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

共 18726 条记录，以下是41-50 订阅

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Smart Glasses for Blind Assistance: A Lightweight and Ergonomic Approach 3

Smart Glasses for Blind Assistance: A Lightweight and Ergono...

引用

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

作者： Shreevatsan, S.S. Annambhotla, Yasasree Lasya Reji, Anaswara Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Individuals who are blind or have reduced vision often struggle to navigate their environments, as traditional tools like canes offer limited information. While smart glasses have the potential to assist, existing models face challenges such as being heavy, uncomfortable, expensive, and lacking effective features for real-time navigation. These limitations hinder usability and diminish the overall user experience. This work presents a cost-effective prototype of lightweight and ergonomic smart glasses designed to enhance navigation and safety for individuals with visual impairments. By integrating obstacle detection sensors, real-time feedback mechanisms, and a user-centric design, the proposed solution aims to promote mobility, independence, and comfort while remaining accessible to a wider user base. © 2025 IEEE.

关键词： Obstacle detectors

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Smart Attendance By Using Face Recognition 3

Smart Attendance By Using Face Recognition

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Naveen Vasantha Kumari, N. Sheetal Alli Presidency College Department of Computer Applications Bangalore India

ISBN: (纸本)9798331515911

Maintaining attendance by hand in contemporary classrooms is a laborious activity that is prone to errors and mistakes. To make this process easier to comply with, we suggest a Face Recognition Based Attendance Management System (FRAMS) which makes use of modern facial recognition technology to eliminate the need for manual attendance. This system utilizes computer vision technology where face detection and recognition take place using real time python open cv software while mingling through a comfortable user interface designed by Tkinter. Haar Cascade classifiers are incorporated for face detection while the facial recognition aspect is conducted through an efficient algorithm referred to as Local Binary Patterns Histograms (LBPH) that is resistant to changes in lighting, facial emotions and facial *** works by taking students pictures using a webcam and a dataset is generated from these pictures. This dataset is then used to prepare the face recognition model. During attendance sessions, live faces are compared against the stored database within the system to recognize and verify users. When the system is capable to identify a person, it automatically registers the person's state using the online spreadsheet which further provides attendance records in a precise manner. The aided system is efficient as it removes the need for attendants roll call, thereby minimizing errors and curtailing proxy *** so, the system was subjected to extensive testing when in use in various situations and its recognition accuracy rate was high in controlled conditions. There were, however, some problems like the differences in lighting and the chances of wrongful identification in the middle of busy pictures. Even so, in light of these challenges, FRAMS is an improvement over the attendee sheets that yesteryear utilized because it's faster and more dependable. For works in progression, it may be possible to enhance the recognition algorithms to cope with increasing

关键词： User interfaces

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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A Hybrid Hand Gesture Classification System using ResNet50, GCN, and SVM for Enhanced Human-computer Interaction 3

A Hybrid Hand Gesture Classification System using ResNet50, ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kollem, Sreedhar Kumar, Gonela Pavan Hepsiba, Kurri Adithya, Pambala Ranadheer, Odela Peddakrishna, Samineni Sr University Dept. of Ece Warangal India VIT-AP University School of Electronics Engineering Andhra Pradesh India

ISBN: (纸本)9798331509675

A novel hand gesture classification system is developed for human-machine interaction to solve the pervasive problems of high computational loads, lighting variations, and real-time accuracy. Thus, this work presents the development of a novel hand gestures classification system that utilizes state-of-the-art computer vision techniques to classify the movement detected by a standard camera. It basically comprises three major parts: ResNet, GCN, and SVM, applied for feature extraction and classification of the specific features. During Stage 1, ResNet50 utilizes convolutional layers in extracting intricate features from input images. These features include a range of patterns, edges, textures, and other advanced characteristics that are important in distinguishing between different classes. In Stage 2, we utilize the superpixel values to extract complex relational features by considering the spatial distances between the superpixels using a graph convolutional network. At this third step of the algorithm, the support vector machine generally focuses on finding the best possible decision boundary, commonly referred to as the hyperplane, that will actually do a better job in distinguishing data points of different classes within this multidimensional feature space. The approach would, instead, improve the recognition rate due to changing light and background conditions by making use of several training datasets. Indeed, the proposed methodology promises to improve the results than that of the existing techniques. Future work will be directed to enhance the system's robustness and test its performance in various real-world scenarios. © 2025 IEEE.

