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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Vineet Kumar Singh, Trilok Dixit, Abhishek Singh, Raushan Kumar Singh, Pradeep Kumar Singh, Juginder Pal Tmu University Dept. of Computer Science and Engg Moradabad India Invertis University Dept. of Computer Science and Engg. Bareilly India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India GLA University Dept. of Computer Engg. and Application Mathura India

ISBN: (纸本)9781665487320

In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network). © 2022 IEEE.

关键词： Database systems

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New Approach of Ensemble Method to Improve Performance of IDS using S-SDN Classifier 11

New Approach of Ensemble Method to Improve Performance of ID...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Amarudin Ferdiana, Ridi Widyawan Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia Universitas Teknokrat Indonesia Engineering and Computer Science Lampung Indonesia

ISBN: (纸本)9781665460309

The application of Machine Learning (ML)-based Intrusion Detection System (IDS) has been widely used. The advantage of ML-based IDS is that it can detect intrusions in the network. However, in its application, there are still false positive detections on the IDS. False positive detection occurs due to improper ML techniques. This research applies an S-SDN model based on Ensemble Learning (EL) to overcome this problem. The S-SDN model is built from three base-learners, namely SVM, Decision Tree, and Naïve Bayes with the Stacking technique. Furthermore, the S-SDN model is used as a classifier on the IDS to detect intrusions. S-SDN was validated using the UNSW-NB15 dataset. Based on the experiment, S-SDN's performance was superior to the old method based on a single classifier. The performance of S-SDN can achieve an accuracy of 83.19%. In comparison, the old method based on a single classifier (SVM) can only achieve an accuracy of 75.89%, and the ensemble classifier (Bagging-DT) is only 80,09%. As for further research, the development of EL-based IDS still needs to be improved. For example, it builds an EL-based model with feature selection techniques and different base learners. © 2022 IEEE.

关键词： Learning systems

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Nonparametric Estimation of Local Bandwidth from Level Crossings Sampling

Nonparametric Estimation of Local Bandwidth from Level Cross...

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Pawlak, Miroslaw Rzepka, Dominik Miskowicz, Marek Agh University of Science and Technology Dept. of Measurement and Electronics Krakow Poland University of Manitoba Canada Dept. of Electrical and Computer Engineering WinnipegR3T 5V6 Canada

ISBN: (纸本)9798350328851

We study the problem of a local bandwidth recovery for nonstationary stochastic signals when the measured information is given in terms of level crossings. We propose a kernel estimate of the local bandwidth from samples generated from level crossings of stochastic signals being the time-warped version of stationary Gaussian processes. The positivity and bandlimited nature of the local intensity is captured by the properly selected class of bandlimited and positive kernel functions. The asymptotic properties of the estimator are derived. © 2023 IEEE.

关键词： Railroad crossings

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Quora Question Sincerity Detection Using BERT-Based Framework

Quora Question Sincerity Detection Using BERT-Based Framewor...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Drishti Arora Garima Aggarwal Farhan Asif Dept. of Computer Science and Engineering Amity University Noida Uttar Pradesh India

In the dynamic landscape of online knowledge exchange, platforms such as Quora have become vital arenas for diverse discussions and information dissemination. However, distinguishing sincere from insincere questions poses a challenge, impacting user experience and content credibility. The research seeks to utilize BERT-based frameworks to classify Quora questions, with a specific focus on discerning between sincere and insincere inquiries. The study underscores the significance of automated insincere content identification in fostering a trustworthy and constructive online environment on Quora, thereby contributing to the improvement of online interaction quality. The research delves into a comprehensive methodology, including data preprocessing, model training, fine-tuning, and performance evaluation. Extensive experimentation demonstrates that the proposed BERT-based approach achieves an impressive F1-score of 96.1%, outperforming existing methods. The contribution of the study lies in its potential to enhance online interactions by automating the identification of insincere content, fostering a conducive environment for meaningful conversations.

