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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是91-100 订阅

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Multimodal Medical Data learning Approaches for Digital Healthcare 6

Multimodal Medical Data Learning Approaches for Digital Heal...

引用

6th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Basystiuk, Oleh Melnykova, Nataliia Lviv Polytech Natl Univ Inst Comp Sci & Informat Technol Dept Artificial Intelligence UA-79000 Lvov Ukraine

the integration of multimodal data has emerged as a game-changing strategy in advancing smart healthcare, allowing for a holistic comprehension of patient health and tailored treatment strategies. this exploration delves into the journey from raw data to insightful wisdom, emphasizing the fusion of various data modalities, notably in CT scans or retinal photographs, to drive smart healthcare innovations. Within this review, we comprehensively examine the fusion of diverse medical data modalities, aiming to unlock a deeper understanding of patient health. Our focus spans various fusion methodologies-from feature selection to rule-based systems, machine learning, deep learning, and natural language processing. Furthermore, we explore the challenges inherent in fusing multimodal data in healthcare settings. the central focus revolves around determining the most efficient and accurate approach, crucial for future research endeavors in Ukrainian language audio-to-text conversion systems. the goal is to ascertain the most effective strategy that will serve as the foundation for further advancements in this domain.

关键词： Speech-to-Text speech recognition video recognition audio recognition multimodal data machine learning deep neural network hybrid approaches

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Emotion Decoding: An Extensive Examination of Electroencephalogram signals Using Explainable machine learning 6

Emotion Decoding: An Extensive Examination of Electroencepha...

引用

6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Nag, Anindya Mondal, Hirak Raihan Kabir, Sheikh Ridwan Islam, Md. Rabiul Ahmed, Md. Estiak Hasan Jamil, S.M. Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh North Western University Department of Computer Science and Engineering Khulna9100 Bangladesh Islamic University Department of Biomedical Engineering Kushtia7003 Bangladesh

ISBN: (纸本)9798350385779

Emotion plays a substantial impact on an individual's mental processes and social interactions. It functions as a connection between an individual's emotions and their behaviors, or it may be stated that it occasionally impacts one's life choices. Given the individual variations in emotional patterns and their manifestations, the investigation of these phenomena should rely on methodologies that are applicable across diverse populations. In order to improve accuracy and pick specific features, the process of emotion detection utilizing brain waves or EEG data necessitates the utilization of effective signal processing techniques. Researchers have been working on several methods of human-machine interface technologies for a considerable period. In recent years, they have achieved significant progress in the automated comprehension of emotions through brain signals. Our research involved the classification and testing of three emotional states using EEG signals collected from the widely accessible EEG Brainwave Dataset: Feeling Emotions from kaggle, utilizing seven machine learning techniques. this study presented a methodology that employed machine learning to identify emotions using the EEG Brainwave dataset. the study also conducted a thorough evaluation of several machine learning algorithms to assess their overall effectiveness. the study employed the Local Interpretable Model-Agnostic Explanations (LIME) to generate interpretable predictions and gain insights into the elements that influence the model's predictions. Despite the interpretability of the ML models, the XGBoost classifier had the greatest accuracy of 99% in recognition. © 2024 IEEE.

关键词： Electroencephalography

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Aspect-Based Sentiment Analysis for Virtual Reality Roaming System 6

Aspect-Based Sentiment Analysis for Virtual Reality Roaming ...

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Wang, Biqing School of Mathematics and Computer Tongling University Tongling China

ISBN: (纸本)9798350353174

through sentiment analysis technology, NPCs (Non-Player-Controlled Character) in virtual reality roaming system are able to recognize the user's emotional state and react accordingly. In aspect-based sentiment analysis, NLP techniques often use manually constructed features combined with machine learning models for classification. However, these methods are complex in feature engineering and lack generalisation capabilities. At the same time, traditional models often ignore the importance of local context for the correct classification of the sentiment polarity of aspect term, and process all input data indiscriminately. In order to solve the above problems and improve the processing efficiency of aspect-based sentiment analysis, an attention weight decay mechanism is proposed. On this basis, a multi-module model is constructed by fusing BERT, BiLSTM and TextCNN, and the performance of the model is verified experimentally. this model can be better applied to virtual reality roaming systems. © 2024 IEEE.

