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Neuro-Cognitive pattern recognition: Advancements in Memory-Related Disorders Identification

Neuro-Cognitive Pattern Recognition: Advancements in Memory-...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Vifert Jenuben Daniel. V Archanaa. N Mohammed Faheem Kousihik. K Suwin Kumar. J.D.T M. Muthulakshmi Department of Artificial Intelligence Amrita school of engineering Amrita vishwa vidhyapeetham Chennai Department of ECE Amrita school of engineering Amrita vishwa vidhyapeetham Chennai

The application of machine learning in medical imaging and diagnostics opens up new pathways for disease identification and classification. Our research investigates the use of various machine learning models for Alzheimer's disease detection and classification using MRI image data, including Support Vector Machines (SVM) with different kernels, Linear Discriminant Analysis (LDA), and the advanced convolutional neural network, EfficientNetB0. The comparative study is an important component of this research, concentrating not only on the models' performance but also on the theoretical assumptions that underpin their efficacy. This investigation sheds light on why certain models outperform others, notably in the context of Alzheimer's disease classification. The research emphasizes the robust performance of linear models such as SVM and LDA, as well as the complexities of convolutional neural networks. Notably, our findings show a tremendous success with a 98.8 % accuracy rate, highlighting the potential of machine learning in improving diagnostic processes. This study contributes to the expanding field of machine learning in medical diagnostics by providing in-depth analysis and theoretical understanding that guide model selection and optimization in high-stakes healthcare applications.

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Intracardiac Electrogram Signals Key Feature Points recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction pattern recognition Testing

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Design of Binary Neural Network soft System for pattern Detection using HDL Tool

Design of Binary Neural Network Soft System for Pattern Dete...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Akshay Chinchanikar Pankaj H. Chandankhede Abhijit Titarmare Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India

Convolution neural network is real time application used to perform complex image processing task. It reduces the total cost of hardware and increases its efficiency. System follows parallel scanning technique with gate level operations. It is inspired by image processing by brain's technique. Eye gathers information send it to brain & brain compute the process with sample images. Then it comes to conclusion. For hardware application GPUs and FPGAs are consider to perform or to process the imagery information because FPGA has the capability of parallel process or concurrent processing hardware facility. Objective of this paper is to design, synthesis & understand effect of different FPGA performances in BNN processer.

关键词： Measurement Convolution Image processing Synthesizers Logic gates Hardware Real-time systems

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An Unsupervised Person Search Method for Video Surveillance 22

An Unsupervised Person Search Method for Video Surveillance

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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Automated Human Emotion recognition and Analysis using Machine Learning 12

Automated Human Emotion Recognition and Analysis using Machi...

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12th international conference on computing Communication and Networking Technologies, ICCCNT 2021

作者： Raj, Kiran S. Kumar, Priyanka Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728185958

Detecting and recognizing human emotion is a big challenge in computer vision and artificial intelligence. Emotions are a big part of human communication. Most of the communication takes place through emotion. The main aim of our project is to develop a robust system which can detect as well as recognize human emotion from the live feed and also detect emotion from speech using NLP. There are some emotions which are universal to all human beings like anger, sadness, happiness, surprise, fear, disgust and neutrality. We have split the work into two modules. The first module is purely based on Video where the model classified different emotions of faces in a live feed. The second module analyses a text and detects the emotion in the text. © 2021 IEEE.

关键词： soft computing

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Design of Intelligent Translation English Corpus Based on Deep Learning

Design of Intelligent Translation English Corpus Based on De...

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computing, Robotics and System Sciences (ICRSS), international conference on

作者： Rong Zeng Zhuorui Zheng School of Foreign Languages Wuhan Business University Wuhan China Hubei Digital Industry Group Co. Ltd. Wuhan China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

In order to further improve the effect of English intelligent translation, this paper combines the idea of deep learning to build an intelligent corpus system. Aiming at the problem of a lot of noise in speech data, the speech data is preprocessed by Mel frequency cepstrum coefficient method, and then transformers are combined with mutual information to recognize speech emotions. Then, by integrating diverse data sources, a wide range of English text data is provided to support complex translation and processing tasks. During the data acquisition phase, English text is collected from online platforms and social media through automated tools. In the data processing stage, advanced text processing technology is used for text analysis and preprocessing to ensure the quality and consistency of data. Finally, this paper verifies the effectiveness of the model proposed in this paper with experimental analysis. In the future, intelligent English translation subjects should take a positive attitude towards science and technology, and regard intelligent translation technology as an effective tool for English translation.

