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Development of Multi-Agent-Based Indoor 3D Reconstruction

computers, Materials & Continua 2024年第10期81卷 161-181页

作者： Hoi Chuen Cheng Frederick Ziyang Hong Babar Hussain Yiru Wang Chik Patrick Yue Optical Wireless Lab Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling *** work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is *** visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional ***,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents *** 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural *** validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our *** challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding *** summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.

关键词： Multi-agent system multi-robot human collaboration visible light communication visible light positioning 3D reconstruction reinforcement learning multi-agent path finding

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Unleashing Deep Reinforcement Learning: A Promising Alternative for Imbalanced Dataset Classification 12th

Unleashing Deep Reinforcement Learning: A Promising Alternat...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Din, Nusrat Mohi Ud Assad, Assif Sabha, Saqib Ul Bhat, Muzafar Rasool Department of Computer Science and Engineering Islamic University of Science and Technology Kashmir India Department of Computer Science Islamic University of Science and Technology Kashmir India

ISBN: (纸本)9789819731794

Class-imbalanced datasets pose a significant challenge for classification tasks in supervised learning, as standard classification algorithms are designed under the assumption that the datasets have balanced class distributions. This can lead to biased models that perform poorly on minority classes. To address this challenge, several approaches have been proposed at both the data level and algorithmic level. In this research study, comparative analysis of Deep Reinforcement Learning (DRL) methods and Oversampling techniques (SMOTE, SVM-SMOTE, ADASYN) along with Deep Learning (DL) algorithms have been performed on two benchmark imbalanced datasets—"Breakhis" and stroke prediction datasets. The experimentation has been carried out by taking different imbalanced ratios of 0.05, 0.1, 0.2, and 0.3 on the Breast Cancer training dataset and 0.05 imbalanced ratio on the Brain Stroke dataset. The comparison of DRL and Oversampling methods has been performed, and the results demonstrate that the DRL model outperformed oversampling techniques on all imbalanced ratios of the "Breakhis" dataset. For the Stroke prediction dataset, Oversampling techniques completely failed in classifying the minority class samples. These findings suggest that the Deep Reinforcement Learning algorithm presents itself as a promising alternative for imbalanced dataset classification in the medical domain and could potentially contribute to improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Leveraging the Potential of Large Language Models

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Informatica (Slovenia) 2024年第8期48卷 1-16页

作者： Prasad, Shreya Gupta, Himank Ghosh, Arup Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Information Technology Graceland University LamoniIA United States

This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future. Povzetek: Študija se osredotoča na izboljšanje obdelave naravnega jezika (NLP) v generativnih AI klepetalnicah z uporabo naprednih vnaprej usposobljenih modelov. Analizirali so pet modelov: TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B in LLAMA-2-13B. Model LLAMA-13B se je izkazal kot najboljši z vrhunskimi rezultati na testih HellaSwag in WinoGrande. Raziskava uporablja orodja, kot je FAISS, in uporabniške vmesnike, kot sta Streamlit in Chainlit za izboljšanje uporabniške izkušnje. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Machine learning

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A fast and distributed C4.5 algorithm for urban big data

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Intelligent Data Analysis 2023年第5期27卷 1379-1408页

作者： Cheng, Wan-Shu Huang, Peng-Yu Huang, Jheng-Yu Chen, Ju-Chin Lin, Kawuu W. Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

The amount of information nowadays is rapidly growing. Aside from valuable information, information that is unrelated to a target or is meaningless is also growing. Big data and broader digital technologies are considered the primary components of smart city governance and planning. Big data analysis is considered to define a new era in urban planning, research, and policy. Effective data mining and pattern detection techniques are becoming very important these days. Processing such a large amount of data entails the use of data mining, a technique that clarifies the association between valid information and excludes irrelevant data to implement a practical decision tree. A large amount of data affects processing time and I/O costs during data mining. This study proposes to distribute data among multiple clients and distribute a large amount of data computation equally to improve the resource cost problem of exploration. Following that, the main server consolidates the computation results and generates the survey results. Experiment results show that the proposed algorithm is superior, thus allowing a larger amount of data to be processed while producing high-quality results. © 2023 - IOS Press. All rights reserved.

关键词： Data mining

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Production of Sustainable Mortar with Sodium Hydroxide Treated Rubber Crumb

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Iranian Journal of science and technology - Transactions of Civil engineering 2025年 1-25页

作者： Kilani, Abiodun Joseph Ikotun, Bolanle Deborah Abdulwahab, Rasheed Department of Civil & Environmental Engineering and Building Science College of Science Engineering and Technology University of South Africa Florida Science Campus Roodepoort South Africa

