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检索条件"任意字段=IEEE 4th International Conference on Intelligent Computer Communication and Processing"

共 5833 条记录，以下是261-270 订阅

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Exploring Advanced Approaches: A Comprehensive Analysis of Machine Learning and Deep Neural Networks in Spectrum Sensing Applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

引用

4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

the increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. this survey paper presents an indepth exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine Learning (ML) and Deep Neural Network (DNN) approaches. the study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. this paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) methodologies. the discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 ieee.

关键词： Hidden Markov models

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Tibetan Question Intention Classification Based on Feature Perception of Question Words 4

Tibetan Question Intention Classification Based on Feature P...

引用

4th international conference on Artificial Intelligence, Robotics, and communication, ICAIRC 2024

作者： Quecairang, Guan Maojia, Yang Te, Rou Chajia, Se Zhenjiacuo, Ci Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9798331531225

Question intent classification is an important part of the Tibetan Q&A system. It is a challenging task to use deep learning methods to classify the intent of Tibetan questions. this paper aims to: (1) the traditional Tibetan question intent classification scheme is too coarse, which is not conducive to the problem intent recognition task in the question answering system. (2) the Tibetan question intent type is highly dependent on the local spatial characteristics with interrogative virtual words as the core, which leads to the problem of poor classification effect, and a novel Tibetan question intent classification scheme is proposed, and each question intent type is defined. On this basis, through a detailed analysis of the semantic features and writing rules of Tibetan interrogators, a semantic information labeling algorithm for Tibetan interrogators was designed, and an BERT_RCNN intentional classification model for Tibetan questions was trained by combining the advantages of Tibetan Pre-Training (TiBERT) and recurrent convolutional neural network. the F1 value of this method can reach 94.87% on the test set, which can accurately identify user needs, compress the search space and optimize model decision-making, and significantly improve the performance of the Tibetan Q&A system. © 2024 ieee.

关键词： Question answering

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Leveraging Deep Quantum Convolutional Neural Networks for Student Facial Expression Identifcation and Mode Assessments 4

Leveraging Deep Quantum Convolutional Neural Networks for St...

引用

4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Hossain, Javed Alam, Md. Nur Akib, Mohammad Yasin Nur Kogoya, Krisdahim Nanjing University of Information Science and Technology Dept. of Artificial Intelligence Nanjing China Nanjing University of Information Science and Technology Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331529635

the incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. this study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58 © 2024 ieee.

关键词： Convolutional neural networks

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Carrot Rolling Detection Based on Machine Vision 4

Carrot Rolling Detection Based on Machine Vision

引用

4th international conference on intelligent Design, ICID 2023

作者： Guan, Lunan Zhang, Chengliang University of Jinan School of Mechanical Engineering Jinan250000 China

ISBN: (纸本)9798350342574

Sorting of carrots is an important step after picking. Usually the sorting of carrots relies on human labor, which leads to a waste of manpower and time. We have developed a rolling carrot visual inspection device, which consists of an image acquisition device, an image processing system and a roller bar conveyor. In this paper, the layout of the camera is investigated. So that the system obtains good quality images when the carrot is rolling, and uses a combination of image processing and neural networks to determine whether the carrot is defective or not. Experiments show that the recognition accuracy for fracture, bending, bifurcation, green shoulders, black spots and fibrous roots, reaches 78.14%, 84.57%, 87.52%, 96.12%, 97.64% and 96.26% respectively. © 2023 ieee.

关键词： computer vision

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QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancement: Fidelity-Improved Data Optimization in Quantum-Classical Systems 4

QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancemen...

引用

4th ieee international conference on Applied Electromagnetics, Signal processing, and communication, AESPC 2024

作者： Kundu, Sourodeep Mishra, Deepti Tarasia, Nachiketa Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed to Be University Bhubaneshwar India School of Economics and Commerce KIIT Deemed to Be University Bhubaneshwar India School of Computer Application KIIT Deemed to Be University Bhubaneshwar India

ISBN: (纸本)9798331528171

Using machine learning to improve quantum algorithms or quantum mechanics to improve machine learning is known as quantum machine learning. In order to include quantum physics into machine learning, we provide a unique model (QEFMLA-FIDO) in this research. the way an artificial neural network simulates the human brain is comparable to this method. Our suggested quantum-inspired technique can accelerate computationally challenging issues when executed on traditional hardware. Additionally, the method may easily be modified to operate on quantum technology, offering a far faster speedup than is achievable on conventional hardware. the Quantum Evolution Algorithm (QEA) improves computing efficiency by operating on both classical and quantum hardware and is motivated by the general development of quantum states. the technique solves complicated problems more quickly than conventional methods by iteratively evolving a system's Hamil-tonian to suit training data. It effectively enhances parameters by optimising the Quantum Fisher Information Matrix (QFIM). the QEA is suitable in real-world situations because it uses quantum parallelism to minimise computing complexity and is noise- resistant. It also has a predictive function that can detect improper outputs before they happen. © 2024 ieee.

关键词： Hamiltonians

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A Deep Machine Learning Approach for Intrusion Detection in IoT 4

A Deep Machine Learning Approach for Intrusion Detection in ...

引用

4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Wadate, Akash J. Deshpande, S.P. DCPE HVPM Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9798350306118

It has proven to be extremely beneficial in various initiatives, including medical automation, defense systems, and even power grids. Essentially, the security surrounding fundamental processing and communications infrastructures is more important compared to the protection of loT networks. However, due to their relatively limited assets and computing capacity, loT devices are vulnerable to a range of attacks. therefore, defending loT networks from attackers is critical, and this can be achieved through the creation and deployment of effective safety regulations, such as intrusion detection technologies that incorporate specific controllers. In this work, we describe a unique traffic flow classification method for loT networks that employs deep machine learning to identify intrusions. We use a recently released loT database to develop general characteristics using domain values at the protocol packet level. Addressing binary or multi-class categorization, which includes loss of assistance, extended loss of assistance, surveillance, and data theft operations over loT devices, we create a feed-forward neural network framework. Findings from the examination based on the proposed strategy using the prepared information demonstrate a high level of precision in classification. © 2023 ieee.

