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Eliminating Toxicity in Text: An NLP Framework for Clean Content Extraction 5

Eliminating Toxicity in Text: An NLP Framework for Clean Con...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Kumar, Sharath Dmello, Daxia Vlora Janani, Devi Avani, K.D. Kirthana Kamath, P. Department of Information Science and Engineering NMAM Institute of Technology Nitte India Mangalore Institute of Technology and Engineering Department of Information Science and Engineering Mangalore India

ISBN: (纸本)9798331518097

This research study proposes a machine learning-based application called ToxiGuard, which is designed to ascertain the toxic language in text. The proposed technique employs the use of the Natural Language Processing (NLP) mechanism and a Naive Bayes based classifier to detect and categorize unhealthy or toxic comments. First the given text data is preprocessed by dividing or tokenizing the sentences into different words, once tokens are formed it will be passed as an argument into the proposed method which removes all the words which are unhealthy based on the trained model. Model takes the decision based on the patterns correlated with toxic or unhealthy language. The proposed mechanism is represented in the form of a graphical user interface (GUI) built with Tkinter. GUI being designed enables user to upload a text file which contains serious of text or sentences analysis is being done and visual representation of the outcome is plotted with help of a bar chart of the maximum frequency of each toxic words. In addition to analysis report outcome of the analysis can be exported for further review process. ToxiGuard focus to help or classify online content by providing effective and user-friendly application in-order to detect harmful or unhealthy language. © 2024 ieee.

关键词： Graphical user interfaces

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Evaluation of Machine Learning and Deep Learning-based Intrusion Detection systems in In-vehicle Networks 9

Evaluation of Machine Learning and Deep Learning-based Intru...

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9th international conference on AI and the Digital Economy, CADE 2023

作者： Li, Junhui Ersotelos, Nikolaos Bottarelli, Mirko Epiphaniou, Gregory School of Computer Science and Creative Technologies University of the West of England Bristol United Kingdom Secure Cyber Systems Research Group WMG University of Warwick Coventry United Kingdom

With the growing complexity and connectivity of intelligent Connected Vehicles (ICVs), In-vehicle communication plays an important role in today’s driving environment. The controller Area Network (CAN) is the most used protocol in an in-vehicle network to exchange data between Electronic control Units (ECUs). Although in-vehicle communications must be regarded as the last line of security defence for ICV, recent reports have indicated vulnerability to threats due to its broadcasting design. In this context, this paper targets to comprehensively review the advantages and limitations of the existing machine learning and deep learning approaches designed for Intrusion Detection systems (IDS). We evaluate their performance and computational resource requirements and propose potential enhancements. Finally, open challenges and future research directions for in-vehicle communication cyber security are highlighted as observations and recommendations. © The Institution of Engineering & technology 2023.

关键词： Vehicles

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Machine learning applications in cyber-Physical Production systems: a survey 27

Machine learning applications in Cyber-Physical Production S...

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27th international conference on automation and Computing, ICAC 2022

作者： Zhang, Zili Liu, Chao Zhang, Jun Peng, Tao Hu, Xinrong Xu, Yuchun Wuhan Textile University School of Computer and Artificial Intelligence Wuhan China Aston University College of Engineering and Physical Science Birminghan United Kingdom Wuhan Institue of Technology School of Computer Science and Engineering Wuhan China Engineering Research Center of Hubei Province for Clothing Information Wuhan China Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion Wuhan China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

cyber-Physical Production systems (CPPS) play a vital role in realizing the vision of Industry 4.0. In the last decade, various machine learning methods have been implemented in manufacturing systems to improve their intelligence. However, few review papers on machine learning applications in CPPS have been published. In this context, this paper presents a survey of machine learning applications in cyber-Physical Production systems. Both bibliometric analysis and qualitative analysis have been conducted based on the related literatures published in the last decade. We identified the major research issues with respect to machine learning applications in CPPS, i.e. anomaly detection, predictive maintenance, fault management, efficiency, quality assurance, and scheduling. The review results show that although machine learning has been extensively applied in manufacturing, its applications in CPPS have not been widely studied. Based on the detailed discussions of the research issues and challenges, this paper indicates the current limitations of CPPS and demonstrates the great advantages and potential for applying machine learning in CPPS in future research. © 2022 ieee.

关键词： Machine learning

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A Dual Approach with CycleGANs-based Face Reconstruction and ViT-based Classification 7

A Dual Approach with CycleGANs-based Face Reconstruction and...

