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An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm 1

18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Peixin Dong, Chen Chen, Zhenyi Zhen, Zihang Jiang, Lei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the development of technology, robots are gradually being used more and more widely in various fields. Industrial robots need to perform path planning in the course of their tasks, but there is still a lack of a simple and effective method to implement path planning in complex industrial scenarios. In this paper, an improved whale optimization algorithm is proposed to solve the robot path planning problem. The algorithm initially uses a logistic chaotic mapping approach for population initialization to enhance the initial population diversity, and proposes a jumping mechanism to help the population jump out of the local optimum and enhance the global search capability of the population. The proposed algorithm is tested on 12 complex test functions and the experimental results show that the improved algorithm achieves the best results in several test functions. The algorithm is then applied to a path planning problem and the results show that the algorithm can help the robot to perform correct and efficient path planning. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Industrial robots

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Dynamic Provision of Workload Allocation on Cloud Resources Using Machine Learning Techniques

Dynamic Provision of Workload Allocation on Cloud Resources ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Saravanakumar, C. Prakash, M. Govindaraj, V. Government Polytechnic College Department of Computer Engineering Chennai600012 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India MPHASIS Ltd SME- Data Science Chennai India

ISBN: (纸本)9798350319200

Cloud tasks are scheduled to the respective VM for execution. Scheduling process selects the resources and allocates the requesting task to them. The resources are selected in advance and then the request is forwarded for processing of tasks. It suffers the overestimation problem which means that the requested task size is less than the allocated resource level. Various scheduling problems are addressed but still the problem arises during the resource allocation. Auto scaling process is implemented in the cloud which allocates the resource based on the demand. The proposed model uses the dynamic method of cloud allocation and provisioning based on the workload level and resource level. Priority queue is implemented prior to the provisioning process for ordering the resource in specific order. The main aim of the proposed method is to allocate the resources using dynamic priority manner. The efficiency is achieved at maximum level by selecting and allocating the workload. © 2023 IEEE.

关键词： Cloud Computing Dynamic Provisioning Machine Learning Scheduling Virtual Machine

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Machine Learning for Subgraph Extraction: Methods, Applications and Challenges 49th

Machine Learning for Subgraph Extraction: Methods, Applicati...

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49th International Conference on Very Large data Bases, VLDB 2023

作者： Yow, Kai Siong Liao, Ningyi Luo, Siqiang Cheng, Reynold School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Guangdong-Hong Kong-Macau Joint Laboratory HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effectiveness and scalability of these traditional algorithms. As a consequence, machine learning techniques—one of the most latest trends—have recently been employed in the database community to address various subgraph problems considering that they have been shown to be beneficial in dealing with graphrelated problems. We discuss learning-based approaches for four well known subgraph problems in this tutorial, namely subgraph isomorphism, maximum common subgraph, community detection and community search problems. We give a general description of each proposed model, and analyse its design and performance. To allow further investigations on relevant subgraph problems, we suggest some potential future directions in this area. We believe that this work can be used as one of the primary resources, for researchers who intend to develop learning models in solving problems that are closely related to subgraphs. © 2023, VLDB Endowment. All rights reserved.

关键词： Machine learning

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Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning

Multi-Granularity History and Entity Similarity Learning for...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mingcong, Shi Zhu, Chunjiang Zhang, Detian Wen, Shiting Li, Qing School of Computer Science and Technology Soochow University Suzhou China Department of Computer Science University of North Carolina Greensboro United States School of Computer and Data Engineering NingboTech University Ningbo China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Temporal Knowledge Graph (TKG) reasoning, aiming to predict future unknown facts based on historical information, has attracted considerable attention due to its great practical value. Insight into history is the key to predict the future. However, most existing TKG reasoning models singly capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. In this paper, we propose Multi-Granularity History and Entity Similarity Learning (MGESL) model for Temporal Knowledge Graph Reasoning, which models historical information from both coarse-grained and fine-grained history. Since similar entities tend to exhibit similar behavioural patterns, we also design a hypergraph convolution aggregator to capture the similarity between entities. Furthermore, we introduce a more realistic setting for the TKG reasoning, where candidate entities are already known at the timestamp to be predicted. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Analysis of Trie-Based Lan File Search Engine

Analysis of Trie-Based Lan File Search Engine

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Shanmugasundaram, C. Umamaheswari, C. Suriyaa, V. Tr, Kartikeyan Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering India St. Thomas College of Arts and Science Department of Computer Science with Data Science India Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350315127

Searching and Accessing files in a computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network. © 2023 IEEE.

