behaviors and high-level evolutionary patterns occurring in TKGs. © 2023 IEEE.

关键词： Evolution Patterns Linear Temporal Logic Temporal Knowledge Graphs

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Soulbound Token for Covid-19 Vaccination Certification

Soulbound Token for Covid-19 Vaccination Certification

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Lunesu, Maria Ilaria Tonelli, Roberto Pinna, Andrea Sansoni, Simone University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy

ISBN: (纸本)9781665453813

Soulbound Tokens were introduced last May by Vitalik Buterin as a new kind of non-fungible, non-transferable token in the Ethereum ecosystem. The main idea behind such tokens is that they are born and die at the same address without ever changing ownership. Because there is typically no one-to-one correspondence between blockchain addresses and people, asserting that such new tokens are associated with a person for life is a strong assertion. Nevertheless, these new tokens promise to envision practical applications related to personal reputation or ownership of specific, non-transferable assets, and their real-world potential applications remain largely unexplored. In this paper, we present a practical application of such tokens, investigating a use case where a real-world problem largely impacting modern life has been exploited: COVID vaccine certification. The approach can easily be extended to other vaccination cases and relies on the use of a DApp where Soulbound Tokens have been implemented. In our scheme, two main actors are involved. The system administrator, represented by a local medical organization, is able to insert into a database the list of vaccines for which certification will be sent, and the citizen, after receiving the vaccination, will be able to receive and visualize the certifications provided by the first actor. © 2023 IEEE.

关键词： Vaccines

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Vehicular Data Retrieval System

Vehicular Data Retrieval System

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Thajudheen, Shaeif Shanmugarathinum, G. Jesudoss, Angelin Gladys Presidency University Dept. of Computer Science Bengaluru India University of Technology and Applied Sciences Dept. of Computer Science Ibri Oman

ISBN: (纸本)9798350317060

The Vehicular Data Retrieval System is comprehensive research that focuses on collecting real-time data from vehicles and transmitting it to a remote server for storage and later visualization in a web application. The system incorporates advanced technologies such as GPS (Global Positioning System) for precise location tracking and data capturing. The primary aim of this research is to create an efficient and scalable solution that enables the retrieval of essential vehicular parameters including speed, engine temperature, fuel level, and coordinates. By harnessing this data, it becomes possible to visualize and analyze vehicle performance over time, leading to valuable insights for various applications, such as fleet management, transportation optimization, and environmental monitoring. This paper presents a detailed analysis of the research's requirements, an extensive literature review, an evaluation of existing systems, and the proposed system design and implementation. Furthermore, the paper discusses testing methodologies, the achieved outcomes, and recommendations for future enhancements to further enrich the vehicular data retrieval and visualization system. © 2023 IEEE.

关键词： Nonbibliographic retrieval systems

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IoT and Edge Computing Using Virtualized Low-Resource Integer Machine Learning with Support for CNN, ANN, and Decision Trees 18

IoT and Edge Computing Using Virtualized Low-Resource Intege...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Bosse, Stefan University of Bremen Dept. Mathematics and Computer Science Bremen Germany

ISBN: (纸本)9788396744784

Data-driven models used for predictive classification and regression tasks are commonly computed using floating point arithmetic preserving accuracy by automatic scaling even in high non-linear functions. With respect to distributed sensor networks like the IoT, sensor data is acquired on low-resource embedded systems and delivered to data servers characterized by big data volumes. In specific use cases and domains, local predictive modelling on low-power devices is desired or required. But heterogeneity of host platforms and dynamic programming disables machine code deployment. This work addresses Tiny ML on very low-resource devices (microcontrollers, less than 32 kB RAM and ROM) by using a stack-based Tiny Virtual Machine providing core ML operations to implement Decision Trees (DT), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). VM program code is always provided in textual format and compiled just-in-time to Bytecode to ensure portability, servicability, and mobility. Two damage diagnostics use-cases demonstrate the suitability of the VM approach, and even time consuming computational tasks do not compromise the overall responsiveness of the platform by using a real-time approach. This work addresses the underlying integer arithmetic operations required to implement efficient and fast computable ML models on microncontrollers. © 2023 Polish Information Processing Society.

