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COCP:Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking

Computers, Materials & Continua 2022年第6期71卷 5523-5543页

作者： Salman Rashid Shukor Abd Razak Fuad A.Ghaleb Faisal Saeed Eman H.Alkhammash School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia College of Computer Science and Engineering Taibah UniversityMedina 42353Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

On-path caching is the prominent module in Content-Centric Networking(CCN),equipped with the capability to handle the demands of future networks such as the Internet of Things(IoT)and vehicular *** main focus of the CCN caching module is data dissemination within the *** of the existing strategies of in-network caching in CCN store the content at the maximumnumber of routers along the downloading ***,content redundancy in the network increases significantly,whereas the cache hit ratio and network performance decrease due to the unnecessary utilization of limited cache ***,content redundancy adversely affects the cache resources,hit ratio,latency,bandwidth utilization,and server *** proposed an in-network caching placement strategy named Coupling Parameters to Optimize Content Placement(COCP)to address the content redundancy and associated *** novelty of the technique lies in its capability tominimize content redundancy by creating a balanced cache space along the routing path by considering request rate,distance,and available cache *** proposed approach minimizes the content redundancy and content dissemination within the network by using appropriate locations while increasing the cache hit ratio and network *** COCP is implemented in the simulator(Icarus)to evaluate its performance in terms of the cache hit ratio,path stretch,latency,and link *** experiments have been conducted to evaluate the proposed COCP *** proposed COCP technique has been compared with the existing state-of-theart techniques,namely,Leave Copy Everywhere(LCE),Leave Copy Down(LCD),ProbCache,Cache Less forMore(CL4M),and *** results obtained with different cache sizes and popularities show that our proposed caching strategy can achieve up to 91.46%more cache hits,19.71%reduced latency,35.43%improved path stretch and 38.14%decreased link *** results confirm that the proposed COCP strateg

关键词： Content-centric networking on-path caching content redundancy security privacy data dissemination internet of things

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Enhancing a Random Forest Model Based on Single Rule Reduction for Tax Evasion Depends on the Values of K in K-Fold Validation Technique 1

Enhancing a Random Forest Model Based on Single Rule Reducti...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Abdullah, Abeer Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Al-Gaphari, Ghaleb Al-Badani, Abdulkader M. Alsabry, Ayman Shujaaddeen Computer Science Faculty of Computer and Information Technology Sana’a University Sana’a Yemen Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen

ISBN: (纸本)9798331533557

This paper developed a new model called Single Rule Random Forest (SrRF) that enhances the performance of the Fandom Forest technique(RF)and reduces its rules, then compared the performance of this model with a set of ML models as follows (DT, RF, GPT) of the datasets provided by the Tax Authority of Yemen which consists of 1083 records using two values for k in K-Fold Cross Validation). The results showed that by using the value k=10, the GPT Classifier gave the highest result of 100 % but this result will cause overfitting, then the SRI-RF Classifier gave the best result with 99.89%, while RF gave the worst result. By using the value k=5, the GPT Classifier also gave the highest result of 100 % but this result also caused overfitting, then the SrRF Classifier gave the best result, while RF gave the worst result. From the above, the researchers also note that training the data using K-fold cross-validation with the value K=10 gave better results. On the other side, our proposed model SRIRF with the value of k=10 gave the best result. © 2024 IEEE.

关键词： Decision trees

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Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas

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International Journal of Hydrogen Energy 2025年 141卷 585-597页

作者： S, Priya Feenita, C. Goel, Uday T, Manoranjitham Duraisamy, Boopathi Subramanian, Balaji Ganeshan, Kavitha Josephin JS, Femilda Albeshr, Mohammed F. Pugazhendhi, Arivalagan Varuvel, Edwin Geo Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Automobile Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Mechanical Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Science and Humanities Agni College of Technology Thazhambur Chennai600130 India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Zoology College of Sciences King Saud University P.O. Box. 2455 Riyadh11451 Saudi Arabia Research and Development Office Asia University Taichung City Taiwan Department of Mechanical Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Autoronics Institute of Automobile Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India

Internal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of da

关键词： Air intakes

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Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

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Chinese Physics B 2023年第3期32卷 153-163页

作者：叶国栋吴惠山黄小玲 Syh-Yuan Tan Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China Guangdong Provincial Engineering and Technology Research Center of Far Sea Fisheries Management and Fishing of South China Sea Zhanjiang 524088China School of Computing Newcastle UniversityNewcastle NE45TGUK

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this *** the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon *** the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the ***,the key stream can be produced depending on the plain image for a higher ***,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic *** the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is ***,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher ***,experiments show that the the proposed algorithm has a better ***,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

