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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Tripathy, Nrusingha Chaudhury, Kumar Surjeet Nibedita, Arpita Nayak, Subrat Kumar Behera, Manjushree Jena, Baidehi Siksha O Anusandhan Deemed to Be University Department of Computer Science and Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India Trident Academy of Technology School of Computing Odisha Bhubaneswar India P.N College Department of Computer Science Khordha India

ISBN: (纸本)9798350355093

Ethereum is an application platform that distributes versions of intelligent contracts to thousands of people globally, utilizing blockchain to decentralize data. Ethereum is a global currency that is used to exchange value without requiring supervision or outside involvement. However, as e-commerce grows, the biggest threat to trade security is the proliferation of illegal activities like phishing, money laundering, and bribery. The need for strong defenses is highlighted by the inherent hazards of blockchain technology, such as the potential for fraud and cyberattacks. The stability and preservation of confidence in the financial system depend on an accessible network that is impervious to these kinds of assaults. Different classification algorithms, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are being used in this work to detect Ethereum fraud. We are utilizing a dataset that includes rows of Ethereum cryptocurrency transactions along with rows of known unauthorized transactions. Notably, the 'XGBoost' model identifies differences that might draw attention and avert possible problems in this task. Supervised learning for Ethereum fraud detection improves the ecosystem's security and integrity. © 2024 IEEE.

关键词： Ethereum

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Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5598-5614页

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences Institute of Computing Technology Beijing100045 China University of Chinese Academy of Sciences Beijing101408 China Zhongguancun Lab Beijing100190 China Beihang University School of Computer Science and Engineering Beijing100191 China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing100191 China JD Technology JD iCity JD Intelligent Cities Research Beijing101111 China Tsinghua University Department of Computer Science and Technology Beijing100190 China Zhongguancun Laboratory Beijing100081 China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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Cross-Modal Recipe Retrieval With Fine-Grained Prompting Alignment and Evidential Semantic Consistency

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IEEE Transactions on Multimedia 2025年 27卷 2783-2794页

作者： Huang, Xu Liu, Jin Zhang, Zhizhong Xie, Yuan Tang, Yongqiang Zhang, Wensheng Cui, Xiaohui Wuhan University School of Computer Science Wuhan430072 China Ministry of Education Wuhan430062 China East China Normal University School of Computer Science and Technology Shanghai200062 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Alignment between the food images and the corresponding recipes is an emerging cross-modal representation learning task. In this task, the recipes are composed of three components, i.e., food title, ingredient lists, and cooking instructions, which require a fine-grained alignment between the features of the two modalities. Existing methods usually aggregate the recipes into global embeddings and then align them with the global image embeddings. Meanwhile, semantic classification is frequently used in these methods to regularize the embeddings of the two modalities. While these methods are efficient, there remain two problems. (1) Forcing the alignment between the global images and recipes embeddings may result in losing the component-specific information. (2) The high diversity of food appearance leads to high uncertainty in the semantic classification of food images and recipes. To solve these problems, we propose a Fine-grained Prompting and Alignment (FPA) model to enhance the feature extraction and bring more component-specific information for fine-grained alignment. Furthermore, to regularize the semantic information contained in the cross-modal features, we design an Evidential Semantic Consistency (ESC) loss to keep the cross-modal semantic consistency. We have conducted comprehensive experiments on the benchmark dataset Recipe1M and the state-of-the-art results on the cross-modal recipe retrieval task demonstrate the effectiveness of our method. © 1999-2012 IEEE.

关键词： Alignment

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models 10

Primeval Identification and Diagnosis of Alzheimer's Disease...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujitha, K.L. Lynsha Helena Pratheeba, H.P. Rajeshwari, R. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Hair Style Recommendations using CNN and AR 9

Hair Style Recommendations using CNN and AR

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Achireddy, Vummadi Sathiya, T. Ravi, Rongala Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Anurag Engineering College Department of Cse Ananthagiri Telangana Kodad India Sona College of Technology Department of Cse Salem India Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

