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Fault diagnosis of chemical process based on CNN-BiGRU 24

Fault diagnosis of chemical process based on CNN-BiGRU

引用

2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Sun, Chengfu Ni, Yang Du, Rui Huaiyin Institute of Technology School of Computer and Software Engineering Huaian China

ISBN: (纸本)9798400718199

targeting issues where the fault features in the chemical production process are hard to discern and the fault diagnosis method's accuracy is low. This paper proposes an improved CNN-BiGRU (Convolutional Neural Networks and Bidirectional Gated Recurrent Unit) model, which is different from the usual serial structure, and adopts a parallel structure to combine the improved CNN and Bidirectional Gated Recurrent Unit. First, CNN is used to extract local features adaptively, and a batch normalization (BN) layer is used in CNN to avoid internal covariate shift and accelerate network training. In order to prevent overfitting and minimize network calculations, the global pooling layer is introduced. Second, BiGRU is used to extract global features from the data, and by incorporating a linear layer into the conventional BiGRU network, the model's accuracy is increased. Eventually, the fusion layer fuses the features that were extracted by the two networks. The experiment's outcomes demonstrate how well the model can raise fault detection accuracy. Taking the Eastman Process of Tennessee process (TE) as the research object, experiments show that the proposed method has excellent performance. © 2024 ACM.

关键词： Failure analysis

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Smart Alarm System for Crime Hotspot Detection using Deep Learning 15

Smart Alarm System for Crime Hotspot Detection using Deep Le...

引用

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

作者： Pawar, Rohan Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

The increasing number of surveillance cameras to watch over human activities necessitates the development of a system that can automatically identify violent and suspicious events. This is an essential piece of equipment for smart buildings. The average yearly death toll from acts of human aggression worldwide is 7.9 per 10,000 individuals. The majority of these acts of violence against people happen suddenly or in remote places. One of the main obstacles to halting these activities is the information delay in this case. In this work, the real time violence detecting technique is employed to thrive on this issue. Certain deep learning algorithms are applied in computer vision to anticipate and identify actions and attributes in videos. When police arrive at dangerous locations in real time, they analyze CCTV footage and begin an investigation before moving further. In smart buildings, this technology is mandatory. Violence Detection in real time will prevent any mis happenings from taking place by raising alerts to the security guards. Our model also has YOLO object detection inculcated in it in order to recognize any weapons or tools that an attacker may be using against people and the security guards will act accordingly. © 2024 IEEE.

关键词： CCTV surveillance technique Deep Learning MobileNetV2 Violence Detection

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DocTrKan: Document Image Enhancement Based on KAN-Enhanced Transformer Networks 3

DocTrKan: Document Image Enhancement Based on KAN-Enhanced T...

引用

3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Zheng, Youkang Liu, Jun Zhou, Sai School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Document images serve as a medium for transmitting information;however, they are influenced by numerous environmental factors during capture, complicating the identification and processing of their content. In the digital era, it is crucial to eliminate noise from document images to enhance readability. To address this challenge, we propose a novel transformer-based document image enhancement algorithm utilizing KAN enhancement, designed to improve both machine-printed and handwritten document images in an end-to-end manner. The encoder operates directly on pixel blocks with positional information, bypassing traditional convolutional layers for feature extraction. A KAN network is incorporated into the encoder to enhance the model's expressive power, reduce parameter count, and improve optimization efficiency. A decoder is employed to reconstruct the clean image. In testing on the DIBCO dataset, DocTrKan surpasses traditional transformer and threshold-based methods in enhancement performance. © 2024 IEEE.

关键词： Electric transformer testing

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Lane Detection and Traffic Sign Detection for Autonomous Driving Systems 2

Lane Detection and Traffic Sign Detection for Autonomous Dri...

