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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Development of Low-Cost Surgical Simulator for Neuroendoscopy using Unity3D and HTC VIVE 16

Development of Low-Cost Surgical Simulator for Neuroendoscop...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Fatima, Aimen Bukhari, Faisal Iqbal, Waheed University of the Punjab Faculty of Computing and Information Technology Department of Computer Science Lahore Pakistan University of the Punjab Faculty of Computing and Information Technology Department of Data Science Lahore Pakistan

ISBN: (纸本)9781665464772

Neuroendoscopy is being utilized more frequently to treat brain tumors and hydrocephalus brought on by tumors. Neuroendoscopy is a minimally invasive operation that requires a lot of skills. Complete training is needed in hospitals to perform this procedure in the operating room. Simulators are necessary to train doctors due to the deficiency of time, resources, cost, and potential risks. With advancements in computer technology, it is becoming more common to simulate and plan surgery using a VR-based simulation. We have created and developed a surgical simulator to provide endoscopic training for the ETV procedure. A method to use multimedia capabilities for learning is to create a virtual 3D simulator using a game engine and render it using VR technology. The user-friendly collaborative environment of the Unity3D game engine offers good features. We have demonstrated how a simplex mesh depiction in a game-based simulator may effectively deform a soft body in actuality. We have successfully developed a method employing Position Based Dynamics to simulate the actual deformation of TM the cancerous brain. According to our study, the HTC Vive is a suitable VR technology for ETV training because it offers excellent precision, quick tactile feedback, a fast frame rate, and a wide field of vision. In the future, this practical tool might replace more costly training simulators. © 2022 IEEE.

关键词： Tumors

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A Predictive Model for Preliminary Indication of Cardiovascular Disease

A Predictive Model for Preliminary Indication of Cardiovascu...

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2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

作者： Arif, Ali Haider Bukhari, Faisal Fatima, Aimen Iqbal, Waheed University of the Punjab Faculty of Computing and Information Technology Department of Computer Science Lahore Pakistan University of the Punjab Faculty of Computing and Information Technology Department of Data Science Lahore Pakistan

ISBN: (数字)9781665459358

ISBN: (纸本)9781665459358

Executing medical diagnoses appropriately and effectively is critical due to the scarcity of available resources in the hospitals of developing countries. Diagnosis is also rendered by professional practice and expertise. Because of this, patients bear unnecessary and disproportionate care costs. The automated system is very beneficial for medical diagnosis. As in some cases, there could be a shortage of resource personnel, and not all doctors have experience in subspecialties. We needed studies of various available techniques and their comparisons for implementing these accurate automated systems. The current research, however, is an alternative precaution to this issue. A preliminary indication of cardiovascular disease using a predictive model is essential as it reduces the risk of developing the disease. The likelihood of having the cardiovascular disease can be predicted using multiple factors. One thousand sixty samples of individuals were taken from different hospitals in Punjab by using a questionnaire survey. Other scaling techniques were applied to the collected data. Various machine learning algorithms with hyperparameter tuning were applied to the collected data. As the results of the predictions were satisfactory, it would help predict cardiovascular disease and act as a critical public awareness to avoid future cardiac disease. © 2022 IEEE.

关键词： Diagnosis

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Vehicle's Density Prediction Based on History data of e-Toll in PT Jasamarga Pandaan Tol Using Hidden Markov Model 5

Vehicle's Density Prediction Based on History Data of e-Toll...

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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Asrori, N.I. Iriawan, N. Winahju, W.S. Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Indonesia

The Gempol-Pandaan toll is a strategic center for regional development. In 2017, the number of vehicles passing through this toll has increased. This condition could impact on traffic jam and road damage because of passes by many cars, especially trucks with loads that exceed their capacity. Therefore, it is necessary to do a vehicle's density prediction not only from the Gempol toll gate but also from Kejapanan, Bangil and Rembang toll gates that will exit through Pandaan toll gate. In this research, vehicle's density is viewed from the probability and number of each vehicles category I, II, III, IV, V. The origin gate and vehicle category can not be observed directly, but the car must pass the toll gate to do tapping the e-toll card, so the method used in this research is the Hidden Markov Model (HMM). Two estimate methods for HMM parameters are employed in this research, the Expectation-Maximization (EM) algorithm and Bayesian approach. The result shows that HMM parameters estimation using Bayesian is better than the EM algorithm. The Bayesian estimated parameter values are closer to the input parameters so that the model is more representative to explain the vehicle's density prediction. © Published under licence by IOP Publishing Ltd.