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ieee 7th international conference on Advanced Technologies, Signal and Image Processing (ATSIP)

作者： Ouannes, Laila Ben Khalifa, Anouar Ben Amara, Najoua Essoukri Univ Sousse Ecole Natl Ingenieurs Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia Univ Jendouba Inst Natl Technol & Sci Kef Le Kef 7100 Tunisia

ISBN: (纸本)9798350351491;9798350351484

Facial recognition technology plays a crucial role in various domains, including security, surveillance, and biometric authentication. However, the accuracy of conventional facial recognition systems is significantly compromised in degraded conditions, characterized by head pose variations, occlusions, and variations in lighting. In this study, we propose an innovative approach to address this challenge by integrating advanced deep-learning techniques. Specifically, we leverage Cycle Generative Adversarial Networks (CycleGANs) for facial reconstruction and vision Transformers (ViT) for recognition in degraded conditions. GANs are employed to enhance the quality of degraded facial images by generating high-resolution reconstructions from low-quality inputs. The reconstructed images are then fed into Transformer-based models, which extract discriminative features for robust recognition. To evaluate the effectiveness of our approach, we conducted extensive experiments using publicly available datasets of degraded facial images: the EURECOM Kinect Face Dataset and the IST-EURECOM Light Field Face Database. Our results demonstrate significant improvements in face recognition accuracy compared to traditional methods, particularly in scenarios with low-resolution or occluded faces. Furthermore, we analyze the computational efficiency and scalability of our proposed approach, highlighting its potential for real-world deployment.

关键词： Degraded face recognition face reconstruction CycleGANs Vision Transformers

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Consensus of Multi-Agent systems via Structural Perturbation and Random Walk with Restart

Consensus of Multi-Agent Systems via Structural Perturbation...

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Advanced Algorithms and control Engineering (ICAACE), international conference on

作者： Xiaoli Li Hailong Su Yan Ren Juan Wang Fangfang Xue School of Digital and Intelligent Industry (School of Cyber Science and Technology) Inner Mongolia University of Science and Technology Baotou China School of Electronic Engineering Xidian University Xian China School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Multi-agent collaborative control is widely applied in fields such as healthcare, traffic control, industrial automation, and military command. The consensus of multi-agent systems is a major research topic in cooperative control. Since low network connectivity can lead to inefficient collaboration of multi-agent systems, this paper proposes a new link prediction method aimed at optimizing the topology of multi-agent networks. The new method combines structural perturbation and random walk with restart, which can effectively utilize the edge and node information in the network to predict missing or possible future links. The new method can enhance the connectivity of the network and improve the collaborative efficiency of multi-agent systems. Comparison experiments with a single method show that the proposed new method significantly accelerates the convergence rate of the collaborative consistency in multi-agent systems.

关键词： Network topology Perturbation methods Collaboration Prediction methods Medical services Traffic control Prediction algorithms Topology Multi-agent systems Convergence

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Self-Adaptive Electronics using Artificial Neural Networks 9

Self-Adaptive Electronics using Artificial Neural Networks

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9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Anita, S. Joon, Rakesh Kumar Devi, S. Rukmani Khandan, K. Lakshmi Howard, Eric Rajendiran, M. R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Ganga Institute of Technology And Management Department of Electronics And Communication Engineering Haryana Kablana124104 India Saveetha College of Liberal Arts And Sciences Simats Deemed To Be University Department of Computer Science Tamil Nadu Chennai600124 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Macquarie University Department of Physics And Astronomy Australia Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Electronic gadgets and systems are growing rapidly, requiring new adaptability methods. This research uses Artificial Neural Networks to self-adapt electronic systems. ANNs, inspired by the brain, can optimize electronic circuits and devices in real ***-adaptive electronics uses ANNs for system control and decision-making. ANNs learn to adapt to changing operational circumstances, environmental factors, and user preferences through supervised and reinforcement learning. Feedback systems let neural networks improve energy usage, system parameters, and performance without operator interaction.A sensor array, artificial neural network-based control unit, and actuators make up the self-adaptive electronics system. The system smoothly integrates sensors and computing. Self-adaptive electronics could be used in loT, wearable, and autonomous systems, the research says. Electronic systems operate in unpredictable and dynamic contexts, thus the architecture is ***, self-adaptive electronics outperform static systems in performance, energy efficiency, and adaptability. Artificial neural networks can enable smarter, more responsive, and autonomous gadgets, according to the findings. Finally, self-adapting artificial neural networks in electronics could lead to intelligent systems. This research advances adaptive electronics, enabling new applications in the ever-changing electronics and technology environment. © 2024 ieee.

关键词： Neural networks

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Performance Enhancement of the Aerodynamics of Savonius Wind Turbine by Wind Flow control using CFD Analysis

Performance Enhancement of the Aerodynamics of Savonius Wind...