关键词： Adversarial machine learning

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DSSS signal Jamming Method Based on DCGAN 6

DSSS Signal Jamming Method Based on DCGAN

引用

6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Ren, Jiangfei Xu, Kui Liu, Yang Lu, Rui Zhang, Mi College of Communications Engineering Army Engineering University Nanjing China

ISBN: (纸本)9798350339994

With the continuous development of anti-jamming technology for the direct sequence spread spectrum (DSSS) communication, all existing efficient jamming methods have certain limitations. Aiming at this problem, a DSSS signal jamming method based on deep convolutional generative adversarial network (DCGAN) is proposed. Firstly, the correlation jamming model of the DSSS signal is studied, and the feasibility of the proposed method is qualitatively analyzed from the perspective of theoretical derivation. Secondly, based on the analysis of the principle of DCGAN, the generative adversarial network model is designed, and the optimized learning rate generates the jamming signal with highly similar time-frequency, frequency and correlation domain characteristics as that of the DSSS signal. Finally, the jamming effect of the proposed method is quantitatively analyzed through simulation experiments and comparison with other four jamming methods. the experimental results show that the proposed method is more threatening to the DSSS signal, and the jamming performance is significantly better than the traditional suppression jamming, slightly inferior to the best correlation jamming, but the implementation difficulty is much lower than the correlation jamming. © 2023 IEEE.

关键词： convolutional DCGAN DSSS jamming

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Electromagnetic signal Adversarial Example Generation Based on Perturbation Region Selection 6

Electromagnetic Signal Adversarial Example Generation Based ...

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6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Zhang, ShangYong Wang, XiaoLei Zhang, Yi Wang, LuLu Chen, Juan Song, QingHua National Innovation Institute of Defense Technology Beijing China Academy of Military Sciences Beijing China China Rongtong Scientific Research Institute Group Co Beijing China

ISBN: (纸本)9798350339994

With the widespread application of deep learning techniques in the field of electromagnetic signal recognition, the generation of adversarial examples for electromagnetic signals has gradually become a hot research topic in the field. Existing techniques for generating adversarial examples for electromagnetic signals suffer from issues such as high perturbation magnitude, low timeliness, and poor stealthiness, which makes it difficult to meet the practical application needs effectively. this thesis proposes an adversarial sample generation method based on region selection, called Region Selection-FGSM (RS-FGSM). this method selects the perturbation regions based on the magnitude of the gradient of the electromagnetic signal, and generates adversarial examples by applying FGSM perturbation within the selected regions. Experimental results demonstrate that the proposed method outperforms the FGSM method in terms of perturbation magnitude, timeliness, and stealthiness. © 2023 IEEE.

关键词： Adversarial Examples Deep learning Electromagnetic signals Region Selection

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Research and Application of Order Analysis Technology Without Tachometer Under Variable Speed Condition 6th

Research and Application of Order Analysis Technology Withou...

引用

conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Yang, Ruibo Wang, Jianguo Inner Mongolia Univ Sci & Technol Coll Mech Engn Baotou 014010 Inner Mongolia Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Under the condition of variable speed, the traditional signal processing method cannot accurately determine the fault location of motor bearing. this paper proposes and studies the order analysis method without tachometer. through the short-time Fourier transform and speed tracking of the collected fault signal, the speed signal of the motor fault bearing is obtained indirectly. the extraction process of the speed signal is completed and the order analysis is carried out to obtain the bearing fault diagnosis results. the results show that this method can effectively judge the fault location of motor bearing.

关键词： Data processing Nonstationary signal Order ratio analysis Resampling Speed tracking

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Secure Dynamic PUF for IoT Security 6th

Secure Dynamic PUF for IoT Security

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6th IFIP international conference on Internet of things, IFIP IoT 2023

作者： Rajput, Shailesh Dofe, Jaya California State University FullertonCA United States

ISBN: (纸本)9783031458774

this student research forum paper is based on our accepted work [1]. the widespread adoption of the Internet of things (IoT) has brought many benefits to our lives. Still, the low-power, heterogeneous, and resource-constrained nature of IoT devices makes it difficult to ensure secure communication and authenticity. Physical Unclonable Functions (PUFs) provide a promising solution by generating a unique and device-specific identity through manufacturing process variations without requiring additional resources. However, recent advances in machine learning algorithms like artificial neural networks and logistic regression have made it possible to predict PUF responses by training the model. machine learning models can use multiple challenges and responses to predict accurate results from the PUF. To address this concern, we propose integrating a dynamically configurable PUF structure into the design to counteract machine learning attacks. the dynamicity of the PUF makes it challenging for machine learning models to predict PUF responses. © 2024, IFIP international Federation for Information processing.

