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Signal, Control and Communication (SCC), International Conference on

作者： Kenz A. Bozed Ahlam Mohammed Elbeskri Jazia R. Ali Zreg Amer R. Zerek Department of Computer System Design Faculty of Information Technology Benghazi University Benghazi Libya Higher institute of Technical Water Affairs Sabratha Libya Sabratha University College of Science Sabratha Libya Electrical and Electronic Eng. Department Faculty of Engineering Zawia University Zawia Libya

ISBN: (数字)9781665402705

ISBN: (纸本)9781665402712

Due to the need for different communication types (Voice, video, and data) over the network, vendors improving enterprise networks for this purpose to minimize delays in order to support the better result, some applications like voice considered real-time applications which suffer from delay, jitter and packet losses, This paper discusses comparison of QoS performance between three QoS algorithms (PQ, FIFO, and WFQ), when using MPLS-TE technology considering the VoIP QoS parameters (end to end delay, jitter, and packet loss, as simulation done by OPNET modeler 14.5.

关键词： Packet loss Quality of service Switches Jitter Streaming media Data models Routing protocols

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The Tuition Payment Queuing system Uses Android-Based First in First Out (FIFO) Algorithm

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IOP Conference Series: Earth and Environmental Science 2021年第1期748卷

作者： Marlince NK Nababan Windania Purba Evta Indra Faculty of Technology and Computer Science Universitas Prima Indonesia Indonesia Department of Information System Faculty of Technology and Computer Sciences

Queuing is an important component of a process in service. Queues are formed in a company or institution due to customers. Disorderly queues are a big problem for all concerned. Where, disorderly queues occur at University Prima Indonesia at the time of tuition payment so that the campus has to carry out a crowded queue process and students have to stand waiting for the next call resulting in less effective and efficient even waste of energy for students and employees. This research designed a system of tuition payment queues using the First In First Out (FIFO) algorithm which aims to reduce the time students are queuing at the checkout at the time of tuition payment so as not to wait too long so that the time of students and campus employees is not wasted too much and reduce the crowded queue time. The result of this study is an android-based tuition payment system, which is useful for reducing student build up when paying tuition. Thus it can be said that the calculation result of P0 queue has a result of 0.25%, and the number of customers 3 people, the average customer in the queue has a value of 2.25, the total customer time is 20 minutes, and the average customer time in the row is 15 minutes, the server busy time is 0.75, and the server's empty time is 0.25%.

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Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data

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Expert systems with Applications 2023年 213卷

作者： Nguyen, Thi Phuong Quyen Phuc, Phan Nguyen Ky Yang, Chao-Lung Sutrisno, Hendri Luong, Bao-Han Le, Thi Huynh Anh Nguyen, Thanh Tung Faculty of Project Management The University of Danang- University of Science and Technology 54 Nguyen Luong Bang Danang Viet Nam Department of Industrial System Engineering International University- Ho Chi Minh City VNU Quarter 6 Linh Trung Ward Thu Duc District HCMC Viet Nam Department of Industrial Management National Taiwan University of Science and Technology No. 43 Sec. 4 Keelung Rd. Daan Dist. Taipei Taiwan Institute of Statistical Science Academia Sinica No. 128 Academia Road Section 2 Nankang Taipei11529 Taiwan Faculty of Computer Science and Engineering University of Technology- Ho Chi Minh City VNU 268 Ly Thuong Kiet Street District 10 HCMC Viet Nam