Mortar is one of the essential construction materials required for the construction of infrastructures, most importantly, as connectors in walling units. The incorporation of Rubber Crumbs (RCs) into mortar is one of the best methods of controlling environmental pollution and improving the quality of construction material. This study evaluates the performance of sodium hydroxide (NaOH) treated Rubber Crumb (RC) on mortar’s structural properties. In the experiment, the physical and chemical characterizations of the materials such as sand, RCs and cement were determined in accordance with SANS 3001-AG1 and SANS 3001-AG20 respectively. The consistency and setting times of mortar were determined according to SANS 50196 – 3: 2006 and EN 196 – 3: 2005. Also, the densities, water absorption, modulus of elasticity, and flexural and compressive strengths of mortar were evaluated according to SANS 50196-1, ASTM C 1437 and SANS 50196-3 standard using 40 × 40 × 160 mm mortar prisms. The morphological natures of the RC were evaluated through SEM and EDS techniques. The experimental results showed that 1.5% RC increased mortar’s compressive and flexural strengths by 9.13% and 5.89%, respectively. The density, modulus of elasticity, variation in modulus of elasticity, and water absorption of mortar reduced by 3.8, 10, 0.2 and 0.27% at the application of 1.5, 1.5, 1.5, and 3.5% of RC respectively. This reduction indicated good properties of RC- mortar for construction purposes. The SEM and EDS micrographs of RC treated with NaOH developed less dense pores leading to the production of better strength mortar. In conclusion, mortar with 1.5% NaOH treated RC is good for construction purposes, most importantly, in building blocks interface and rendering of walls. © The Author(s) 2025.

关键词： Compressive strength Flexural strength Modulus of elasticity Mortar Rubber crumbs

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An improved image compression framework in modern multimedia applications using tetrolet transformation

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Multimedia Tools and Applications 2025年第6期84卷 3305-3338页

作者： Yadav, Satya Prakash Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology U.P Gorakhpur India

The fast advancement of the multimedia era has led to an explosion in the use and technology of large amounts of digital snapshots. It has created a developing call for Image compression techniques that can reduce the dimensions of Image without compromising their perceptual excellence. Lossless picture compression techniques have emerged as a popular answer in this regard, as they maintain the precise statistics of the original Image. However, current lossless compression techniques face demanding situations in accomplishing high compression ratios without sacrificing the picture best. This paper proposes a more robust lossless Image compression framework based on the Tetrolet transformation. The framework introduces an adaptive quantization method, which adjusts the quantization step length based totally on the neighborhood Image characteristics, resulting in better compression overall performance for exclusive sorts of Image. Context modeling is also included in the framework to make the most of the inter-Image correlations inside the Image. This method uses context information from the Detroit-converted coefficients, which might be used to estimate the probability of prevalence for every Image value. The proposed model obtained 94.09% accuracy, 92.90% Precision, 95.25% Recall, 92.91% F1-Score and 93.87% Compression Duration. Using as it should be modeling these chances;the framework improves the entropy coding performance and ultimately results in better compression ratios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image compression

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Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Panigrahi, Bhawani Sankar Kanna, R. Kishore Das, Pragyan Paramita Sahoo, Susanta Kumar Dutta, Tanusree Department of Computer Science & Engineering GITAM Institute of Technology GITAM University Vishakhapatnam India Chennai India Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar India Odisha Sarang India Hyderabad India

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centers, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies. © (2024) Panigrahi, B.S. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Machine learning

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Software effort estimation using convolutional neural network and fuzzy clustering

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Neural Computing and Applications 2024年第23期36卷 14449-14464页

作者： Azzeh, Mohammad Alkhateeb, Abedalrhman Bou Nassif, Ali Department of Data Science Princess Sumaya University for Technology Amman Jordan Department of Computer Science Lakehead University Thunder Bay Canada Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

Adopting an efficient software process model is critical for building high-quality software applications. An important factor impacting the software development process is an accurate estimate of human effort required to complete the software project. While machine learning methods were historically used to develop estimation models, there has been little investigation into the potential of deep convolutional neural networks (DCNNs) for improving software effort estimation. One of the biggest obstacles in using DCNN for this purpose is the common nature of software datasets, which often consist of vectorized samples rather than matrices. To defeat this obstacle and reduce vagueness in software attribute measurement, this study uses Fuzzy theory to generate an appropriate two-dimensional datapoint representation. The fuzzy clustering is commonly used to split dataset samples into separate clusters, which can help to generate Fuzzy membership functions. This approach makes it easier to generate a two-dimensional array representation for each data sample based on the membership values, allowing it to be used as input to the DCNN model. The efficiency of the proposed model was thoroughly evaluated using PROMISE benchmark datasets. The findings based on mean absolute errors and standardized accuracy show that our proposed model produced very good performance with low error rates and outperformed several current state-of-the-art effort estimation models. Nonetheless, further research is needed to determine the impact of different cluster numbers and features on the performance of our model. In conclusion, this study emphasizes the possibility for incorporating DCNN into software effort estimates and highlights the viability of utilizing fuzzy modeling and clustering techniques to enhance the data representation of software datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy clustering

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In-service performance of emergency shutdown valves and dependent operational relationships in the offshore oil and gas industry

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Petroleum 2023年第4期9卷 613-620页

作者： Keith Cameron Andrew Lewis Diogo Montalvao Mohammad Reza Herfatmanesh School of Physics Engineering and Computer ScienceUniversity of HertfordshireUnited Kingdom Department of Design and Engineering Faculty of Science and TechnologyBournemouth UniversityUnited Kingdom

Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.

关键词： Emergency Shutdown Valve Safety instrumented systems Closure time IEC-61511

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