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نتیجه جستجو - Graph theory

تعداد مقالات یافته شده: 25
ردیف عنوان نوع
1 Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
پیاده سازی انتخاب ویژگی گراف-نظری توسط الگوریتم بهینه سازی تقریبی کوانتومی-2022
Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
Index Terms: Feature selection | graph theory | parameterized quantum circuit | quantum approximation optimization algorithm | quantum computing.
مقاله انگلیسی
2 An uncertainty law for microbial evolution
قانون عدم قطعیت برای تکامل میکروبها-2020
Medical practice would benefit from a thorough understanding of constraints and uncertainty in mi- crobial evolution. Higher order epistasis refers to unexpected effects of multiple mutations even if both single mutations and pairwise effects have been accounted for. Recent studies show that higher order epistasis is abundant in nature, for bacteria as well as higher organisms. However, the importance of higher order effects has been debated. It has been suggested that such effects cannot be interpreted, and should not be considered. Here, we show conclusively that higher order epistasis changes the adaptive prospects for a population. The conclusion is based on an exhaustive search of 193,270,310 hyper-cube graphs and applications of graph theory. Our results are more precise, yet more universal, than related research since they depend on mathematical theory, rather than sampling or simulations. Moreover, the uncertainty we establish for microbial evolution, due to higher order epistasis is not sensitive for detailed model assumptions, such as the baseline being additive or log-additive fitness.
Keywords: Fitness landscapes| epistasis | higher order epistasis | predictability
مقاله انگلیسی
3 Network properties of healthy and Alzheimer brains
خواص شبکه مغز سالم و آلزایمر-2020
The application of graph theory in diffusion weighted resonance magnetic images have allowed the description of the brain as a complex network, often called structural network. For many years, the small-world properties of brain networks have been studied and reported. However, few studies have gone beyond of clustering and characteristic path length. In this work, we compare the structural connection network of a healthy brain and a brain affected by Alzheimer’s disease with artificial small-world networks. Based on statistical analysis, we demonstrate how artificial networks can be constructed using Newman–Watts procedure. The network quantifiers of both structural matrices are identified inside a probabilistic valley. Despite of similarities between structural connection matrices and artificial small-world networks, increased assortativity can be found in the Alzheimer brain. Due to limited experimental data, we cannot define a direct link between Alzheimer’s disease and assortativity. Nevertheless, we intend to call attention for an important network quantifier that has been neglected. Our results indicate that network quantifiers can be helpful to identify abnormalities in real structural connections, for instance Alzheimer’s disease that disrupts the communication among neurons. One of our main results is to show that the network indicators of the Alzheimer brain are almost identical with the small-world network, except the assortativity.
Keywords: Network | Human brain | Alzheimer’s disease | Small-world
مقاله انگلیسی
4 Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males
بررسی خستگی مؤثر بر شبکه های عملکردی مغزی مبتنی بر الکترونسفالوگرافی در هنگام رانندگی واقعی در مردان جوان-2019
In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.
Keywords: Electroencephalography (EEG) | Driver fatigue | Phase lag index | Graph theory | Functional connectivity | Brain network
مقاله انگلیسی
5 Cross-subject network investigation of the EEG microstructure: A sleep spindles study
بررسی شبکه ای موضعی از ساختار EEG: یک مطالعه sleep spindles-2019
Background: The microstructural EEG elements and their functional networks relate to many neurophysiological functions of the brain and can reveal abnormalities. Despite the blooming variety of methods for estimating connectivity in the EEG of a single subject, a common pitfall is seen in relevant studies; grand averaging is used for estimating the characteristic connectivity patterns of a group of subjects. This averaging may distort results and fail to account for the internal variability of connectivity results across the subjects of a group. New Method: In this study, we propose a novel methodology for the cross-subject network investigation of EEG graphoelements. We used dimensionality reduction techniques in order to reveal internal connectivity properties and to examine how consistent these are across a number of subjects. In addition, graph theoretical measures were utilized to prioritize regions according to their network attributes. Results: As proof of concept, we applied this method on fast sleep spindles across 10 healthy subjects. Neurophysiological findings revealed subnetworks of the spindle events across subjects, highlighting a predominance for occipito-parietal areas and their connectivity with frontal regions. Comparison with existing methods: This is a new approach for the examination of within-group connectivities in EEG research. The results accounted for more than 85% of the overall data variance and the detected subnetworks were found to be meaningful down-projections of the grand average of the group, suggesting sufficient performance for the proposed methodology. Conclusion: We conclude that the proposed methodology can serve as an observatory tool for the EEG connectivity patterns across subjects, providing a supplementary analysis of the existing topography techniques.
