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Use of Neighborhood-Based Bridge Node Centrality Tuple for Preferential Vaccination of Nodes to Reduce the Number of Infected Nodes in a Complex Real-World Network 1

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7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Meghanathan, Natarajan Burden, Kapri Robinson, Miah Department of Electrical and Computer Engineering and Computer Science Jackson State University 1400 Lynch Street JacksonMS39217 United States

ISBN: (数字)9783031535529

ISBN: (纸本)9783031535512

We investigate the use of the Neighborhood-based Bridge Node Centrality (NBNC) tuple to choose nodes for preferential vaccination so that such vaccinated nodes could provide herd immunity and reduce the spreading rate of infections in a complex real-world network. The NBNC tuple ranks nodes on the basis of the extent they serve as bridge nodes in a network. A node is a bridge node, if when removed its neighbors are either disconnected or at least sparsely connected. We hypothesize that preferentially vaccinating such bridge nodes would block an infection spread from a neighbor of the bridge node to an another neighbor that are otherwise not reachable to each other. We evaluate the effectiveness of using NBNC vis-a-vis degree centrality for preferential vaccination to reduce the spread of infections by conducting simulations of the spread of infections per the SIS (Susceptible-Infected-Susceptible) model on a collection of 10 complex real-world social networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Bridge Node Centrality Tuple Infection Spread Simulations SIS Model Vaccination

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Assessment of Gut Microbiota in Obese Male and Female Children Using 16S rRNA Gene Profiling 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： El Yacoubi, Fatima Azzahrae Kettani, Zineb Sehli, Sofia Allali, Imane Boudar, Zakia Dini, Nezha Al Idrissi, Najib Rhalem, Wajih Ghazal, Hassan Lab of Immunology Infectious Disease and Tropical Biotechnology School of Pharmacy Mohammed VI University of HealthSciences Casablanca82403 Morocco Laboratory of Genomics and Bioinformatics School of Pharmacy Mohammed VI University of HealthSciences Casablanca82403 Morocco Mother and Child Department Cheikh Khalifa International University Hospital School of Medicine Mohammed VI University of Sciences and Health Casablanca Morocco Mohammed V University in Rabat Rabat Morocco Laboratory of Sports Sciences and Performance Optimization Royal Institute of Sports Royal Institute for Managerial Training in Youth and Sport Department of Sports Sciences Salé10102 Morocco Department of Surgery School of Medicine Mohammed VI University of Sciences and Health Casablanca Morocco Moroccan Society of Digital Health Rabat Morocco

ISBN: (数字)9783031523854

ISBN: (纸本)9783031523847

The relationship between gut microbiota and obesity has been investigated in numerous studies. However, inter-individual heterogeneity influenced by multiple factors may explain the inconsistent findings in human studies. Our primary aim was to identify the specific gut microbial composition that distinguishes obese male and female children from normal-weight children based on their body mass index (BMI) and to compare the results with previous analyses. Methods: Gut microbiome 16S rRNA sequences data from participants divided into three groups based on age (6–16 years), gender, and weight status: 13 obese female and 6 normal-weight female children, 13 obese male and 5 normal-weight male children, and a combined group of 13 obese male and 13 obese female children, has been analyzed using he EZBiocloud public data and analytics portal. Results: Gut microbiome composition of obese female and male children groups showed significant differences in terms of their alpha and beta diversity indices. Obese female children had significantly higher alpha diversity indices than male children with obesity. The increase in alpha diversity indices observed among female children with obesity indicates higher microbial diversity within their gut microbiota. Moreover, the analysis of beta diversity uncovered notable distinctions in the gut microbiota composition among subgroups of obese male and female children. We propose that bacterial microbiota, such as Bifidobacterium lactis and Lactobacillus rhamnosus, could potentially function as health indicators in obese children of both sexes. These microbial genera might be useful in aiding obese children to manage weight problems and alleviating metabolic disorders associated with obesity. Conclusions: Gender plays a crucial role in the composition of the gut microbiota of obese children. This finding emphasizes the necessity of considering gender-specific factors in both obesity research and management. Furthermore, using prebiotic and