关键词： three-dimensional improved logistic chaotic map(3D-ILM) Rivest–Shamir–Adleman(RSA)algorithm image encryption confusion entropy

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Influences of double diffusion upon radiative flow of thin film Maxwell fluid through a stretching channel

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Chinese Physics B 2023年第8期32卷 327-335页

作者： Arshad Khan Ishtiaq Ali Musawa Yahya Almusawa Taza Gul Wajdi Alghamdi College of Aeronautical Engineering National University of Sciences and Technology(NUST)Sector H-12Islamabad 44000Pakistan Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl-Ahsa 31982Saudi Arabia Department of Mathematics Faculty of ScienceJazan UniversityJazanSaudi Arabia Department of Mathematics City University of Science and Information TechnologyPeshawar 25000Pakistan Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 80261Saudi Arabia

This work explores the influence of double diffusion over thermally radiative flow of thin film hybrid nanofluid and irreversibility generation through a stretching *** nanoparticles of silver and alumina have mixed in the Maxwell fluid(base fluid).Magnetic field influence has been employed to channel in normal *** that are going to administer the fluid flow have been converted to dimension-free notations by using appropriate *** analysis method is used for the solution of the resultant *** this investigation it has pointed out that motion of fluid has declined with growth in magnetic effects,thin film thickness,and unsteadiness *** of fluid has grown up with upsurge in Brownian motion,radiation factor,and thermophoresis effects,while it has declined with greater values of thermal Maxwell factor and thickness factor of the thin *** distribution has grown up with higher values of thermophoresis effects and has declined for augmentation in Brownian motion.

关键词： Maxwell fluid flow magnetohydrodynamic(MHD) hybrid nano fluid flow stretching channel double diffusion entropy generation HAM technique

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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Towards sustainable separation and recovery of dichloromethane and methanol azeotropic mixture through process design, control, and intensification (vol 98, pg 213, 2022)

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JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY 2023年第6期98卷 1551-1554页

作者： Kong, Z. Y. Yang, A. Segovia-Hernandez, J. G. Putranto, A. Sunarso, J. Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Sarawak Malaysia College of Safety Engineering Chongqing University of Science & Technology Chongqing People's Republic of China Universidad de Guanajuato Campus Guanajuato División de Ciencias Naturales y Exactas Departamento de Ingeniería Química Guanajuato Gto Mexico Discipline of Chemical Engineering School of Engineering Monash University Bandar Sunway Malaysia

Background Here we analysed the possibility of improving the sustainability performance for the recovery of dichloromethane and methanol from a binary azeotropic mixture using different energy-intensified extractive distillation-based processes: side-stream extractive distillation (SSED), thermally coupled extractive distillation (TCED), and extractive dividing wall column (EDWC). The sustainability performance of the different processes was analysed based on three main factors: total annual cost (TAC), CO 2 emissions, and condition number. Results The EDWC was found to give the best improvement in terms of TAC and CO 2 emissions by about 18% and 21%, relative to conventional extractive distillation (CED). These however were traded-off by the increase in conditional number (CN) by 186 times, signifying a complex dynamic characteristic for the EDWC. Thus, the SSED was suggested as an alternative sustainable option as it also provides significant improvement in TAC and CO 2 emissions by about 11%, and 18% with respect to the CED, whilst providing the least reduction in operational controllability, as evidenced by the marginal increase in the CN of about 1.5 times. We also investigated the dynamic performance of the SSED and found that the SSED provides identical dynamic performance in handling both ±10% throughput and ±5% feed composition disturbances as those of the CED. Conclusion Among the different processes, SSED is the best sustainable alternative that provides compromised steady-state (i.e. TAC and CO 2 emissions) and dynamic (i.e. control) performance for the recovery of dichloromethane and methanol. © 2022 Society of Chemical Industry (SCI).

关键词： resource recovery extractive distillation binary azeotropic separation energy-intensified techniques design and control

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Machine Learning Algorithms for the Detection of Threats in IoT Healthcare 1

Machine Learning Algorithms for the Detection of Threats in ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Arun, V. Shenbagavalli, P. Sridhar, T. Manivannan, B. Mahesh, T.R. Anitha, K. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India Karunya Institute of Technology and Sciences Division of Digital Sciences India Alliance University Alliance College of Engineering and Design Bangalore India Computer Science and Engineering Namakkal India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350359756

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy. © 2023 IEEE.

关键词： Internet of things

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

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Computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： Bander Alzahrani Ahmed Barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial *** example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real ***,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management *** addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication ***,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD ***,the AKA scheme involves many vulnerabilities in terms of security and *** this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling *** scheme supports key security features,including anti-forgery attacks,and confirms user *** security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle *** simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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