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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Javeriya, Thonduru Anjum Kabilan, M. Manoj, Golla Sree, Yarragonda Bhavya Jagadish, Rongala Venkata Satya Mir, Mahmood Hussain Debnath, Saswati Girish, B.M. Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Department of Computer Science and Information Technology Karnataka Bengaluru India Alliance College of Engineering and Design Department of Mechanical Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350385199

Because of COVID-19, businesses have had to change how they operate, like having people work from home or having fewer employees in the office. This 'new normal' is sticking around for a while. Workers have had to adjust to these changes, which can make them feel tired and Pressure. Stress happens when people feel overwhelmed and uncertain. There are lots of things that can cause stress for workers, like getting paid less, having too much work to do, feeling lonely while working from home, working longer hours, not having a good place to work, not feeling like part of a team, feeling like they can't do their job well, and worrying about getting sick. If bosses can recognize when workers are starting to feel stressed, they can help fix the problem, which can make things better for both the worker and the company. © 2024 IEEE.

关键词： COVID-19

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Profanity Detection from Audio Recordings Using Natural Language Processing Techniques

Profanity Detection from Audio Recordings Using Natural Lang...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment 8

Fusion of Traditional and Deep Learning Models for Underwate...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Dhivya, R.M. Nagasai, V. Masilamani, V. Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai600127 India

ISBN: (纸本)9798331531539

Underwater image quality is significantly affected by the unique challenges of the underwater environment, including light attenuation, color distortion, backscatter, and the presence of particulate matter. These factors reduce visibility, alter color perception, and introduce noise and artifacts that degrade the overall quality of captured images. Ensuring high-quality underwater images is essential for various applications, including marine biology, environmental monitoring, underwater navigation, and industrial operations. This research proposes a novel approach to evaluating underwater image quality by integrating conventional machine learning methods with advanced deep learning architectures. The fusion process, employing Support Vector Regression (SVR), known as FTDUIQA (Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment), combines well-established deep learning-based models - CNNIQA (Convolutional Neural Network for No-Reference Image Quality Assessment), DBCNN (Deep Bilinear Convolutional Neural Network), and HyperIQA (HyperNet for Image Quality Assessment) - with the classical IQA model, MSAEQA (Multi-scale and Antagonistic Energy Distribution). Additionally, we incorporate a modified deep learning model, MAUIQA (Multi-scale AttentionNet for Underwater Image Quality Assessment), which has been adapted from its image super-resolution application. This hybrid approach leverages the strengths of both deep learning and traditional techniques to provide a robust and comprehensive assessment of underwater image quality. © 2024 IEEE.

关键词： Convolutional neural networks

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Stroke-GAN Painter:Learning to paint artworks using stroke-style generative adversarial networks

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Computational Visual Media 2023年第4期9卷 787-806页

作者： Qian Wang Cai Guo Hong-Ning Dai Ping Li School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina School of Computing and Information Engineering Hanshan Normal UniversityChaozhouChina Department of Computer Science Hong Kong Baptist UniversityHong KongChina School of Design The Hong Kong Polytechnic UniversityHong KongChina

It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke *** advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for *** this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two ***-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke *** design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted *** overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of *** demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.

关键词： AI painting painting strokes artistic style

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Nostalgic Resonance: Exploring the Impact of Rebranding on Consumer Behavior 4

Nostalgic Resonance: Exploring the Impact of Rebranding on C...

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

作者： Titus, Ray Chinnaiyan, R. Sungheetha, Akey Rajesh Sharma, R. Babu, Tina Nair, Rekha R Asob Alliance University Bengaluru India Alliance University Alliance School of Engineering and Design Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350307757

This research aims to delve into consumer behavior, irrespective of demographic factors, and explore the impact of nostalgia on both established and upcoming brands. The study seeks to understand how consumers respond to brands already on the market and those making a comeback. Additionally, it aims to analyze consumer buying behavior and willingness to purchase products post-relaunch. A comparative study between different age groups and their brand awareness will be conducted to assess its influence on buying behavior for relaunched brands. Identifying a research gap, previous studies have provided insights into nostalgia marketing, but there is a lack of specific data representing nostalgia's role in overall consumer behavior, particularly for certain products. Furthermore, despite numerous brands undergoing relaunches globally and in India, the factors determining the success or failure of these relaunched brands remain unclear. This research aims to fill these gaps, contributing valuable insights into the nuanced relationship between nostalgia, brand relaunches, and consumer behavior. © 2024 IEEE.

关键词： Consumer behavior

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Research on YOLO model optimization of multi-modal data processing attention mechanism based on deep learning 4

Research on YOLO model optimization of multi-modal data proc...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wu, Qiongfei Xie, Qubo Wuhan Institute of Design and Science School of Intelligent Engineering Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350361643

In this study, a YOLO model method combining LightGBM and multi-modal data processing attention mechanism optimization is proposed to enhance the effectiveness and accuracy of object detection. The traditional YOLO model has the problem of insufficient utilization of multi-modal data when processing image data, while LightGBM has significant advantages in processing structured data. Therefore, this paper designs and implements an optimization framework by embedding CBAM module into the extraction of features part of YOLOv5 model to improve the capability of feature representation. Concurrently, LightGBM is employed to handle structured data, and a fusion module is used to combine the features from both sources with appropriate weighting. © 2024 IEEE.

