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检索条件"机构=Department of Computer Science of the Faculty of Engineering and Computer Science"

共 431541 条记录，以下是4491-4500 订阅

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Advancing Software-Defined Vehicles: An End-to-End Framework with Digital Twin Based Attestation for OTA Updates 17

Advancing Software-Defined Vehicles: An End-to-End Framework...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Agrawal, Krish Rohan, Jha Luthra, Nishkarsh Sekhar, Pilla Venkata Sharma, Hrishesh Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update's compatibility and functionality with the vehicle's current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle's architecture (as per the twin) on the cloud. The idea is via the SDVs' digital twin - since each and every ECU is virtualized - the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements. © 2025 IEEE.

关键词： Automotive industry

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Sentiment Analysis of YouTube Comments Using Machine Learning Algorithm

Sentiment Analysis of YouTube Comments Using Machine Learnin...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Singha, Rahul Das, Swarna Das, Kapil Hasan, Md. Premier University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

The extensive user engagement on YouTube leads to a flood of comments, creating challenges for content creators who aim to understand audience sentiment. Previous studies have mainly concentrated on distinguishing between positive, negative, and neutral sentiments in comments. This study involves creating a dataset of YouTube comments and classifying them into positive, negative, interrogative, corrective, imperative, and neutral categories, which will help content creators visualize other sentiments alongside positive, negative, and neutral sentiments. Based on the sentiment, they can improve their content and also enhance user interaction. Six machine learning algorithms - Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) - are applied, with the SVM model demonstrating outstanding accuracy, reaching 94%. © 2024 IEEE.

关键词： Adversarial machine learning

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Heart Diseases Prediction with Sound Wave using Support Vector Machine 2

Heart Diseases Prediction with Sound Wave using Support Vect...

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Hariharasudhan, S. Komarasamy, Dinesh Aravindh, S. Darshan, S. Saranya, S. Mohana Iyapparaja, M. Kongu Engineering College Department of Computer Science & Engineering Perundurai India VIT School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350349221

Heart illnesses are now an increasing occurrence, and regular human heart testing gets more and more significant. The Phonocardiogram (PCG), a useful diagnostic technique for examining heart sounds, offers insightful data regarding the clinical state of the heart. This research provides an algorithmic method employing PCG sound data to distinguish between abnormal and normal cardiac sounds. Both signal types undergo Cepstrum analysis, and the heart sounds' features are retrieved. Following that, a support vector machine classifier is trained and evaluated using these extracted features, with an accuracy of 95 percentage, generating a method for characterizing heart sound normal or abnormal for PCG signals. © 2024 IEEE.

关键词： Heart

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Advancement in EEG Signal Feature Extraction for Enhanced Seizure Detection and Prediction 8

Advancement in EEG Signal Feature Extraction for Enhanced Se...

引用

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

作者： Khattar, Sonam Kaur, Ravinder Chandigarh University Department of Computer Science Mohali India Thapar Institute of Engineering and Technology Department of Computer Science Punjab Patiala140413 India

ISBN: (纸本)9798331505462

Epileptic seizure is a highly severe neurological disorder that abruptly interrupts the daily life of those afflicted. In order to advance the technology utilized for managing epileptic seizures, new diagnostic procedures have concentrated on creating electroencephalogram (EEG) techniques that are based on machine learning and deep learning models. The main aim of this research is to focus on some of thecommonly used methods for feature extraction as mentioned in the literature. It will also delve into potential uses of these methods. There are many ways described in this article to process and extract features from EEG signals. Those include time, frequency, decomposing, time-frequency and spatial. Finally, we look at different areas where artificial intelligence is utilized including assistive technology, classification of brain related diseases brain-computer interface systems and integration of machine learning techniques. These applications contribute to a general architecture for analyzing EEG signals. Lastly, potential directions in EEG signal analysis involving feature extraction will be discussed. This study's findings will guide researchers find out which machine learning models that are most effective when it comes to deep learning as well as how feature extraction can be done so that it can enhance detection accuracy of epilepsy seizure based on EEG data. © 2024 IEEE.

关键词： Frequency domain analysis

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Study and Analysis of Key-Predistribution Schemes Based on Hash Chain for WSN

Study and Analysis of Key-Predistribution Schemes Based on H...

引用

International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Yadav, Kanhaiya Kumar Ahlawat, Priyanka Department of Computer Science and Engineering NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789819950904

In recent years, Wireless Sensor Network (WSN) has become a leading area of research. WSN is ad hoc, without infrastructure wireless network that connects a huge number of wireless sensors. WSN consists of multiple sensor nodes which have limited processing speed, storage capacity, communication bandwidth and base stations. They can be thought of as the network’s "sensing cells" and "brain", respectively. They are emerging as a better technology in the future due to their large range of applications in surveillance and people-related domains. This paper proposes a study and analysis of hash-based key predistribution for a better understanding of the uses of key predistribution or random key predistribution over the wireless sensor network to prevent the makers and also provide better connectivity, lower storage overhead, lower communication overhead and less computation complexities. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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REPLAYING WITH REALISTIC LATENT VECTORS IN GENERATIVE CONTINUAL LEARNING 3

REPLAYING WITH REALISTIC LATENT VECTORS IN GENERATIVE CONTIN...