关键词： Hidden Markov models

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Molecular dynamics simulation of thermal behavior of paraffin/Cu nanoparticle PCM in a non-connected rotating ribbed tube

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International Communications in Heat and Mass Transfer 2025年 165卷

作者： Liu, Yaoyang Basem, Ali Al-zahy, Younis Mohamed Atiah Singh, Narinderjit Singh Sawaran Al-Bahrani, Mohammed Abduvalieva, Dilsora Salahshour, Soheil Esmaeili, Sh. Jiangxi Cas pharmaceutical Engineering Technology Co. LTD Jiangxi Nanchang330000 China Chinese Academy of Sciences Hatch Innovation Investment Co. Ltd Beijing100000 China Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Physics College of Education Misan University Maysan Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia Chemical Engineering and Petroleum Industries Department Al-Mustaqbal University Babylon51001 Iraq Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Applying molecular dynamics simulations, this study investigates the influence of different atomic ratios of Cu nanoparticles on the atomic and thermal behavior of a paraffin/Cu composite within a non-connected rotating ribbed tube. A simulation box measuring 50 × 150 × 50 Å3 is employed, with periodic boundary conditions in y and z-coordinates. LAMMPS simulation is running for a total of 20 ns. The simulation model is validated through an equilibration phase for 10 ns, achieving a temperature of 300 K and a total energy of 1.450 kcal/mol. The results indicate that the maximum density decreases to 0.0852 atom/Å3 as the atomic ratio of Cu nanoparticles increases from 1 to 7 %. Additionally, the velocity and temperature increased to 0.00493 Å/fs and 766 K. Furthermore, the thermal conductivity increased from 0.63 to 0.68 W/m·K, and the heat transfer increased from 5.25 to 5.36 W/m2. The charging and discharging times decrease to 6.24 and 7.11 ns. These trends are reversed at an atomic ratio of 10 %: the maximum density increased, the velocity and temperature decreased, the heat flux and thermal conductivity decreased to 5.33 W/m2 and 0.67 W/m·K, and the charging/discharging times increased to 6.26 ns and 7.18 ns, respectively. These results indicate that an optimal concentration of 7 % Cu nanoparticles improved thermal conductivity. The paper examined the influence of nanoparticle saturation on thermal stability, demonstrating that excessive agglomeration adversely impacts heat conduction. This study offered for the development of superior nanoparticle-enhanced phase change materials in confined systems with unconnected rotating ribs, to improve heat dissipation and stability. © 2025 Elsevier Ltd

关键词： Positive temperature coefficient

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Covert Communications with Enhanced Physical Layer Security in RIS-Assisted Cooperative Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Xingwang Liu, Musen Dang, Shuping Luong, Nguyen Cong Yuen, Chau Nallanathan, Arumugam Niyato, Dusit Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University Singapore School of Electrical and Electronics Engineering 639798 Singapore Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanyang Technological University Singapore College of Computing and Data Science 639798 Singapore

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) technologies are recognized for their programmability and high energy efficiency respectively, which will be the key parts of the future sixth generation (6G) mobile communication technology. The combination of the two technologies can improve communication security by reducing the probability of detection and decoding through enhanced transmission and backscatter transmission in different communication slots. In this paper, a dual-function RIS that supports cooperative relaying for covert communications is proposed. It operates in different communication slots (enhanced transmission slot and backscatter slot), but the performance is affected by phase errors due to function switching. A source covertly communicates with an intended destination via the help of RIS and cooperative relay. There is an illegal monitor aims to detect and eavesdrop the covert message. For this system, the outage probability (OP), intercept probability (IP), and detection error probability (DEP) in different communication slots are derived to examine the system reliability and security. Moreover, the system security probability (SSP) is proposed, and a block coordinated ascent (BCA)-based iterative algorithm is used to jointly optimize the power allocation coefficients to maximize the SSP. Simulation results show that increasing the number of elements can improve the security performance and mitigate the negative impact of RIS phase errors. © 2002-2012 IEEE.

关键词： Mobile telecommunication systems

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Machine learning for modelling unstructured grid data in computational physics: A review

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Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： Reinforcement learning

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Part-Based Convolutional Neural Network and Dual Interactive Wasserstein Generative Adversarial Networks for Land Mark Detection and Localization of Autonomous Robots in Outdoor Environment Note: Sub-titles are not captured in Xplore and should not be used 1

Part-Based Convolutional Neural Network and Dual Interactive...

