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

共 40057 条记录，以下是1111-1120 订阅

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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A Machine Learning Based Auto-Grading Model for Question-Based Algebra Problems using RoBERTa 4

A Machine Learning Based Auto-Grading Model for Question-Bas...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Vidula, N.A. Reddy, T. Anisha Manogna, Sodum Balakrishnan, Roshni M Pati, Peeta Basa Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798350349900

The use of Machine learning models into educational assessment has revolutionized the grading process for problemsolving *** research introduces a machine learning auto-grading model tailored for question-based algebra problems, utilizing RoBERTa, a natural language processing *** primary objective is to investigate the adaptability of RoBERTa, originally designed for natural language understanding, for representing non-natural language mathematical expressions. The study also explores the impact of different embedding strategies on the performance of autograding systems for student responses to equation *** mathematical and textual code inputs were transformed into vectors, and the vectors were then fed into several models. Evaluation metrics such as RMSE, MSE, R2, and MAE are utilized to assess the effectiveness of each embedding approach in grading student responses accurately. The use of k-fold cross-validation will improve the dependability of the assessment measures, offering insights into the model's consistency across different subsets of the data. The comparative analysis of different embedding strategies offers guidance on selecting the most suitable approach for enhancing the autograding process. © 2024 IEEE.

关键词： Embeddings

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Classifying Alzheimer's Disease Stages via Bootstrap Aggregation of CNN Architectures on MRI Scans 1

Classifying Alzheimer's Disease Stages via Bootstrap Aggrega...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Neeharika, S. Rao, D. Rajeswara Nagaveni, V. Lalitha V.R. Siddhartha Engineering College Computer science Dept. Vijayawada India

ISBN: (纸本)9798350365016

Alzheimer's disease (AD) presents a pressing challenge in healthcare globally, necessitating timely and accurate diagnosis for effective management. Magnetic Resonance Imaging (MRI) holds promise for AD detection due to its ability to reveal structural brain changes. However, manual MRI analysis is labor-intensive and prone to human error. To tackle these issues, this study proposes a novel method for AD classification using ensemble learning with Convolutional Neural Network (CNN) architectures-specifically, CNN, Inception V3,DenseNet121, and MobileNet-leveraging bootstrap aggregation. This ensemble approach diversifies training data subsets, leading to enhanced accuracy. The proposed model achieved an impressive 84% accuracy with bootstrapping and 94 % without. Additionally, specificity values underscore the model's robustness, with 0.93 and 0.97 for bootstrapped and non-bootstrapped ensembles, respectively. This innovative approach not only improves accuracy but also addresses scalability and generalization challenges, offering promise for AD diagnosis and management based on neuroimaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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Context Aware Recommendation System Using LLM 8

Context Aware Recommendation System Using LLM

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Madhav, Kagolanu Venkata Chandra Veeramaneni, Asritha Vivek, Vinnakota Sai Belwal, Meena Khare, Sangita Amrita School of computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331505462

This paper proposes a unique approach to emotion recognition and employs Vision Transformer (ViT) model for the sake of giving personalised suggestions of music selections and environments. ViT is able to determine the user emotion based on his facial expression detection technology. Those emotions will provide the critical inputs that will allow recommendations to be modified to context-aware standards the mood you're in the time of year, the specifics of the month. Proprietary technology creates music playlists suitable to the mood of the moment as it suggests the best locations for experiences based on an emotional context and other external factors such as the season and time of month. As we move deeper into the future, this merging of emotion detection technology and customized recommendations can deliver an unparalleled user experience by offering a user exactly what they would like in their location of the area they are you engaged in. It just ensures that the location to suggest should not only be ideal to the user mood but must also be practical as well by taking into account the locations weather and local events. The whole concept of this is to create a more rounded and complete approach to curating a fitness routine tailored to the individual, taking into account their mental health as well as external issues. © 2024 IEEE.

关键词： Emotion Recognition

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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Epileptic Seizure Detection using Variational Quantum Classifier

Epileptic Seizure Detection using Variational Quantum Classi...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Kumar, Lalit Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy, a neurological disorder characterized by recurrent and unpredictable seizures, demands timely and accurate detection for effective management and patient safety. While traditional machine learning approaches have shown promise in seizure detection, this study explores the potential of quantum computing, specifically utilizing a Variational Quantum Classifier. The Variational Quantum Classifier, grounded in quantum mechanics, aims to enhance computational capabilities for seizure detection. Leveraging quantum computing's intrinsic parallelism and superposition properties, the model focuses on processing complex and high-dimensional Electroencephalogram signals commonly used in epilepsy diagnosis. The model undergoes training on a dataset containing both seizure and non-seizure instances, optimizing parameters for increased accuracy. Performance evaluations utilize metrics such as sensitivity, specificity, and overall classification accuracy. © 2024 IEEE.

