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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kavthekar, Sameer Vaidya, Shreya Pujari, Vishwesh Mane, Sunil Department of Computer Engineering and Information Technology COEP Technological University Pune411005 India

ISBN: (纸本)9789819731794

Over the last decade, deep learning has become one of the fastest-growing fields in computer science. It finds applications in several sectors, such as Healthcare, Agriculture, Financial Services, and Crime Investigation. However, with the rapid development of Generative Adversarial Networks in 2017, the concept of deepfakes emerged. The term deepfake refers to an artificially synthesized image or video generated using techniques like Face Swapping and Face Expression Reenactment. Such face manipulation techniques have become highly refined, making deepfakes impossible to detect by the naked eye. Deepfake videos can potentially be used for various malicious purposes. Spreading misleading news via politicians or celebrities is one such example. Such ill-intended videos can exacerbate the increasingly prevalent problem of false information online. Therefore, being able to differentiate between real and fake videos is crucial. This work proposes a solution based on Masked Image Modelling using Auto Encoders and Vision Transformers to tackle the problem of Deepfake Detection. The solution consists of two sub-models working in parallel, namely the Multiscale Vision Transformer and the Masked Autoencoder, ConvNeXt. A novel facial quality detection algorithm is developed, which helps improve the data quality by overcoming the challenge of misrepresented facial data. The proposed model, MIM-ViT, achieves competitive results on popular datasets like the Deepfake Detection Challenge Preview and Face Forensics++ with a test accuracy of 80.22 and 98.1% along with an Area Under the Receiver Operating Characteristics score of 84.48 and 99.8% respectively. The proposed model generalizes well and performs competitively on unseen data, achieving an AUCROC score of 68.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Suicidal Thought Detection using Max Voting Ensemble Technique 2

Suicidal Thought Detection using Max Voting Ensemble Techniq...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Goel, Roma Digalwar, Mayuri Department of Computer Science Engineering Indian Institute of Information Technology Nagpur441108 India

Online social networking (SN) data presents a data stream that is rich in context and temporal information. It holds promise for predicting suicidal thoughts and behaviors. The fusion of SN data with machine learning algorithms offers a potential path forward. This research proposes a Max Voting Ensemble classifier model applied to a Reddit dataset for the identification of suicidal ideation. The preprocessing involves data cleansing, tokenization, and lemmatization. Additionally, TF-IDF and Word2Vec word embedding techniques are applied. Diverse machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), AdaBoost, and XGBoost, are implemented. The results of selected Machine Learning Classifiers (MLCs) are amalgamated using a Max Voting Ensemble classifier. The research findings clearly indicate that the Max Voting Ensemble classifier yields improved precision of 91.39% coupled with a substantial accuracy of 87.5%. The application of Ensembling Techniques (ET) to SN data holds the potential to address the complexities and modeling challenges inherent in predicting acute Suicidal Ideation within these dynamic time scales. © 2024 Elsevier B.V.. All rights reserved.

关键词： Logistic regression

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Anomaly Detection from CCTV Camera Feed 10

Anomaly Detection from CCTV Camera Feed

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Aakash Chauhan, Lakshay Sharma, Shubham Deb, Sujay Indraprastha Institute of Information Technology Delhi Computer Science & Engineering Department Delhi India

ISBN: (纸本)9798331533229

The paper presents a novel approach for detecting anomalies like arson, explosion, vandalism, etc in public spaces using CCTV camera feeds. We propose a deep learning-based system that analyzes video streams to identify unusual events or behaviours that deviate from normal patterns. Our method combines Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs) for feature extraction with Long Short-Term Memory (LSTM) networks to model temporal dependencies in the video sequences. The system is trained on the UCF Crime Dataset to learn normal activity patterns and can flag potential anomalies in emulated real-time situations. To enhance accuracy, we incorporate additional computer vision technique, like optical flow, with CNN/SNN and LSTM to get various combinations of models (OF-CNN, OF-CNNLSTM, OF-SNN, OF-SNNLSTM, CNNLSTM) for improved anomaly detection. The proposed system has potential applications in enhancing public safety and security monitoring in urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Analysis of optical networks in presence of nodes noise and crosstalk

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Journal of Optical Communications 2024年第3期45卷 547-554页

作者： Shukla, Rahul Deo Pratap, Ajay Suryavanshi, Raghuraj Singh Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

Optical packet switching has gained lot of momentum in last decade due to the advantages of optical fiber over copper cables. Optical switching is beneficial in optical networks which form connections of links and switching nodes. In these high speed networks minimum delay and high throughput are two important parameters which are considered. To minimize network delay shortest path algorithm is used for route selections. In previous studies while choosing shortest path distance among various nodes is considered. In this work we have shown that it is necessary to consider both distance and number of hops while choosing path from source to destination to minimize power per bit used for the transmission. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Optical switches

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State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping

State Representations as Incentives for Reinforcement Learni...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Petropoulakis, Panagiotis Graf, Ludwig Malmir, Mohammadhossein Josifovski, Josip Knoll, Alois School of Computation Information and Technology Technical University of Munich Department of Computer Engineering Germany