关键词： Superpixels

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Crop Disease Detection using Hybrid Model of CNN and Capsule Networks 3

Crop Disease Detection using Hybrid Model of CNN and Capsule...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chayadevi, M.L. Shetty, Sameeksha Shetty, Shriddhi Bnm Institute of Technology Affiliated to Vtu Dept. of Cse Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Incorporation of DL techniques have greatly advanced in accuracy of prediction in various fields. In this research, our goal is to diagnose crop damage in fields by enabling farmers to identify the crop disease and take suitable actions. The proposed solution leverages hybrid model of CNN and CapsNets for image-based classification, accessible via a web interface, to facilitate this accurate crop disease prediction. After reviewing over 50 CNN articles, we identified that usage of Capsule Networks along with CNN enhances image classification acquiring better spatial relationships. © 2025 IEEE.

关键词： Productivity Accuracy Computational modeling Crops Predictive models Pest control Feature extraction Object recognition Diseases Image classification

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CNN Approach for Live Speech Emotion Detection 3

CNN Approach for Live Speech Emotion Detection

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Subramani, Nevedha Sivasankar, Srividhya Saravanan, K. Sakthivel, K. K. S. Rangasamy College of Technology Tiruchengode India Computer Science and Business Systems K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331509675

The application of convolutional neural networks (CNNs) in real-time speech recognition in real-time speech improves the real-time processing capability and the ability to accurately and effectively control speech in many places. CNNs are useful for this task because they can learn complex features from noise sources that represent the frequency and amplitude of sounds over time. Using multiple convolutional techniques, these networks can capture spatial and temporal patterns in audio data, making them very good at distinguishing loud speech. This approach is particularly useful for applications in smart assistants, fast change services, and hearing aids, where immediate detection and response are important. The main advances include the development of well-designed models and training methods to increase model accuracy and speed. These models can be further developed to recognize specific sounds, tones, or words to provide personalized experiences for users. As commercial performance improves, CNNs for instant speech recognition have become easier to use, paving the way for widespread use of equipment, security, and technology among humans. © 2025 IEEE.

关键词： Convolutional neural networks

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

引用

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

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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Joint Optimization of Computation Offloading, Dynamic Pricing and Admission control in Mobile Edge Computing-Enable Blockchain 24

Joint Optimization of Computation Offloading, Dynamic Pricin...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Liu, Yi Zhang, Wenjie Zheng, Yifeng Chen, Liming Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Fujian Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Fujian Zhangzhou China

ISBN: (纸本)9798400710834

In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners’ offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility. © 2024 Copyright held by the owner/author(s).

关键词： Computation offloading

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Bubble Prediction in Stock Markets: Leveraging SVM and Technical Indicators for High Accuracy 3

Bubble Prediction in Stock Markets: Leveraging SVM and Techn...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Manogar, E. Doss, P.Lourdu Mahimai Gunasekaran, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering TamilNadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331509675

Predicting stock market bubbles is indispensable for risk control and refinement of trading strategies. Therefore, this study presents a Machine-Learning Framework to detect price bubble formations based on Support Vector Machine (SVM) and through important technical indicators. Among these are momentum indicators, such as the Relative Strength Index (RSI), and the Moving Average Convergence Divergence (MACD) histogram, which are complemented with volatility measures from Bollinger Bands and with normalized volume indicators, all to improve predictive accuracy. Recent historical stock data were fed to the model during training and testing, yielding exceptional accuracy: 98.54% on the test set and 98.95% on the training set. The performance evaluation was carried out based on Classification Reports, Confusion Matrices, Receiver Operating Characteristic (ROC) curve, and Precision-Recall metrics, thus confirming the performance efficiency of the developed framework. With this approach, bubbles' early detection allows traders and analysts a powerful tool to make informed decisions in risk mitigation or strategic enhancement. © 2025 IEEE.

关键词： Support vector machines

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