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1st IEEE International Conference on Advances in data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Alli, Smaran Baidya, Sabur Carnegie Mellon University Department of Data Science PA United States University of Louisville Department of Computer Science and Engineering KY United States

ISBN: (纸本)9798350344158

Invasive weeds have continued to ravage crops across the world and with their population starkly rising, intelligent technologies need to be incorporated to mitigate the spread of these weeds. This research aims at employing Deep Learning models on invasive weed seedlings images in order to be able to detect them in images before they have grown. Due to the lack of images available on weed seedlings, more images were collected of weed seedlings, which were then combined with the limited available pre-existing images found in the Aarhus dataset. However, due to the dataset being still comparably small, a Few-Shot Object Detector (FSOD) is employed for better object detection performance. This FSOD was created through transfer learning with the universal COCO dataset. We also investigate data augmentation techniques by varying different attributes of images. We compare our proposed data-augmented FSOD with a 16-layer CNN-based object detector and also with the models without any augmentation. With experimental results, we show that the FSOD model achieves a higher mean Average Precision (mAP50) than the 16-layer CNN for both augmented (0.839 to 0.834) and non-augmented data (0.814 to 0.808). The FSOD detector also runs faster than the CNN model, and thus can be useful in real-time autonomous detection of the weed seedling and hence, in mitigating spreading of these invasive weeds. © 2023 IEEE.

关键词： Object detection

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Towards Simulation-Based Circular Product Design 30

Towards Simulation-Based Circular Product Design

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30th CIRP Life Cycle engineering Conference, LCE 2023

作者： Aher, Gaurav Boudjadar, Jalil Ramanujan, Devarajan Department of Mechanical and Production Engineering Aarhus University Aarhus8200 Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus8200 Denmark

Assessing the circular economy (CE) performance of products has become an essential consideration in product design processes. While a growing body of literature is exploring the use of CE indicators to guide design processes, they typically assume broad design changes (e.g., material changes, end-of-life treatment) that may not accurately capture trade-offs in CE indicators, technical performance, and design feasibility. This paper aims to address the above gaps with the goal of incorporating CE assessment into simulation-based design. To this end, this paper defines a novel framework for using simulation-based design to analyze the effects of parametric changes in product design attributes on CE performance. First, the proposed framework describes the lifecycle information needed for computing resource-based, product-level CE indicators at the design stage. Depending on the set of selected CE indicators, corresponding simulation models are used to compute the effects of changing design parameters, and setting design/performance constraints, on the product's CE performance. Thus, the proposed framework enables the selection and optimization of product design attributes for achieving a specific level of CE performance, given specific technical performance requirements and other design constraints. The application of this framework is demonstrated using a case study conducted on a flywheel. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Product design

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Label Aggregation with Self-Supervision Enhanced Graph Transformer 26

Label Aggregation with Self-Supervision Enhanced Graph Trans...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Jiacheng Tang, Feilong Hou, Xiaofeng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Aggregating noisy labels produced by the crowd of workers to generate true labels is a challenging problem in crowdsourcing. The key behind label aggregation is to effectively utilize the hidden information (e.g., characteristics of workers and questions which are often missing) in the labeling process. Existing methods mainly generated aggregation models based on the complicated Bayesian model or some strong assumptions. Recently, deep learning-based methods attempt to automate label aggregation but need various labels. These all make them hard to deploy to real-world applications. In fact, abundant information in the process of crowdsourcing itself can be extremely helpful to aggregate the labels. In this paper, we propose ATHENA (lAbel aggregaTion witH sElf-supervision eNhanced grAph transformer) to aggregate labels by utilizing the self-supervision signals in crowdsourcing. Firstly, we propose a transformer-based graph neural network that can learn from the crowdsourcing topology and features. Then, we use self-supervision signals inherently included in the dataset to help to aggregate the labels. To be specific, we identify the answer-based self-supervision signal that can predict the answer of any user given to different tasks. In our evaluations, we compare the proposed ATHENA with the other 11 representative methods on 10 datasets. Our experimental results demonstrate that ATHENA is highly effective in aggregating labels and obtains much better performance than existing methods. © 2023 The Authors.

