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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Ragam, Prashanth Karthik, Guntha Jagadesh, B.N. Jyothi, Sankati School of Computer Science and Engineering VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India Department of ECE Stanely College of Engineering and Technology for Women Telangana Hyderabad India Software Development Cell VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India

ISBN: (纸本)9789819966899

Wireless sensor networks (WSNs) and Internet of Things (IoT) have received remarkable attention from the past few years in various applications. Modeling of path loss (PL) for the deployment of a developed WSN system is a crucial task owing to the time-consuming and elegant operation. However, radiofrequency (RF) engineers adopted either deterministic or stochastic empirical models to estimate the PL. In general, empirical models utilize predefined influenced parameters including path loss (dB), path loss exponent (ϒ), and other significant parameters. Although, empirical models differ significantly from original measurement due to consideration of different terrains. In this study, an endeavor has been made to develop a machine learning-based model to estimate the path loss for a standard ZigBee communication network operating on a 2.4 GHz carrier frequency deployed in an urban area. An experimental setup was designed and tested in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to collect the influence parameters such as received signal strength indicator (RSSI), frequency, distance, and transmitter antenna gain. Besides that, environmental parameters such as temperature and humidity are also included. In this context, a three-layer, feed-forward backpropagation multilayer perception neural network (BPNN) machine learning and Log-Distance empirical models were employed to estimate the PL. The obtained results reveal that the BPNN model noticeably enhanced the coefficient of determination (R2) and reduced root mean square error (RMSE) compared with the empirical model. The R2 and RMSE metrics were obtained as 0.97220 and 0.03630 in the NLOS scenario as well as 0.99820 and 0.00773 in the LOS environment, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Analysis the Research Hotspots and Key Technical of Intelligent Manufacturing 22

Analysis the Research Hotspots and Key Technical of Intellig...

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4th International Conference on Management science and Industrial engineering, MSIE 2022

作者： Cai, Yu Wu, Jiahao Zaheer, Muhammad Department of Computer Software Engineering Institute of Guangzhou Guangzhou51098 China College of Computer Information and Engineering Nanchang Institute of Technology Nanchang330044 China Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9781450395816

Intelligent manufacturing is one of the keys to open up the future of life, and multidisciplinary interactions have shown its strong vitality. This study conducts a scientometric review of the global research published since 1987 on intelligent manufacturing, through citation, keyword, clustering and keywords burst analysis. A total of 3994 research documents are taken as data sources, which are retrieved from the Web of science Core Collection database from 1987 to 2021. Using the CiteSpace analysis tool, this paper discussed the development process of the relevant years of intelligent manufacturing, analyzed the research hotspots and their technology, presented the emerging models and techniques, and identified the latest research evolution and trends. The results will provide reference for scientific scholars in future research in the field of intelligent manufacturing. © 2022 ACM.

关键词： Industrial research

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

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Enhancing Cybersecurity using Machine Learning Strategies for Phishing Defense

Enhancing Cybersecurity using Machine Learning Strategies fo...

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International Conference on Communication and Electronics Systems (ICCES)

作者： C. Jamunadevi S. Pandikumar Naveen N S. Prasath Dhivyashree S Department of Computer Technology PG Kongu Engineering College Perundurai Erode Department of MCA Acharya Institute of technology Bangalore Department of IT Excel Engineering College Komarapalayam Department of Computer Science VET Institute of Arts and Science (Co-education) college Thindal MSc (Software Systems) Kongu Engineering College Perundurai Erode

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Cybersecurity experts continue to be concerned about phishing attacks because it causes a real risk to people, businesses, and economies all around the world. Phishing attacks cause a critical threat to global cybersecurity, necessitating effective classification methods for phishing URL operations. So, this work aims to categorize phishing URL operations into distinct types: deceptive, spear, whaling, and pharming. Leveraging machine learning techniques including logistic regression, support vector machine, Naive Bayes, decision tree, K-NN, and Perceptron, evaluate their efficacy in accurately identifying and classifying phishing attacks. The findings underscore the importance of these models in providing cybersecurity professionals with actionable insights into the diverse tactics employed by malicious actors. Among the evaluated models, logistic regression emerges as the preferred choice, demonstrating superior performance in accurately categorizing phishing URLs. This work contributes to enhancing cybersecurity defenses by offering a comprehensive framework for understanding and mitigating the impact of phishing attacks on digital ecosystems.

