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检索条件"机构=Dept.of Computer Engineering and Computer Science"

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Hybrid Fake News Detection Model: Bagging and Logistic Regression Approach 14

Hybrid Fake News Detection Model: Bagging and Logistic Regre...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Shivam Tiwari, Naveen Kumar Bajpai, Abhishek Yadav, Shashank Dept. of Computer Science and Engineering Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9798331531935

In Today’s world or from Past Scenario’s Fake News has a Huge Impact on Everyone’s Lives including Politics, Sports, Education, External Foreign Affairs, Defense, etc. This project aims to develop a model for identifying and detecting fake news. In this model, we implement an ensemble method that leverages the TF IDF vectorization technique to extract meaningful textual features and employ a Bagging Classifier with Logistic Regression as the base estimator to enhance classification accuracy. To build this classifier, we utilized the scikit learn library. Our work was implemented using Jupyter Notebook, an online platform. For feature extraction, we employed a well-known vectorizer called Term Frequency Inverse Document Frequency (TF-IDF). We split our data into training sets (75%) and test sets (25%). The proposed model achieves an accuracy of 99.15% on the test dataset, with a precision of 99.45%, a recall of 99%, and an F1-score of 99.17%. It outperforms standalone Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers. © 2025 IEEE.

关键词： Logistic regression

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Adapting Federated Learning to Dynamic and Non-Stationary Data: An Online Learning Approach

Adapting Federated Learning to Dynamic and Non-Stationary Da...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jaemin Kim Donghyeon Hur Dongwook Won Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Online learning-based optimization represents a novel approach that integrates the principles of federated learning and online learning to effectively handle dynamic data distributions and continuous learning scenarios. This paper offers a comprehensive survey of the latest developments in online federated learning, focusing on optimizing communication efficiency in federated learning environments through online learning techniques. We delve into foundational concepts, key methodologies, challenges, and diverse applications of this emerg-ina field.

关键词： Surveys Privacy Federated learning Scalability Focusing Medical services Reliability Protection Optimization Industrial Internet of Things

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Serial vs parallel execution of Principal Component Analysis using Singular Value Decomposition

Serial vs parallel execution of Principal Component Analysis...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Mallegowda M Tanupriya R Vishnupriya C Anita Kanavalli Dept of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

Principal component analysis (PCA) is a crucial technique in data science and machine learning for reducing dimensionality and identifying crucial characteristics. But when dealing with big datasets, it uses Singular Value Decomposition (SVD), which is computationally costly. To address this issue, this study compares the effectiveness of parallel and serial PCA implementations. Serial execution uses a step-by-step data processing method, which is simple but falls short when dealing with large datasets. The task is divided across many processing units in parallel execution, which is preferred in high-performance computing environments, and thus results in significant speed increases for large datasets. While parallel execution shines with huge datasets and time-sensitive activities, serial execution improves with smaller datasets and simple scenarios. Depending on variables like dataset size and available computer resources, one of these techniques may be preferred over the other. In conclusion, this paper examines the advantages of utilizing parallel computing for PCA via SVD, offering a quick way to speed up calculations for high-dimensional datasets. It emphasizes how flexible it is to choose serial or parallel execution depending on the particular dataset's properties and processing needs.

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Montgomery Modular Multiplication via Single-Base Residue Number Systems

Montgomery Modular Multiplication via Single-Base Residue Nu...

引用

computer Arithmetic (ARITH)

作者： Zabihollah Ahmadpour Ghassem Jaberipur Jeong-A Lee Dept. of Computer Science and Engineering Shahid Beheshti University Tehran Iran Dept. of Computer Engineering Chosun University Gwangju Republic of Korea

