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Perspective of signal processing-based on brain-computer interfaces using machine learning methods

Perspective of signal processing-based on brain-computer int...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： He, Ruyue Department of Electronics and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781643684321

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The 'Discussion' part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms. © 2023 IOS Press. All rights reserved.

关键词： Brain computer interface

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Ecological Vulnerability Assessment of Helan Mountain in Ningxia Based on SPR Model 3rd

Ecological Vulnerability Assessment of Helan Mountain in Nin...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： He, Yuanrong Chen, Yuhang Song, Yongfei Zhang, Liheng Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Ningxia Survey and Monitor Institute of Land and Resources Yinchuan China

ISBN: (纸本)9789819624676

In order to explore the ecological vulnerability of Helan Mountain in the context of the ecological protection project, this paper, based on the SRP model, selects 15 evaluation indicators to construct an ecological vulnerability evaluation system for the Helan Mountain, and utilizes the fuzzy hierarchical analysis method to reveal the ecological vulnerability of the study area in 2021 from the distribution of spatial characteristics, different land types, and the relationship between key ecological restoration areas and vulnerability in three dimensions. The results showed that (1) the overall spatial distribution of ecological vulnerability in the study area is dominated by slight and mild vulnerability in the southwest and severe vulnerability in the northeast, and the hinterland of the more developed cities is dominated by moderate and severe vulnerability due to the impacts of industrialization and urbanization as well as the population density at the county scale. (2) Ecological vulnerability is significantly affected by the type of land use, with the lowest vulnerability in paddy fields and forested land, and relatively higher in urban land and rocky land. (3) The analysis of 169 key restoration areas in Helan Mountain of Ningxia showed that 140 of them were severely fragile and 21 were extremely fragile, including Rujigou mine area where coal fire spontaneous combustion was serious. The research can provide data support and decision support for ecological protection and coal fire rectification of Helan Mountain in Ningxia Province. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Forest ecology

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Investigation on Smart Campus Management Platform Based on Digital Twin 3

Investigation on Smart Campus Management Platform Based on D...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Hu, Pan DianChi College Yunnan University Yunnan Kunming China

With the improvement of digitalization and the development of information technology, intelligent campus management has become an important research direction in the current education field. Based on digital twin technology, this article proposed a design scheme for a smart campus management platform to address the current problems in campus management. This could manage and serve students, teachers, parents, and other aspects, and achieve the mining and analysis of school big data. Through the implementation and application of the platform, the efficiency and accuracy of campus management could be improved, and the modernization of education could be further promoted. The smart campus management platform of digital twin utilized digital twin technology to digitize the campus environment, equipment, students, teaching resources, and other information of the school, thus achieving three-dimensional visualization, intelligent analysis, and feedback. The experimental results showed that the security of the smart campus management platform designed in this article exceeded 90% and performed well in system testing. © 2023 Elsevier B.V.. All rights reserved.

关键词： Big data

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Traditional Indian Textile Designs Classification Using Transfer learning 3rd

Traditional Indian Textile Designs Classification Using Tran...

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3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Varshney, Seema Vasantha Lakshmi, C. Patvardhan, C. Department of Physics and Computer Science Dayalbagh Educational Institute Agra India Department of Electrical Engineering Dayalbagh Educational Institute Agra India

ISBN: (纸本)9789811958670

Traditional Indian textile designs are very rich and varied and reflect the culture of the area in which they are popular. Unfortunately, artisans for these forms of art are dwindling because of the onslaught of mechanization and low productivity of handmade textiles. The art has to be learned painstakingly and creating new designs in sync with the market trends is a time taking task. Any attempt at developing technology for the preservation of these art forms is, therefore, the need of the hour. In this work, an attempt has been taken to create a database of traditional Indian textiles designs and use transfer learning with CNNs for classifying woven, painted, printed, and embroidered fabric pattern designs of Indian traditional textiles. This is the first attempt in this direction and paves the way for further work in this domain aimed toward the development of content-based image retrieval tools and also for developing automatic new designs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Research on Speech recognition Technology Based on machine learning

Research on Speech Recognition Technology Based on Machine L...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Lijie Wang Chengdu Neusoft University Chengdu China

An automatic speech recognition software based on STM32FI03 is developed. The purpose is to solve the problems in the storage and processing of speech recognition. A language processing method based on quantitative deep learning is proposed based on the analysis of language. Firstly, the difference between the characteristics of speech and the background noise in the time domain is used to set the corresponding threshold to achieve the recognition of the target. A method of speech feature extraction based on Mayer transform is proposed, which cannot solve the complex speech features effectively. Meier cestrum and Mel-Meier cestrum coefficients are calculated for different types of speech signals, and mixed feature parameters based on Fisher criterion are constructed. This paper presents a speech recognition method based on convolutional neural network. Compared with common English language, it is found that this method has better accuracy and achieves better results in a shorter time. Because the system is small in size and easy to carry, it can easily operate language on various devices.