关键词： Deep learning Translation Text analysis Social networking (online) soft sensors Speech recognition Big Data Transformers Artificial intelligence Text processing

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Online pattern recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble 11

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAACS)

作者： Pareek, Vishakha Chaudhury, Santanu Singh, Sanjay CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Indian Inst Technol Delhi Delhi 110016 India

ISBN: (纸本)9781665426053

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. The presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. The responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. The Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. The network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. The proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： 2D-CNN pattern recognition Ensemble Gas Sensor

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Memristor Emulator Circuit Design using MOCCII

Memristor Emulator Circuit Design using MOCCII

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Modeling, Simulation & Intelligent computing (MoSICom), international conference on

作者： Basit Shafat Makhdoomi Rajeev Kumar Ranjan Department of Electronics Engineering Indian Institute of Technology (ISM) Dhanbad India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

The development of memristor emulators with particular operating parameters poses a notable challenge, prompting the development of memristor emulator circuits. The design of the memristor emulator introduced in this paper utilizes the Analog Current Mode building block MOCCII (Multi Output Second Generation Current Conveyor), along with a multiplier and a few passive electronic components (two resistors and a capacitor). In comparison with existing memristor emulators based on multipliers, our design distinguishes itself by employing fewer transistors used within the block and operating over a broader frequency range. The proposed memristor emulator demonstrates reliable performance up to a frequency of approximately 40 MHz, exhibiting characteristics akin to those of an ideal memristor. Circuit simulations are conducted using a $0.18 \mu \mathrm{~m}$ SCL pdk with a dual power supply of $\pm 1.25 \mathrm{~V}$. The functionality of the proposed circuit is validated by varying the capacitance at different frequencies and adjusting the frequency across various capacitance values. Transient analysis confirms the non-volatile nature of the device.

关键词： Power demand Circuit simulation Scalability Capacitors Memristors Capacitance pattern recognition Transistors Integrated circuit reliability Transient analysis

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Analysing Emotional and Topical patterns in Conspiracy Theory Narratives: a Discourse Comparative Study on the 2023 Hawaii Wildfires

Analysing Emotional and Topical Patterns in Conspiracy Theor...

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pattern recognition Systems (ICPRS), international conference on

作者： Nikolai Dubinko Petra Saskia Bayerl Marjory Da Costa Abreu Helen Gibson CENTRIC Sheffield Hallam University Sheffield United Kingdom Department of Computing Sheffield Hallam University Sheffield United Kingdom

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

Today, platforms like YouTube provide a fertile environment for conspiracy theory communities, which can build their own fan bases and attract the attention of a wider audience. Moreover, they easily disseminate unverified narratives and misinformation, which has the potential to influence public opinion in a variety of online *** aim of this study is to investigate emotional and topical patterns inherent to conspiracy theory narratives based on comments posted in three YouTube videos on the 2023 Hawaii wildfires. The first video from one of the popular conspiracy theory channels provides an explanation from the perspective of an unproven plot. The second, a mainstream media video, offers conventional coverage of the event, while the third one features an opinion of the event from the point of view of an independent video blogger. Videos were strategically chosen to allow comparisons between mainstream and conspiracy-related *** research applies methods based on LLMs (Large Language Models) to analyse sentimental and emotional responses in the comments as well as the representation of discussed topics. Thus, the analysis seeks to understand if conspiracy theory narratives influence the public perception of the Hawaiian Fires and assess the belief in these narratives along with their emotional palette. Early findings show that emotional and sentimental tones differ between the three comment sections. Comments under the conspiracy theory video demonstrate more disapproval, negative feelings, and less empathy than the general expression of mainstream and blogger comment sections. Furthermore, ideas raised in the comments of the conspiracy theory videos have substantial presence in the blogger’s video discussion and are less substantial in the mainstream video. The results demonstrate that the inclusion of emotions and topical patterns can be beneficial for the identification of conspiracy-related communication.

关键词： Fans Wildfires Video on demand Large language models Blogs Genetic expression pattern recognition

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Warm-Start Variational Quantum Policy Iteration

Warm-Start Variational Quantum Policy Iteration

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Quantum computing and Engineering (QCE), IEEE international conference on

作者： Nico Meyer Jakob Murauer Alexander Popov Christian Ufrecht Axel Plinge Christopher Mutschler Daniel D. Scherer Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-compatible quantum-enhanced subroutine. Its scalability is supported by an analysis of the structure of generic reinforcement learning environments' laying the foundation for potential quantum advantage with utility-scale quantum computers. Furthermore, we introduce the warm-start initialization variant (WS-VarQPI) that significantly reduces resource overhead. The algorithm solves a large FrozenLake environment with an underlying 256×256-dimensional linear system, indicating its practical robustness.

关键词： Linear systems Quantum advantage Scalability Decision making Reinforcement learning Linear algebra Robustness Hardware Noise measurement Matrix decomposition

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