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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311-320 订阅

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Convolutional neural network-based signal discrimination for P300 brain-computer interface

Convolutional neural network-based signal discrimination for...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yongzhi Liao Liumei Zhang Yaoxin Wang School of Computer Science Xi’an Shiyou University Xi’an China

the P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. this paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. the test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects.

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Epilepsy Seizure Detection via Optimized Kernel Extreme learning machine Method

Epilepsy Seizure Detection via Optimized Kernel Extreme Lear...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Shuang Guo Jingqi Fu School of Mechatronic Engineering and Automation Shanghai University Shang Hai China

Epilepsy is a neurological disease that can have an impact on the health of patients. therefore, the detection of epilepsy diseases is very important. In this paper, empirical mode decomposition is used to decompose epileptic EEG signals, and mixed features are extracted from signal components that contain more feature information. Linear feature mean, fluctuation index, and nonlinear feature sample entropy constitute the feature vectors for epileptic EEG classification. After that, an adaptive sparrow search algorithm is used to optimize the kernel based extreme learning machine for classification, and a five fold cross validation method is used to ensure the performance of the classifier. Experimental results show that the method proposed in this paper has a high accuracy and better performance.

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Improved YOLO-based Pedestrian Detection Algorithm 23

Improved YOLO-based Pedestrian Detection Algorithm

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6th international conference on Image and Graphics processing, ICIGP 2023

作者： Chen, Cong Zhu, Huasheng Ren, Qiaofeng Luo, Rui Lan, Hong School of Information Engineering Nanchang Institute of Technology China

ISBN: (纸本)9781450398572

When the YOLO algorithm is directly used for pedestrian detection, it suffers from the problem that the algorithm is complex and not suitable for deployment in mobile terminal. therefore, an improved YOLO-based pedestrian detection algorithm is proposed. the algorithm is based on YOLOv5n by introducing CBAM attention mechanism, which makes the network pay more attention to the feature information of channel and space, and is conducive to extracting the important features of the target. Secondly, the lightweight CARAFE module is selected for the up-sampling module on the neck layer, which can reduce the complexity of the algorithm and obtain a larger perceptual field to improve the learning ability of the network. Finally, the Focal-EIoU loss function is chosen to solve the problem of inaccurate bounding box regression due to variable human morphology in pedestrian detection tasks. Experimental results show that the mAP value of this algorithm on INRIA Person Dataset reaches 98.4%, which is 2.4% higher than the original algorithm. the size of the improved algorithm model is less than 4 MB, which improves the detection performance while maintaining a small scale, and is more conducive to deployment on mobile terminal. © 2023 ACM.

关键词： signal detection

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HLS-Based FPGA Implementation of Multi-Label learning for Radar signal Recognition

HLS-Based FPGA Implementation of Multi-Label Learning for Ra...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Lei Guo Wen Pan Jian Zhao Hongbo Li Beijing Institute of Space Long March Vehicle Beijing China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

In modern Electronic Support (ES) systems, the Radar signal Recognition (RSR) component necessitates real-time processing of overlapping signals. In comparison to CPUs and GPUs, Field-Programmable Gate Arrays (FPGAs) exhibit enhanced energy efficiency and low-latency streaming capabilities, rendering them better suited for energy-sensitive or real-time machine learning endeavors. this paper presents a proposed system that leverages multi-label deep learning technology to autonomously recognize overlapping signals. the system is deployed on the FPGA platform, and optimization of the multi-label learning inference network is achieved through High-Level Synthesis (HLS) and loop pipeline techniques. the implemented solution comprises an energy-efficient HLS-based FPGA convolutional multi-label accelerator specifically designed for RSR applications, utilizing the Virtex-7 platform. the system operates at a frequency of 150MHz, resulting in a 35.2% increase in throughput and a significant 90.3 % reduction in power consumption compared to GPU-based platform.