引用

2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Biswas, Aishi Chowdhury, Anushka Roy Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781837242672

In the realm of autonomous driving, this study focuses on two critical tasks for scene understanding: lane detection and traffic sign detection. These tasks are essential to ensuring safe and reliable navigation in self-driving systems. A combination of traditional computer vision techniques and modern deep learning methods are used to achieve accurate detection and analysis of lanes and traffic signs in real-world driving scenarios. For lane detection, we utilize edge detection and the Hough Transform, while for traffic sign detection, we apply machine learning algorithms to recognize and classify various signs. This hybrid approach demonstrates the potential of integrating classical computer vision algorithms with deep learning models to enhance the perception capabilities of autonomous vehicles. The model achieves a balanced performance, with accuracy metrics ranging from 80-90% across both tasks, indicating a robust ability to detect lanes and signs effectively under varied conditions. This work serves as a foundation for developing more advanced scene understanding systems in autonomous driving technology. In the future, we aim to enhance the model's adaptability through continuous learning and incorporating additional sensory data, such as LiDAR and radar. Additionally, expanding the dataset and testing the models in diverse environmental conditions, including different lighting and weather scenarios, will further improve the robustness and reliability. © The Institution of engineering & technology 2024.

关键词： Hough transforms

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Gesture Guide: Empowering Deaf and Mute Individuals with ISL Recognition and Guidance System using Hand signs 5

Gesture Guide: Empowering Deaf and Mute Individuals with ISL...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Rithesh Manikandan, S. Prasuna, Gnaneethi Lakshmi Sivanesh, G. Divya Lakshmi, K. School of Computer Scienceand Engineering Vellore of Institute of Technology Vellore India

ISBN: (纸本)9798350385649

Indian Sign Language (ISL) is a crucial language used by the deaf and mute community. The paper presents a systematic approach that utilizes Indian Sign Language video as input and converts it into text. By leveraging support vector machines, convolutional neural networks, recurrent neural networks, Media Pipe, and deep learning methods, the proposed system effectively addresses the challenges faced by the deaf and mute community in accessing educational resources and engaging in daily activities. The approach takes a step-by-step, methodical approach to improve the efficiency and accuracy of hand gesture recognition, enhancing accessibility and inclusion for the deaf and mute community. © 2024 IEEE.

关键词： Support vector machines

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IoT Network Intrusion Detection with Deep Learning and Voice Alerts 5

IoT Network Intrusion Detection with Deep Learning and Voice...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dharshiniya, S. Daniel Madan Raja, S. School of Computer Science and Technology Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The growing complexity and scale of Internet of Things (IoT) networks have made them a prime target for cyber-attacks, necessitating the creation of Intrusion Detection Systems (IDS) to secure confidential data. A key challenge in existing IDS models is that a high loss value can indicate inadequate detection capabilities, potentially leading to missed attacks especially in time-sensitive environments. This study proposes a robust IDS framework by integrating an Autoencoder for feature extraction with Temporal Fusion Transformer (TFT) for attack detection of network intrusion in IoT environments. UNSW-NB15 dataset, was used to assess the model's efficiency. Data preprocessing techniques, including data cleaning and the applying the SMOTE, were employed to ensure balanced dataset representation. The Autoencoder's encoding layer reduces dimensionality to capture essential features and extracted that feature and that as an input to models like the TFT. The Temporal Fusion Transformer (TFT) leverages LSTM encoding, temporal attention, and Gated Residual Networks to capture complex time-series dependencies with the extracted features which help to detect attacks like Fuzzers, Dos, Backdoor, Exploits, Generic, Reconnaissance, Shellcode, Worms. After detecting the attacks, the voice alert is automatically sent to the admin which helps to maintain the robustness. The accuracy achieved by the proposed IDS is 99.06%, with a recall of 98.66%, precision of 91.59%, F1-score of 94.27% and a loss of 0.030 when compared to other traditional algorithms. These results demonstrate the model's effectiveness in identifying various types of cyber-attacks while maintaining a low loss when compared to others. The integration of the voice alert system further enhances the security of the IDS by providing immediate notifications of detected intrusion, thereby facilitating timely responses. © 2024 IEEE.

关键词： Network intrusion

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Opinion Analysis and Text Pattern Detection from Social Media Data Using Integrated Machine Learning Approach 2

Opinion Analysis and Text Pattern Detection from Social Medi...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Abdullah, A. Sheik Mishra, Utkarsh Bala Adithyan, G. Sriram, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