Keywords:EEG networks | PCA | EEG microstructure | Sleep spindle networks | Graph theory | Pattern recognition
مقاله انگلیسی
6 IOT and big data based cooperative logistical delivery scheduling method and cloud robot system
اینترنت اشیا و داده های بزرگ مبتنی بر همکاری لایسنسسی برنامه ریزی تحویل و سیستم ربات ابر-2018
Many studies have been done for logistics delivery scheduling technologies, but the cooperating and relaying of resources in the process of logistics delivery remains elusive. We proposed IOT and big data based cooperative logistical delivery scheduling method and cloud robot system, After obtaining the big data of logistics delivery resources and requirements from logistics delivery companies through the IOT and/or Internet, establishing the map of logistics delivery routes based on the big data of logistics delivery resources, the logistics delivery route corresponding to the logistics delivery requirements is selected from the map of logistics delivery routes by using the shortest route algorithm of the graph theory, and then the logistics delivery resources corresponding to the logistics delivery route are scheduled to the corresponding logistics delivery requirements, which can greatly improve the cooperative scheduling of logistics delivery resources among different logistics delivery companies to enhance the level of logistics delivery resources utilization, reduce the logistics delivery logistics delivery costs, and improve customer experience.
Keywords: logistical delivery, cooperative scheduling, IOT, big data, cloud robot
مقاله انگلیسی
7 The lean and resilient management of the supply chain and its impact on performance
مدیرت زنجیره تامین متکی و برجهنده و تاثیر آن روی عملکرد-2018
The relationship between lean management and resilience in the supply chain, whether negative or positive, is still not clear from the existing literature. This paper aims to investigate the relationship and links between lean and resilient supply chain (SC) practices and their impact on SC performance. To achieve this objective, the aerospace manufacturing sector (AMS) is chosen as the study sector because of the importance of both paradigms. Interpretive Structural Modeling (ISM) approach is used in order to identify linkages among various lean and resilience practices and SC performance metrics through a single systemic framework. ISM is an interactive learning process based on graph theory where experts knowledge is extracted and converted into a powerful well-structured model. For that purpose, a heterogeneous panel of experts in the AMS was formed, providing a complete view of all SC levels in the sector. The final ISM model revealed that lean SC practices act as drivers for resilient SC practices, since implementing the former in isolation could lead to a more vulnerable SC. The findings also show that lean SC practices lead to a higher performance improvement than resilient SC practices. This is due to the fact that resilient SC practices do not exert influence over all SC performance metrics as it occurs with lean SC practices. In addition, several managerial implications regarding the most convenient practices in terms of the companys objectives are drawn from this study.
keywords: Lean supply chain management |Resilient supply chain management |Interpretive structural modeling |Aerospace manugacturing sector
مقاله انگلیسی
8 Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics
شبکه اجتماعی سایری: پتانسیل سیستم فیزیکی سایبری برای تعریف رفتارهای انسانی با استفاده از تجزیه و تحلیل داده های بزرگ-2018
The growing gap between users and the big data analytics requires innovative tools that address the chal lenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of big data application and data science leads toward a new paradigm of human behavior, where various smart devices integrate with each other and establish a relationship. However, majority of the systems are either memoryless or computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of big data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of ‘Socio-Cyber Network’, where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. The proposed graph theory provides a better understanding of extraction knowledge from the data and finding relationship between different users. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.
Keywords: Big data ، Socio-cyber network ، Human behavior ، Graphs ، Friendship ، Trust index
مقاله انگلیسی
9 DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing
DTRM: مکانیزم اعتبار جدید برای افزایش اطمینان داده ها برای محاسبات ابری با کارایی بالا-2018
Cloud computing and the mobile Internet have been the two most influential information technology revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure an important but often overlooked aspect of big data — data veracity. Troublesome internal attacks launched by internal malicious users is one key problem that reduces data veracity and remains difficult to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory based user group division, and reputation transferring methods are integrated into the reputation query and evaluation process. The extensive simulation results based on real datasets show that the DTRM outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.
Keywords: Cloud computing ، Reputation mechanism ، Trustworthiness ، Data veracity
مقاله انگلیسی
10 The lean and resilient management of the supply chain and its impact on performance
مدیریت لاغر و انعطاف پذیر از زنجیره تامین و تاثیر آن بر عملکرد-2018
The relationship between lean management and resilience in the supply chain, whether negative or positive, is still not clear from the existing literature. This paper aims to investigate the relationship and links between lean and resilient supply chain (SC) practices and their impact on SC performance. To achieve this objective, the aerospace manufacturing sector (AMS) is chosen as the study sector because of the importance of both para digms. Interpretive Structural Modeling (ISM) approach is used in order to identify linkages among various lean and resilience practices and SC performance metrics through a single systemic framework. ISM is an interactive learning process based on graph theory where experts knowledge is extracted and converted into a powerful well-structured model. For that purpose, a heterogeneous panel of experts in the AMS was formed, providing a complete view of all SC levels in the sector. The final ISM model revealed that lean SC practices act as drivers for resilient SC practices, since implementing the former in isolation could lead to a more vulnerable SC. The findings also show that lean SC practices lead to a higher performance improvement than resilient SC practices. This is due to the fact that resilient SC practices do not exert influence over all SC performance metrics as it occurs with lean SC practices. In addition, several managerial implications regarding the most convenient practices in terms of the companys objectives are drawn from this study.
Keywords: Lean supply chain management ، Resilient supply chain management ، Interpretive structural modeling ، Aerospace manugacturing sector
مقاله انگلیسی
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