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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Enhancement of wood filled polymer by microwave induced heat treatment

Enhancement of wood filled polymer by microwave induced heat...

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2023 International Conference on Advances in Materials, Mechanics, Mechatronics and Manufacturing, IC4M 2023

作者： Subasinghe, M.P.M. Soon, Kok Heng Ko, Rudy Vui Keong Jayamani, Elammaran Choo, Hui Leng Faculty of Engineering Computing and Science Swinburne University of Technology Q5B Sarawak Kuching93350 Malaysia School of Engineering Faculty of Innovation and Technology Taylor's University No. 1 Jalan Taylor's Selangor DE Subang Jaya47500 Malaysia

A domestic microwave oven was modified using a thermocouple to measure temperature while heating to experiment the effect of microwave irradiation on Meranti wood. The main aspect of this research paper would be to evaluate the performance of heating on how it affects the wood by microwave irradiation. Wood is the testing specimen of this control system. The purpose of this study was to see if the microwave drying method had an effect on young's moduli, tensile strength, and permeability. The results of the experimental tests demonstrate that Young's moduli increased as wood powder volume increased. The samples with 10 wt% of microwave-treated wood powder had the highest elasticity modulus, tensile strength and permeability of 4043 MPa, 15.41 MPa and 24.43% respectively. It was followed by Fourier-transform infrared spectroscopy (FTIR) analysis and followed by SEM (Scanning electron microscope analysis). Microwave treated wood at high intensities increased the elastic modulus and tensile strength by deeply impregnating the wood into the composite, according to morphological analysis performed using SEM. Copyright © 2024 Elsevier Ltd. All rights reserved.

关键词： Tensile strength

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach 5

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Athilakshmi, R. Jansi, R. Upadhyay, Roshan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350321425

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. The proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. The use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. This study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. These findings demonstrate the potential of an automated system to improve DR diagnosis and management. © 2023 IEEE.

关键词： Computer vision

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees 41

FedLMT: Tackling System Heterogeneity of Federated Learning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Jiahao Zhou, Yipeng Wu, Di Hu, Miao Guizani, Mohsen Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Abu Dhabi United Arab Emirates

Federated learning (FL) is an emerging machine learning paradigm for preserving data privacy. However, diverse client hardware often has varying computation resources. Such system heterogeneity limits the participation of resource-constrained clients in FL, and hence degrades the global model accuracy. To enable heterogeneous clients to participate in and contribute to FL training, previous works tackle this problem by assigning customized sub-models to individual clients with model pruning, distillation, or low-rank based techniques. Unfortunately, the global model trained by these methods still encounters performance degradation due to heterogeneous sub-model aggregation. Besides, most methods are heuristic-based and lack convergence analysis. In this work, we propose the FedLMT framework to bridge the performance gap, by assigning clients with a homogeneous pre-factorized low-rank model to substantially reduce resource consumption without conducting heterogeneous aggregation. We theoretically prove that the convergence of the low-rank model can guarantee the convergence of the original full model. To further meet clients' personalized resource needs, we extend FedLMT to pFedLMT, by separating model parameters into common and custom ones. Finally, extensive experiments are conducted to verify our theoretical analysis and show that FedLMT and pFedLMT outperform other baselines with much less communication and computation costs. Copyright 2024 by the author(s)

关键词： Federated learning

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Feature Reduction of Relational Oil Drilling Data Before Propositionalization and Harmonization by Measuring Relational Data Missingness

Feature Reduction of Relational Oil Drilling Data Before Pro...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Ting, Clement Pek Wen Then, Patrick Hang Hui Centre for Digital Futures School of Information and Communications Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Knowledge Discovery in Database (KDD) is a process undertaken by many data scientists who aim to extract knowledge from previously collected, secondary data. KDD is difficult when the scientist must analyze big data without prior due to the amount of knowledge required to understand and select relevant features for propositionalization, which is the conversion of raw relational data (stored in several tables) to propositional data (stored as one table) which is more compatible with established feature selection and data mining techniques. In this paper, we describe the Database Attribute Health Feature Reduction process (DAHFR) which is used as a filter-method feature selection metric for Feature Selection before Propositionalization (FSbP) to reduce the scope of features for observation, analysis, harmonization, and propositionalization before data mining. We employ FSbP using DAHFR during the KDD of multi-sourced oil drilling data which is contained in about 20,000 features in about 700 tables in 1 database per source. Each database follows an identical schema but has varying feature-wide missingness and semantic patterns which makes effective propositionalization difficult. DAHFR is a semi-automated process which allows the user to measure relational missingness of each feature for feature reduction and compare missingness patterns across databases. DAHFR is an improvement to existing feature selection processes because it requires minimum initial knowledge of the database’s business rules and could be used with relational data without the need for direct data observation nor prior propositionalization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Integrating ChatGPT into Medical Education: A Combined SEM-ML Approach 2

Integrating ChatGPT into Medical Education: A Combined SEM-M...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Salloum, Said Shaalan, Khaled Alfaisal, Raghad Salloum, Ayham Gaber, Tarek School of Science Engineering and Environment University of Salford Manchester United Kingdom The British University in Dubai Faculty of Engineering & It United Arab Emirates Universiti Pendidikan Faculty of Computing and Meta-Technology Sultan Idris Malaysia College of Medicine University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350370188

The educational realm has undergone profound transformations due to technological advancements, evolving from conventional classrooms to digital platforms, such as online learning modules and virtual simulations. Within this context, ChatGPT, an innovation in artificial intelligence, stands out for its capacity to enhance and personalize learning experiences in medical education. While the potential of tools like ChatGPT is acknowledged, there is a need to understand the determinants of their acceptance, especially in the backdrop of dynamic medical curricula. Drawing on The Technology Acceptance Model (TAM) and focusing on perceived value, we collected 563 questionnaires across multiple academic institutions. We then analyzed this data using partial least squares-structural equation modeling (PLS-SEM), and machine learning (ML) algorithms. Findings highlighted ChatGPT's pronounced influence on user acceptance, with perceived value emerging as a primary driver. Interestingly, task-technology congruence did not show significant predictive power. Notably, the J48 classifier showcased superior predictive capacity over other models for the dependent variable. This study offers invaluable insights for stakeholders in medical education, suggesting the importance of aligning AI tools like ChatGPT with user perceptions and needs to ensure successful adoption and integration into the learning landscape. © 2024 IEEE.

关键词： Simulation platform

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CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

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Multimedia Tools and Applications 2024年第39期83卷 86557-86591页

作者： Geetha, R. Jegatheesan, A. Dhanaraj, Rajesh Kumar Vijayalakshmi, K. Nayyar, Anand Arulkumar, V. Velmurugan, J. Thavasimuthu, Rajendran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Pune India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77% accuracy, 99.92% specificity, 99.60% precision, 99.81% detection rate, and a 99.72% F1-score in the CIC IDS dataset, and 99.68% accuracy, 99.30% precision, 99.83% specificity, 99.11% detection rate, and a 99.21% F1-score in evaluating the BoT-IoT dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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