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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Verhoeven, Richard Van Berlo, Bram Meratnia, Nirvana Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands

ISBN: (纸本)9798350329674

Advances in GPU have facilitated design and execution of complex and computation-intensive deep learning models. As the model complexity increases, the risk of encountering problems due to very large model size, individual tensor size, Not a Number (NaN) value, and memory leak increases as well. When untreated, these problems lead to substantial increase of execution time, generating unpredictable results, and memory leak exceptions. In this paper, we address these problems and particularly large tensor support, C++ kernel changes, and recompilation of the TensorFlow framework. In addition, issues related to NaN value debugging with existing debugging toolkits and solutions to alleviate memory leaks will be explored. Based on experience gained from our analysis, we propose solutions related to better tensor dimension sanity checks, alternative tensor loop procedures, different ways of applying kernels to tensors, a debug trace file filter method, and ways how memory leak exceptions can be resolved. While these problems and solutions may be applicable to running any complex and computation-intensive deep learning model, we described how we encountered them in a use case, in which we designed a deep learning model for activity and gesture recognition using radio data aiming to mitigate domain shift problem. © 2023 IEEE.

关键词： Tensors

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Finite Difference Method for Neutrosophic Fuzzy Second Order Differential Equation under Generalized Hukuhara Differentiability

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Neutrosophic Sets and Systems 2023年 58卷 311-329页

作者： Kamal, Bassem Khalid, Huda E. Salama, A.A. El-Baghdady, Galal I. Department of Mathematics and Engineering Physics Faculty of Engineering Delta University for Science and Technology Egypt Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Saied University Egypt Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Egypt

This paper solves the boundary value problem of the second-order differential equation under the neutrosophic fuzzy boundary condition. The proposed solution is approximated using the finite difference method but deneutrosophication utilizing strongly generalized Hukuhara differentiability. The error calculation is then processed by comparing the proposed numerical solution with the analytical solution presented in our earlier work [20]. © (2023). All Rights Reserved.

关键词： Boundary conditions

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Design and evaluation of Swift routing for payment channel network

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Blockchain(Research and Applications) 2024年第2期5卷 62-72页

作者： Neeraj Sharma Kalpesh Kapoor V.Anirudh Department of Computer Science and Engineering Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia Department of Mathematics Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia

Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby sacrificing the ability to confirm transactions *** uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a *** performance of most of the existing centralized path-finding algorithms does not scale with network *** rapid growth of Bitcoin PCN necessitates considering distributed ***,the existing decentralized algorithms suffer from resource *** present a decentralized routing algorithm,Swift,focusing on fee *** concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the ***,we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud *** secret path also helps in edge load sharing,which results in an improvement of *** routing achieves up to 21%and 63%in fee and throughput optimization,*** results from the simulations follow the trends identified in the theoretical analysis.

关键词： Blockchain Payment channel networks Bitcoin Lightning network Routing protocols Layer 2 protocols

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A Comparison of Machine Learning Techniques for Yemeni Universities Admission Examinations Predictions 1

A Comparison of Machine Learning Techniques for Yemeni Unive...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hagree, Salah Al-Sanabani, Maher Al-Gaphari, Ghaleb Al-Shalabi, Ahmed Abdullah Alawdi, Ala'a Al-Dilami, Redwan Mohsen, Ayedh Abdulaziz Alfahad, Khalil T. Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Aljazeera University Yemen Computer Science Department Faculty of Computer and Information Technology Thamar University Yemen Faculty of Computing and IT University of Science and Technology Sana’a Yemen School of Mathematics and Computer Science Wuhan Textile University China

