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Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Yuhong Wu Yunshan Lv Ling Zhang School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system; then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency.

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Prognosis of Relocation Disease in Animals using Aggregation Method with Optimization Techniques

Prognosis of Relocation Disease in Animals using Aggregation...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Khushwant Singh Mohit Yadav R K Yadav Bharat Bhushan Naib Sandeep Bhatia Deepak Kumar Panda Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak India Department of Mathematics University Institute of Sciences Chandigarh University Mohali India Department of Data Science Christ University Bengaluru India School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In the most cutting-edge setting, health data is processed by machine learning algorithms, which are used to forecast illnesses. Dementia, especially Alzheimer's disease, is a leading cause of diminished quality of life in the elderly. Early diagnosis by medical professionals increases the likelihood of reducing the aggressiveness of the disease. In this study, we develop a new uncertainty-based clustering model to handle the centroid selection ambiguity and the issue of noisy instances and outliers that lower the efficiency of prediction models. This work employs an uncertainty-based optimization technique to handle the unknown pattern of Alzheimer Disease (AD) patients, since it is relatively tough to handle unknown patterns with unsupervised learning algorithms. Converting the instances in the AD dataset to the membership value of the dependent variable allows for an accurate determination of whether they belong as AD patterns or non-AD patterns. This proposed study takes a migration-based optimization method to animal migration, where the best instances are chosen as centroids and fresh instances are evaluated for clustering; this minimizes outliers throughout the clustering process by using comparable patterns. To make sure, we check the fitness values of each instance; the ones with the highest values are called centroids. To control the unknowns when dealing with outliers, the fuzzy Euclidean distance is employed. By comparing it to current state-of-the-art clustering methods, the OASIS dataset simulation results show that the proposed uncertainty-based Animal Migration optimization method (UAMO) performs better.

关键词： Accuracy Uncertainty Animals Optimization methods Clustering algorithms Predictive models Prognostics and health management Alzheimer's disease Stress Unsupervised learning

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ExpertDRL: Request Dispatching and Instance Configuration for Serverless Edge Inference with Foundation Models

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IEEE Transactions on Mobile Computing 2025年

作者： Zeng, Yue Zhou, Junlong Ye, Baoliu Qu, Zhihao Guo, Song Gong, Tianjian Li, Pan Nanjing University of Science and Technology Department of Computer Science and Engineering Jiangsu Nanjing210094 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China Hong Kong University of Science and Technology Hong Kong Southwest University College of Electronic and Information Engineering Chongqing400715 China

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle Detection Network in High Resolution Aerial Imagery

EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Shubham Gupta Chiranjoy Chattopadhyay Debasis Das Suman Kundu Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

关键词： Visualization Accuracy Image resolution Explainable AI Error analysis Vehicle detection Object detection

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Enhancement of Recommendation Systems through Collaborative filtering to assure Customer Requirement

Enhancement of Recommendation Systems through Collaborative ...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Annie Silviya S H S. Vinod Pathmanaban. J Sriman B Suryaa Narayanan K Santhosh Kumar E Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Computer Science and Engineering Sairam Institute of Technology Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology (RIT) Chennai

Online advertising is the most popular and significant marketing tool in today's world. Traditional advertising systems failed to meet the needs of varied users, since they display ads without considering the individual's characteristics. Now-a-days ads are displayed based on the previous search history of activities of the users, but, human mind change most frequently and the ads are being displayed regardless of the user's emotion. In this paper, we introduce an improved advertising system that predicts the user's sentiment based on the genre of the content being viewed and queues ads accordingly that uses both Collaborative and Content based Filtering that paved way for Hybrid Recommender System. This will enhance the user experience and the quality of the advertisements and advertising will be improved.

关键词： databases Scalability Collaboration Turning Knowledge discovery Information filters User experience

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Multi-Task Generalizable Communication: Beyond the Information Bottleneck

Multi-Task Generalizable Communication: Beyond the Informati...

