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Human Brain Wave Concentration pattern Prediction Design Concept 4

Human Brain Wave Concentration Pattern Prediction Design Con...

引用

4th international conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Rahmad, Iwan Fitrianto Zarlis, Muhammad Universitas Sumatera Utara Faculty of Computer Science Medan Indonesia

ISBN: (纸本)9781665453950

By not concentrating on someone while driving or working this can lead to serious accidents, with the increasing number of road accidents becoming one of the most important problems that cannot be ignored. Traffic accidents are one of the leading causes of death in Indonesia, with human error playing a major role. According to Police data on the electronic media site ***, it is stated that in Indonesia an average of 3 people die every hour due to traffic accidents. therefore, using the concept of predicting the concentration pattern of human brain waves to reduce the accident rate while driving by using a machine learning approach. To detect a person's concentration, it can be done by measuring existing brain waves using sensors, there are several waves found in the human brain including alpha, beta, delta, theta, gamma waves. features using the frequency method and time method as well as a combination of frequency and time after using the PCA method to select features, so that the input from the classification method will be better. © 2022 IEEE.

关键词： Brain

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Deep learning for Diagnosis of Endometrial Cancer and Atypical Endometrial Hyperplasia

Deep Learning for Diagnosis of Endometrial Cancer and Atypic...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Aihua Zhao Xin Zhu Xin Du Wenwen Wang Wenqing Ma Shixuan Wang Graduate School of Computer Science and Engineering The University of Aizu Fukushima Japan Department of Gynecology Maternal and Child Hospital of Hubei Province Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Obstetrics and Gynecology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Endometrial cancer(EC) is the most common and rapidly increasing female cancer globally. Atypical endometrial hyperplasia (AEH) is a precancerous condition of EC. Although hysteroscopy serves as the primary modality for diagnosing lesions, it relies on the subjective judgment of hysteroscopists. therefore, this study proposed a computer-aided diagnostic system utilizing the EfficientNet network as a baseline, incorporating ParNet attention mechanism and class weighting to accurately classify EC/AEH from benign lesions. this study included 49,556 hysteroscopy images from 1,237 cases as a training set and 3,412 hysteroscopy images from 85 cases as a testing set. AUC, accuracy, sensitivity, specificity, PPV, Kappa, and F 1 -Score of the proposed method are 0.941, 89.4%, 93.7%, 87.1%, 73.3%, 0.755, and 0.8225, respectively. the proposed model may be used as a computer-aided tool for the diagnosis of EC/AEH.

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Food Detection and recognition Using Deep learning - A Review 4

Food Detection and Recognition Using Deep Learning - A Revie...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Banerjee, Anushrie Bansal, Palak thomas, K.T. Christ University Department. of Data Science Lavasa Pune India

ISBN: (纸本)9781665474368

Studies show poor lifestyle choices and unhealthy eating patterns cause issues like obesity and other ongoing illnesses that raise the risk of heart attacks, such as hypertension, abnormal blood sugar levels, and diabetes. To improve this situation a lot of health apps have been built which use modern dietary monitoring systems that automatically evaluate dietary intake using machine learning and deep learning techniques rather. For these reasons indepth investigations on food detection, classification, and analysis have been conducted. Some of the top methods for automatic food recognition created have been discussed in this paper. We also propose an idea for detection of Indian food items using image classification. According to our findings of the papers we reviewed, convolutional neural networks (CNN) have been extensively been used in food detection as it has been giving better results compared to other models. We also observed that Vision transformers perform better in situations where the dataset is large and a hybrid model would give better accuracy. A review of potential applications for food image analysis, shortfalls in the area, and open issues concludes the paper. © 2022 IEEE.

关键词： Image classification

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Metagenomic Face: Applying machine learning to Facial Microbiome data for pattern recognition and Disease Classification

Metagenomic Face: Applying Machine Learning to Facial Microb...

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Intelligent Technologies (CONIT), international conference on

作者： M S Guru Prasad Raghavendra M Devadas Vani Hiremani Afsar Jahan J Praveen Gujjar Aryan Dahiya Department of Computer Science and Engg Graphic Era (Deemed to be University) Dehradun India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework’s efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis.

关键词： Support vector machines Measurement Logistic regression Metagenomics Microorganisms Accuracy machine learning algorithms

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the New Answer to Drug Discovery: Quantum machine learning in Preclinical Drug Development

The New Answer to Drug Discovery: Quantum Machine Learning i...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yew Kee Wong Yifan Zhou Yan Shing Liang Haichuan Qiu Yu Xi Wu Bin He Basis International School Guangzhou Guangzhou China

the Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based machine learning network (QML) and Quantum Computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum Computer, Hit to Lead, Lead Optimization Simulation, machine learning.

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Minimal Electrode EEG for BCI Emotion Detection

Minimal Electrode EEG for BCI Emotion Detection

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Yuxin Li Hao Fang Wen Liu Chuantong Cheng Hongda Chen School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Institute for Imaging Data and Communications School of Engineering The University of Edinburgh Edinburgh UK State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China Brain Machine Fusion Intelligence Institute Jiangsu Industrial Technology Research Institute Suzhou China

Electroencephalography (EEG)-based emotion recognition is a potential research direction in the field of brain-computer interfaces (BCIs). However, its deployment on wearable devices still suffers from the challenges of low accuracy, heavy computation, and complex electrode placement. this study focuses on advancing the efficiency and cost-effectiveness of EEG-based BCIs for emotion recognition. Our approach begins with an investigation of electrode placement in relation to emotion detection, leveraging the SEED dataset to identify an optimal configuration that uses a minimal number of electrodes while maintaining high recognition accuracy. Employing a variety of machine learning and deep learning algorithms, we compare detection accuracy across different electrode combinations. through these experiments and subsequent analysis, we identify an effective combination of two electrodes, T7 and T8, with the SVM method achieving an impressive 92.8 % accuracy. this finding laid the foundation for the design of our wearable, closed-loop BCI device with EEG-based emotion recognition capability.