This study proposes a novel approach to time-series anomaly detection by solving the longest common subsequence (LCS) problem of two time-series data. The conventional LCS problem is used to measure the similarity of two input strings. However, it cannot be applied to real-time data collected from sensors in smart manufacturing, which are mostly real numbers instead of strings. Thus, two algorithms—an extension of fixed gap longest common subsequence (extent FGLCS) and the modified dynamic programming-based LCS (MDP-LCS) algorithm—are proposed to deal with real-time series data. Further, a gap search constraint is investigated to limit the search range in finding the LCS. This study also provides a threshold to define the matching of a pair that contains real numbers rather than a string in the conventional LCS or FGLCS problems. In addition, the proposed methods employ the gap search constraint and the threshold to reduce computational time. Furthermore, the proposed algorithms can be implemented on both univariate and multivariate time-series data. The LCS length, known as the similarity of the two time-series data, is used to detect the anomalies based on the user's expectation. The accuracy and computational time of the proposed algorithm show a significant positive result compared with the exact solution provided by the dynamic programming-based LCS algorithm. Moreover, this study also uses the proposed methods for anomaly detection in a case study of big multisensory data in smart manufacturing. The result shows that the MDP-LCS can detect approximately 98% of normal data and more than 85% of anomalies with a shorter computational time. © 2022 Elsevier Ltd

关键词： Time series

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Instance-based Deep Transfer Learning on Cross-domain Image Captioning 21

Instance-based Deep Transfer Learning on Cross-domain Image ...

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21st International Electronics Symposium, IES 2019

作者： Perdana, Rizal Setya Ishida, Yoshiteru Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan Department of Information System Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781728144498

Image captioning task achieves imposing result in generating text description of image by training on the large image and sentence pairs dataset (e.g., MSCOCO). Applied the large publicly available datasets to the specific new task will lead to the problem known as domain shifting due to the different probability distribution. In this research, we propose a Multimodal Instance-based Deep Transfer Learning (MIBTL) for cross-domain image captioning. Generally, transferring knowledge from the source domain into the target domain is pertinent due to the limited number of the dataset from the target domain. The limited number of the dataset also lead to a problem called overfitting. The instance-based strategy intuitively takes into account the influence of data by selecting the most representative data from source domain as the supplement for training on the target domain. We employ deep hash binary code representation of image and text pair to determine the distance between two data points. The experiment conducted by two types of transfer condition includes a slight shift (from MSCOCO to Flickr30k) and significant shift (from MSCOCO to CUB-200 and Oxford-102). The result shows that MIBTL can outperform other baselines to achieve state-of-the-art performance in cross-domain image captioning transfer learning. © 2019 IEEE.

关键词： Probability distributions

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The Phenology and Water Level Time-Series Mangrove Index for Improved Mangrove Monitoring

SSRN

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SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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Total completion time minimization for scheduling with incompatibility cliques

arXiv

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arXiv 2020年

作者： Jansen, Klaus Lassota, Alexandra Maack, Marten Pikies, Tytus Department of Computer Science Faculty Of Engineering Kiel University Germany Department of Algorithms and System Modelling ETI Faculty Gdańsk University of Technology Poland

This paper considers parallel machine scheduling with incompatibilities between jobs. The jobs form a graph and no two jobs connected by an edge are allowed to be assigned to the same machine. In particular, we study the case where the graph is a collection of disjoint cliques. Scheduling with incompatibilities between jobs represents a well-established line of research in scheduling theory and the case of disjoint cliques has received increasing attention in recent years. While the research up to this point has been focused on the makespan objective, we broaden the scope and study the classical total completion time criterion. In the setting without incompatibilities, this objective is well known to admit polynomial time algorithms even for unrelated machines via matching techniques. We show that the introduction of incompatibility cliques results in a richer, more interesting picture. Scheduling on identical machines remains solvable in polynomial time, while scheduling on unrelated machines becomes APX-hard. We identify several more subcases of theses problems that are polynomial time solvable or NP-hard, respectively. Furthermore, we study the problem under the paradigm of fixed-parameter tractable algorithms (FPT). In particular, we consider a problem variant with assignment restrictions for the cliques rather than the jobs. We prove that it is NP-hard and can be solved in FPT time with respect to the number of cliques. Moreover, we show that the problem on unrelated machines can be solved in FPT time for reasonable parameters, e.g., the parameter pair: number of machines and maximum processing time. The latter result is a natural extension of known results for the case without incompatibilities and can even be extended to the case of total weighted completion time. All of the FPT results make use of n-fold Integer Programs that recently have received great attention by proving their usefulness for scheduling problems. Copyright © 2020, The Authors. All rights