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Sindhu, S. Saravanan, M. Srm Institute of Science and Technology Faculty of School of Computing Department of Data Science and Business Systems Kattankulathur India Srm Institute of Science and Technology Faculty of School of Computing Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9781665476553

Robot localization is a fundamental competency required by an autonomous robot because the robot's location knowledge is an essential precursor to making decisions about future actions. Accurate localization of robots or autonomous vehicles is the most important requirement for autonomous applications. In this manuscript, a part-based convolutional neural network and dual interactive Wasserstein generative adversarial networks for landmark detection and localization of autonomous robots in an outdoor environment are proposed (P-CNN-DIWGAN-LMD-LZ). This research contains two phases landmark detection phase and the localization phase. In the landmark detection phase, the part-based convolutional neural network (P-CNN) is proposed to detect landmarks in capturing images. This landmark detection process creates 3 categories of responses for every detected landmarks instance: bounding box, label, and score. The bounding box has positioning with the sizing of detected landmarks at the input imagery. Label implicates detected landmark's class name. A score signifies an abjectness score that scales bounding box membership for landmarks or background classes. In the localization phase, a dual interactive Wasserstein generative adversarial network (DIWGAN) is proposed to determine robot location coordinates. Finally, the proposed method attains high robot localization recall at high accuracy in the real-world environment. Here, the outdoor robot localization dataset is taken from the KITTI dataset. The proposed method is implemented in Python;its performance is estimated under certain performance metrics, like mean absolute error (MAE), cosine proximity (CP), and accuracy. The performance of the proposed method shows higher accuracy compared with existing approaches, like DQP-ODA-LMD-LZ, TDL-LMD-LZ, and 3D-RISS-MLEVD- LMD-LZ. © 2022 IEEE.

关键词： Generative adversarial networks

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Development of an Enhanced Quality Score Calculation Method for Accurate Assessment of Hotel Quality 5

Development of an Enhanced Quality Score Calculation Method ...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Athurugiriya, A. A. A. Gaweshini Sumathipala, Prasanna Hemachandra, K.M. Tharindu Anupa Sri Lanka Institute of Information Technology Faculty of Computing Department of Data Science Malabe Sri Lanka

ISBN: (纸本)9798350358131

In this research paper, an enhanced hotel quality scoring method (EHQSM), is presented as a revolutionary way to analyze hotel quality. The EHQSM combines sentiment analysis, HOLSERV Plus dimensions classification, and amenity scores to deliver a more thorough review. The study generates an overall sentiment score by doing sentiment analysis on reviews categorized into HOLSERV Plus dimensions then thoughtfully averaging it using dimensionspecific weights collected from a survey. The availability of necessary amenities is considered when calculating the amenity score. The novel feature of this approach is how the amenity score, and sentiment score are carefully combined and efficiently weighted using values from the survey. The result is an improved quality score that accurately and thoroughly describes the quality of hotel services. With the goal of facilitating informed decisions for both hotel owners and attentive tourists, this technique establishes an updated standard for the evaluation of hospitality quality. © 2023 IEEE.

关键词： Sentiment analysis

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Enhancing Hotel Customer Satisfaction Assessment with Semantic Search-based Scoring 5

Enhancing Hotel Customer Satisfaction Assessment with Semant...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Hemachandra, K.M Tharindu Anupa Sumathipala, Prasanna Athurugiriya, A.A.A Gaweshini Sri Lanka Institute of Information Technology Department of Data Science Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

Within the hospitality sector, ensuring customer satisfaction takes precedence, particularly in economies such as Sri Lanka, where tourism serves as a vital growth catalyst. Interestingly, disparities often arise between customer ratings and their written evaluations. This study presents an innovative strategy that utilizes semantic search techniques to extract comprehensive insights from customer feedback, leading to the development of a robust hotel scoring system. By exploring intricate emotions via advanced linguistic analysis, this method reveals underlying cues that conventional metrics often overlook. The method's effectiveness is validated through a comparative study with established hotel ratings. This advancement not only refines precise customer satisfaction assessment for hotels but also bears strategic significance for countries aiming to fortify their tourism sectors. In this era centered around data, this research provides a pivotal standpoint for optimizing hospitality evaluation, thereby enriching guest experiences, and guiding well-informed operational choices. © 2023 IEEE.

关键词： Sentiment analysis

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