关键词： Probiotics

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The Effect of Endotracheal Tube Resistance and Respiratory Frequency on PCO2 Management During Pressure-Controlled Ventilation: A Modelling Study 1

引用

International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Bourti, Mohamed Belaguid, Abdelaziz National School of Arts and Crafts Mohammed V University of Rabat Rabat Morocco Faculty of Medicine and Pharmacy Department of Physiology Mohammed V University of Rabat Rabat Morocco

ISBN: (数字)9783031523854

ISBN: (纸本)9783031523847

Pressure Controlled Ventilation (PCV) is a commonly used mode of mechanical ventilation that allows for better control of airway pressure. The Endotracheal Tubes (ETTs) used in this mode of artificial ventilation can, under certain conditions, affect Partial Pressure of Carbon Dioxide (PCO2) values in addition to other well-known factors such as Respiratory Frequency (RF) and Tidal Volume (VT). We developed a mathematical model of the respiratory system during PCV, incorporating, in addition to the resistance and compliance of the respiratory system, the linear and non-linear mode resistance of a variety of ETTs with Internal Diameters (ID) ranging from 6.5 to 9 mm. The impact of these ETT resistances on PCO2 was studied under different RF. Our results demonstrated the validity of the models, and in addition to the known effect of RF, the non-linear resistance of the ETT has a significant impact on PCO2 during PCV particularly for leads with an ID of less than 7 mm. For this reason, the use of small-diameter Endotracheal tubes should be taken into consideration when setting mechanical ventilation parameters to ensure adequate ventilation without complication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Carbon dioxide

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A Security Framework for the Detection of Targeted Attacks Using Honeypot 1

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5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Subhash, P. Qayyum, Mohammed Likhitha Varsha, C. Mehernadh, K. Sruthi, J. Nithin, A. and AI&DS VNR VJIET Pragathi Nagar Telangana Hyderabad500090 India Computer Science Engineering King Khalid University Abha62529 Saudi Arabia

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

The reliance on the Internet is growing steadily day by day, making susceptible to various security risks such as code injection, session hijacking, Denial-of-Service attacks, etc. These attacks threaten the CIA triad, that is, Confidentiality, Integrity, and Availability. As a result, ensuring uninterrupted security has become a demanding undertaking. Of all the options available, a honeypot is one of the best security mechanisms an organization can rely on. It is a system used as a trap for threat actors to believe it is a real system. The study of tricks and their attack vectors enables an understanding of potential security vulnerabilities, allowing for the implementation of measures to safeguard assets before any compromise occurs. This work presents the development of a real organizational network on the AWS Cloud, with a focus on enhancing cyber security measures. The network includes an all-in-one honeypot, TPOT, and vulnerable web servers on one server, while a secure web server and database server are deployed on another. The system aims to detect nine different types of attacks, such as DoS, brute force, and XSS, leveraging the T-Pot framework to analyze attack parameters. The crucial aspect of log monitoring is addressed through AWS Cloud Watch, which logs all processes on the connected instances. Additionally, Route 53 health checks are used to analyze traffic levels and implement necessary mitigation strategies. This comprehensive network setup offers a robust defense against potential cyber threats, ensuring the organization's security and enabling proactive measures to safeguard its digital assets. Proposed security framework exhibits the significant results in detecting multiple targeted attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Watches

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Using Digital Services to Form a "Smart City" Ecosystem 1

引用

International Scientific Conference on Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East, AFE 2022

作者： Kharlamova, Tatiana Lavrova, Tatiana Lemeshchenko, Peter Peter the Great St. Petersburg Polytechnic University 29 Polytechnicheskaya Street St. Petersburg195251 Russia St. Petersburg State University of Economics 21 Sadovaya Street St. Petersburg191023 Russia Belarusian State University 4 Prospekt Nezavisimosti Minsk220030 Belarus

