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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Obayemi, Ajibola Nguyen, Khuong School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom University of London Department of Computer Science Royal Holloway London United Kingdom

ISBN: (纸本)9798350374889

Multimodal models can experience multimodal collapse, leading to sub-optimal performance on tasks like fine-grained e-commerce product classification. To address this, we introduce an approach that leverages multimodal Shapley values (MM-SHAP) to quantify the individual contributions of each modality to the model's predictions. By employing weighted stacked ensembles of unimodal and multimodal models, with weights derived from these Shapley values (MM-SHAP), we enhance the overall performance and mitigate the effects of multimodal collapse. Using this approach we improve previous results(f1-score) from 0.67 to 0.79. © 2024 IEEE.

关键词： Marketplaces

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Paper Defects Recognition Based on Deformable Convolution

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2022年第2期54卷 37-50页

作者： Qu, Yun-Hui Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University China Department of Electric and Control Engineering Shaanxi University of Science & Technology China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, convolutional neural network has strict requirements for the size of the input image. This requires that in the process of practical engineering application, for the collected paper defect images to be classified, the area containing paper defect must be segmented during preprocessing, and then the size of the paper defect area must be adjusted to meet the input requirements of the adopted classifier. To solve the above problems, the two-stage target detection network Faster R-CNN (Region-Convolutional Neural Network) was used in paper defects recognition to solve the problem of the size requirements of the input image;In addition, the deformable convolution layer was added after the traditional convolution layer to learn the characteristics of paper defects more efficiently and accurately, so as to improve the accuracy and accuracy of paper defects recognition and classification;Finally, the deformable RoI (Region-of-Interest) pooling layer was used to replace the RoI pooling layer of classic Faster R-CNN to locate and classify the paper defects area more accurately. Experiments show that the proposed algorithm has a further improvement in accuracy and scalability compared with the previous algorithm. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Convolution

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi section Humanitarian technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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Novel design of recurrent neural network for the dynamical of nonlinear piezoelectric cantilever mass–beam model

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European Physical Journal Plus 2024年第1期139卷 1-34页

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Zameer, Aneela Shoaib, Muhammad Graduate School of Engineering Science and Technology (Computer Science and Information Engineering) National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Nilore 45650 Pakistan AI Center Yuan Ze University Taoyüan 320 Taiwan

Piezoelectricity spans a wide range of fields, showcasing its practical significance. In the realm of consumer electronics, piezoelectric materials are utilized in buzzers, sensors, and actuators. These components find applications in everyday devices such as mobile phones, wearable gadgets, and home appliances. The aim of study is to present a novel design of layered recurrent neural network (LRNNs) for solving nonlinear piezoelectric cantilever mass–beam (PE-CMB) model representing the excitation frequency dynamics. The proposed PE-CMB model is mathematically represented with highly nonlinear second-order differential system. The synthetic dataset is created using Adams numerical scheme for piezoelectric cantilever beam system, and the data are randomly segmented for training samples to formulate the network and testing samples to record performance on unbiased data and validation samples for controllability of adaptive procedure of training the weights to approximate the solutions of PE-CMB with LRNNs. The efficiency and consistent performance of the LRNNs design are validated through an evaluation process encompassing measures such as achieved accuracy using mean squared error-derived cost functions, plots illustrating learning curves, adaptively controlled parameters, solution dynamics along with associated error analysis, and a statistical analysis involving distribution of error for each instances via histograms and regression metric. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Yogapriya, G. Kumari, R. Shalini Suganthi, S. Agalya, K. Elangovan, T. Pandian, D. Selva School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai India Department of Architecture Periyar Maniammai Institute of Science and Technology Tamilnadu Thanjavuri India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India

ISBN: (纸本)9798350334357

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction. © 2023 IEEE.

关键词： Convolutional neural networks

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Exploring the Use of WebAssembly in HPC 23

Exploring the Use of WebAssembly in HPC

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian and Institute of Information Technology Kottayam Kerala India

ISBN: (纸本)9798400700156

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. © 2023 ACM.

关键词： Containers

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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2024 Arab ICT Conference, AICTC 2024

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering Department Computer Science and Digital Technologies UEL London United Kingdom

ISBN: (纸本)9798350342086

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Adversarial machine learning

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Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 51-62页

作者： Rawat, Romil Oki, Olukayode A. Chakrawarti, Rajesh Kumar Adekunle, Temitope Samson Lukose, Jose Manappattukunnel Ajagbe, Sunday Adeola Department of Computer Architecture and Communications University of Extremadura Badajoz06006 Spain Department of Information Technology East London Walter Sisulu University South Africa Department of Computer Science and Engineering Sushila Devi Bansal College Bansal Group of Institutions Indore India Colorado State University United States Department of Information Technology Walter Sisulu University South Africa Department of Computer & Industrial Production Engineering First Technical University Ibadan200255 Nigeria

Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensic techniques. We've also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Artificial intelligence

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Unsupervised Fouling Reconstruction in the Pipe Bend

Unsupervised Fouling Reconstruction in the Pipe Bend

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Iablonskyi, Denys Rasgado-Moreno, Carlos-Omar Ratassepp, Madis Klami, Arto Haggstrom, Edward Salmi, Ari University of Helsinki Department of Computer Science Department of Physics Helsinki Finland Tallinn University of Technology Department of Civil Engineering and Architecture Tallinn Estonia University of Helsinki Department of Computer Science Helsinki Finland University of Helsinki Department of Physics Helsinki Finland

ISBN: (纸本)9798350346459

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals. © 2023 IEEE.

关键词： Nondestructive examination

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