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检索条件"任意字段=10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems"

共 1698 条记录，以下是171-180 订阅

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2022 IEEE 10th international conference on Computer Science and Network Technology, ICCSNT 2022

2022 IEEE 10th International Conference on Computer Science ...

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10th IEEE international conference on Computer Science and Network Technology, ICCSNT 2022

ISBN: (纸本)9781665470612

the proceedings contain 34 papers. the topics discussed include: an improved ant colony algorithm for multi-objective flexible job-shop scheduling problem;hybrid particle swarm optimization for unrelated parallel machine hybrid flow shop scheduling problem;parallel simulation of high-speed trains using ray-based cloud computing;optimization of CT image quality assessment metric based on genetic algorithm;power systems observability analysis based on parallel gaussian belief propagation;a dual-graph attention-based approach for identifying distribution network topology;efficient multi-authority attribute-based encryption with policy hiding and updating;graph partitioning-based query acceleration of power graph database;study of express package data processing under specific scenario;knowledge graph construction base on power accident emergency plan;power triplet extraction based on knowledge enhanced representation;and a span information fusion-based end-to-end relation extraction model for power knowledge graph.

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Artificial Intelligence-Based Water Environment analysis of Green Buildings in Marine Cities 10th

Artificial Intelligence-Based Water Environment Analysis of ...

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10th international conference on Coastal and Ocean Engineering, ICCOE 2023

作者： Yuan, Jingwen Kim, Chul Soo Department of Marine Design Convergence Engineering Pukyong National University Busan Korea Republic of Department of Industrial Design Pukyong National University Busan Korea Republic of

ISBN: (纸本)9789819753529

the development of green buildings has become an inevitable trend today. the rapid development of green buildings over the past 20 years has highlighted the challenges of moving from the start-up phase to stable operation. the over-reliance on technology and disregard for regional culture has led to a high degree of homogenisation of green buildings across regions. Utilizing the ecological architectural wisdom of traditional architecture, extracting its architectural systems adapted to the regional environment, and scientifically interpreting and transforming them through the use of modern materials and technologies, has important inspirational value and significance for the development of the modern green building industry. this paper examines the design of water environment in the process of green building design. It is also a rethinking of the region’s modern green building panoply construction, deliberately thinking about universal high technology, providing ideas for the appropriate development of green building systems in today’s coastal cities, and realizing the synergistic development of traditional green building and modern green technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Conceptual Identification Within the Decomposition of Fuzzy Homogeneous Classes of Objects 8

Conceptual Identification Within the Decomposition of Fuzzy ...

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8th international conference on Computational Linguistics and Intelligent systems. Volume III: Intelligent systems Workshop, ISW-CoLInS 2024

作者： Terletskyi, Dmytro O. Yershov, Sergey V. V. M. Glushkov Institute of Cybernetics of NAS of Ukraine Academician Glushkov avenue 40 Kyiv03187 Ukraine

Conceptual identification of fuzzy knowledge is one of the important knowledge-processing methods, which can be used for such tasks as concept matching, computation of concept similarity, re-engineering of conceptual hierarchies, etc. Since wildly used approaches to conceptual identification, which are based on the formal concept analysis and fuzzy formal concept analysis, do not consider the internal semantic dependencies among the attributes, it may lead to the construction of semantically inconsistent concepts. therefore, in this paper, we propose a new approach to the conceptual identification of fuzzy knowledge within the decomposition of nodes of fuzzy object-oriented dynamic networks. the decomposition of fuzzy homogeneous classes of objects is considered the space for the identifying their fuzzy sub-concepts within the corresponding identification lattice. To implement the proposed approach, we developed the algorithm for identifying semantically consistent subclasses of fuzzy homogeneous classes of objects. the algorithm constructs a semantically consistent lattice of fuzzy class subclasses and discovers all subclasses and superclasses for a selected fuzzy class subclass, creating a corresponding identification lattice. In addition, we introduce a notion of a subclass neighborhood within its identification lattice, which allows the consideration of a conceptual locus of the subclass instead of the subclass itself. It makes it possible to operate with subclasses of a fuzzy class in a broader sense, calculating their similarities and differences. To explain the proposed approach, we have provided a detailed example of the conceptual identification of a particular fuzzy homogeneous class of objects, demonstrating the application of the developed algorithm. © 2024 Copyright for this paper by its authors.