ISBN: (数字)9783031379789

ISBN: (纸本)9783031379772

The global landmarks defined by the Concept of Sustainable Development reinforce the requirements for the digital support of management processes in all areas of the modern economy. The most promising step in this process is to integrate the capabilities of various digital tools and technologies into a single ecosystem. Thanks to the use of a common concept, a single ideology and unified requirements, building ecosystems provides better conditions for progressive transformations in the sustainable system of production and consumption, compared to traditional integrated structures. In this context, the research aimed at substantiating the use of digital services in tourism to build the infrastructure of a "Smart city" is quite demanded. The article provides an overview of scientific publications related to the digital opportunities of the ecosystem and considers modern trends in the development of digital technologies in tourism. On this basis, the authors formulate the goals and objectives of the study, as well as determine a set of relevant research methods. Their application provides the necessary methodological and statistical substantiation of the results obtained, including the implementation of government initiatives in the field of a smart city, the expansion of new digital services in the tourism sector and the development of universal platforms for digital interaction. According to the results of the study, a conclusion about the need to expand the range of digital services is made. This justifies their integration into the ecosystem of "Smart city", based on its importance for the sustainable socio-economic development of the country. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ecosystems

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Development of an Online Collectable Items Marketplace Using Modern Practices of SDLC and Web Technologies 1

引用

5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Khulbe, Saransh Gumber, Divyam Pratap Singh, Shailendra School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

This research paper is intended to study and implement the development of a PHP-based system of an online collectable items marketplace which can onboard multiple users and sellers while keeping a singular admin account. The research intensely focuses on adapting modern techniques of the software development lifecycle (SDLC), Pattern matching algorithms to optimize search times, secure multilayer encryption techniques for login and purchase modules, overall security from XSS attacks in the input fields while documenting the work and meanwhile proposing a system with a rich feature set to satisfy user requirements. The development cycle one has been intensely studied with usability testing and a tabular representation of the results. Furthermore, the analysis of these results has been captured graphically and conclusively a complete Web application is developed compliant to the essential benchmarks of today’s time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cryptography

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Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Sontakke, Prashanth Jafari, Fahimeh Saeedi, Mitra Amirhosseini, Mohammad Hossein University of East London London United Kingdom

ISBN: (数字)9789819965441

ISBN: (纸本)9789819965434

Using daily data from 1st April 2016 to 3rd March 2022, this study aims to explore the use and effectiveness of machine learning algorithms in forecasting the price of Bitcoin. The paper examines the forecasting performance based on different time lags within the selected periods: (1) before pandemic and (2) including pandemic. The second time frame is selected to examine the effect of the Covid pandemic on the Bitcoin market fluctuations. This research employs four machine learning models, including linear regression, support vector regression, extreme gradient boosting, and long short-term memory. These are refined and calibrated to produce the most accurate forecasts. The performance of the algorithms was measured and compared using regression metrics. The results show that before the pandemic, the linear regression model performed the best for next-day predictions, while extreme gradient boosting performed best overall and for longer-term predictions. For the period including the pandemic, extreme gradient boosting and linear regression performed the best, consistently outperforming long short-term memory and support vector regression. The prediction models for data before the pandemic have demonstrated improved performance, whereas the selected model for the period including the pandemic exhibited satisfactory results. This is because Bitcoin prices displayed the highest volatility during the Covid pandemic. The study finds that extreme gradient boosting performs best overall and for longer-term predictions, while linear regression performs the best for next-day predictions before the pandemic. Moreover, the study reports satisfactory results for Bitcoin price prediction for the period including the pandemic, despite the high volatility of prices. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Feature Selection Using Artificial Bee Colony and Discernibility Matrix in Rough Set Theory—A Hybrid Approach 1

引用

World Conference on Information systems for Business Management, ISBM 2023

作者： Sekhar, Leena C. Sabu, M.K. MES College Kerala Marampally Aluva India Cochin University of Science and Technology Kerala Kochi India

