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Modelling stress-strain curves and forming limit curves using deep learning 21

Modelling stress-strain curves and forming limit curves usin...

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21st international conference on Applied Mechanics and Mechanical engineering, AMME 2024

作者： Eldin Salem, Hossam Elatriby, Sherif A. Mohamed, Mohamed Department of Mechanical Engineering Helwan University Helwan Egypt Nile University Egypt Impression Technologies Ltd Coventry United Kingdom

The sheet metal forming process stands one of the most principal applications in production engineering with the goal of producing defect free products possessing the mechanical properties that met the working conditions requirements. The efficient metal forming process relies on a deep understanding of the material behaviour during the forming process. To achieve this, feasibility studies for new incoming components helps determine if a part is feasible for manufacturing or if design modifications are required. This simulation requires an accurate material card to replicate the real material behaviour, requiring extensive material testing data such as stress-strain curves and forming limit curves (FLC). Artificial intelligence, specifically deep learning, is a promising tool to overcome such challenges. In this study, one of the deep learning algorithms, the artificial neural network (ANN), is utilized to create material testing data for building the material cards for feasibility simulation. © Published under licence by IOP Publishing Ltd.

关键词： Sheet metal

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Care Compass: Monitoring made easy, Care made effective 3

Care Compass: Monitoring made easy, Care made effective

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manhas, Prabal Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Chandigarh University Punjab India Chandigarh University Dept.of Computer Science and Engineering Punjab Mohali India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331515911

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today's youth. The 21st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals. © 2025 IEEE.

关键词： Electronic health record

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Attention Enhanced Feature Fusion for Mental stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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Portable Executable Analysis for Malware Detection for Reducing Risk 1st

Portable Executable Analysis for Malware Detection for Reduc...

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1st international conference on intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Kumar, Avinash Chaudhary, Meena Department of Computer Science Engineering Mangalayatan University UP Aligarh India

ISBN: (纸本)9783031823763

In the current era where data and information is the main fuel for business, education, banking, marketing and digital applications used by people. These applications consist of personal and sensitive data. These digital applications are using various technologies that makes it heterogeneous. This heterogeneity creates vulnerabilities in many cases and thus resulting in various threats like, virus, Trojan, spyware and other malicious programs. Malware is one of the deadliest threats in cyber security and it is very challenging to detect malware when entering a system because of its intelligent coding. The reverse engineering is used for detecting malware in classical way which is a time taking process and involves maximum human interventions. Therefore, there is also need of automated defensive mechanism to detect these malwares in order to reduce the duration of mitigation. The less time for accurate detection would result in stopping malware spreading in other part of the organization once it attacks the organization. Therefore, this paper focuses on the Machine learning concept as one of the major solution for malware detection. This paper also addresses the classical reverse engineering concept that is used to analysis malware. The paper uses, Random Forest, XGBoosting Decision Tree, Gradient Boosting Classifier and SGD Classifier for analysis. Further the Machine learning models that gives highest accuracy is XGBoosting that gives accuracy of 94.86% followed by random forest which 94.66%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reverse engineering

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From Pixels to Insights: Image datasets for AI/ML in Software-Defined Networking 21

From Pixels to Insights: Image Datasets for AI/ML in Softwar...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Pant, Pranav Mallick, Rishikant Sahu, Santosh Kumar Kumar Vashishtha, Lalit Kalinga Institute of Industrial Technology Department of Computer Science Bhubaneswar India Geodata Processing and Interpretation Center Oil and Natural Gas Corporation Uttarakhand Dehradun India

ISBN: (纸本)9798350358230

In the modern landscape of pervasive digital data, the need for robust intrusion detection systems (IDS) has become paramount to safeguard networks from malicious activities. This research explores the synergistic integration of big data analytics and AI/ML techniques, with a focus on leveraging Apache Spark as a platform for intrusion detection. The study delves into the core principles underlying intelligent IDS systems enhanced by AI, leading to heightened accuracy and adaptability in countering dynamic cyber threats. The research introduces an innovative SDN DDoS attack image dataset, refined to optimize machine learning and deep learning models for IDS. Through novel image generation methodologies, the dataset is enriched, substantially enhancing model performance. Comparative analyses are conducted, contrasting the original dataset with the refined versions, utilizing diverse learning strategies. The findings underscore the substantial advancements achieved, revealing notably improved model accuracy. The research also hints at the potential adoption of convolutional neural networks to further elevate classification capabilities, instigating prospective investigations into optimizing IDS models. This study not only contributes to the evolution of intrusion detection systems but also sheds light on the symbiotic interplay between big data analytics, AI/ML, and the realm of cybersecurity. © 2023 IEEE.

关键词： data Analytics

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automated Detection of Encryption Algorithms Using AI Techniques 5

Automated Detection of Encryption Algorithms Using AI Techni...