关键词： Quantum computers

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Deepfake Image Forgery Detection using Local Feature Descriptors 15

Deepfake Image Forgery Detection using Local Feature Descrip...

引用

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

作者： Dienash, S. Swapna, T.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Deepfake Image Forgery,generates fake images after manipulating the original source images. This increases the risk of losing trust in images or videos. This paper proposes an efficient methodology to detect Deepfake images from real images. The methodology involves extracting Local Feature De scriptors(Local Binary Pattern (LBP),Frequency Decoded Local Binary Pattern (FDLBP),Binary Gabor Pattern (BGP)). The fusion of these features are fed to the DenseNet to classify the image as fake or real. The model uses Convolutional Neural Network (CNN) architecture that utilizes all three Local Feature Descriptors that extract features of the images. The model has an accuracy of 93.65% which is performing better than Deep learning models. © 2024 IEEE.

关键词： Binary Gabor Pattern (BGP) DenseNet Frequency Decoded Local Binary Pattern (FDLBP) Local Binary Pattern (LBP) Local Feature Descriptors (LFD)

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NutriSpy: Investigate Your Food's Health Quotient 3

NutriSpy: Investigate Your Food's Health Quotient

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Thanay Kumar, Y.R. Joshi, Sudhanva S Sujan, A.T. Ujwal Karanth, K.V. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

NutriSpy is a comprehensive web and mobile application designed to enhance users' health and wellbeing through personalized food and exercise recommendations. At its core, NutriSpy integrates three powerful features: a tailored workout recommendation system, an advanced food identification tool, and a robust diet advisory service. By leveraging state-of-the-art machine learning algorithms, NutriSpy accurately identifies food items, assesses their nutritional and caloric content, and generates customized dietary guidance. The food identification system employs image recognition and natural language processing to provide precise nutritional information from photographs or descriptions of meals. This is combined with a diet advice system that considers individual health profiles, dietary restrictions, and fitness goals to offer personalized nutrition plans. Additionally, NutriSpy's workout recommendation system aligns exercise routines with users' dietary needs and fitness aspirations, creating a harmonious balance between food intake and physical activity. NutriSpy's sophisticated technology facilitates real-time adjustments and recommendations, making it a dynamic tool for promoting healthier lifestyles. By analyzing user data and integrating current health trends, NutriSpy not only aids in immediate dietary and fitness decisions but also contributes to long-term health improvements. This study explores the development and functionality of NutriSpy, detailing its components, underlying technologies, and potential impact on fostering healthier living habits. © 2024 IEEE.

关键词： Diet Recommendation system Exercise Recommendation System Food Recognition System

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ma, Zhuxuan Cao, Hui Zhao, Yue University of Arkansas Dept. of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350376067

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components. © 2024 IEEE.

关键词： Busbars

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Automated tiered Detection of Cyber-Attacks in Operational Distribution Networks 8

Automated tiered Detection of Cyber-Attacks in Operational D...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Brintha, N.C. Balaji, M. Arun Teja, B. Chandra Anwesh, G. Shashank, P. Eswar, C. Kapil Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Tamil Nadu Krishnankovil India

ISBN: (纸本)9798350386578

The increasing dependence on digital technologies within power grids brings forth a pressing concern: the vulnerability to cyber-attacks poses a significant threat to its security and reliability. In response, this research proposes an innovative approach leveraging ensemble learning techniques to predict and mitigate cyber-attacks in Smart Grids. By combining the strengths of diverse models such as Extra Trees, XGBoost, Random Forest, and Bagging, the ensemble model aims to bolster attack detection accuracy and robustness. Through real-time monitoring, anomaly detection, and rapid response mechanisms, the framework enhances the resilience of Smart Grids against evolving cyber threats. The research focuses on refining a predictive model capable of distinguishing legitimate data from malicious injections, thus ensuring the ongoing security and reliability of critical energy infrastructure. This innovative ensemble learning approach represents a promising strategy to fortify modern power grids, safeguarding against sophisticated cyber threats and ensuring continued operational integrity. © 2024 IEEE.

关键词： Cyber attacks

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