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IEEE International Conference on Communications (ICC)

作者： Cheuk Ting Leung Rohan Ghosh Mehul Motani Department of Electrical & Computer Engineering National University of Singapore Institute of Data Science Institute for Digital Medicine N.1 Institute for Health

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Conventional communication systems focus on re-covering the messages sent by the transmitter at the receiver, by undoing the errors introduced by the channel. In semantic communication, the goal is instead to preserve the semantics (i.e., meaning) of the intended message $X$ . A well-known framework adopted by the literature in modelling semantic communication is task-oriented communication. There, an encoder generates a feature representation $Z$ (e.g., a trained neural network) to transmit across the channel to fulfill a certain task (e.g., classification with labels $Y$ ). This naturally leads to the study of the information bottleneck (IB) principle - which proposes reducing the mutual information $I (X; Z)$ (compression) while simultaneously maxi-mizing $I (Z; Y)$ (fitting). Although this objective seems initially meaningful, we posit that both the standard task-oriented setting for studying semantic communication and the IB approach may not be optimal. In this paper, we first propose a novel paradigm of multi-task generalizable communication, where, at test time, the messages may originate from a different unseen classification task compared to those during training. We intuitively reason that the multi-task generalizable setting is more feasible and relevant in the context of real-world semantic communication, compared to task-oriented communication. Next, we propose a Reconstruction Loss Aware (RLA) approach, which yields better feature encodings that are more generalizable towards unseen tasks. We empirically observe that, in the proposed paradigm, the IB principle is not optimal. We also demonstrate that RLA is better (15%-20% higher accuracy) compared to a benchmark approach for task-oriented communication, for diverse non-overlapping unseen tasks.

关键词： Training Transmitters Semantics Neural networks Receivers Benchmark testing Multitasking

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A Unified Framework for Convergence Analysis in Social Networks

A Unified Framework for Convergence Analysis in Social Netwo...

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IEEE Conference on Decision and Control

作者： Ti-Chung Lee Jun-Kai Huang Youfeng Su Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper proposes a unified framework for the stability analysis of discrete-time nonlinear systems from social networks, including the Friedkin-Johnsen opinion model, two opinion dynamics models in the study of social power, and a general nonlinear polar opinion model. Three novel convergence results are proposed to treat various conditions based on LaSalle invariance principle. Several applications are provided to illustrate the power of the proposed framework.

关键词： Analytical models Social networking (online) Stability analysis Nonlinear systems Convergence

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Overcoming data Inequality across Domains with Semi-Supervised Domain Generalization

arXiv

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arXiv 2024年

作者： Park, Jinha Cho, Wonguk Kim, Taesup Department of Electrical and Computer Engineering Seoul National University Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of

While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various sources and populations. This inequality across domains poses challenges in modeling for those with limited data, which can lead to profound practical and ethical concerns. In this paper, we address a representative case of data inequality problem across domains termed Semi-Supervised Domain Generalization (SSDG), in which only one domain is labeled while the rest are unlabeled. We propose a novel algorithm, ProUD, which can effectively learn domain-invariant features via domain-aware prototypes along with progressive generalization via uncertainty-adaptive mixing of labeled and unlabeled domains. Our experiments on three different benchmark datasets demonstrate the effectiveness of ProUD, outperforming all baseline models including single domain generalization and semi-supervised learning. Source code will be released upon acceptance of the paper. Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics

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Analysis of Multi-Class Convolution Neural Network on Garbage Classification System

Analysis of Multi-Class Convolution Neural Network on Garbag...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vikas K R Ullas P Sri Sai Teja M S Kumar Swamy S Ashwini Kodipalli Trupthi Rao Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

The buildup of solid waste in metropolitan areas is a major worry that, if not effectively handled, might lead to environmental contamination and be dangerous to human health. To manage a range of waste products, it's crucial to have an advanced/intelligent waste management system. The act of separating garbage into its many components is one of the most crucial parts of waste management, and it is often carried out manually by hand-picking. To simplify this, we suggest a customized architecture for multiclass Convolutional Neural Networks (CNNs). The design includes elements that are individual-specific, enabling the model to adjust to the traits of each class. Extensive evaluation has shown that our CNN architecture, utilising a personalised approach, outperforms traditional CNN models like ResNet50 and VGG16 in multiclass classification situations. The effectiveness and resilience of our suggested architecture are demonstrated by experimental results on our dataset, opening the path for increased classification accuracy of 71.15%.

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Clustering Single-Cell RNA-seq data: Impact of data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： data accuracy

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