ISBN: (纸本)9798331533557

Admissions examinations are crucial in shaping students' academic and career paths. Machine learning (ML) offers a promising avenue for transforming the assessment process in universities, schools, and colleges. This paper presents a new ML-based method to evaluate high school graduates seeking admission to eleven colleges across nine Yemeni universities, applicable to both scholarship and self-financing students. The proposed approach leverages several ML techniques, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra Trees (ET), Decision Tree (DT), AdaBoost, and XGBoost. In this study, ML algorithms were applied to admissions examinations at nine Yemeni universities, including Sana’a, Ibb, Dhamar, Amran, Jiblah, Mihwit, Al-Dhalea, Saada, and Hajjah. The dataset comprised 15,898 student applications for eleven colleges, encompassing both scholarship and self-financing programs. Experiment one evaluated the performance of various ML techniques. Gradient Boosting (GB) and Random Forest (RF) demonstrated superior results, achieving the highest accuracy, precision, recall, and F-score. Specifically, GB and RF attained 91.00% and 90.00% accuracy, 87.60% and 88.80% precision, 89.90% and 87.50% recall, and 89.30% and 87.60% F-score, respectively. Experiment two further refined the analysis, comparing ET and RF to other ML methods. ET and RF consistently outperformed LR, KNN, SVM, DT, GB, AdaBoost, and XGBoost. In this experiment, ET and RF achieved exceptional accuracy, precision, recall, and F-score rates of 99.00% and 99.00%, 97.50% and 97.20%, 98.00% and 97.80%, and 97.80% and 97.50%, respectively. © 2024 IEEE.

关键词： Random forests

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A survey of fault tolerant consensus in wireless networks

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High-Confidence Computing 2024年第2期4卷 1-11页

作者： Yifei Zou Li Yang Guanlin Jing Ruirui Zhang Zhenzhen Xie Huiqun Li Dongxiao Yu School of Computer Science and Technology Shandong UniversityQingdao 266237China Research Center for Mathematics and Interdisciplinary Sciences Shandong UniversityQingdao 266237China

Wireless networks have become integral to modern communication systems,enabling the seamless exchange of information across a myriad of ***,the inherent characteristics of wireless channels,such as fading,interference,and openness,pose significant challenges to achieving fault-tolerant consensus within these ***-tolerant consensus,a critical aspect of distributed systems,ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised *** survey paper provides a comprehensive overview of faulttolerant consensus mechanisms specifically tailored for wireless *** explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless *** paper systematically categorizes these consensus mechanisms based on their underlying principles,communication models,and fault *** investigates how these mechanisms handle various types of faults,including communication errors,node failures,and malicious *** highlights key use cases,such as sensor networks,Internet of Things(IoT)applications,wireless blockchain,and vehicular networks,where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

关键词： Consensus Fault-tolerance Byzantine-resilience Wireless networks

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A Study of Android Security Vulnerabilities and Their Future Prospects

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 854-869页

作者： Alsumayt, Albandari Elbeh, Heba Elkawkagy, Mohamed Alfawaer, Zeyad Alghamedy, Fatemah H. Alshammari, Majid Aljameel, Sumayh S. Albassam, Sarah Alghareeb, Shahad Alamoudi, Khadijah Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Dammam31441 Saudi Arabia Department of Science Technology and Mathematics College of Art and Sciences Lincoln University Jefferson CityMO United States Department of Information Technology College of Computers and Information Technology Taif University 21944 Taif Saudi Arabia Saudi Aramco Cybersecurity Chair Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam31441 Saudi Arabia

Nowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities,

关键词： Smartphones

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Numerical performances based artificial neural networks to deal with the computer viruses spread on the complex networks

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International Journal of computer mathematics 2024年第3期101卷 314-330页

作者： Alderremy, A.A. Gómez-Aguilar, J.F. Sabir, Zulqurnain Aly, Shaban Lavín-Delgado, J.E. Razo-Hernández, José R. Department of Mathematics Faculty of Science King Khalid University Abha Saudi Arabia /UAEM Universidad Autónoma del Estado de Morelos Cuernavaca Mexico Department of Mathematics and Statistics Hazara University Mansehra Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Mathematics Faculty of Science Al-Azhar University Assiut Egypt Guerrero Mexico Irapuato Mexico

This paper shows the outcomes of computer virus propagation (CVP) model, represented with susceptible, exposed, infected, quarantine and recovered computers (SEIRQ), classes based mathematical model using the stochastic procedures. The systematic study of the CVP based SEIRQ model represents that the equilibrium state of virus-free is stable globally with reproduction not more than one, while the viral symmetry is attractive globally. The numerical performances of the CVP based SEIRQ model are presented by using the stochastic computational framework based on the artificial neural networks (ANNs) together with the Levenberg-Marquardt backpropagation (LBMB) called as ANNs-LBMB. The learning procedures via ANNs-LBMB for solving the CVP based SEIRQ model are implemented to indorse the statics using the testing, authorization, and training. Thirteen numbers of neurons and the data selection for training 72%, testing 12% and validation 16% are selected to solve the model. For the numerical outcomes of the CVP based SEIRQ model using the ANNs-LBMB, a dataset is considered through the Adams approach. The accuracy and reliability performances of the scheme are presented by using the values of the absolute error (AE) along with the observations of state transitions (STs), regression, mean square error (MSE) and error histograms (EHs). © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Neural networks