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Frontotemporal Dementia Detection Model Based on Explainable machine learning Approach 1

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Poonam, Km Guha, Rajlakshmi Chakrabarti, Partha P. IIT Kharagpur West Bengal Kharagpur721302 India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Early diagnosis of neurodegenerative disorders is one of mainstream neuroscience’s most significant challenges. Although there is no cure for this group of disorders, the importance of medical treatment primarily depends on when the patient is diagnosed. One of the devastating neurodegenerative diseases is Frontotemporal Dementia (FTD). the primary FTD spectrum disorders include Behavioral Variant FTD (BV), Non-fluent Variant Primary Progressive Aphasia (PNFA), and Semantic Variant (SV). FTD detection has been thoroughly researched. However, research findings typically have less impact on clinical practice for the following reasons: (1) FTD subtypes detection has not been studied substantially;(2) most research relies heavily on a single diagnostic modality, particularly neuroimaging;and (3) current research focuses primarily on improving the accuracy of machine learning and deep learning models whereas neglecting their interpretability. So, Clinicians find it difficult to interpret these complex models. In this study, to assist medical specialists with supporting diagnostic tools, we investigate how well a machine learning algorithm performs in developing an accurate FTD subtype. the dataset consists of neuropsychiatric inventory data of 288 subjects from the frontotemporal lobar degeneration neuroimaging initiative (NIFD) database. Experiments are carried out by investigating different machine-learning classification algorithms and providing various explainers for the models’ global interpretability via Shapley Additive Explanations (SHAP) and local interpretability via Local Interpretable Model-agnostic Explanations (LIME). Obtained results show high accuracy in detecting FTD subtypes when Random Forest and Extreme Gradient Boosting techniques achieve a test accuracy of 85.05% and 86.21%, respectively. © 2023, IFIP international Federation for Information processing.

关键词： Neurodegenerative diseases

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machine learning and Audio signal processing for Predictive Maintenance: A review

Machine Learning and Audio Signal Processing for Predictive ...

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IEEE Colombian conference on Automatic Control (CCAC)

作者： Rommel Prieto Diego Bravo Physics Department Universidad Del Cauca Popayán Colombia

this work shows a systematic review of literature of machine learning and audio processing for applications in preventive maintenance. It intends to prove how audio signal processing combined with machine Leanring techniques can produce a powerfool tool to detect anomalies and malfunctions in electromechanical devices. the document describes a review of art state and the algorithms that can be used for preventive maintenance applications. When reviewed, the literature proves that machine learning and Deep learning approaches provide high accuracy results as tools for PdM, and that Support Vector machines, k-Nearest Neighbors and Convolutional Neural Networks are the most used approaches as they achieve the highest evaluation metrics, and prove sound, vibration and current to be the most popular signals to train ML-PdM oriented models.

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the Proactive Approach to Cyber-Attack Prevention: Countermeasures Against Ransomware

The Proactive Approach to Cyber-Attack Prevention: Counterme...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Jun Tang School of Fuzhou Melbourne Polytechnic Fuzhou Fujian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

the relentless escalation of ransomware attacks necessitates the proactive approach to cyber-attack prevention. this paper presents a comprehensive framework for anticipating and mitigating ransomware threats, leveraging predictive analytics, threat lifecycle analysis, and robust countermeasures. through a multi-faceted methodology, the study introduces machine learning models for threat prediction, proactive security measures, and specific strategies against ransomware. A case study illustrates the practical application and effectiveness of these strategies in a real-world scenario. the findings advocate for a dynamic cybersecurity posture, emphasizing the importance of adaptability and vigilance in the face of evolving digital threats.

关键词： Prevention and mitigation machine learning signal processing Predictive models Ransomware Time factors Predictive analytics Cyberattack Faces Resilience

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A Software for learning thai Fingerspelling Sign Language into thai Consonants with machine learning 6th

A Software for Learning Thai Fingerspelling Sign Language in...