ISBN: (数字)9798350384321

ISBN: (纸本)9798350384338

Montgomery modular multiplication (MMM) in residue number systems (RNS) uses a base extension (BE) technique. This is to avoid division, which is hard, slow and costly in RNS. It is somewhat less costly and faster than the reverse conversion, via Chinese remainder theorem (CRT) and reduction factor method. However, it is used one after the other, for each of the equally large bases. In this work, we modify the conventional RNS-MMM algorithm via replacing the two unparalleled BE undertakings with three parallel CRT-like operations with the same complexity, as BE. As for the reduction factors, we use a special case of the Kawamura’s algorithm that leads to definitive result. The proposed RNS-MMM method allows for squaring the working dynamic range, or halving the bit-width of the balanced residue channels. Moreover, the common practice of dynamically changing the working moduli set in security and crypto applications is less critical due to doubled size of the pool of available moduli. The proposed circuits are simulated, tested and synthesized via Synopsys Design Compiler on the TSMC 65-nm technology, to show 69% less delay and 28% less area-time-product at the cost of 14% more energy consumption, with respect to the most relevant reference work.

关键词： Energy consumption Costs Heuristic algorithms Circuits Dynamic range Cathode ray tubes Digital arithmetic

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Comprehensive Approach to Retinal Disease Classification from Optical Coherence Tomography Images Using Average Ensemble Learning

Comprehensive Approach to Retinal Disease Classification fro...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Abid Hossain Masum Sakibul Hasan Rony Habiba Dewan Arpita Abdullah Al Ryan Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The retina, a highly sensitive part of the eyes, is crucial for vision, particularly when affected by disease. Early identification of ocular diseases is made possible by optical coherence tomography (OCT), which offers excellent quality longitudinal images of the retinal layers. We present an iterative average ensemble method for enhanced OCT image classification accuracy. We combine three pre-trained models— ResNet101v2, DenseNet201, and NASNetMobile—into an ensemble, leveraging their unique strengths to improve performance. Tested on a real-world retinal OCT dataset of 84,495 JPEG images categorized into Normal, CNV, DME, and Drusen, our ensemble model achieves 91.51% accuracy, surpassing traditional CNNs and alternative cutting-edge techniques. For comparison, DenseNet201 achieved 89.75%, NASNetMobile 86.17%, and ResNet101v2 89.21%, highlighting the effectiveness of our model in diagnosing retinal diseases.

关键词： Measurement Accuracy Optical coherence tomography Transform coding Retina Ophthalmology Ensemble learning Iterative methods Diseases Image classification

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Evaluating Large Language Model Understanding of Due Process

Evaluating Large Language Model Understanding of Due Process

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Joshua P. Johnson Adrian P. Lauf Dept. of Computer Science and Engineering University of Louisville Louisville US

In this paper the Llama-2 and GPT-2 large language models are evaluated for their fundamental understanding of basic due process concepts. The reference implementations and versions fine-tuned on judicial opinions wer... 详细信息

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

关键词： Law Computational modeling Large language models Decision making Reliability Machine intelligence Context modeling

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CART-OD: Optimizing Object Detection Models via Correlation-Aware Prediction

CART-OD: Optimizing Object Detection Models via Correlation-...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khakurel, Utsab Rawat, Danda B. Raglin, Adrienne Basak, Anjon Howard University Dept. of Electrical Engineering and Computer Science WashingtonDC United States Devcom Army Research Lab Content Understanding Branch AdelphiMD United States

ISBN: (纸本)9798350362480

A Convolutional Neural Network (CNN) relies on intricate pixel correlations to predict objects within images, demonstrating improved performance when identifying objects alongside commonly co-occurring objects. For instance, a helicopter can be more accurately predicted when it appears with an aircraft carrier compared to when it stands alone. Influence on detecting an object due to the presence or absence of another object can be regarded as a form of contextual bias. This phenomenon poses a significant challenge in object detection. Addressing this bias is crucial. Our study explores correlation-aware prediction techniques to disentangle and leverage connections between objects. We propose CART-OD, a novel object detection approach that enhances model performance by adjusting the confidence scores of correlated objects during inference. By strategically intervening during prediction, CART-OD targets instances where commonly co-occurring objects appear separately, effectively mitigating the influence of object correlation. The adjustment in real-time makes the approach computationally efficient and scalable to handle large-scale datasets. Results indicate that CART-OD significantly enhances precision, recall, and mean average precision (mAP) in detecting objects that appear without their most correlated counterparts, underscoring the potential of correlation-aware strategies in object detection tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Shear Wave Elastography with High Precision and Relaxed Frame Rate Utilizing 2D Radial Basis Function Reconstruction

Shear Wave Elastography with High Precision and Relaxed Fram...