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Research on Radar PRI Modulation pattern recognition Based on Recurrent Neural Network

Research on Radar PRI Modulation Pattern Recognition Based o...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Wei Qu Gang Yao Lei Meng Department of Electronics and Optics Space Engineering University Beijing China Beijing Institute of Tracking and Telecommunications Technology Beijing China Beijing Remote Sensing Information Research Instituteline Beijing China

In this paper, the Recurrent Neural Network (RNN) model is introduced into the identification of radar emitters, which verifies the feasibility of RNN application to radar emitter identification, and the design process is simplified and the radar emitter identification method is easy to apply in engineering. Based on the radar emission source recognition model, the Pulse Repetition Interval(PRI) modulation pattern recognition research based on recurrent neural network was carried out. Firstly, aiming at the problems of gradient vanishing and gradient explosion in RNN, a PRI modulation pattern recognition method based on LSTM is proposed, and the recognition method process is introduced in detail. According to the characteristics of pulse train data, a data preprocessing method suitable for recurrent neural network is proposed. Finally, simulation verifies the effectiveness of the proposed method and tests the performance of the algorithm under different interference conditions.

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MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model 27th

MCANet: Multimodal Caption Aware Training-Free Video Anomaly...

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27th international conference on pattern recognition, ICPR 2024

作者： Dev, Prabhu Prasad Hazari, Raju Das, Pranesh Machine Learning Laboratory Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9783031781247

Towards Video Anomaly Detection (VAD), existing methods require labor-intensive data collection and model retraining, making them costly and domain-specific. the proposed method, termed as Multi-modal Caption Aware Network (MCANet), introduces a novel paradigm that identifies anomalies in video sequences without requiring prior domain knowledge. this training-free VAD approach dynamically generates and analyzes textual descriptions of video frames by utilizing off-the-shelf vision-language model (VLM), audio-language model (ALM) and large language model (LLM). MCANet has four primary modules. the first module utilizes image-text similarities to clean noisy captions generated by the image captioning model, while the second module applies audio-text similarities to refine noisy captions produced by the audio captioning model. the third module employs a LLM to consolidate scene dynamics over time. Finally, the fourth module enhances the results by aggregating scores from semantically similar frames based on video-text similarity. To validate the effectiveness of the proposed method, experiments are conducted on two large-scale benchmark datasets (UCF-Crime and XD-Violence). Experimental results demonstrate that MCANet surpasses existing unsupervised and one-class approaches without requiring any training or data collection. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Research on Image recognition based on Reinforcement learning

Research on Image Recognition based on Reinforcement Learnin...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Jiabin Luo Rongzhen Luo School of Mathematics and Statistics Northeastern University at Qinhuangdao Hebei China Department of Mechanical Engineering University Baoding Campus of North China Electric Power University Hebei China

Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. With the development of images processing methods, the image-level objectives recognition accuracy has been greatly enhanced by utilizing the machine learning models or neural networks. However, existing methods are mainly concentrated on the primary features of input images and concentrate on some certain areas, which ignore the environment features and the deep investigation of the image data-set. In this paper, we propose a novel image recognition method to identify the objectives and obtain the policy gradients for decreasing orders. Furthermore, we compare our proposed models with existing traditional machine learning methods to evaluate the performance of recognition accuracy. From our extensive experimental results, we can conclude that our proposed methods can achieve the subjective detection from numerous images data-set with reasonable computation costs.

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Evaluating Performance of Adam Optimization by Proposing Energy Index 4th

Evaluating Performance of Adam Optimization by Proposing E...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Bhandari, Mohan Parajuli, Pramod Chapagain, Pralhad Gaur, Loveleen Nepal College of Information Technology Lalitpur Nepal CG Institutue of Management Kathmandu Nepal Amity International Business School Amity University Noida India

ISBN: (纸本)9783031070044

the adjustment of learning rate (η ), bias and additional parameters throughout back propagation are crucial for the performance of machine learning algorithms. Regarding optimization for algorithms, adam optimization technique tune the learning parameters by utilising the exponential decay of past gradients and their squares. However, the optimizer requires the engagement of frequently occurring features from the datasets that play a significant role for performance improvement in machine learning algorithms. In this paper, the energy model of a neuron is designed to calculate the energy index from frequently occurring features and introduced in adam optimizer. the classification performance of the proposed energy modeled adam optimizer is experimented on Logistic Regression (single layered) and Support Vector machine (hyperplane based) machine learning algorithms utlising CIFAR10, MNIST and Fashion MNIST datasets. Optimized with proposed optimizer, Logistic Regression achieved training accuracy of 90.79%, 99.02% and 95.87% whereas Support Vector machine achieved training accuracy of 39.04%, 80.80% and 82.29% for CIFAR10, MNIST and Fashion MNIST datasets respectively. © 2022, Springer Nature Switzerland AG.

关键词： Support vector machines

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