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6th international conference on Technology in Education, ICTE 2023

作者： Pisuchpen, Supisara Singhtaun, Chansiri Suwannachote, Laddawan Kasetsart University Laboratory School Center for Educational Research and Development Faculty of Education Kasetsart University Bangkok Thailand Department of Industrial Engineering Faculty of Engineering Kasetsart University 50 Ngamwongwan Road Lat Yao Chatuchak Bangkok10900 Thailand

ISBN: (纸本)9789819982547

Children with hearing impairment or deafness often face difficulties in spelling and reading. this research aims to develop a program that translates thai fingerspelling sign language into thai consonants. the objective is to use this program as a teaching tool to help children with hearing impairment understand and learn the thai language more easily. the program created is a utilization of Artificial Intelligence (AI) techniques, where the program captures real-time hand gesture images in fingerspelling sign language and then translates the results into thai consonants. It enables them to accurately create hand signs and makes the learning process more engaging and interesting. the program development begins with Hand Detection algorithm, which involves detecting the positions of 21 landmarks on the hand after using image processing techniques. After that, the hand gesture images for all the thai consonants are captured and saved to be used as ground truth datasets. Next, the ground truth datasets are fed into the machine learning process to create a trained model. this enables the program to match the hand gesture images accurately with the correct consonant flashcards, using Google’s Teachable machine. the results of testing the program revealed that the program has an average accuracy of 79.50%. the program was deployed with 12 users, consisting of 2 teachers and 10 elementary school students from Setsatian School for the Deaf under the Royal Patronage of His Royal Highness Crown Prince Maha Vajiralongkorn. After conducting the user satisfaction survey and applying statistical analysis methods, the results revealed an overall satisfaction rating of "very satisfactory," scoring 83.80% across all aspects. the program developed can be used as a teaching aid for children with hearing impairments to facilitate their learning and academic progress. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Detection of Traffic Congestion from Surveillance Videos using machine learning Techniques 6

Detection of Traffic Congestion from Surveillance Videos usi...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Govinda Rao, S. Rambabu, R. Anil Kumar, B.S. Srinivas, V. Varaprasada Rao, P. Griet Department of Cse Hyderabad India Riet Department of Cse Rajahmundry India Cse Griet Hyderabad India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Smart Cities applications, automated traffic control and management is the most trending research area. With the improving needs of developed towns and cities traffic congestion, now a days this the traffic congestion control and its applications has large needed facing problem in the increased population cities. Peeled eye camera photos and videos can be watched efficiently to detect traffic congestions in most of the areas in the grown populated cities. the earlier researchers had observed more on traffic signal controls through photos executed by using different algorithms of machine learning. there is existing research available on traffic signal controls through image processing and various machine learning methods. the image features are extracted and interpreted for the same. Deep learning algorithm, convolutional neural network (CNN) is proposed for effective detection of traffic congestion. there were existing works available in traffic detection using machine learning and deep learning approaches. machine learning, Nowadays, traffic surveillance systems collect a lot of videos or images and store them for the live monitoring purposes. Deep learning techniques are used sparingly in traffic surveillance and control systems. Various images with various weather conditions are collected and are used as training dataset. Advantages of deep learning have been exploited in many applications, which use computer vision and image analysis. One of such applications is traffic monitoring, in which large amounts of video or images are processed for effective learning. the traffic surveillance can only monitor, which cannot detect the traffic on particular time. © 2022 IEEE.

关键词： Traffic congestion

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Enhancing IoT Security through AI-Based Anomaly Detection and Intrusion Prevention 6

Enhancing IoT Security Through AI-Based Anomaly Detection an...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ramaiah, Narayana Swamy Andrews, S. Kevin Shenbagharaman, A. Gowtham, M.S. Bhaskar, Bandi Tiwari, Mohit Sankalchand Patel University SPCE Dept of Computer Engineering Gujarat Visnagar India Dr. Mgr Educational and Research Institute Department of Computer Applications Tamil Nadu Chennai India National Engineering College Department of CSE Tamilnadu Kovilpatti India Karpagam Institute of Technology ECE Department Tamilnadu Coimbatore India School of CS & AI SR University Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350304480

Enhancing the Internet of things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. the difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like machine learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. this enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. the outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863. © 2023 IEEE.

关键词： Anomaly detection Artificial intelligence Internet of things Intrusion prevention machine learning Security

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