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Application of Big data in the Research of Intelligent Information Management System

Application of Big Data in the Research of Intelligent Infor...

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Advancement in Technology (ICONAT), international conference for

作者： Qianqian Sun Kunlun Xue Huangdao District People’s Hospital Qingdao Shandong China Qingdao Municipal Public Security Bureau Huangdao Branch network police Brigade Shandong China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machine learning cannot solve the information management problem in the research of intelligent information management system, and the effect is not satisfactory. Therefore, this paper proposes the application of intelligent information management system based on big data, and the application of intelligent information management system research an analysis was carried out. Firstly, The key is your guiding theory, es, to reduce the interference factors in data analysis and mining. Then, the dataset theory is used to form a big data analysis and mining scheme, and the data analysis and mining results are comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, big data is better than traditional machine learning in terms of data analysis and mining accuracy, data analysis and mining influencing factor time.

关键词： data analysis Accuracy Law enforcement Simulation machine learning Interference Big data Information management data mining MATLAB

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A Novel ML classifier Using Modern Hopfield Neural Networks

A Novel ML classifier Using Modern Hopfield Neural Networks

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Computing and machine Intelligence (ICMI), international conference on

作者： Mohamed Abdelkarim Hanan Ahmed Kamal Doaa Shawky Siemens Digital Industries Software Faculty of Engineering Cairo University Zewail City of Science and Technology

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machine learning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, pattern recognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.

关键词： Training Associative memory Accuracy MIMICs Hopfield neural networks machine learning pattern recognition

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Stockwell Transform and data mining based Fault Diagnosis Method to protect Microgrid 3

Stockwell Transform and Data Mining based Fault Diagnosis Me...

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3rd international conference for Emerging Technology, INCET 2022

作者： Kumar Joga, S Ramana Sinha, Pampa Maharana, Manoj Kumar Jena, Chitralekha Mishra, Alok Roy, Ashutosh Kiit Deemed to Be University School of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9781665494991

Increase in industrial and residential loads gives arise to the need of local power generation. The concept of local power generation to local loads in a small area made a new local power grid known as Microgrid. The monitoring and control of Microgrid is the important concern of electrical engineers and power engineering researchers. Current fluctuations are common in Microgrid due to various factors. In literature many methods are there to diagnose the fault occur in Microgrid. machine learning techniques are one of the future method to detect and island the fault in Microgrid. Signal Processing techniques helps to analyze the signal condition for further diagnosis. combination of these two methods for fault diagnosis in Microgrid yields better result compared to other previous methods that present in the literature. In this paper, a Microgrid consisting of PV Distributed Energy Resources (DER) and Wind System with local loads is simulated and all the simulations are carried out in MATLAB Simulink. © 2022 IEEE.

关键词： Failure analysis

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An Autonomous Emotion recognition Strategy Employing Deep learning for Self-learning 3

An Autonomous Emotion Recognition Strategy Employing Deep Le...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression recognition Challenge (FERC-2013) dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process's conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learni

关键词： Affective computing Deep learning Emotion recognition Facial expression analysis machine learning Neural networks Self-learning

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Length of Stay Prediction for Diabetes Mellitus Patients on Indonesia Health Insurance data using Logistic Regression and Process mining 3

Length of Stay Prediction for Diabetes Mellitus Patients on ...

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3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Cahyanti, Fitri Eka Kurniati, Angelina Prima Alharbi, Amirah Adiwijaya Telkom University School of Computing Bandung Indonesia Umm Al-Qura University Computer Science Department Makkah Saudi Arabia

ISBN: (纸本)9798350394559

Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the international Diabetes Federations alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. This study focuses on predicting the Length of Stay (LOS) for Diabetes Mellitus patients, utilizing Logistic Regression and various machine learning models, particularly Random Forest. Concurrently, Process mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the healthcare facilitys location, patient age, and arrival time, are unveiled. Logistic Regression exhibits commendable accuracy at 0.76 with rapid prediction times of 0.00082 seconds, while Random Forest excels with an impressive accuracy of 0.88 and a prediction time of 0.07 seconds. Specific medical procedures, such as those involving 'Diabetes and mild Nutritional/Metabolic Disorders,' reveal prolonged sojourn times, averaging 41.2 days. Best conformance-checking results emerge with a dependency threshold of 0.6 and a loop-two threshold of 32, yielding a Precision of 0.97, Trace Fitness of 0.66, and Generalization of 0.60. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Despite Random Forest superior performance, Logistic Regression demonstrates notable accuracy and efficiency. Analysis of sojourn times underscores the importance of efficient healthcare services, particularly in cases involving 'Diabetes and Mild Nutritional/Metabolic Disorders.' Adjusting threshold values significantly impacts model accuracy, with lower threshold values potentially leading to overfitting. © 2023 IEEE.

关键词： Health insurance

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