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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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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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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 804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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Real Equation Systems with Alternating Fixed-Points 34

Real Equation Systems with Alternating Fixed-Points

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34th International Conference on Concurrency Theory, CONCUR 2023

作者： Groote, Jan Friso Willemse, Tim A.C. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959772990

We introduce the notion of a Real Equation System (RES), which lifts Boolean Equation Systems (BESs) to the domain of extended real numbers. Our RESs allow arbitrary nesting of least and greatest fixed-point operators. We show that each RES can be rewritten into an equivalent RES in normal form. These normal forms provide the basis for a complete procedure to solve RESs. This employs the elimination of the fixed-point variable at the left side of an equation from its right-hand side, combined with a technique often referred to as Gauß-elimination. We illustrate how this framework can be used to verify quantitative modal formulas with alternating fixed-point operators interpreted over probabilistic labelled transition systems. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Model checking

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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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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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Achieving High-Level Software Component Summarization via Hierarchical Chain-of-Thought Prompting and Static Code Analysis

Achieving High-Level Software Component Summarization via Hi...

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2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023

作者： Rukmono, Satrio Adi Ochoa, Lina Chaudron, Michel R.V. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798350381382

Comprehension of software systems is key to their successful maintenance and evolution. This comprehension comes at different levels of abstraction: At the low level, one must focus on comprehending functions;while at the high level, one must abstract and comprehend the system's requirements. Diverse Automated Source Code Summarization (ASCS) techniques have been proposed to comprehend systems at the lower level. However, techniques for abstracting higher-level explanations fall short. Research on related fields, such as software architecture recovery, has tried to address system comprehension at the higher level by attempting to detect abstractions of design decisions from source code. Nevertheless, this is an on-going effort and many steps in the process are still unsolved. In this paper, we lever-age the emergent abilities of Large Language Models (LLMs) together with the achievements in the ASCS and static code analysis fields to design an approach that produces component-level summaries of software systems. Particularly, we address the unreliability of LLMs in performing reasoning by applying a chain-of-thought prompting strategy, which allows us to emulate inductive reasoning. We follow a bottom-up approach, where we start by comprehending lower-level software abstractions (e.g., functions), and then we compose these findings-in a cascading style-to comprehend higher-level ones (e.g., classes, components). We demonstrate the feasibility of our approach by applying it to the open-source Java project JHotDraw version 5.1. We believe our approach offers a stepping stone in developing robust automated software summarization approaches that can be applied generally across domains and types of software system. copy;2023 IEEE. © 2023 IEEE.

关键词： Abstracting

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