This research begins with the collecting of a varied dataset of annotated facial photos to ensure accurate representation across populations. The photographs will be pre-processed to standardize their size, illumination, and orientation. On this dataset, we will fine-tune a pre-trained CNN, such as ResNet50, to identify various face forms before evaluating accuracy against a separate test set. Furthermore, we will create a dataset of facial photos combined with various haircuts classified by face shape, and use the CNN to extract relevant features and embeddings. A matching algorithm will match user facial features to hairdo embeddings, ranking recommendations according to similarity metrics. The AR component will leverage ARKit or ARCore for real-time face tracking, allowing users to realistically test on different haircuts overlayed on their live camera feeds. The final solution will be available on mobile and online platforms, with a user-friendly design and options for saving preferred haircuts and providing feedback. This project tackles the demand for a more engaging and individualized hairstyle picking experience, utilizing advanced AI and AR technologies to improve customer happiness and interactivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Potential of Machine Learning in Automated Real-Time Data Analysis Systems 15

Exploring the Potential of Machine Learning in Automated Rea...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandrakala, P. Meshram, Vishal A. Meena, Mansingh Nithya, M.S. Alam, Intekhab Kumar, V. Mathan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper explores the current country of gadget learning and its capability to allow real-time records analysis. Automated actual-time statistics analysis structures enable business specialists to reply to situations by extracting complicated patterns faithfully. Gadget learning techniques can assist these units by allowing algorithms to analyze previously amassed information and generalize these consequences to novel scenarios. This paper examines the latest developments in device learning algorithms that will likely be leveraged in automatic actual-time facts evaluation structures. The report also considers challenges associated with data education and the mission's generation selection. ultimately, the paper discusses numerous processes for integrating gadget getting to know into real-time records analytics systems that could permit agencies to take complete advantage of the significant ability of machine mastering. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.M. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Algorithms for the Prize-Collecting k-Steiner Tree Problem

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Tsinghua science and technology 2022年第5期27卷 785-792页

作者： Lu Han Changjun Wang Dachuan Xu Dongmei Zhang School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China

In this paper,we study the prize-collecting k-Steiner tree(PCkST) *** are given a graph G=(V,E) and an integer *** graph is connected and undirected.A vertex r ∈ V called root and a subset R?V called terminals are also given.A feasible solution for the PCkST is a tree F rooted at r and connecting at least k vertices in *** a vertex from the tree incurs a penalty cost,and including an edge in the tree incurs an edge *** wish to find a feasible solution with minimum total *** total cost of a tree F is the sum of the edge costs of the edges in F and the penalty costs of the vertices not in *** present a simple approximation algorithm with the ratio of 5.9672 for the *** algorithm uses the approximation algorithms for the prize-collecting Steiner tree(PCST) problem and the k-Steiner tree(kST) problem as *** we propose a primal-dual based approximation algorithm and improve the approximation ratio to 5.

关键词： prize-collecting Steiner tree approximation algorithm

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An Intelligent Deep Learning Strategy for Breast Cancer prediction using feature ensemble learning

An Intelligent Deep Learning Strategy for Breast Cancer pred...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prakash, E.P. Satheshkumar, K. Anandhasilambarasan, D. Prasath, B. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350305708

One in six women will be diagnosed with breast cancer (BC) by the time they reach their forties, making it the leading cause of death among this demographic. It's the leading killer in the world, yet it's hard to spot and diagnose in its early stages. Among women, BC is a foremost origin of death in third world countries. The prevention and treatment of these tumors can be greatly aided by screening and early diagnosis. At the moment, BC is the leading malignancy in females, even more so than ovarian cancer. When researchers get access to patient medical records, they can uncover hidden patterns in healthcare. The National-Cancer-Institute (NCI) reports that mortality rates from breast cancer can be reduced with early detection. In order to accurately identify patients with cancer, this work presents a voting classifier based on ensemble learning that integrates the logistic-regression(LR) &stochastic-gradient-descent(SGD) with deep-convoluted features. The ensemble voting classifier is fed deeply-convoluted features mined from the microscopic characteristics. This concept offers a refined framework for differentiating amid benign and malignant tumors. The results show that the highest level of classification accuracy, 100%, may be attained when utilizing voting classifiers with complex features. When compared to the traditional methods, the proposed method showed an improvement in accuracy. Thus, the research aids doctors in diagnosing and avoiding breast cancer. © 2023 IEEE.

关键词： breast-cancer classification deep-convoluted-features ensemble learning healthcare prediction

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