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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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3DSC2-UNet: Advancing Breast Cancer Segmentation Techniques Using the 3D Skipped Convolution Capsule-UNet

3DSC2-UNet: Advancing Breast Cancer Segmentation Techniques ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ramadass, Parthasarathy Sharmila, C. Mahendran, Rakesh Kumar Hema, V. Kavitha, E. Sanmugasundaram, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr.M.G.R Educational and Research Institute Department of Physics Chennai India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Breast cancer (BC) persists as a substantial global health hurdle, demanding ongoing progress in the realms of detection, diagnosis, and therapeutic approaches. This thorough examination delves into cutting-edge technologies and methodologies that have surfaced in the quest for enhanced results in BC management. Yet, investigations employing solitary imaging modalities have showcased restricted clinical effectiveness owing to their relatively diminished diagnostic precision and dependability when compared to multimodal systems. As a result, our aim is to create a 3D Skipped Convolution Capsule UNet designed for the segmentation of breast tumors, employing both mammogram and ultrasound imaging modalities. For the integration of these modalities, we utilize the Dynamic Fusion Net (DF-Net), incorporating two modules named the Molecular-Level Aligner Module and Biphasic Sequential Integration Module (BSInU). The process of segmentation takes place within our innovative framework, 3DSC2-UNet, encompassing four primary elements: (a) Graphical feature extraction, (b) Convolutional Capsule (ConvCaps) encoder module, (c) Synaptic skip connection, and (d) Convolutional decoder (Conv Decoder). The overall accuracy attained is 99.97%, accompanied by a Dice Similarity Coefficient (DSC) of 0.89 and an Average Surface Distance (ASD) of 2.5mm across the mammogram dataset. Moreover, the precision stands at 99.65%, accompanied by a Dice Similarity Coefficient (DSC) of 0.88 and an Average Surface Distance (ASD) of 2.4mm across the ultrasound dataset. The suggested 3DSC2-UNet, harnessing both mammogram and ultrasound, showcases exceptional performance in accuracy, ASD, and DSC metrics. This model holds the capability to diminish unwarranted biopsy procedures, thereby advancing its prospects for clinical implementation. © 2024 IEEE.

关键词： Ultrasonic imaging

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Navigating the Blockchain Landscape with a Comprehensive Survey of Smart Contracts, Ethereum, and Consensus Mechanisms 4

Navigating the Blockchain Landscape with a Comprehensive Sur...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Suresh Kumar, N. Dolly Brinthakumari, S. Partheeban, N. School of Computer Science and Engineering Ramanagara India Jagan Institute of Management Studies Department of Information Technology Delhi India New Horizon Institute of Technology and Management Department of Computer Engineering Thane India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350393354

In the dynamic landscape of blockchain technology, this research paper meticulously explores and contrasts two pivotal paradigmsSmart Contracts and Traditional Contractswithin the overarching framework of decentralized agreements. In the pursuit of this, the research diligently examines the subtle differentiations inherent in the various aspects of execution, intermediaries, trust, transparency, speed, costs, flexibility, security, and enforceability. While Smart Contracts, executed automatically through code on blockchain platforms such as Ethereum, offer unprecedented efficiency and transparency, Traditional Contracts, reliant on manual execution and potential intermediaries like lawyers, maintain a familiar yet potentially more cumbersome approach. The research extends its focus to Ethereum, a groundbreaking blockchain platform, spotlighting its instrumental role in upholding data security and integrity. The subsequent challenge under consideration is Ethereum's key aspects, such as decentralization, immutability, cryptography, smart contracts, consensus mechanisms, transparent ledger, tamper resistance, encryption, and permissioned access. A pivotal facet of this study centers on the comprehensive examination and comparison of consensus mechanisms employed in blockchain systems. This research assesses and compares key parameters of prominent blockchain consensus mechanisms, emphasizing factors like accuracy, time efficiency, scalability, and energy efficiency. The research culminates in a robust conclusion that synthesizes key findings, emphasizing Ethereum's role in ensuring data security and integrity, and the intricate landscape of consensus mechanisms. © 2024 IEEE.

关键词： Smart contract

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Dynamic Outcome Prediction of an NBA Match 18

Dynamic Outcome Prediction of an NBA Match

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kandhway, Kundan Indian Institute of Science Education and Research Department of Electrical Engineering and Computer Science Madhya Pradesh Bhopal462066 India

ISBN: (纸本)9798350331011

In this paper we formulate the problem of predicting the outcome (winner) of an ongoing National Basketball Association (NBA) match as a supervised machine learning problem. In this approach, as the match progresses, the outcome prediction dynamically adapts to the current match situation which does not happen in static prediction problems. Dynamic outcome prediction of the match seems to have received less attention in the literature than static predictions. We compute the features used in the classification problem based on the most up-to-date match situation till that point in time. A total of 32 features are used. We present results for all the regular season matches of the 2022-23 NBA season. Dynamic prediction accuracy varies from about 62% at the beginning of the match to about 78% at the beginning of the final quarter of the match. We also evaluate feature importance in this classification problem. © 2024 IEEE.