引用

IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Sajjad Afrakhteh Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

In the last decade, ultrasound elasticity imaging has advanced as a technique for measuring tissue stiffness. However, achieving an exceptionally high frame rate in shear wave elastography (SWE) is crucial for accurately assessing tissue stiffness and capturing the brief propagation of shear waves. Therefore, this study aims to address this challenge and reduce the necessary frame rate for high-precision SWE. In this study, after generating acoustic radiation force (ARF) by a push pulse, we propose adopting a relaxed frame rate determined by a targeted relaxation factor (RxF). Next, we propose a post-processing technique based on spatio-temporal interpolation (IP) to compensate for the temporal information lost due to the low frame rate acquisition. We utilize radial basis functions (RBF) to perform the reconstruction. The experimental study of the results shows that we can reduce the frame rate requirement of SWE imaging by a factor of 4 while achieving the close elasticity estimates as the original high frame rate.

关键词： Interpolation Ultrasonic imaging Ultrasonic variables measurement Imaging Elasticity Acoustics IP networks Elastography Image reconstruction Frequency control

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Enhancing Movie Recommendation Efficiency Using Ensemble Learning Techniques

Enhancing Movie Recommendation Efficiency Using Ensemble Lea...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Nitasha Aniket Kumar Pandey Ankit Yadav Subrat Mishra Medhanshi Dept. of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

This research paper explores the “Movie Dataset” to create a better movie recommendation. The data includes many features such as genre, ratings, players, and user preferences. Our main goal is to find the best combination model combining SVD (Singular Value Decomposition), SVD++ (SVD with Implicit Feedback) and KNN Baseline (K Nearest Neighbors with Baseline) recommendation algorithms to improve prediction accuracy and recommendations. The rapid expansion of digital media platforms has created a lot of information in the entertainment industry, making movie recommendations very important for the development of consumers. Through our research, we focus on the development of personalized movie recommendations and user satisfaction in the field of digital entertainment.

关键词： Technological innovation Accuracy Entertainment industry Nearest neighbor methods Predictive models Media Motion pictures

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Clustering Epileptic Spike Waveforms for Predicting Seizure Onset Zone 31

Clustering Epileptic Spike Waveforms for Predicting Seizure ...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Mohammadpour, M. Kapeller, C. Korostenskaja, M. Scharinger, J. Guger, C. Johannes Kepler University Dept. of Computer Science Linz Austria G.tec Medical Engineering Linz Austria G.tec Neurotechnology NY United States

ISBN: (纸本)9798331529710

Interictal spikes, observed through electrocorticography (ECoG), offer critical insights that assist in the surgical planning for patients with drug-resistant epilepsy. However, the temporal dynamics and shape of these spikes remain unclear. To address this issue in support of epilepsy surgery, our study aimed to analyze the shape of interictal spikes and measure their distance to the seizure onset zone (SOZ). Twelve patients' data from the iEEG portal were retrospectively studied. For analysis, half an hour of ECoG data was used from each patient, with the data being truncated before the onset of a seizure. Spikes were first detected and grouped in a sequence, then clustered into interictal epileptiform discharges (IEDs) and non-IED groups using a clustering algorithm. The distance of the spikes from IED and non-IED groups to SOZ was quantified and compared using the Wilcoxon rank-sum test. Spikes in the IED group were often in SOZ or close to it, while spikes in the non-IED group were within the distance of the SOZ or non-SOZ area. At the group level, the distribution for sharp wave, positive baseline shift, slow wave, and slow wave to sharp wave ratio was significantly different for IED and non-IED groups. The distance of the IED cluster was 23.36mm and significantly closer to the SOZ than the 37.52mm for non-IEDs. These findings offer valuable insights into the shape and spatiotemporal dynamics of spikes, which could help localize the SOZ and assist neurosurgeons in making more informed decisions for epilepsy surgery. © 2024 IEEE.

关键词： clustering dynamic time warping. Electrocorticography interictal spike seizure onset zone

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