关键词： Formal concept analysis

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analysis and Optimization of Weak Coverage of LTE Network in Universities 10

Analysis and Optimization of Weak Coverage of LTE Network in...

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10th IEEE Joint international Information Technology and Artificial Intelligence conference, ITAIC 2022

作者： Liang, Jiayong Wang, Yuyuan Xie, Dongjie Jing, Yanmei College of Physics and Electronic Information Yunnan Normal University Kunming China Yunnan Institute of Measuring and Testing Technology Kunming China Library Yunnan Normal University Kunming China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

With the maturity of LTE network construction and the increasing number of college students, the explosive increase in the number of base stations and users makes the current network environment more complex, resulting in more and more weak coverage problems in LTE networks. Good network quality helps to improve the perception of campus users. However, it is actually found that there are still poor signals and weak coverage areas when using mobile networks in colleges and universities. therefore, it is of great significance to analyze and optimize the weak coverage problem. In this paper, software tools such as Huawei's drive test equipment and Wang You Ren Wo Xing software are used to measure engineering parameters, and then use Atoll software to perform coverage prediction simulation analysis and optimization of the mobile network. Methods to improve the quality of mobile networks are presented. © 2022 IEEE.

关键词： Wireless networks

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A Bibliometric analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE international conference on Control System, Computing and Engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

this study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. the Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. the ACM international conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. the most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). the output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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An analysis on data mining for sentiment analysis using different classification algorithms 10

An analysis on data mining for sentiment analysis using diff...

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10th IEEE international conference on Communication systems and Network Technologies, CSNT 2021

作者： Ojha, Divyank Singh, Rana Pratap Jadon, Kuldeep Singh Dept. of Computer Science & Engg. ITM Gwalior India

ISBN: (纸本)9780738105239

A rapid rise in the field of data mining has occurred in recent years. analysis of Sentiment is an idea that includes numerous happenings including sentiment extraction, classification of sentiment, classification of subjectivity, a summary of the opinion, or spam detection amongst others. opinion mining (OM) or Sentiment analysis (SA) is a mathematical study of views, emotions, sentiments, assessments, and behaviors of people, including products, organizations, services, issues, individuals, topics, events also their attributes. the advancement in web technology tools has given rights to their users to share their opinions on things that they interact with within their daily life. they can post it online on different platforms available. this massive amount of data is used to find out major curiousness that concerns people most and is very beneficial to many firms. To get benefited from this unstructured, inconsistent data written in natural language, first, it needs to be clean and transform and then we will be able to perform different operations like classification and aspect-based sentiment analysis. In this paper, we have thoroughly discussed 4 data mining algorithms utilized for prediction & classification. © 2021 IEEE.

关键词： Support vector machines

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Efficient Neural Network analysis with Sum-of-Infeasibilities 28th

Efficient Neural Network Analysis with Sum-of-Infeasibilitie...

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28th international conference on tools and algorithms for the construction and analysis of systems (TACAS) Held as Part of the 25th European Joint conferences on theory and Practice of Software (ETAPS)

作者： Wu, Haoze Zeljic, Aleksandar Katz, Guy Barrett, Clark Stanford Univ Stanford CA 94305 USA Hebrew Univ Jerusalem Jerusalem Israel

ISBN: (纸本)9783030995249;9783030995232

Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activation functions. Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation. the cost function, referred to as the Sum-of-Infeasibilities (Sol), is designed so that its minimum is zero and achieved only if all the activation functions are satisfied. We propose a stochastic procedure, DeepSol, to efficiently minimize the Sol. An extension to a canonical case-analysis-based complete search procedure can be achieved by replacing the convex procedure executed at each search state with DeepSol. Extending the complete search with DeepSol achieves multiple simultaneous goals: 1) it guides the search towards a counter-example;2) it enables more informed branching decisions;and 3) it creates additional opportunities for bound derivation. An extensive evaluation across different benchmarks and solvers demonstrates the benefit of the proposed techniques. In particular, we demonstrate that Sol significantly improves the performance of an existing complete search procedure. Moreover, the SoI-based implementation outperforms other state-of-the-art complete verifiers. We also show that our technique can efficiently improve upon the perturbation bound derived by a recent adversarial attack algorithm.

关键词： neural networks sum of infeasibilities convex optimization stochastic local search

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Technologies, tools, and resources - Driving forces in construction sector digitalization 10

Technologies, tools, and resources - Driving forces in const...