关键词： Intrusion detection

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Image Captioning for Chest X-Rays Using GRU - Based Attention Mechanism 4

Image Captioning for Chest X-Rays Using GRU - Based Attentio...

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Kodavati, Trinaya Bharathi Mohan, G. Prasanna Kumar, R. Elakkiya, R. Chennai601103 India Birla Institute of Technology and Science Department of Computer Science and Engineering Dubai345055 United Arab Emirates

ISBN: (纸本)9798350306118

Medical image captioning is a field that combines medical imaging with natural language processing (NLP). Since the development of deep learning techniques in both NLP and medical image analysis, this area of study has gained significant progress. this research aims to develop a system for providing a brief description when given an image of chest x-ray using a GRU-based Attention Model, which gave better training and validation accuracy and BLEU scores when compared to a simple encoder-decoder, attention mechanism implemented with context flow and global flow and other state-of-the-art models using chexnet. the Chest X-Ray Images dataset, consisting of a large number of images of different chest x-rays suffering from pneumonia with different points of view (anterior-posterior), is utilized for training and validation of the model. the proposed model is trained using a combination of chest x-ray images along with their text features. the findings demonstrate the potential of using the GRU-based attention model for detection and classification of pneumonia, which can be applied for diagnosis in hospitals, clinics, emergency departments and screening programs. © 2023 ieee.

关键词： Hospitals

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Real Time Pothole Detection System Using Deep Learning Techniques: A Systematic Review 4

Real Time Pothole Detection System Using Deep Learning Techn...

引用

4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Saraswathi, S. Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529635

Real-time pothole detection serves a crucial role in maintaining road infrastructure and ensuring the safety of drivers. Traditional methods for identifying potholes are often labour-intensive and inefficient, prompting the exploration of automated techniques using deep learning. this comprehensive review aims to synthesize the current state of research on pothole detection systems leveraging deep learning algorithms. By examining a range of studies published over the past decade, the review highlights various methodologies, including You Only Look Once (YOLO), convolutional neural networks (CNNs), deep CNN, and hybrid models, as well as the different datasets and image processing techniques employed. the review categorizes the approaches based on their accuracy, computational efficiency, and practical implementation challenges. the review also identifies research gaps in the literature survey, such as the demand for standardized datasets and real-time processing capabilities, and suggests directions for future research. By providing a systematic overview of deep learning-based pothole detection systems, the purpose of this review is to help researchers and practitioners develop more reliable and scalable solutions for maintaining road infrastructure. © 2024 ieee.

关键词： Convolutional neural networks

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Research on Visual Tracking System Software of Cutting Robot Based on Adaptive Algorithm 4

Research on Visual Tracking System Software of Cutting Robot...

引用

4th ieee international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhang, Teng China Coal Technology & Engineering Group Nanjing Design & Research Institute Co. Ltd Nanjing China Nanjing Yhd Intelligent System Co. Ltd Nanjing China

ISBN: (纸本)9798350359589

this paper studies various tracking algorithms based on the characteristics of visual tracking system. According to the characteristics of cutting robot, tangential tracking and normal approximation are studied. An adaptive correction algorithm for motion trajectory of precision cutting manipulator is presented. the algorithm compares it with the PSR fraction to achieve the target tracking algorithm. the translation filter and proportional filter are modified. the device collects image information from CCD equipped with suitable lighting equipment and lens, uses image processing board to preprocess, binarize, compresses and encodes the image information, and then recognizes and interprets it by computer. And by a servo motor and drive to complete a 1:1 linear tracking, experiments show that the image tracking software of the cutting robot designed in this paper can reach the latest discriminative correlation filter tracking method, and it has better adaptability. Experimental results show that this method can achieve target tracking well. © 2024 ieee.

关键词： Adaptive algorithm control software cutting robot visual tracking

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An Implementation of Machine Learning Based Healthcare Chabot for Disease Prediction (MIBOT) 4

An Implementation of Machine Learning Based Healthcare Chabo...

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Ganguly, Gaurav Kumar, Sourav University of Engineering & Management Jaipur Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350306118

In an era where quality healthcare is pivotal for a successful life journey, challenges arise when seeking medical attention amid the ongoing pandemic. the traditional approach of consulting doctors in person or visiting hospitals is hindered, emphasizing the necessity for innovative solutions. Harnessing the capabilities of Natural Language processing (NLP) and machine learning, this study introduces an advanced chatbot application. Built upon supervised machine learning, the proposed chatbot system not only offers disease diagnosis and comprehensive treatment insights but also does so preemptively, prior to consulting a physician. this system boasts a user-friendly GUI-based text assistant, facilitating seamless interaction with the chatbot. Unlike existing models, this approach intelligently presents user symptoms and corresponding risk factors, tailored to the user's ailment, while also offering optimal recommendations. Moreover, the chatbot clarifies the appropriate instances for in-person medical consultations. By addressing the underutilization highlighted in previous research, this study signifies the chatbot's potential advantages. By embracing this cost-free application, individuals can circumvent cumbersome hospital visits. the comparison with the reference model demonstrates the enhanced efficacy of the proposed approach in addressing healthcare challenges, ensuring convenient and accessible medical guidance. © 2023 ieee.

关键词： Hospitals

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