Opinion analysis using social media data has become essential for corporations, politics, and social research because it provides insightful information about the public's attitudes toward various issues. In the realm of e-commerce, understanding consumer preferences and emerging trends is crucial for businesses to adapt and thrive in a competitive market. This paper focuses on trend detection techniques in fashion e-commerce, aiming to analyze market successes and failures. By harnessing social media data, the objective is to equip retailers with predictive tools, enabling them to anticipate trends, optimize logistics, and meet consumer demands effectively. The study utilizes Python and sentiment analysis tools, including the Vader Polarity Score, to evaluate social media content from platforms like Flipkart and Amazon. The research identifies trending keywords and assesses their relevance in the market by analyzing product names, ratings, reviews, and sentiment scores. This research empowers fashion retailers to make informed decisions by leveraging social media data and sentiment analysis. The findings spotlight current market trends and provide a framework for anticipating future consumer preferences. Implementing such techniques can revolutionize e-commerce strategies, enhance product offerings and ensure sustained market relevance. © 2024 IEEE.

关键词： Sentiment analysis

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Navigating Spatial Insights: Comparative Exploration of Deep Learning Algorithms for Flood Detection using Remote Sensing Data 2

Navigating Spatial Insights: Comparative Exploration of Deep...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Bhavuk Vanzara, Jui Sajidha, S.A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Detecting floods, which are highly destructive natural disasters, is crucial for minimizing their impact on both life and infrastructure [1]. Timely identification is essential, and satellites play a key role in assessing the extent of damage and pinpointing areas in urgent need of assistance following events such as floods, earthquakes, or wildfires. The "SEN12-FLOOD"dataset has emerged as a vital resource for flood detection, utilizing both synthetic aperture radar (SAR) and optical imaging. However, a significant challenge lies in the clarity of the images, making it difficult to accurately identify the extent of damage. In response, we propose a comprehensive methodology that incorporates data preprocessing, the comparison of multiple models, and efficient labeling techniques to enhance the accuracy of flood detection. The data preprocessing phase involves the removal of empty folders and irrelevant images, streamlining the dataset for optimal analysis. Additionally, we introduce band stacking, combining satellite imagery bands B2, B3, B4, and B8 to improve image clarity, providing a refined input for flood detection models. The methodology extends to model comparison, wherein multiple models are evaluated to identify the most effective one for accurate flood detection. © 2024 IEEE.

关键词： Satellite imagery

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Privacy-Protected Data Mining and CNN for E-Healthcare Systems 5

Privacy-Protected Data Mining and CNN for E-Healthcare Syste...

引用

5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Nevatia, Isha Chaudhary, Vraj Pandian, M. Thurai Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376685

The E-Health care system is crucial in modern health care. These systems are designed to secure medical data repositories. The main goal of this study is to ensure both security and privacy by enhancing fully homomorphic data collection. It also aims to improve classification accuracy in disease detection. The existing method of Privacy-Preserving Data Mining (PPDM) is utilized to maintain privacy in healthcare, and the new method using a Convolutional Neural Network (CNN) algorithm is employed to identify diseases more accurately. This new approach enhances performance by achieving higher accuracy, recall, and reducing time complexity. © 2024 IEEE.

关键词： Differential privacy

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Cybersecurity Advancements: A Comprehensive Survey of Machine Learning-based Preprocessing Techniques for Enhanced Website Phishing Detection 15

Cybersecurity Advancements: A Comprehensive Survey of Machin...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kamble, Naresh Mishra, Nilamadhab Vellore Institute of Technology School of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331300579

In a time of escalating digital threats, cybersecurity takes on paramount significance, as website phishing attacks pose considerable risks to individuals and organizations. This extensive survey delves into the ever-evolving realm of website phishing detection, with a specific spotlight on the pivotal role of machine learning-based preprocessing techniques in fortifying security measures. Research explores a wide spectrum of preprocessing methods, including URL analysis, content examination, and anomaly detection, all geared towards early-stage phishing detection. We also investigate the latest advancements in data feature extraction, dimensionality reduction, and data representation, all of which play a crucial part in the ongoing battle against website phishing. Through a methodical analysis of leading methodologies and illustrative case studies, this survey strives to provide a comprehensive understanding of how machine learning-based preprocessing enhances website phishing detection. Additionally, it examines the challenges, opportunities, and promising research directions within this domain. The insights presented here contribute to a knowledge base that serves as a valuable resource for security practitioners, researchers, and policymakers, helping them make informed decisions and implement effective countermeasures in the relentless fight against website phishing threats. © Grenze Scientific Society, 2024.

关键词： Phishing

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