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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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SAECNet: Self-Attention Encryption and Compression Network Based on Bidirectional Cyclic Multi-Scroll Memristor Neural Network

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IEEE Internet of Things Journal 2025年

作者： Li, Peizhen Feng, Xiufang Lu, Herbert Ho-Ching Zhou, Shuang Zhang, Hao Taiyuan University of Technology School of Software China University of Western Australia School of Engineering Electrical Electronic and Computer Engineering Australia Chongqing Normal University School of Mathematics China Taiyuan University of Technology School of Computer Science and Technology China

This paper introduces a memristor-based neural network with bidirectional cyclic constructed from square wave pulse functions. Additionally, we design an image encryption and compression network framework incorporating a self-attention mechanism based on this memristor neural network. Specifically, this study introduces a novel model of a high-dimensional multi-scroll memristor neural network (MMNN) that incorporates three neurons and a synapse made from memristive elements. Its complex dynamic behavior is analyzed in depth. Theoretical analysis and numerical simulations demonstrate that the proposed MMNN can generate an unlimited number of hyperchaotic multi-scroll attractors and various other types of dynamics, which can be fine-tuned by modifying the system's parameters or initial conditions. Additionally, a simulated equivalent circuit for MMNN proves the validity and practicality of the multi-scroll attractor behavior. Finally, an image encryption and compression network framework based on end-to-end and MMNN and the self-attention mechanism is proposed, called SAECNet. Extensive performance evaluation highlights the system's high accuracy in compression and reconstruction, strong encryption capabilities, and solid defense against potential attacks. © 2025 IEEE. All rights reserved.

关键词： Hopfield neural networks

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IPL Data Analysis by Web Scraping and Using PowerBI

IPL Data Analysis by Web Scraping and Using PowerBI

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Mogith P Mothiswar T B G Mohammad Abubak J. Jagannathan N. Balaganesh Software Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information.

关键词： Visualization Technological innovation Fans Data analysis Key performance indicator Data visualization Market research Knowledge discovery Cleaning

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Evaluating Feature Prioritization in a Reduced Feature Set Using Explainable AI for Intracranial Aneurysm Classification

Evaluating Feature Prioritization in a Reduced Feature Set U...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Iram Abdullah Ali Javed Department of Computer Science Air University Kamra Pakistan Department of Software Engineering University of Engineering & Technology Taxila Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The timely management of an unruptured aneurysm can prevent hemorrhage. Multiple anatomical, morphological, and hemodynamic features associated with an aneurysm can predict the risk of rupture and classify the nature of the aneurysm as either ruptured or unruptured. However, considering all features may create ambiguities in the model due to inter-dependencies, such as the correlation between aneurysm height and width to aneurysm area. In computer-aided medical diagnostics, focusing on relevant features that diagnose the disease is more crucial than using an abundance of features that may not contribute to the diagnosis and may cause unnecessary complexity. The use of independent or sparsely correlated features improves the accuracy of the classifier. The proposed research analyzes the interdependency of features by calculating the correlation between features. Based on the analyses, the high correlation threshold is determined through rigorous experimentation. From the highly correlated features, one feature is selected. Consequently, the reduced feature set is recommended. The reduced feature set performs better with simpler classifiers. Instead of choosing a classifier that struggles with a broad range of features, an appropriately optimized feature set can enhance performance and improve accuracy. The proposed approach is tested on a publicly available dataset of aneurysms, leading to an improvement in classification accuracy from 82.6% to 87%, due to the use of an appropriate feature set. Moreover, the best model was evaluated using two different explainable AI frameworks to assess the contribution of each feature in prediction. However, the conflicting feature prioritization in each explainable framework suggests that these models prioritize different features for the same trained model, leading to ambiguity. This inconsistency indicates that the explainable models lack generalization and fail to consistently analyze feature importance.

关键词： Accuracy Correlation Explainable AI Computational modeling Redundancy Training data Aneurysm Predictive models Medical diagnosis Random forests

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Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection 19

Advancing Blackboard Framework: Responsive Trigger Mechanism...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Muhd Mukhtar, Hana Munira Husin, Husna Sarirah Ismail, Suriana Rahmat, Azizah Ismail, Roslan Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia School of Computer Science Taylor’s University Kuala Lumpur Malaysia Centre of Innovative Digital Education & Emerging Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the "control problem," remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect—trigger mechanisms and feasible conditions—for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process. © 2025 IEEE.

关键词： Expert systems

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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Design of AXI Bus-Based IP Core for Image Processing 12th

Design of AXI Bus-Based IP Core for Image Processing

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12th CCF Conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Enhanced Interpretability in Depression Classification with the Use of Machine Learning Models with Explainable AI

Enhanced Interpretability in Depression Classification with ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hirak Mondal Kamrul Golder Towfeka Benta Towhid Bushra Jamil Anindya Nag Anupam Tarafder Anindya Mozumder Md Asaduzzaman Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Northern University of Business and Technology Khulna Bangladesh Computer Science and Engineering Khulna University Khulna Bangladesh Software Engineering Hubei University of Technology Hubei China

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Depression, a prevalent mental health concern, demands efficient and accurate classification methods to support timely interventions. This study utilizes two mental health datasets to classify depression levels into high, medium, and low categories. Data preprocessing and visualization techniques were applied to extract meaningful patterns, followed by the implementation of six widely used machine learning algorithms: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Among these, the RF model demonstrated the highest accuracy, achieving 0.98 without cross-validation and 0.99 with cross-validation. Class-wise evaluation using precision, recall, and F1 score further established RF’s superior performance. To enhance interpretability and transparency, Local Interpretable Model-agnostic Explanations (LIME) was employed, providing insights into the RF model’s decision-making process. This research underscores the efficacy of the RF model for depression classification and highlights the importance of integrating explainable AI techniques in mental health diagnostics.

关键词： Radio frequency Support vector machines Accuracy Machine learning algorithms Explainable AI Decision making Mental health Nearest neighbor methods Depression Random forests

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Star Operation in Self-Attention for 3D Human Pose Estimation

Star Operation in Self-Attention for 3D Human Pose Estimatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao Wang Xiaochuan Wang Ruijun Liu Xiaoming Chen Haisheng Li School of Computer and Artificial Intelligence Beijing Technology and Business University School of Software Engineering Beijing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent transformer-based methods have achieved notable success in 3D human pose estimation. However, the most utilized self-attention mechanisms compute the attention matrix by performing a dot product on inter-vector features, which may overlook finer element-wise interactions. In this paper, we introduce star-attention, which integrates the star operation into the self-attention module. This approach retains the non-linearity and high dimensionality characteristics of matrix multiplication in self-attention while enhancing the feature representation by capturing interactions at a finer granularity. Specifically, we validated the promotion of the proposed star-attention module on MixSTE as an example. Experimental results demonstrate that our approach achieves competitive performance on benchmarks compared to state-of-the-art methods, producing smoother 3D poses across successive frames.

关键词： Three-dimensional displays Pose estimation Stars Benchmark testing Signal processing Transformers Acoustics Speech processing

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