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2024 international conference on Advancement in Renewable Energy and intelligent systems, AREIS 2024

作者： Debnath, Pinku Chakraborty, Debanjali Chakraborty, Sumit National Institute of Technology Agaratla Department of Mechanical Engineering Tripura India

ISBN: (纸本)9798350387230

The Savonius Rotor (SR) is gaining more attention for electrical energy generation in urban area. Although Savonius wind rotor produces negative static torque coefficient at every rotor angle, it has been used for simple construction. The curtain has been used to control the wind flow direction in upstream side of the rotor. The performance investigation of the rotor has been done in ANSYS fluent. The focus of the numerical investigation is to increase the Savonius Wind Turbine (SWT) efficiency with and without curtain arrangement in upstream rotor side. As the rotor speed increases with increase of wind speed, so curtain guide the flow direction. The RANS turbulence model is exploited for simulating the fluid flow in front of the rotor. From the simulation it is found that the non-dimensional performance parameters i.e power coefficient (Cp) and torque coefficient (Ct) increases with increase in Tip Speed Ratio (TSR) without curtain design. But the peak value of coefficient of power and torque are noted at lower tip speed ratio with magnitude of 0.67 and 0.68 are obtained from curtain arrangement., which is comparatively higher than without curtain in front of the rotor. © 2024 ieee.

关键词： Computational fluid dynamics

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Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

Medical Visual Prompting (MVP): A Unified Framework for Vers...

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2024 ieee international conference on systems, Man, and cybernetics, SMC 2024

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Sun Yat-Sen University Cancer Center Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (纸本)9781665410205

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. © 2024 ieee.

关键词： Image segmentation

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FlowEmbed: Binary function embedding model based on relational control flow graph and byte sequence 29

FlowEmbed: Binary function embedding model based on relation...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Yongpan Dong, Chaopeng Li, Siyuan Luo, Fucai Su, Renjie Song, Zhanwei Li, Hong Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China State Grid Fujian Electric Power Company China

ISBN: (纸本)9798350330717

Binary function embedding models are applicable to various downstream tasks within IoT device software systems and have demonstrated advantages in numerous binary analysis tasks, such as vulnerability (homologous) function search and compilation optimization option identification. However, current binary function embedding methods either learn embedding based on code sequence, which lack the program semantics of functions (e.g., control flow, etc.) or based on program structure graphs, which omit global sequential information. As a result, these methods fall short in enabling models to learn the complete semantic of function. In this paper, we introduce FlowEmbed, a novel approach that synergistically integrates control flow and global semantic learning to facilitate exhaustive code comprehension. Initially, FlowEmbed harnesses a distinct relational control flow graph combined with the power of BERT and RGCN models to aptly capture the nuances of control flow semantics. Moreover, by deploying the DPCNN model on a byte sequence constructed from function machine code, FlowEmbed adeptly discerns the inherent global sequential semantics of binary functions. Through rigorous evaluations spanning three IoT-related tasks, FlowEmbed's efficacy becomes evident, showcasing notable improvements: a 20.6% improvement in compilation optimization option identification, a 1.8% improvement in binary function similarity analysis, and an 11.9% improvement in homologous function search. Collectively, these results underscore FlowEmbed's superior capability, positioning it as a invaluable asset in a binary analysis application. © 2023 ieee.

关键词： binary code search binary code similarity detection binary function embedding deep learning static analysis

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Rule-based Intrusion Detection System using Logical Analysis of Data

Rule-based Intrusion Detection System using Logical Analysis...

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2023 international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Kumar, Anjanee Das, Tanmoy Kanti National Institute of Technology Raipur Department of Computer Applications G.E. Road Chhatisgarh Raipur492010 India

ISBN: (纸本)9781665474511

Any organisation's network infrastructure is insecure as different cyber-attacks have constantly mounted and destabilised these systems. There is a rapid upsurge in the usage of the internet in the modern era. This extensive use of the internet has given a chance to attackers to do malicious activities on the network field. To combat these attacks, we need an Intrusion Detection System (IDS). IDS is a robust technological system that protects the system by detecting any intrusions in it. In this study, different machine learning algorithms, which include Support Vector Machine (SVM), Naive Bayes, Random Forest (RF), and Decision Tree (DT), are compared with the method of Logical Analysis of Data (LAD) on NSL-KDD dataset. NSL-KDD is the benchmark dataset used in the network field. The results have been compared on the basis of accuracy, recall, F1-score, G-mean, detection time and ROC-AUC curve. Based on the result obtained, it is evident that the LAD method has outperformed in comparison with other ML-based methods and also detects intrusions in real time. © 2023 ieee.

关键词： Network security

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