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10th IEEE international conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Parusheva, Silvia Aleksandrova, Yanka University of Economics - Varna Department of Computer Science Varna Bulgaria

ISBN: (纸本)9781665440769

the construction sector is characterized by a high degree of complexity of production processes and greater conservatism, which determines the difficulties in the digitalization transformation. Information technologies and tools are the main driving forces of its digitalization. the paper highlights the role of building information modeling as a leading technology in construction, along with the application of virtual, augmented, and mixed reality and mobile technologies. Sensors and other tools and technologies belonging to the Internet of things, as well as the use of drones, have great potential for revolutionizing the construction sector. Artificial Intelligence and Machine Learning help analyze large amounts of data in construction and help make timely, accurate and efficient decisions. the study highlights the importance of resources as a driving force for the digitalization of construction with a special emphasis on the field of human resources. © 2021 IEEE.

关键词： Drones

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Sentiment and Emotion-Aware Multi-Criteria Fuzzy Group Decision Making System 10

Sentiment and Emotion-Aware Multi-Criteria Fuzzy Group Decis...

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10th international conference on Fuzzy systems and Data Mining, FSDM 2024

作者： Yerkin, Adilet Shamoi, Pakizar Kadyrgali, Elnara School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan

ISBN: (纸本)9781643685694

In today’s world, making decisions as a group is common, whether choosing a restaurant or deciding on a holiday destination. Group decision-making (GDM) systems play a crucial role by facilitating consensus among participants with diverse preferences. Discussions are one of the main tools people use to make decisions. When people discuss alternatives, they use natural language to express their opinions. Traditional GDM systems generally require participants to provide explicit opinion values to the system. However, in real-life scenarios, participants often express their opinions through some text (e.g., in comments, social media, messengers, etc.). this paper introduces a sentiment and emotion-aware multicriteria fuzzy GDM system designed to enhance consensus-reaching effectiveness in group settings. this system incorporates natural language processing to analyze sentiments and emotions expressed in textual data, enabling an understanding of participant opinions besides the explicit numerical preference inputs. Once all the experts have provided their preferences for the alternatives, the individual preferences are aggregated into a single collective preference matrix. this matrix represents the collective expert opinion regarding the other options. then, sentiments, emotions, and preference scores are inputted into a fuzzy inference system to get the overall score. the proposed system was used for a small decision-making process - choosing the hotel for a vacation by a group of friends. Our findings demonstrate that integrating sentiment and emotion analysis into GDM systems allows everyone’s feelings and opinions to be considered during discussions and significantly improves consensus among participants. © 2024 the Authors.

关键词： Natural language processing systems

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A Combined ELM and CNN Model Architecture for Accurate Viral Family DNA Classification 11

A Combined ELM and CNN Model Architecture for Accurate Viral...

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11th IEEE international conference on Intelligent Computing and Information systems, ICICIS 2023

作者： El-Tohamy, Ahmed Maghawry, Huda Amin Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Viruses are an important group of pathogens that can cause a wide range of diseases in humans and other organisms. Accurate and efficient classification of viruses is crucial for the development of diagnostic tools and therapies, as well as for understanding the evolutionary relationships and diversity of these viruses. there has been a surge of interest in the application of machine learning and deep learning approaches for DNA viral classification in recent years. these methods can effectively handle the large and complex datasets that are often encountered in this context. A deep learning architecture is proposed in this study for the classification of DNA virus into its respective family. the model was trained on a newly collected dataset that covers a wide variety of viral family genomes. the architecture utilizes Convolutional Neural Networks (CNN) as a feature extraction layer and Extreme Learning Machines (ELMs) as a classification layer, using k-mer encoding. the use of CNN as a feature extraction layer and ELMs for classification allows for the effective and efficient classification of DNA virus into their diverse virus families. the proposed architecture was tested on a dataset obtained in FASTA format, as provided by the National Center for Biotechnology Information (NCBI). the dataset consists of the major 10 virus families. the architecture achieved an accuracy of 94.54% on the test set, with a confusion matrix showing strong performance across all 10 virus families. A comparative analysis was also performed with other k-mer based machine learning algorithms and the proposed architecture achieved the highest accuracy amongst them. © 2023 IEEE.

关键词： Deep neural networks

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