关键词： Decision trees

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CNN-based Community Detection: A Comparison Study 5

CNN-based Community Detection: A Comparison Study

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Bekkair, Abdelfateh Oulad-Naoui, Slimane Bellaouar, Slimane Krimat, Rababe Roumaissa Haddaoui, Alla Universite de Ghardaia Dept. of Mathematics and Computer Science Algeria

ISBN: (纸本)9798350381450

Community detection is indispensable for comprehending the structural organization and functional modules of complex networks. Convolutional neural networks (CNNs) have emerged as powerful instruments for graph data analysis in recent years. In this paper we start by providing a classification of CNN community detection approaches in networks based on node and edge transformations. Then we conduct a comparative analysis of these two categories. We assess the performance of the two CNN models on diverse network datasets using normalized mutual information a widely accepted evaluation metric. According to the findings of the study, CNN models can identify cohesive communities within networks. Moreover, they demonstrate that the node-based transformation method is more straightforward than the edge-based transformation method, while still producing satisfactory community detection results. This indicates that the node-based transformation method simplifies community detection and requires fewer computational resources. © 2023 IEEE.

关键词： Convolutional neural networks

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MANETs Connectivity Management for Age of Information Decreasing in Real-Time Applications 5

MANETs Connectivity Management for Age of Information Decrea...

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5th International science and Technology Conference Modern Network Technologies, MoNeTec 2024

作者： Mikov, Alexander I. Mikov, Alexander A. Kuban State University Information Technologies Dept. Krasnodar Russia Kuban State University Computer Technologies & Applied Mathematics Dept. Krasnodar Russia

ISBN: (纸本)9798350369007

Mobile networks that implement real-time distributed tasks of searching for certain regions on the surface are considered. It is shown that the time for solving such problems significantly depends on the age of the information available in network nodes, and the age, in turn, depends on the duration of disconnectedness periods of network. Algorithms for controlling the connectivity of mobile sensor search networks have been proposed, which significantly increase the speed of finding areas containing objects of a given type. The effectiveness of the algorithms has been assessed. Based on the simulation results, the possibility of solving optimization problems for determining the composition of network node equipment is shown. © 2024 IEEE.

关键词： Graph theory

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Harnessing LSTMs for Enhanced Prediction of Psychotic Episodes in Schizophrenia Spectrum 13

Harnessing LSTMs for Enhanced Prediction of Psychotic Episod...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tsakmaki, Paraskevi V. Tasoulis, Sotiris Georgakopoulos, Spiros V. Plagianakos, Vassilis P. University of Thessaly Dept. of Computer Science and Biomedical Informatics Lamia Greece University of Thessaly Dept. of Mathematics Lamia Greece

ISBN: (纸本)9798350308365

In the quest to enhance predictive models for schizophrenia spectrum disorders, Long Short-Term Memory networks (LSTM) have been pivotal due to their adept.han-dling of temporal data sequences. This ability to process time-dependent data, enhances the monitoring of longitudinal patterns that are indicative of relapse. This study leverages LSTMs to analyze Heart Rate Variability (HRV), a key marker in neuropsychiatric evaluations, focusing on individual patient data. Emphasizing individualization, each LSTM model is carefully tai-lored to reflect the unique behavioral and physiological patterns of the patient. By customizing the analysis to accommodate the distinct fluctuations in HRV, LSTMs have shown a remarkable capacity to decode the complex, patient-specific patterns, as evidenced by a notable predictive average score of 0.972± 0.068. This figure not only validates the LSTM model's effectiveness in grappling with the diverse aspects of psychosis but also highlights its exceptional ability to adapt to the unique temporal dynamics inherent in each patient's mental health progression. © 2024 IEEE.

关键词： Diseases

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