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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18716 条记录，以下是131-140 订阅

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Apple Leaf Disease Detection and Classification Using L1 Regularized Multi Head vision Transformer 3

Apple Leaf Disease Detection and Classification Using L1 Reg...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Math, Gayatri Vishwanath, P. Balsem, Zayd Ajsan Supriya Jena, Suvendu Kumar Pda College of Engineering Department of Electronics and Communication Engineering Kalaburagi India Vtu Belagavi India Hke Society's Sir M Visvesvaraya College of Engineering Department of Electronics and Communication Engineering Raichur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Indira Gandhi Institute of technology Department of Computer Science Engineering and Applications Sarang Dhenkanal India

ISBN: (纸本)9798331508456

Timely identification of apple leaf diseases is important to avoid disease spread and ensure healthy growth of apple industry. Recent developments in digital cameras and electronic devices integrated with Machine Learning (ML) and Deep Learning (DL) based apple leaf disease detection has emerged as efficient to traditional visual inspection models. Therefore, this research proposes a L1 regularized Multi Head vision Transformer (L1-MHViT) for apple leaf disease detection and classification. The L1-MHViT utilized multi-head attention to capture complex patterns and improve classification performance. Furthermore, it incorporates advantages of convolution-based and transformer-based models to enhance the detection and classification accuracy. Furthermore, the Median Filter (MF) is applied for preprocessing which removes noise and enhance the image quality thereby helps to enhance the accuracy of the consequent steps. Then, EfficientNet is applied for feature extraction which is known for higher accuracy and effective scaling of model size. The effectiveness assessment of proposed L1-MHViT uses standard metrics which include precision (Pre), recall (Rec), F1 score (F1) and accuracy (Acc). The L1-MHViT achieved 99.46% pre, 99.54% rec, 99.50% F1 and 99.82% are for Apple dataset which is better than baseline models. © 2025 IEEE.

关键词： Feature Selection

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PFHaD: Page Fault Handler Assisted Defense Against Data-Oriented Attacks 3rd

PFHaD: Page Fault Handler Assisted Defense Against Data-Orie...

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3rd international conference on Advances in Computing Research, ACR 2025

作者： Zhang, Tianning Huang, Hao School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031876462

As memory corruption vulnerabilities evolve, attackers have shifted focus from traditional control-flow attacks to non-control data attacks, which manipulate data influencing a program′s behavior without altering its control flow. This shift bypasses conventional defenses like control Flow Integrity (CFI), posing new security risks. In response, we propose a novel defense mechanism called Page Fault Handler-assisted Defense (PFHaD). PFHaD leverages hardware page faults to protect critical data in memory, providing robust defense against non-control data attacks with minimal performance overhead. Unlike traditional defenses, PFHaD also prevents information leakage by making critical data pages inaccessible. The solution does not require modifications to hardware, compilers, or operating systems, making it ideal for commercial-off-the-shelf (COTS) software. Experimental results demonstrate that PFHaD effectively mitigates a wide range of non-control data attacks while maintaining low performance impact, offering a scalable and efficient solution to modern memory corruption threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Program compilers

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Deep Convolutional Neural Networks for Autism Spectrum Disorders 3rd

Deep Convolutional Neural Networks for Autism Spectrum Disor...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Patel, Drashti Mittal, Shreyan Rahevar, Mrugendrasinh Patel, Abhishek Department of Computer Science and Engineering CSPIT Charotar University of Science and Technology Gujarat Changa India U & P U Patel Department of Computer Engineering CSPIT Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9789819743582

Autism Spectrum Disorder (ASD) stands as a complex and heterogeneous neurodevelopmental condition, encompassing a range of challenges in social interaction, communication, and behavior. Timely and accurate detection of ASD is crucial to initiate tailored interventions and support, optimizing developmental outcomes for affected individuals. This study delves into a novel avenue for ASD detection, leveraging the capabilities of Deep Convolutional Neural Networks (Deep CNN) applied to textual data. The model training and evaluation stages incorporate established metrics, assessing accuracy, precision, recall, and F1-score. The significance of this research extends beyond its technical prowess. By harnessing the power of deep-learning, this approach bridges the gap between cutting-edge technology and clinical practice. The implications of this study reverberate across multidisciplinary domains, from healthcare to machine learning, underscoring the potential for artificial intelligence to positively impact the lives of those affected by ASD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Predictive Analytics and Automation in Supply Chain Management with Internet of Things (IoT) 3

Predictive Analytics and Automation in Supply Chain Manageme...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Yesodha, Karthik Kalyan Raj Kumar Rajendran, Prabhakaran Bhalerao, Manoj Gupta, Ketan Yuvaraj, N. Supply Chain and Manufacturing Caterpillar Inc BloomingtonIL United States Cscs Llc CummingGA United States Hub Group Oak BrookIL United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

Predictive analytics and automation with the Internet of Things (IoT) is a growing trend in supply chain management. The generation permits cost-efficient operations and organizations to reveal and control their supply chain systems. The combination of predictive analytics algorithms and automatic structures can offer insights into supply chain operations, expect adjustments in customer demands, and allow agencies to plan better and put things together for the future. IoT can also be used to screen supply chain techniques, become aware of regions for development, and proactively manipulate potential disruptions. U sing IoT -based analytics and automation, advantages that include increased visibility in supply chain operations, elevated customer satisfaction, and cost-effective supply chain processes may be achieved. Furthermore, predictive analytics and automation can help improve choice-making with actual-time records insights and ensure everyone's tactics are optimized for efficient performance. © 2025 IEEE.