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dept.Voting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce dept.Voting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, dept.Voting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, dept.Voting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction Data augmentation

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Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data

Data Augmentation's Effect on Machine Learning Models when L...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Damien A. Dablain Nitesh V. Chawla Dept. Computer Science and Engineering University of Notre Dame Notre Dame USA

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Real-world data is often imbalanced, such that the number of training instances varies by class. Data augmentation (DA) of under-represented classes is commonly used to improve model generalization in the face of class imbalance. Despite its ubiquity, the impact of data augmentation on machine learning (ML) models is not clearly understood. Here, we undertake a holistic examination of the effect of DA on under-represented classes. Unlike other studies, which focus on a single ML model type, we examine three different classifier families: convolutional neural networks, support vector machines, and logistic regression models; five different DA techniques and two different data modalities - image and tabular. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and front-end feature selection. These changes occur with respect to all classes, not just the ones that DA is applied to. Further, our empirical analysis shows that data augmentation's positive influence on generalization does not necessarily occur as a result of reducing weight norms. Rather, weight and support vector specialization play important roles in generalization. The specialization process may be a form of memorization that is spawned by variances introduced by augmented data. We investigate the seeming contradiction between improved generalization versus weight and support vector specialization.

关键词： Training Support vector machines Logistic regression Neural networks Machine learning Data augmentation Feature extraction Data models Vectors Faces

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Real Estate Price Prediction Model Analysis Using Machine Learning 1

Real Estate Price Prediction Model Analysis Using Machine Le...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Rajput, Kapil Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Diwakar, Manoj Lamba, Sumita Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Pune India Graphic Era Deemed to be University Department of CSE Dehradun India School of Computing DIT University India

ISBN: (纸本)9798350353648

The research illustrates use of ML algos in the field of housing price prediction. The models have been analyzed on real datasets downloaded from Kaggle created by Amitabha Chakraborty. We know that the source on the research of ML prediction in India are scarce, but we encounter conditions which pose need of efficient methodologies to solve this problem. This research gives a glance about the use of ML techniques on the dataset. After careful analysis of data processed and running tests in multiple ways, the results indicate the best suited algorithms for this work. © 2024 IEEE.

关键词： Prediction models

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Lip Reading Into Text Using Deep Learning

Lip Reading Into Text Using Deep Learning

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Krishna Manglani Utkarsh Bahuguna Subhash Chand Gupta dept. of Computer Science Amity School Of Engineering and Technology Uttar Pradesh India

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies.

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Water Region Extraction in Aerial Images Using Deep Learning

Water Region Extraction in Aerial Images Using Deep Learning

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Nikita Pandey Saumya Kumar Satish Kumar Yadav Shashwat Srivastava Shomil Singh Deepak Gupta Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Water bodies are essential component of Earth’s surface and accurate water region mapping is crucial for disaster management and environmental monitoring. Existing methods to map water bodies relies on traditional modelling methods that need extensive manual calibration struggle with scalability and lack advanced pattern recognition. This paper evaluates the effectiveness of deep learning-based semantic segmentation models, DeepLabV3+ and U-Net, using aerial images resized to $\mathbf{2 5 6 x} 256$ pixels in mapping water regions in aerial images. DeepLabV3+, with a ResNet50 backbone, and U-Net are trained on a comprehensive flood-affected dataset with preprocessing techniques such as resizing and augmentation. Model performance is assessed using metrics like IoU, F1 score, accuracy, precision, and recall. The results indicate that DeepLabV3+ outperforms U-Net, showing promise for automating water region mapping and improving flood surveys and urban planning.

关键词： Training Measurement Deep learning Surveys Technological innovation Accuracy Computational modeling Semantic segmentation Urban planning Water resources

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Peephole Optimization for Quantum Approximate Synthesis

arXiv

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arXiv 2024年

作者： Clark, Joseph Thapliyal, Himanshu Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States

Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this technique partitions an entire circuit into a series of peepholes and produces multiple approximations of each partitioned subcircuit. A single approximation of each subcircuit is then selected to form optimized result circuits. We propose a series of improvements to the final phase of this architecture, which include the addition of error awareness and a better method of approximating the correctness of the result. We evaluated these proposed improvements on a set of benchmark circuits using the IBMQ FakeWashington simulator. The results demonstrate that our best-performing method provides an average reduction in Total Variational Distance (TVD) and Jensen-Shannon Divergence (JSD) of 18.2% and 15.8%, respectively, compared with the Qiskit optimizer. This also constitutes an improvement in TVD of 11.4% and JSD of 9.0% over existing solutions. © 2024, CC BY.

关键词： Quantum electronics

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