关键词： Uniform resource locators Support vector machines Logistic regression Phishing Biological system modeling Ecosystems Machine learning Decision trees computer crime Business

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Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers

arXiv

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arXiv 2024年

作者： Li, Mengke Li, Da Yang, Guoqing Cheung, Yiu-Ming Huang, Hui Shenzhen China VCC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https://***/research/2024/PointFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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Separating Spectral and Spatial Feature Aggregation for Demosaicking

Separating Spectral and Spatial Feature Aggregation for Demo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xuanchen Li Bo Zhao Yan Niu Li Cheng Haoyuan Shi Zitong An College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of Alberta Canada

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Existing demosaicking neural models typically follow the same design principles as those adopted by the rest image restoration tasks; yet one unique & often overlooked problem with demosaicking is it also suffers from the moiré artifact. This observation inspires us to examine the key reason that moiré happens only to demosaicking instead of other image restoration tasks. In this process, We identify the spectral inconsistency concealed in the input of demosaicking neural networks; our findings also clarify the failure of the traditional dense spatiospectral feature aggregation in mitigating spectral inconsistency. Based on the analysis, a new solution is proposed to address moiré while preserving fine image details. In particular, we decouple the traditionally used spatio-spectral feature aggregation into comprehensive spectral aggregation and local spatial aggregation. Throughout a diverse range of experiments with quantitative and qualitative results, our approach is shown capable of significantly reducing artifacts such as moiré and over-smoothness, as well as drastically boosting state-of-the-art performance without increasing computational cost.

关键词： Image color analysis Neural networks Transformers Boosting Image restoration Computational efficiency High frequency

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SecureMed: A Blockchain-Based Privacy-Preserving Framework for Internet of Medical Things

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Rafique, Wajid Khan, Maqbool Khan, Salabat Ally, Juma Said Department of Computer Science and Operations Research University of Montreal Canada Department of IT and Computer Science PAF-IAST Pakistan College of Computer Science and Software Engineering Shenzhen University China Department of Electronics and Telecommunication Engineering Mbeya University of Science and Technology Tanzania United Republic of

The Internet of Medical Things (IoMT) connects a huge amount of smart sensors with the Internet for healthcare service provisioning. IoMT's privacy-preserving becomes a challenge considering the life-saving data collected and transferred through IoMT. Traditional privacy protection techniques use centralized management strategies, which lead to a single point of failure, lack of trust, state modification, information disclosure, and identity theft. Edge computing enables local computation of IoMT data, which reduces traffic to the cloud and also helps in accomplishing latency-sensitive healthcare applications and services. This paper proposes a novel framework (i.e., SecureMed) that uses blockchain-based distributed authentication implemented at the edge cloudlets to enforce privacy protection. In SecureMed, IoMT devices interact with edge cloudlets using smart contracts. It uses trusted edge nodes to implement an authentication algorithm that uses public/private key matching to authenticate IoMT. Experimental evaluation performed using the Pythereum blockchain shows that SecureMed outperforms the traditional blockchain scheme based on latency, bandwidth consumption, deployment time, scalability, and accuracy. Therefore, it can be used to protect the edge-enabled IoMT from privacy attacks and to ensure end-to-end healthcare service provisioning. © 2023 Wajid Rafique et al.

关键词： Blockchain

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking

arXiv

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arXiv 2024年

作者： Feng, Yinglan Feng, Liang Liu, Songbai Kwong, Sam Tan, Kay Chen Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science Chongqing University China College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary Multitasking (EMT) paradigm, an emerging research topic in evolutionary computation, has been successfully applied in solving high-dimensional feature selection (FS) problems recently. However, existing EMT-based FS methods suffer from several limitations, such as a single mode of multitask generation, conducting the same generic evolutionary search for all tasks, relying on implicit transfer mechanisms through sole solution encodings, and employing single-objective transformation, which result in inadequate knowledge acquisition, exploitation, and transfer. To this end, this paper develops a novel EMT framework for multiobjective high-dimensional feature selection problems, namely MO-FSEMT. In particular, multiple auxiliary tasks are constructed by distinct formulation methods to provide diverse search spaces and information representations and then simultaneously addressed with the original task through a multi-slover-based multitask optimization scheme. Each task has an independent population with task-specific representations and is solved using separate evolutionary solvers with different biases and search preferences. A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions during the search process. Comprehensive experimental results demonstrate that our MO-FSEMT framework can achieve overall superior performance compared to the state-of-the-art FS methods on 26 datasets. Moreover, the ablation studies verify the contributions of different components of the proposed MO-FSEMT. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Learning-Aided Evolutionary Search and Selection for Scaling-up Constrained Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Liu, Songbai Wang, Zeyi Lin, Qiuzhen Li, Jianqiang Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong

The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great room for improvement in balancing populations convergence, diversity and feasibility on complex constrained multiobjective optimization problems (CMOPs). Besides, their effectiveness deteriorates dramatically when facing the CMOPs with scaling-up objective space or search space. We are thus motivated to design a learning-aided CMOEA with promising problem-solving ability and scalability for various CMOPs. In the proposed solver, two learning models are respectively trained online on constrained-ignored task and feasibility-first task, which are then used to learn the two improvement-based vectors for enhancing the search by differential evolution. In addition, the union population of parent and child solutions is divided into multiple subsets with a hierarchical clustering based on cosine similarity. A comprehensive indicator, considering objective-based performance and constraint violation degree of a solution, is developed to select the representative solution from each cluster. The effectiveness of the proposed optimizer is verified by solving the CMOPs with various irregular Pareto fronts, the number of objectives ranging from 2 to 15, and the dimensionality of search space scaling up to 1000. IEEE

关键词： Evolutionary algorithms

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