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Chidambaranathan, Senthilnathan Santhanaraj, M. Ajitha, E. Syed Fiaz, A.S. Suchithra, B. Kathirvel, T. Virtusa Corporation NJ United States Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India St. Joseph's Institute of Technology Computer Science and Engineering India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798331518097

In today's digital era, cryptographic algorithms play a vital role in safeguarding sensitive information, maintaining confidentiality, and defending against cyberattacks. With the growing variety of encryption methods and modes, identifying the specific algorithm used to generate a ciphertext has become increasingly challenging. To address this, we propose a machine learning solution that automatically identifies cryptographic algorithms using the Random Forest classifier. The system processes a vast dataset called 'encrypted-data,' which contains 39 different algorithms and modes, by extracting essential features like byte frequency and entropy from the ciphertexts. These features form a 257-dimensional Feature vector used for classification. Random Forest models are trained with these data. During testing, models are loaded is used to predict the encryption algorithm for a given ciphertext. Our method offers a scalable and efficient approach to cryptographic identification, surpassing traditional techniques. © 2024 IEEE.

关键词： Encryption algorithms

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Metasurface Technology and Device for Ultra Precision Optical Measurement 21

Metasurface Technology and Device for Ultra Precision Optica...

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21st international conference on Optical Communications and Networks, ICOCN 2023

作者： Han, L.L. Chen, D. Ren, X.R. Tang, X.S. Gong, W.H. Wang, Z.W. Zhang, W. Chegel, V. Gusev, E.Yu. Polyakov, V.V. Ageev, O.A. Jinan250104 China School of Optoelectronic Engineering Jinan250353 China Southern Federal University Institute of Nanotechnologies Electronics and Equipment Engineering Taganrog347922 Russia Southern Federal University Research and Education Centre 'Nanotechnologies' Taganrog347922 Russia

Precision measurement is gradually shifting from laboratories to real-world environments and production platforms to meet the increasing requirement of intelligent manufacturing. Metasurfaces, 2D engineered artificial... 详细信息

ISBN: (纸本)9798350343502

关键词： Optical data processing

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Determining the Most Significant Metadata Features to Indicate Defective Software Commits 21

Determining the Most Significant Metadata Features to Indica...

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21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Dey, Rupam Kumar Khojandi, Anahita Perumalla, Kalyan The University of Tennessee Industrial and Systems Engineering Knoxville United States

ISBN: (纸本)9798350345889

Defects are largely inevitable in the software development life cycle. Since we cannot avoid them during the development process, we can only desire to fight back with our limited resources in terms of time and monetary investment. Like in many other fields, machine learning models can be of help to mitigate the problem of defects by predicting both bug frequency and defective modules at different granularity levels. However, machine learning models are as good as the quality of the pre-selected set of features under consideration. Therefore, importance must be given while selecting only the necessary features from the original set of features. In this study, we compared various machine learning models with varying feature selection techniques and found the superiority of random forest-based machine learning techniques with wrapper methods. Random forest-based models with the wrapper method were able to detect all the buggy classes successfully on the validation data set. © 2023 IEEE.

关键词： Defects

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Performance improvement of multi-parameter measurement optical fiber sensors based on machine learning methods 21

Performance improvement of multi-parameter measurement optic...

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21st international conference on Optical Communications and Networks, ICOCN 2023

作者： Zhou, Yi-Fan Han, Bo Jin, Po Zhang, Ya-Nan Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

A machine learning based spectral data analysis method is proposed to solve the problem of limited measurement range of optical fiber sensors. This method expands the measurement range of the sensor and improves measu... 详细信息

ISBN: (纸本)9798350343502

关键词： Spectrum analysis

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Physics-Informed Machine learning for UAV Control 21

Physics-Informed Machine Learning for UAV Control

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21st international conference on Electrical engineering, Computing Science and Automatic Control, CCE 2024

作者： Osorio Quero, Carlos Alexander Martinez-Carranza, Jose Computer Science Department Puebla Mexico

ISBN: (纸本)9798350377545

The integration of Dynamic Mode Decomposition (DMD) control with Physics-Informed Neural Networks (PINNs) offers a novel approach to enhancing UAV quadcopter control systems. This method leverages DMD techniques and PINNs to solve the Riccati equation, which is essential for precise UAV position estimation. By framing the UAV control problem within physics-based constraints, the approach ensures that the learned models adhere to the physical laws governing UAV dynamics. Utilizing a comprehensive dataset-including UAV flight parameters such as position, velocity, and control inputs-DMD extracts fundamental dynamic modes from the data, providing a reduced-order representation that captures the dominant UAV dynamics. This reduced representation is then incorporated into the PINN framework to accurately solve the Riccati equation. The combination of DMD and PINNs results in a robust control strategy that significantly improves position estimation accuracy and optimizes control performance. Real-Time validation of this method was conducted in a Unity-based physics simulation environment, incorporating factors like gravity and perturbation noise. The results demonstrate substantial improvements in estimation accuracy and control stability compared to traditional methods. © 2024 IEEE.

关键词： Dynamic mode decomposition

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