关键词： Supply chain management

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Automated Assessment of Pizza Quality Using computer vision and Deep Learning Techniques 3

Automated Assessment of Pizza Quality Using Computer Vision ...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Arakeri, Megha Lakshmana Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India Seamovation Labs Pvt. Ltd Bangalore India

ISBN: (纸本)9798331523893

Quality control in the pizza-making process is essential to meet consumer expectations. In the age of online delivery, verifying the delivered pizza is necessary. The pizza usually consists of a base, sauce, cheese, vegetables, and meat. Low-quality pizza delivery may lead to consumer dissatisfaction and hinder pizza production business growth. Hence, this paper proposes a method to evaluate sauce spread, identify toppings, and assess their distribution on cooked and uncooked pizzas to address this issue. The proposed methodology uses a feature pyramid network for background and foreground separation in the pizza image. Then, the sauce spread thickness is classified into 3 categories using the U-Net model. Finally, a multi-label CNN and color segmentation were applied to the image to evaluate the spread of toppings on the pizza. Experiments were carried out on a standard image dataset, and it was observed that the proposed model achieved 95% accuracy in pizza quality evaluation. © 2025 IEEE.

关键词： Damage detection

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AI Fairness in Medical Imaging: controlling for Disease Severity 2nd

AI Fairness in Medical Imaging: Controlling for Disease Se...

引用

2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Mukherjee, Pritam Summers, Ronald M. National Institutes of Health Clinical Center 10 Center Dr BethesdaMD20892-1182 United States

ISBN: (纸本)9783031727863

A new criterion for assessing fairness of AI models in medical imaging is proposed. The key idea is to control for disease severity, which as a mediator, affects the presentation of disease in medical images, and hence the performance of AI algorithms. Existing fairness criteria such as equalized odds do not capture this effect, as is illustrated by an example. Additionally, a new metric is proposed based on the information theoretic notion of adjusted mutual information. The metric is easy to compute, captures the overall bias of an AI model, and can be used to compare multiple models, which is illustrated using an example. In this example, three chest X-ray classification models, trained on NIH, CheXpert and PadChest datasets, respectively, are used to predict on a subset of MIMIC-CXR cases, for which the severity scores of pulmonary edema are available, and the bias of the three models are computed and compared along two sensitive attributes: sex and ethnicity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Disease control

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Deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition 3rd

Deep Learning for Cognitive Task and Seizure Classification ...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Variational mode decomposition

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Simulation analysis approach for the integration time of deep-space navigation optical cameras 3

Simulation analysis approach for the integration time of dee...

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2024 3rd international conference on Aerospace and control Engineering, ICoACE 2024

作者： Lei, Kaiyu Yu, Chengwu Sang, Hairui An, Yuehan Wu, Yanpeng Jiang, Tiantian Wang, Zexin Xing, Yan Beijing Institute of Control Engineering Beijing100190 China

In deep space exploration tasks, owing to the dissimilar lighting environment between outer space and Earth ground, the integration time of the navigation camera needs to be evaluated during its design or mission execution. This article puts forward an integration time analysis approach based on computer graphics simulation technology, which is capable of analyzing the rational integration time range in accordance with diverse environments and tasks. Our method encompasses two pipelines: "radiometric calibration" and "scene simulation". The scene simulation pipeline can simulate the image of the camera in the virtual scene based on the coefficients obtained from radiometric calibration. In contrast to previous methods, the evaluation process proposed in this article is able to simulate different task scenarios and conduct "one-case-one-meeting" analyses for different mission objectives and instrumentations. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Radiometry

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Synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations 3rd

Synergizing Collaborative and Content-Based Filtering for En...

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3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Walia, Madhav Raj, Shivanshu Aishwary, M. Lakshmi, T. Jaya Department of Computer Science and Engineering School of Engineering and Applied Sciences SRM University Andhra Pradesh Guntur India

ISBN: (纸本)9789819743582

This study combined content-based and collaborative filtering algorithms to create an all-inclusive movie recommendation system. In order to find trends and provide suggestions based on the tastes of comparable users, collaborative filtering analyzes user–item interaction data. Movie qualities are evaluated using content-based filtering in order to suggest related products. Preprocessing techniques like data cleansing, filtering, and text processing are used in the implementation to extract pertinent information and textual elements. While the textual characteristics are converted into numerical representations using methods like literal_eval and CountVectorizer, the metadata contains information on genres, release dates, cast, crew, and keywords. Using the vectorized characteristics of two videos, the cosine similarity between them is computed. Techniques for collaborative and content-based filtering are used to deliver accurate and customized. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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