关键词： Aggregates

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Advancements in Convolutional Neural Networks for Accurate and Efficient Skin Cancer Classification: A Comprehensive Survey 8th

Advancements in Convolutional Neural Networks for Accurate a...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Kashid, Shraddha S. Deshmukh, Gopal B. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Deshpande, Arati V. Ghule, Vijaykumar R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819766741

The cancer of the skin, particularly melanoma, is considered to be one of the most life-threatening types of cancer. The major cause of skin cancer is an unrepaired deoxyribonucleic acid (DNA) disruption in skin cells which results in heritable skin imperfections or mutations that result from the disruptions. It is very important to note that skin cancers are most curable at their earliest stages, but they tend to spread to multiple parts of the body and appear as lesions. Among the lesion parameters that doctors use to identify and distinguish between non-cancer skin cancer and melanoma skin cancer is similarity, structure, spread, color, etc. These parameters provide the doctors with a valuable tool to diagnose skin cancer early on. Due to the parameter study and significant increase in skin cancer incidence rates, many researchers decided to quickly identify and diagnose skin cancer. Early detection of skin cancer is essential to lowering the mortality rate. Using deep learning and transfer learning models, researchers have been able to detect skin cancer using various algorithms. A recent study demonstrates the capability of deep learning and deep transfer learning methods to classify and detect skin cancer early. The results of this study suggest that a comprehensive investigation into the methods of Deep Learning (DL) algorithms for locating skin cancer at several stages and types is required based on the analysis of the results of this study. An investigation into articles published in reputed journals was carried out with regard to the diagnosis of skin cancer based on the findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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A Comprehensive Analysis of the Internet of Things (IOT) in the Context of 5G Wireless Systems 4

A Comprehensive Analysis of the Internet of Things (IOT) in ...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mohseni, Milad Kanwer, Budesh Singh, Navdeep Krishna, M Hari Rajalakshmi, B. Alkhafaji, Mohammed Ayad Islamic Azad University Department of Computer and Engineering Tabriz Iran Poornima Institute of Engineering & Technology Department of Artificial Intelligence & Data Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The fast development of 5G wireless technologies (IoT) has been a major contributor to the rapid growth and expansion of the Internet of Things! This in-depth study investigates the mutually advantageous relationship between 5G and the Internet of Things (IoT), focusing on how the combination assists in the development of massive device networks, enhances connectivity, and enables the development of real-time applications. There are now new opportunities for innovative applications in smart cities, industrial automation, healthcare, and other industries owing to 5G, which enables Internet of Things devices to communicate with one another at a speed and efficiency that has never been seen before. In spite of the benefits, however, there are challenges to be faced, such as the construction of infrastructure to support the expansion of the Internet of Things and the assurance of robust security. This article provides an analysis of these components and a blueprint for leveraging the advantages of Internet of Things (IoT) with 5G while minimizing any potential downsides. © 2024 IEEE.

关键词： Internet of things

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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing 2

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital Image Processing 3

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Prabu, S. Natarajan, B. George, Roshan Sujitha, S. Bharathi, P Divya Rajeswari, S. School of Computer Science and Engineering VIT University Chennai India Amrita Vishwa Vidyapeetham Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India

ISBN: (纸本)9798350317022

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage. © 2023 IEEE.

关键词： Edge Detection Feature Extraction Feature Selection Image Processing Image Representation

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Detecting Malicious Blockchain Attacks through Flower using Horizontal Federated Learning: An Investigation of Federated Approaches 14

Detecting Malicious Blockchain Attacks through Flower using ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Samuel, Aiden Gigi Puthusseri, Sia Viji Eazhakadan, Edryn Shajan Shetty, Monali Fr. Conceicao Rodrigues College of Engineering Artificial Intelligence & Data Science Department Mumbai India Fr. Conceicao Rodrigues College of Engineering Computer Engineering Department Mumbai India

ISBN: (纸本)9798350335095

This research study focuses on the detection of malicious attacks on WEB 3 decentralized applications in blockchain systems using Federated Learning (FL) with the Flower framework. The scope of the study involves evaluating the effectiveness of FL in enhancing the security and reliability of decentralized networks by detecting various types of malicious attacks. A comparative analysis is conducted to assess the performance of FL against traditional machine learning techniques that rely on centralized data storage and direct user data access. The study highlights the limitations of traditional approaches in the context of blockchain systems, emphasizing the need for privacy-preserving collaborative learning mechanisms like FL. By leveraging the Flower framework, FL enables collaborative learning among edge devices without central data storage, ensuring data privacy and improving security. The research findings demonstrate the superiority of FL in terms of privacy preservation and collaborative learning capabilities, establishing its potential for detecting and preventing malicious attacks in blockchain systems. These findings contribute to the wider adoption of blockchain technology by addressing the security concerns associated with decentralized networks. © 2023 IEEE.

关键词： Blockchain

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