关键词： Object detection

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Research on Multi-Modal Artificial Intelligence Information Fusion Technology in Embedded computer Systems 3

Research on Multi-Modal Artificial Intelligence Information ...

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3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Wu, Qiongfei Xie, Qubo Chen, Yi Wuhan Institute of Design and Science Department of Information Engineering Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350380989

Multi-scale decomposition (MSD) has some problems, such as missing details and generating noise. This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques. Aiming at the problem that MSD is easy to produce noise, low-pass filter optimization function and structure texture decomposition model are introduced to effectively solve the problem of decomposition technology. An adaptive filtering algorithm based on least square method is proposed, and the structure-texture analysis method based on image is combined to overcome the noise problem of traditional algorithms theoretically. Secondly, for structured texture, the fusion algorithm of convolutional neural network and Gaussian fairing is studied to enhance the effective acquisition of image details and remove noise. The phase-shift matching algorithm is used for high frequency band to achieve effective fusion of high frequency signals. Then the inversion is carried out to obtain the final composite image. The images are studied qualitatively and quantitatively from the perspectives of visual effect, mutual information, feature mutual information, structural similarity, information entropy, PSNR, etc. © 2024 IEEE.

关键词： convolutional neural network low-pass filter multi-scale decomposition Multimodal image fusion

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Hybrid Machine Learning Model for Predicting Damage Scale in Sunflower Leaves affected by Aster Yellow Disease 2

Hybrid Machine Learning Model for Predicting Damage Scale in...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Jain, Anuj Kumar Pradeep Ghantasala, G.S. Bhandari, Rahul Kaur, Gaganpreet Rajesh Sharma, R. Sungheetha, Akey Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354461

Through this work a thorough summary of a predictive model specifically designed to evaluate Aster Yellows infections in sunflower leaves is presented. Exhibiting remarkable accuracy are the model's performance measures, which span from 97.74% to 97.89% for various Damage Scale categories and include precision, recall, and F1-Score. These measurements demonstrate how well the model performs in accurately categorizing cases within each group and highlight how consistently it measures the severity of Aster Yellows disease. The support values clarify the significant representation of cases on every Damage Scale, demonstrating the model's resilience to a variety of situations. This work trains and evaluates the model using the 6880-picture dataset, yielding an astonishing 97.81% overall accuracy, recorded repeatedly at 97.82% on average due to the high averages (of macro, weighted, or micro numbers within each kind). It not only shows high performance across the dataset but also its consistency across Damaged Scales is further supported. And not just the model's reliability, modest variations in support rates also increase its dependability: highlighting its capacity to manage multiple diverse Aster Yellows infection scenarios. Through the model, they can clearly distinguish between different levels of damage with an accuracy of 99 percent overall, which betokens its potential application in precision farming. Now, thanks to these compelling test numbers, farmers and agricultural experts will no longer have to guess at how to distinguish and treat sunflower plants suffering from Aster Yellows infection. In summary: The abstract deftly depicts this model's quantitative performance, general versatility, and possible pragmatic utility, making it a bright spot in the history of precision farming. © 2024 IEEE.

关键词： Plant diseases

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A Novel Split Learning-Based Consumer Electronics Network Traffic Anomaly Detection Framework for Smart City Environment

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4197-4204页

作者： Kim, Dong-Jo Amma, N. G. Bhuvaneswari Sarveshwaran, Velliangiri Sunchon National University Department of Image Design Sunchon57922 Korea Republic of Vellore Institute of Technology School of Computer Science and Engineering Chennai600 127 India Srm Institute of Science and Technology Department of Computational Intelligence Chennai603 203 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi621301 Taiwan

Due to technology advancement, connectivity and intelligence have revolutionized the consumer electronics (CE) world and IoT systems pose a new cybersecurity threat because smart devices are constantly connected to sensors, which are connected directly to enormous cloud servers. To address the cybersecurity vulnerabilities associated with IoT in smart cities, we provide a Software Defined Network (SDN) orchestrated anomaly detection system based on Split Learning (SL). The traffic generated by the CE is pre-processed and the relevant traffic features are selected. Then the model is split into at least two sub-models using SL to detach model training from the necessity for direct access to raw data. Client-side sub-models are trained on local training data on client devices, whereas server-side sub-models are trained on servers. Experiments show that the proposed SL-based CE network anomaly detection framework decreases computation needs on the client side by just computing a sub-model rather than the complete computationally costly model. The proposed solution has the potential to detect compromised CE network traffic in smart city environments. As evidenced by our results, the proposed framework performed significantly better than the existing state-of-the-art anomaly detection system in terms of accuracy. © 1975-2011 IEEE.

关键词： Smart city

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