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Energy Aware Routing in Wireless Sensor Networks for Agricultural Monitoring: A Deep Q-Network Based Framework

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SN computer science 2024年第7期5卷 1-21页

作者： Dey, Ranadeep Thakurta, Parag Kumar Guha Kar, Samarjit Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Mathematics National Institute of Technology Durgapur India

Instead of earlier traditional farming, wireless sensor networks (WSNs) can be effectively used in the precision agriculture to improve the farmer’s livelihood. Whereas, hierarchical routing based protocols in WSNs are beneficial in agricultural monitoring as the network transmission load towards the sink is reduced. It decreases the energy requirement for routing the data. Following this, a deep Q-network (DQN) based energy aware multi-path routing of data towards the sink node in the WSNs is proposed in this paper. It improves the energy efficiency of sensor nodes, which in turn can eventually increase the network lifetime. Employing the proposed energy efficient routing technique, effective monitoring of the soil-moisture data in an agricultural field is taken into account. Initially, the soil moisture data from different depths of an agricultural field is collected, and it is then forwarded to an aggregator node. The principle component analysis is used for the data aggregation to obtain accurate correlated data with low power requirements. Finally, the aggregated data is routed from the aggregator node to the network gateway via the sink nodes in an energy efficient manner. The proposed DQN based routing uses the Q-table to store the Q-values for different state-action pairs. The high and positive Q-value of DQN is determined by the selection of better state-action pair. Various simulation results show that 3% saving of average residual energy is obtained by the proposed work over the existing approaches, which leads in approximately 12% increased network lifetime to route the data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep Q-network Energy efficiency Lifetime Precision agriculture Soil-moisture WSNs

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On efficacy of Meta-Learning for Domain Generalization in Speech Emotion Recognition

On efficacy of Meta-Learning for Domain Generalization in Sp...

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

作者： Gandhi, Raeshak King Tsouvalas, Vasileios Meratnia, Nirvana Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands

ISBN: (纸本)9781665453813

Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech, vital for building human-centered context-aware intelligent systems. Here, domain shift, where models' trained on one domain exhibit performance degradation when exposed to an unseen domain with different statistics, is a major limiting factor in SER applicability, as models have a strong dependence on speakers and languages characteristics used during training. Meta-Learning for Domain Generalization (MLDG) has shown great success in improving models' generalization capacity and alleviate the domain shift problem in the vision domain;yet, its' efficacy on SER remains largely unexplored. In this work, we propose a 'domain-shift aware' MLDG approach to learn generalizable models across multiple domains in SER. Based on our extensive evaluation, we identify a number of pitfalls that contribute to poor models' DG ability, and demonstrate that log-mel spectrograms representations lack distinct features required for MLDG in SER. We further explore the use of appropriate features to achieve DG in SER as to provide insides to future research directions for DG in SER. © 2023 IEEE.

关键词： Intelligent systems

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Optimizing Hospital Patient Flow by Predicting Aftercare Requests from Fuzzy Time Series 29th

Optimizing Hospital Patient Flow by Predicting Aftercare Re...

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29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： M. de Carvalho, Renata van der Sommen, Stef F. de Carvalho, Danilo Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783031468452

Predictive modelling can be a huge benefit when it comes to optimizing patient flows in a hospital. Hospital beds are considered critical resources, thus the need for optimizing patient flow is evident. This paper focuses on predicting the out-flow of hospital patients to external aftercare facilities, to mitigate the waiting times that currently dominate this flow and have a negative influence on the patient recovery process. In order to achieve this, we analyze hospital patient time series data in the form of aftercare requests. Such predictions allow hospital and aftercare facilities to be aligned such that, as soon as a patient is medically ready for discharge, the aftercare facility can immediately allocate the patient, avoiding for such patient to stay longer in the hospital occupying a bed while waiting for a place in the aftercare facility. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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