ISBN: (数字)9789819983490

ISBN: (纸本)9789819983483

In terms of time and space, machine learning algorithms are more sophisticated. The dataset's size is a key factor in lowering its complexity. The technique performs better and uses less storage space when the dataset is smaller. To attain promising performance of machine learning algorithms, a feature selection (FS) process is employed and the dimensionality gets reduced considerably. In FS, nature-inspired swarm intelligent algorithms have salient contributions. In this research, a hybrid feature selection algorithm is proposed by combining the Artificial Bee Colony (ABC) algorithm with the notion of discernibility matrix in rough set theory. The algorithm initially ranks all the features of the dataset with the discernibility-based Discrimination Frequency Relevance Measure (DFRM) algorithm. Then, FS is performed with ABC algorithm by considering the top-ranked features. To evaluate the utility of the generated feature subset, a classifier is employed. Using an existing ABC FS algorithm, the performance of the suggested technique is compared. The suggested hybrid technique works better than the current ABC algorithm, according to an experimental evaluation using datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rough set theory

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An Efficient Framework for Crime Prediction Using Feature Engineering and Machine Learning 1

引用

5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Vengadeswaran Binu, Dhanush Rai, Lokesh Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The growth of crime in society produces insecurity among people and severely impacts the country’s economic development. Understanding crime patterns is necessary to provide a proactive response to curb criminal activities. Most crime prediction models harnessing machine learning/deep learning techniques fail to exhibit significant results for the vague and inconsistent dataset. This is due to the non-consideration of suitable pre-processing and feature engineering techniques, resulting in an unreliable and inaccurate model. Hence in this work, an efficient framework for crime prediction using Feature engineering and Machine learning is proposed. Initially, the vague and inconsistent input dataset is pre-processed using Feature Scaling and Encoding input values. After pre-processing DBSCAN clustering is applied to extract geospatial features, which is used for improving model accuracy. Then SelectKbest library is used to select the 10 best features for the prediction model. Further, the SMOTE library is used to oversample crime types with the lowest sample size. Finally, for classifying the crime types, XGBoost and LGBM classifier models are evaluated. The proposed crime prediction model was validated by deploying an Amazon EC2 P3 instance in the cloud environment. The proposed model yields an improved accuracy of 62% for the XGBoost Classifier and 63% for the LGBM Classifier compared with KNN and Decision Tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Hybrid Cyber-Attack Detection Model on Cyber-Physical systems Using Machine Learning Techniques 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Gulzar, Qawsar Mustafa, Khurram Department of Computer Science Jamia Millia Islamia New Delhi India

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Physical objects and infrastructure can now be connected to the web and to one another using cyber-physical systems, which incorporate sensing, computation, control, and networking. Protection of these cyber-physical systems is becoming a necessity for their major stakeholders, including government agencies. Their performance affects several facets of human existence, including health, economy, and welfare. Cyber-Physical systems (CPSs) are vulnerable to security breaches, and an attacker could potentially acquire access to the process controls for a prolonged amount of time. As a part of our research, we present a hybrid cyber-attack detection model, integrating the incremental principal component analysis (IPCA) with the two machine learning methods in parallel: k-nearest neighbor (KNN) and logistic regression (LR). Two machine learning methods with hyperparameters are combined with model adjustment to enhance their ability to predict attacks. To improve the validation accuracy, the incremental principal component analysis (IPCA) is taken into consideration. The experiments are performed using the Secure Water Treatment (SWaT) dataset, comprising details about the normal operation of the system as well as the malfunctioning of the system caused due to cyber-attacks. First, we evaluate the suggested model's efficacy in light of previous research, and it shows that out of two parallel machine learning techniques, KNN does perform exceptionally well in terms of precision attaining a value of 0.997, recall of 0.996, and F1-score of 0.996 in comparison to LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cyber Physical System

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