关键词： machine learning

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Detection of Abductors Using machine learning Algorithms 1

引用

6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Baba, Mohammad Asif Varun, thupakula Sai Shalivahana, Madasu Shaik, Subhani Iyengar, N. Ch. Sriman Narayana Telangana Hyderabad India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

the objective of this project is to help the police personnel and higher authorities to track down missing people quickly. the usual process to track down a missing person is using different types of investigation methods which include case diaries, spot visit, collection of evidences, searches and seizures, witness examinations, etc., that requires lot time and experience in that field. Most of the times, investigation methods work pretty well, but it is time consuming and can be unsuccessful if the person has been shifted to different location. this project proposes a system that would help the police and the public by speeding up the process of searching. We are using image processing and machine learning algorithms along with Postgres SQL database to insert, update, and delete the information regarding the missing person and help the authorities to find the abductor as soon as possible (Sisodia et al., Evaluation of machine learning models for employee churn prediction, 2017). the AI is used to push the image that is closest to the victim’s facial features. there are various NGO’s and Government initiatives to help with it. this paper tries to implement a new way to help the police personnel and higher authorities. We connect this system to the public cameras where the public cameras keep on recording all the details in its environment, and when it matches an image with the image present in the database, then it captures the image and store the location into the database. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Language Detection Using Natural Language processing 6

Language Detection Using Natural Language Processing

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Rao, P. Bhaskar Kotagiri, Chaithanya Madamshetti, Rishanka Minkoori, Abhisree Gande, Sahasra Bvritn Narsapur India Bvritn Cse Narsapur India

ISBN: (纸本)9798350394702

the Natural Language Detection Model is a project that uses machine learning techniques to estimate the language of a given document. the project makes use of a data set containing text information for 17 distinct languages and employs methods that include labelled encoding, preprocessing of text, or a Bag of Word model. the predictive model undergoes training using the Multinomial Naive Bayes approach and achieves an excellent accuracy of 0.97. this study demonstrates the ability of NLP to identify languages or its possible uses in translation by machines and multilingual content tracking. the model's precise predictions and strong performance verify the model's efficacy in real-world circumstances. the project starts by installing modules and uploading the language recognition data set, then separates both dependent and independent variables. the text data is subsequently processed with preprocessing for text techniques, and then a Bag with Words model is generated with CountVectorizer. the dataset is divided into sets for training and testing, and the model is trained using the Multinomial Naive Bayes method on the training data. the model that has been trained is then applied to the test set to forecast the language of the text. the project showcases the successful application of natural language processing (NLP) approaches for language recognition and emphasizes the value of hands-on project experience for budding machine learning enthusiasts. © 2023 IEEE.

关键词： machine learning

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Magnetostrictive Sensor Based Bridge Health Assessment Using machine learning Classifiers 6

Magnetostrictive Sensor Based Bridge Health Assessment Using...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Dolui, Cherosree Devi, Sagolshem Farishta Dasadhikari, Shamik Roy, Debabrata Indian Institute of Engineering Science and Technology Department of Electrical Engineering Howrah India

ISBN: (纸本)9798350324204

Bridge Health Monitoring (BHM) is a crucial research domain in engineering, involving the analysis of infrastructure conditions like bridges using sensor data. Structural Health Monitoring (SHM) systems comprise distinct phases, with pivotal stages being feature extraction and pattern recognition. signal processing techniques aid in feature extraction, while machine learning algorithms play a crucial role in pattern recognition, all geared towards accurately assessing bridge health. Applying machine learning techniques permits the automation of the SHM procedure and intelligent damage detection. this study introduces an innovative method utilizing machine learning technologies, leveraging data from Galfenol-based Magnetostrictive Sensors. these sensors convert vibrations into electrical data, offering insights into bridge health. A Decision Tree classifier categorizes data with an 80:20 split for training and testing, achieving a 98.72% accuracy. the code also demonstrates its predictive capability with new experimental data. Furthermore, various classifiers, including the Support Vector Method (SVM) and Neural Network, are tested, revealing their comparable accuracy. this research provides a user-friendly avenue for SHM and assists researchers in selecting optimal classification methods. © 2023 IEEE.

关键词： Structural health monitoring

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