引用

3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Jeong, Hyemin Kim, Seong-Woong Choi, Dong-Wan Department of Computer Science and Engineering Inha University Korea Republic of

In generative continual learning for GANs, replay-based methods mitigate forgetting of past knowledge by retraining either synthesized images from the previous generator (generative replay) or real images from the memory buffer (memory replay) during training each new task. However, despite its strength at memory efficiency, generative replay often fails to produce realistic images, due to the gap between synthetic and real images especially in challenging datasets. Although memory replay can address this issue by storing real images, it still suffers from the practical limit of memory space, gradually deteriorating the quality of generated images. In this paper, we propose a novel mixed replay method, called Realistic Latent Vector Fitting (RactoFit), which not only effectively resolves the drawbacks of generative and memory replay but also combine their strengths, memory utilization yet realism preservation. To efficiently utilize the memory space, our proposal is to maintain realistic latent vectors corresponding to previous real images, rather than storing those images themselves. In addition to replaying with these stored latent vectors, we also design a new optimization technique for latent vectors that can generate more realistic images from the previous generator, not just taking random vectors as in generative replay. Through our experimental analysis, we demonstrate that our method outperforms the baseline methods of continual learning for GANs in terms of the standard metrics for generative models, along with qualitative results clearly showing the highest level of realism of our generated images. © 2024 CoLLAs. All Rights Reserved.

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Unmasking Pneumonia with Deep Learning: Enhancing Medical Diagnosis through Advanced AI Models 14

Unmasking Pneumonia with Deep Learning: Enhancing Medical Di...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Puri, Naman Ashhad, Abdul Kumar, Nitish Amity University Department of Computer Science Engineering Noida India

ISBN: (纸本)9798350344837

Pneumonia has emerged as a global health concern, with a higher mortality rate compared to Covid-19. In 2019, over two million people succumbed to this infectious disease, including nearly 600,000 infants under the age of 5. The traditional method of disease identification, reliant on manual examination by radiologists, is plagued by its unreliability due to suboptimal accuracy. Advancements in computational resources, particularly the enhanced computing capabilities of GPUs, have opened opportunities to train highly complex models. This research undertakes a comparative study of various deep learning models for the purpose of pneumonia detection. The primary objective is to perform binary image classification for pneumonia recognition using three distinct models: a baseline Sequential model built from scratch using TensorFlow, VGG16 Autoencoder, and Keras ViT. The aim is to assess their efficacy on the test set. The dataset comprises 5856 Chest X-Ray images, both anterior and posterior views, categorized as Normal or Pneumonic. © 2024 IEEE.

关键词： Diagnosis

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DeepEmoNet: An Efficient Deep Emotional Neural Network for Facial Expression Recognition 3

DeepEmoNet: An Efficient Deep Emotional Neural Network for F...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Likhitha, Dulla Sistla, Venkatrama Phani Kumar Vfstr University Department of Computer Science and Engineering Vadlamudi India

ISBN: (纸本)9798350375190

Recognizing human emotions is essential for successful interaction. This research focuses on applying deep learning techniques to identify emotions through facial expressions. Facial expressions reveal a lot about how individuals feel, making them extremely important for communication. The research focuses on developing a system that can look at facial photos and reliably determine the emotions being expressed. The primary objective was to distinguish between various emotions employing a deep learning approach. An accuracy level of approximately 88% was successfully attained by leveraging convolutional neural networks (CNNs). To conduct the analysis, the FER 2013 database was utilized, which contains grayscale images depicting facial expressions, each with dimensions of 48× 48 pixels. © 2024 IEEE.

关键词： Emotion Recognition

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Personalized Blood Glucose Forecasting Models Using Machine Learning

Personalized Blood Glucose Forecasting Models Using Machine ...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Jyoti Saloni, Saumya Pathak, Mayank Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350361537

Accurate blood glucose forecasting can enable improved decision support and glycaemic control for diabetes patients. However, complex patient-specific behaviours make reliable personalized predictions challenging. This work develops data-driven machine learning models to provide personalized blood glucose forecasts. Long short-term memory (LSTM) neural networks are adapted to incorporate patient context through modular architecture layers. The models are trained on continuous glucose monitoring (CGM) data from diabetes patients. Customization techniques such as transfer learning and similarity matching are introduced to tailor the forecasting models to individual patient characteristics and subgroups. Experiments demonstrate significantly improved predictive performance over population-based models, with over 85% accuracy on 30-minute ahead forecasts. The customized LSTM framework has the potential to provide more effective blood glucose predictions and strengthen personalized diabetes management. © 2024 IEEE.

关键词： Prediction models

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EmotionNet: Pioneering Deep Learning Fusion for Real-Time Speech Emotion Recognition with Convolutional Neural Networks 6

EmotionNet: Pioneering Deep Learning Fusion for Real-Time Sp...

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

作者： Rahman, Md. Mominur Hossain, Md. Alamgir Hasan, Tahmid Ahmed, Md. Kawsar Sultana, Rabeya Islam, Md Shihabul Prime University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Speech emotion detection is driven by complex neurological mechanisms within the brain's nervous system, reflecting an individual's emotional state and psychological makeup. This unique characteristic serves as a foundation for many applications, including personalized user experiences, mental health diagnostics, and demographic analysis based on emotional responses. Traditional approaches to emotion recognition frequently face challenges such as variability in speech patterns, environmental noise, and speaker diversity. Within the evolving landscape of human-computer interaction, this research introduces a novel synthesis of Convolutional Neural Networks (CNNs) with the domain of speech emotion recognition, thus surpassing the boundaries of traditional methodologies. This research analyzed 957 audio samples from 5 individuals using benchmark datasets, extracting Mel Frequency Cepstral Coefficients (MFCC) to classify emotions accurately-including 'neutral', 'happy', 'angry', 'fear', 'surprise', 'calm', 'disgust', and 'sad'. Departing from traditional approaches reliant on manual feature engineering, our end-to-end model autonomously learns from raw speech, with our CNNs achieving an exceptional 96.7% accuracy. This marks a significant advancement in speech emotion recognition through deep learning, paving the way for more intuitive, emotion-aware human-computer interactions. © 2024 IEEE.

关键词： Emotion Recognition

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