ISBN: (纸本)9781665410205

Choosing an appropriate representation of the environment for the underlying decision-making process of the reinforcement learning agent is not always straightforward. The state representation should be inclusive enough to allow the agent to informatively decide on its actions and disentangled enough to simplify policy training and the corresponding sim2real transfer. Given this outlook, this work examines the effect of various representations in incentivizing the agent to solve a specific robotic task: antipodal and planar object grasping. A continuum of state representations is defined, starting from hand-crafted numerical states to encoded image-based representations, with decreasing levels of induced task-specific knowledge. The effects of each representation on the ability of the agent to solve the task in simulation and the transferability of the learned policy to the real robot are examined and compared against a model-based approach with complete system knowledge. The results show that reinforcement learning agents using numerical states can perform on par with non-learning baselines. Furthermore, we find that agents using image-based representations from pre-trained environment embedding vectors perform better than end-to-end trained agents, and hypothesize that separation of representation learning from reinforcement learning can benefit sim2real transfer. Finally, we conclude that incentivizing the state representation with task-specific knowledge facilitates faster convergence for agent training and increases success rates in sim2real robot control.22Supplementary materials can be found on the project webpage: https://***/PetropoulakisPanagiotis/igae © 2024 IEEE.

关键词： Reinforcement learning

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A Novel Framework for Securing information in Cyber Security System using Authentication, Intrusion Detection and Deep Learning-based Risk Prediction Tasks 5

A Novel Framework for Securing Information in Cyber Security...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Naik, R. Lakshman Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9798350361155

The cyber security has become a hot research topic nowadays. The term cyber security is frequently employed replaceable with the term data security. In recent days, each system faces the cyber security threat virtually and the model framework should have the capacity to combine the security features and work as the system's integral elements. Various works have been conducted to discover the answer to the evaluation challenge of cyber security and numerous architectures have been designed. However, the techniques face distinct issues though it is functioning well initially at the development time. Hence, an efficient and advanced cyber security framework is implemented in this paper to prevent cyber attacks using deep learning. Initially, to predict the risks in cyber security devices, the necessary data is fetched from the available resources. The gathered data is forwarded to the authentication operation and then the authenticated data is given to the intrusion detection stage. After that, the intrusion-detected data is subjected to the risk prediction stage, where the Weighted Recurrent Neural Network (WRNN) is supported to forecast the risk level of the network. Here, the Dung Beetle Optimizer (DBO) is utilized to tune the weights optimally in WRNN. This authentication, intrusion detection, and risk prediction operations have enriched the security of the cyber networks. Lastly, the efficacy and the security of the implemented model are analyzed with its related models by utilizing several performance metrics. © 2024 IEEE.

关键词： Forecasting

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High-Fidelity Image Style Transfer by Hybrid Transformers1

High-Fidelity Image Style Transfer by Hybrid Transformers1

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2024 IEEE International Conference on Visual Communications and Image Processing, VCIP 2024

作者： Hsu, Zhe-Wei Yang, Shih-Hsuan Tung, Bo-Jiun National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331529543

Image style transfer is a technique in computer vision by which the artistic style of one image is applied to the content of another while keeping the structural features. Image style transfer finds applications in creating artwork, design and branding, entertainment and media, and many other fields. Current image style transfer methods fail to satisfactorily retain global characteristics and local details simultaneously. This paper proposes a hybrid transformer architecture that incorporates mixed convolutional network modules. By integrating the transformer and the convolutional modules, the global and local features are captured and fused. Experimental results demonstrate that the proposed method achieves more favorable visual fidelity, reducing the combined content and style loss by at least 10% as compared with the state-of-the-art approaches. © 2024 IEEE.

关键词： Distribution transformers

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Stability Investigation and Improvement for DC Cascade Systems with Simplified Impedance-based Stability Criterion

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1044-1052页

作者： Junbin Fang Zhikang Shuai Yang Li Zhibing Wang Xiangyang Wu Xia Shen Z.John Shen College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Renewable Energy Department of China Electric Power Research Institute Beijing 100192China Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616USA IEEE

In DC distributed power systems(DPSs),the complex impedance interactions possibly lead to DC bus voltage oscillation or *** previous research,the stability analysis of DPSs is implemented based on mathematical analysis in control *** specific mechanisms of the instability of the cascade system have not been intuitively *** this paper,the stability analysis of DPSs based on the traditional Nyquist criterion is simplified to the resonance analysis of the seriesconnected port impedance(Z=R+jX)of source and load *** reveals that the essential reason for impedance instability of a DC cascade system is that the negative damping characteristic(R<0)of the port the overall impedance amplifies the internal resonance source at reactance zero-crossing *** simplified stability criterion for DC cascade systems can be concluded as:in the negative damping frequency ranges(R<0),there exists no zero-crossing point of the reactance component(i.e.,X=0).According to the proposed stability criterion,the oscillation modes of cascade systems are classified.A typical one is the internal impedance instability excited by the negative damping,and the other one is that the external disturbance amplified by negativity in a low stability ***,the impedance reshaping method for stability improvement of the system can be further *** validity of the simplified criterion is verified theoretically and experimentally by a positive damping reshaping method.

关键词： DC cascaded system,impedance model impedance reconstruction stability criterion

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