关键词： Polynomial approximation

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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ASBSO: An improved brain storm optimization with flexible search length and memory-based selection

arXiv

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arXiv 2021年

作者： Yu, Yang Gao, Shangce Wang, Yirui Cheng, Jiujun Todo, Yuki The Faculty of Engineering University of Toyama Toyama930-8555 Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai200092 China The School of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number, and lacks of flexibility and variety which makes a poor search efficiency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy are proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism which is capable of evaluating and storing the degree of improvements of solutions is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSO's search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability and robustness of ASBSO. Copyright © 2021, The Authors. All rights reserved.

关键词： Swarm intelligence

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An Investigation of IOT-Based Consumer Analytics to Assist Consumer Engagement Strategies in Evolving Markets

An Investigation of IOT-Based Consumer Analytics to Assist C...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Sujay Mugaloremutt Jayadeva Nagendra Prasad Krishnam B. Raja Mannar Amar Prakash Dabral Dharam Buddhi Neha Garg Department of Health System Management Studies (Faculty of Management Studies) JSS Academy of Higher Education & Research School of Management and Commerce (SOMC) Malla Reddy University Hyderabad Telangana India Lecturer - Business Administration St. Theresa Intl College Thailand Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttarakhand Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In order to establish sustainable operations, Internet innovation has been widely adopted in the retail sector; however, certain operational difficulties, such as security and confidentiality have been uncovered in practise. As a result, linked fields must do more thorough theoretical study. In order to compete with online merchants and promote sustainable growth, this study examines how traditional brick and mortar stores might use IoT technology. First, by taking into account the costs of customer inconvenience associated with shopping through various retail partners and the influence of technology, pricing Plans were created for the store and the retailer of concrete blocks, respectively. Furthermore, by contrasting different scenarios, the benefits of internet to retail shops and consumer excess are analysed. After that, the basic models were expanded to take into account consumer - perceived sensitivity regarding IoT technology and price competitiveness in order to test the robustness of the analytical conclusions. Additionally, actual data-based numerical analyses are performed.

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SRML: Structure-relation mutual learning network for few-shot image classification

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Pattern Recognition 2025年 168卷

作者： Li, Xiaoxu Wang, Lang Zhu, Rui Ma, Zhanyu Cao, Jie Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China Faculty of Actuarial Science and Insurance Bayes Business School City St George's University of London LondonEC1Y 8TZ United Kingdom Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Lanzhou City University Lanzhou730050 China Department of Statistical Science University College London LondonWC1E 6BT United Kingdom

Few-shot image classification aims at tackling a challenging but practical classification setting, where only few labelled images are available for training. Metric-based methods are main-stream solutions for few-shot image classification, but many of them extract features that are either irrelevant to target objects in the query images or insufficient to describe the local shape or structural patterns within images, which can lead to mis-identification of the target objects, especially when the images are of multiple objects. To resolve this issue, we propose the structure-relation mutual learning (SRML) network, which first learns both the intra-image structural features and the inter-image relational features in a parallel fashion via two parallel branches, the structural feature extractor (SFE) and the relational feature extractor (RFE), and then harnesses mutual learning to enable knowledge exchange between them. In such a manner, the structural features learnt from the SFE branch not only contain the structural patterns within the images, but also focus more on the target objects, guided by the relational knowledge from the RFE branch. In return, the RFE branch can exploit the more-focused structural knowledge to better match the target objects in the support and query images. We conduct extensive experiments on four few-shot classification benchmark datasets to showcase the superior classification of the proposed SRML network, achieving a 3.17% improvement in classification accuracy over the leading competitor, RENet Kang et al. (2021). The code of this work can be found in https://***/Rilliant7/SRML. © 2025 The Authors

关键词： Image correlation

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