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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

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A Cancellable Semi-Supervised Gait Recognition in 5G: The Future of Biometric

A Cancellable Semi-Supervised Gait Recognition in 5G: The Fu...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Joyce Tlhoolebe Neo Rafifing Pulafela Akofhang Majoo Oabile Lesley Boitshoko Tlamelo Emmanuel School of Computer and Artificial Intelligence Southwest Jiatong University Chengdu China School of Computer Science and Information Systems North-West University Northwest South Africa Department of Knowledge and Information Management University of Johannesburg Johannesburg South Africa Department of Computer Science and Information Systems Botswana International University of Science & Technology Palapye Botswana Department of Computer Science Botswana Accountancy College Gaborone Botswana

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

The rapid advancement of 5G technology needs a wide range of infrastructure solutions to handle various scenarios. 5G technology has increased the number of vulnerable endpoints that connect to a network, presenting hackers with more opportunities to exploit. Also, in addition to extremely high capacity and rigorous latency requirements, the security provisioning is getting difficult in 5G networks. Therefore, a strong authentication and authorization system is required to provide to service security for smooth interaction between various users and access to network services. The biggest concern with 5G is to secure biometric templates. With 5G, hackers will have greater bandwidth and faster speeds to perform more assaults than ever before. Cancelable semi-supervised gait recognition in 5G refers to a method of recognizing and identifying individuals based on their walking patterns (gait) using a combination of supervised and unsupervised learning techniques, while maintaining the ability to revoke and update the recognition model in a 5G network environment. Old gait recognition techniques utilize a limited number of labelled sequences for training, therefore, in this paper, we propose to develop a cancellable semi supervised gait recognition scheme using feature adaptive random projection technique. The study indicates improved privacy and security of biometric templates. Our semi supervised model returned a 96% classification rate which is a better performance compared to the supervised and unsupervised algorithms.

关键词： Training Adaptation models Privacy 5G mobile communication computer hacking Biological system modeling Pattern recognition Security Gait recognition Unsupervised learning

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Intrusion Detection in 5G Cellular Network Using Machine Learning

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computer systems science & Engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Research on Conceptual Model of the Remote Sensing Big Data Workflow 5

Research on Conceptual Model of the Remote Sensing Big Data ...

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5th International Conference on E-Business, information Management and computer science, EBIMCS 2022

作者： Du, Ying Zhang, Shuo Kaabar, Mohammed K.A. Yue, Xiao-Guang School of Computer and Information Engineering Henan University Kaifeng475004 China Institute of Mathematical Sciences Faculty of Science University Malaya Kuala Lumpur50603 Malaysia Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus Department of Information and Knowledge Management School of Consumer Intelligence and Information Systems University of Johannesburg Apk Campus Johannesburg2006 South Africa

ISBN: (纸本)9781450397834

Remote sensing big data workflows are formed by combining workflow and the remote sensing data processing system, and have the capabilities of remote sensing data processing, analysis, and visualization. There are many issues such as numerous sensors, sophisticated processing algorithms, and lacking of a unified process management about the operation control of the remote sensing data processing system. Therefore, the concept and characteristics of remote sensing big data workflows is reanalysed and the metamodel of remote sensing big data workflows from different aspects is constructed in order to establish a conceptual framework of them, and thus establish concept framework of Remote Sensing Big Data Workflow. © 2022 ACM.

关键词： Remote sensing

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Online knowledge Integration for 3d Semantic Mapping: A Survey

SSRN

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SSRN 2025年

作者： Igelbrink, Felix Renz, Marian Günther, Martin Powell, Piper Niecksch, Lennart Lima, Oscar Atzmueller, Martin Hertzberg, Joachim German Research Center for Artificial Intelligence Research Department Plan-based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Group Osnabrück Germany Osnabrück University Institute of Computer Science Semantic Information Systems Group Osnabrück Germany

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts. © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

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Synthetic and systems Biotechnology 2023年第3期8卷 498-508页

作者： Yalun Wu Qianqian Yuan Yongfu Yang Defei Liu Shihui Yang Hongwu Ma State Key Laboratory of Biocatalysis and Enzyme Engineering School of Life SciencesHubei UniversityWuhan430062HubeiChina Biodesign Center Key Laboratory of Systems Microbial BiotechnologyTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjin300308China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhan430062HubeiChina Artificial Intelligence and Knowledge Engineering Lab School of Computer Science and Information EngineeringHubei UniversityWuhan430062HubeiChina

High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology *** of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still *** mobilis *** ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical *** five GEMs of *** have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format *** this study,a high-quality GEM iZM516 of *** ZM4 was *** information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of ***,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of *** growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate *** addition,the potential endogenous succinate synthesis pathway of *** ZM4 was proposed through simulation and analysis using ***,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using *** results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species *** study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell fac

关键词： Genome-scale metabolic models(GEMSs) Non-model industrial microorganism Zymomonas mobilis Biolog phenotype microarray Succinate 1,4-Butanediol

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Power Consumption Dashboard Using Iot

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Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University 2023年第10期44卷 327-335页

作者： Sowmiya, S. Sakthivel, P. Nithya, C. Ravi, P. RanjithKumar, M. Computer Science and Engineering Mahendra Engineering College Mahendrapuri Mallasamudhram Namakkal India Information Technology Sona College of Technology Salem India Computer Science and Business System Knowledge Institute of Technology Namakkal India Information Technology K S R Institute for Engineering and Technology Tiruchengode Namakkal India Computer Science and Business Systems Knowledge Institute of Technology Namakkal India

"Power Consumption Dash Board Using IoT" was developed using a web application. The planned system includes a home energy monitoring system and cloud service notifications. An Internet of Things (IoT) platform was implemented to monitor energy consumption data at home and send the energy consumption data to the IoT cloud. The system uses the WiFi connected to the RaspberryPiPico to measure the energy consumption of the house. Energy consumption data is collected and stored in IoT cloud services. Consumers can monitor the energy consumption of their home on a daily basis and thus control how much energy is used on a daily basis. The data collection experiment aims to determine household energy consumption. Also, the website shows data on energy consumption before and after the load is connected to the system. IoT dashboard available online that allows users to check their daily energy consumption using IoT dashboard. IoT Cloud performs event analysis based on collected energy consumption data and alerts the owner with an alert, an email recommendation and a telegram notifying the customer when the energy consumption limit is exceeded, helping to reduce excessive energy consumption. © 2023 Editorial Board of Journal of Harbin Engineering. All rights reserved.

关键词： Internet of things

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Positive and unlabeled learning with controlled probability boundary fence 24

Positive and unlabeled learning with controlled probability ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Changchun Li Yuanchao Dai Lei Feng Ximing Li Bing Wang Jihong Ouyang College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines.

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Online knowledge Integration for 3D Semantic Mapping: A Survey

arXiv

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arXiv 2024年

关键词： knowledge graph

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Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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