关键词： Local area networks

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Quality of Service-aware 6G-Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1136-1147页

作者： Roy, Bipasha Guha Roy, Deepsubhra Guha Dutta, Piyali Khan, Surbhi Bhatia Asiri, Fatima Ayadi, Manel Institute of Engineering and Management Kolkata India Institute of Engineering and Management Kolkata India University of Salford Chitkara University Institute of Engineering and Technology Chitkara University Department of Data Science United Kingdom University Centre for Research and Development Chandigarh University India Centre for Research Impact and Outcome Punjab Rajpura140401 India King Khalid University College of computer science Informatics and computer systems department Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia

Internet of Things connectivity in home health monitoring is a high-in-demand application area. The electronics industry and procedural researchers seek high-end, secured, on-time, cost-effective ways to build reliable quality of service (QoS) proved autonomous systems using existing wireless techniques. Also, the continuous availability of a traffic-free gateway, particularly in isolated places, is necessary for large-scale data gathering and real-time data update processes to function the sensor nodes in an Internet of Things (IoT) network. Improved Narrowband IoT (NB-IoT) is one of the most desirable networks offered by 6th generation (6G) connectivity in IoT-associated remote-monitoring proceedings. In this article, we propose a QoS-aware narrow bandwidth allocation-based prototype model for healthcare monitoring in long-distance high-speed trains during the patient transfer from home to the healthcare center or vice versa. This article demonstrates a possible enhancement in social aspects of cognitive IoT applications with large data systems in industrial informatics. © 1975-2011 IEEE.

关键词： Frequency allocation

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Machine Translation for Morphologically Rich Low-Resourced South African Languages 4

Machine Translation for Morphologically Rich Low-Resourced S...

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Masethe, Hlaudi Daniel Mothapo, Lawrence M Ojo, Sunday O. Owolawi, Pius A. Masethe, Mosima Anna Giunchigilia, Fausto Tshwane University of Technology Department of Data Science South Africa Durban University of Technology Department of Information Technology South Africa Tshwane University of Technology Department of Computer Engineering South Africa Sefako Makgatho Health Sciences University Department of Computer Science and Information Technology South Africa University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350387988

In the context of machine translation, many of South Africa's languages are categorized as low-resourced, despite the country's vast linguistic diversity. Creating efficient multilingual translation systems that can support languages like Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, isiXhosa, Xitsonga, siSwati and isiZulu are severely hampered by the lack of high-quality training data. Low translation accuracy and fluency are the result of existing translation models' frequent inability to perform well in these languages. As a result, speakers of these languages encounter difficulties using increasingly accessible online resources for information and services. By utilizing the MarianMT model to create a reliable multilingual machine translation system, this work seeks to address these issues by enhancing the quality of translation for South African languages with limited resources. This study presents a comprehensive approach to multilingual machine translation focused on low-resourced South African languages, utilizing the MarianMT model. Our objective was to enhance translation quality for languages with limited training data, including Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, IsiXhosa, Xitsonga, siSwati and isiZulu. We conducted extensive experiments, resulting in a BLEU score of 62%, indicating a significant improvement in translation. Our evaluation metrics demonstrated strong performance, with precision at 78%, recall at 81%, F1 score at 79%, and overall accuracy at 82%. © 2024 IEEE.

关键词： Neural machine translation

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Multi-Hop D2D Cluster Formation and Resource Allocation for Scalable Video Multicast 22

Multi-Hop D2D Cluster Formation and Resource Allocation for ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Nie, Hao Wang, Wei Dai, Rui Liu, Chenyan Wang, Zhixing Xu, Peng Huazhong University of Science and Technology Wuhan China University of Cincinnati Department of Electrical Engineering and Computer Science Cincinnati United States Centrin Data Vally Technology Co. Ltd Wuhan China

ISBN: (纸本)9798331509712

Scalable video coding (SVC) associated with adaptive modulation and coding (AMC) has provided an excellent solution for transmitting real-time videos. However, some users have bad channel conditions with the base station (BS) and need help getting a good quality of service (QoS). With the fifth generation (5G) of the cellular network, these users can also receive most of the data by device-to-device (D2D) transmissions. In this paper, we focus on live video delivery scenarios. We adopt the multicast Multi-Hop D2D and reuse spectrum spatially to help broadcast the scalable videos. We aim to maximize all users' average quality of service (QoS) by jointly optimizing D2D cluster heads (CHs) selection and channel reuse. The problem is an integer non-linear programming problem, and we decompose the problem into two subproblems: the D2D cluster formation problem and the resource allocation problem. To solve the first subproblem, we propose a greedy algorithm to form D2D clusters that can broadcast more efficiently. We convert the second subproblem to a combination of two partial graph coloring problems in two conflict graphs. An algorithm similar to the recursive largest first (RLF) algorithm but considering the conflicts in both graphs is provided for the second subproblem. We execute numerical simulations to validate the performance of our proposal. © 2024 IEEE.

关键词： Scalable video coding

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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