关键词： Forecasting

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Autonomous Network Optimization and Dynamic Channel Allocation for Cognitive Radio-Based Consumer IoT

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 53-61页

作者： Abbas, Laraib Shoaib, Umar Omar, Marwan Bashir, Ali K. University of Gujrat Department of Computer Science Gujrat Pakistan Illinois Institute of Technology Information Technology and Management United States Manchester Metropolitan University Department of Computing and Mathematics Ukraine Chitkara University Institute of Engineering and Technology Center for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India

The heterogeneous environment of next-generation Consumer Internet of Things (CIoT) demands efficient resource utilization and reliable network services. On the contrary, the proliferation in the diverse nature of smart consumer IoT devices is causing spectrum scarcity and uneven utilization of available resources. Cognitive Radios (CRs) provide the most suitable solution for spectrum scarcity through dynamic spectrum access. To achieve spectral efficiency and provide consumer-centric network services we propose a novel Cognitive Radio based Autonomous Network Management framework called (CR-ANM). The framework combines the benefits of cognitive radios, Network Function Virtualization (NFV), and Software Defined Networking (SDN), to decouple the control plane from the data plane and is divided into two further operations called Dynamic Priority Determination (DPD) and Efficient Channel Allocation (ECA). DPD is responsible for determining the SUs priority using a fuzzy logic-based decision controller. Whereas ECA optimizes the channel allocation process and allocates the best available channel to SU. Which increases the channel availability by 77% and reduces the service drop rate by 81.8%. Both schemes run as Virtual Utility Functions (VUFs) on dedicated virtual machines assigned by the SDN controller. This approach increases energy efficiency for low-power consumer IoT devices and improves network reliability. © 1975-2011 IEEE.

关键词： Fuzzy logic

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Enhancing Steel Melting Shop Operations by Object Detection Using Template Matching Algorithm 2

Enhancing Steel Melting Shop Operations by Object Detection ...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Srinivasan, V. Parthasarathy, M. Kumar, M. Pavin Vaishnavi, S. Sri Ramakrishna Engineering College Department of Mechanical Engineering Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381979

Ladle monitoring systems are essential for optimizing steel melting operations. They improve material and equipment flow, reduce wait times, increase overall production, enable quick problem response, and ensure steel quality. Traditional ladle tracking methods utilizing sensors require a wide frequency band for data transmission. However, sensor technology has limitations such as high cost, limited range, signal interference, poor integration, wear, and tear. Thus, an additional layer of security is needed. To address these issues, a proposed solution suggests implementing a machine vision system for ladle tracking and management in steel factories. This vision-based system uses a live video captured by a camera as input to detect the ladle. The template matching algorithm is employed to compare the ladle template with the image's covered window, allowing the system to identify the ladle's ID and the zone where it is located. Various techniques for template matching are compared, and the most suitable one for the application is selected based on the results. The matched templates are stored in a database using Python SQLite3. The ladle tracking positions can be accessed through a web application, which also provides the capability to track individual ladles. The results demonstrate that implementing a vision-based tracking system for ladles in steel factories offers optimal outcomes with minimal hardware expenses. © 2023 IEEE.

关键词： Object detection

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Enhancing Cloud computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems 4

Enhancing Cloud Computing Security through Deep Learning and...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shukla, Sunil Singh, Jagendra Ramya, T. Rahul, Satyakam Mallick, Arnab Kumar Pandey, Pawan Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Mit Adt University School of Computer Science Engineering Pune India Indian Institute of Technology Madras Department of Computer Science Engineering Chennai India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350349900

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of 94.4%, with a false positive rate of 2.2% and a computational efficiency of 1 5 8. 4 milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of 96.2%, a false positive rate of 1.1%, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud. © 2024 IEEE.

关键词： Cloud security

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IDRISI-D: Arabic and English Datasets and Benchmarks for Location Mention Disambiguation over Disaster Microblogs 1

IDRISI-D: Arabic and English Datasets and Benchmarks for Loc...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： Suwaileh, Reem Elsayed, Tamer Imran, Muhammad Computer Science and Engineering Department Qatar University Qatar Qatar Computing Research Institute Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781959429272

Extracting and disambiguating geolocation information from social media data enables effective disaster management, as it helps response authorities;for example, locating incidents for planning rescue activities and affected people for evacuation. Nevertheless, the dearth of resources and tools hinders the development and evaluation of Location Mention Disambiguation (LMD) models in the disaster management domain. Consequently, the LMD task is greatly understudied, especially for the low resource languages such as Arabic. To fill this gap, we introduce IDRISI-D, the largest to date English and the first Arabic public LMD datasets. Additionally, we introduce a modified hierarchical evaluation framework that offers a lenient and nuanced evaluation of LMD systems. We further benchmark IDRISI-D datasets using representative baselines and show the competitiveness of BERT-based models. © 2023 Association for Computational Linguistics.

关键词： Location

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