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International Conference on Innovative Mechanisms for Industry applications (ICIMIA)

作者： G Jyostna Himanshu Rai Goyal Manzoore Elahi M. Soudagar Swapnil Parikh Harshal Patil Kamal Alaskar Department of CSE-DS Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Application BharatiVidyapeeth (Deemed To Be University) Institute of Management Kolhapur Maharashtra India

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid Machine Learning (ML) model that combines Support Vector Machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model’s performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers.

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Momentum Recursive Darts

SSRN

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SSRN 2023年

作者： Ma, Benteng Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

DARTS has emerged as a popular method for neural architecture search (NAS) owing to its efficiency and simplicity. It employs gradient-based bi-level optimization to iteratively optimize the upper-level architecture parameters and lower-level super-network weights. One of the key challenges in DARTS is the accurate estimation of gradients for both upper-level and lower-level object functions, which existing methods struggle with, leading to significant errors in gradient approximation. To address this issue, we propose a new approach called MR-DARTS that incorporates a momentum term and a recursive scheme to improve gradient estimation. Specifically, MR-DARTS leverages historical information by using a running average of past observed gradients to enhance the quality of current gradient estimation in both upper-level and lower-level functions. Our theoretical analysis shows that the variance of our estimated gradient decreases with each iteration. By utilizing momentum and a recursive scheme, MR-DARTS effectively controls the error in stochastic gradient updates that results from inaccurate gradient estimation. Furthermore, we utilize the Neumann series approximation and Hessian Vector Product scheme to reduce computational requirements and memory usage. We evaluate our proposed method on several search spaces, including NAS Bench-201 and four variants of the DARTS search space, and demonstrate its effectiveness through comprehensive experiments. © 2023, The Authors. All rights reserved.

关键词： Image recognition

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Predicted High-Temperature Superconductivity in Rare Earth Hydride ErH2 at Moderate Pressure

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Chinese Physics Letters 2022年第12期39卷 84-88页

作者： Yiding Liu Qiang Fan Jianhui Yang Lili Wang Weibin Zhang Gang Yao College of Mathematics and Physics Leshan Normal UniversityLeshan 614004China Institute of Atomic and Molecular Physics Sichuan UniversityChengdu 610065China School of New Energy Materials and Chemistry Leshan Normal UniversityLeshan 614004China Institute of Computer Application China Academy of Engineering PhysicsMianyang 621900China College of Physics and Electronics Information Yunnan Key Laboratory of Optoelectronic Information TechnologyYunnan Normal UniversityKunming 650500China School of Physical Science and Technology Southwest UniversityChongqing 400715China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China

Hydrides offer an opportunity to study high critical temperature(high-Tc)superconductivity at experimentally achievable ***,the pressure needed remains extremely *** density functional theory calculations,herein we demonstrate that a new rare earth hydride ErH2could be superconducting with Tc~80 K at 14.5 GPa,the lowest reported value for compressed hydrides to ***,due to Kondo destruction,superconductivity was prone to exist at 15 *** also reveal an energy gap at 20 GPa on the background of normal metallic *** 20 GPa,this compressed system could act as a host of superconductor judged from a sharp jump of spontaneous magnetic susceptibility with an evanescent spin density of state at Fermi ***,electron pairing glue for ErH2at these three typical pressures was attributed to the antiferromagnetic spin fluctuation.

关键词： Rare Kondo superconductor

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Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution

arXiv

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arXiv 2024年

作者： Zou, Yang Chen, Zhixin Zhang, Zhipeng Li, Xingyuan Ma, Long Liu, Jinyuan Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710129 China Graduate School of Information Production and Systems Waseda University Fukuoka8080135 Japan School of Software Dalian University of Technology Dalian116024 China School of Mechanical Engineering Dalian University of Technology Dalian116024 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://***/hey-it-s-me/*** Codes 68T45 Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

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TB Chest X-Ray Diagnostic Technique Using Deep Learning

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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Delhi Section Conference (DELCON), IEEE

作者： Manish Kumar Goyal Dhananjay Kumar Yadav Khushmeen Kaur Brar Manisha Mittal Ananta Ojha Laith H. Alzubaidi Ashish Pathani Dept. of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Dept. of Electronics and Communication Engineering Chandigarh University Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India The Islamic University Najaf Iraq Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained.

关键词： Support vector machines Tuberculosis Lungs Transfer learning X-rays Bacteria Pattern recognition X-ray imaging Medical diagnostic imaging Kidney

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Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection

arXiv

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arXiv 2022年

作者： Cao, Congqi Lu, Yue Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710129 China

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framework based on context recovery and knowledge retrieval, where the two streams can complement each other. For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame. Furthermore, we propose a maximum local error mechanism to alleviate the problem of large recovery errors caused by complex foreground objects. For the knowledge retrieval stream, we propose an improved learnable locality-sensitive hashing, which optimizes hash functions via a Siamese network and a mutual difference loss. The knowledge about normality is encoded and stored in hash tables, and the distance between the testing event and the knowledge representation is used to reveal the probability of anomaly. Finally, we fuse the anomaly scores from the two streams to detect anomalies. Extensive experiments demonstrate the effectiveness and complementarity of the two streams, whereby the proposed two-stream framework achieves state-of-the-art performance on four datasets. © 2022, CC BY-NC-SA.

关键词： Recovery

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Indian stock market prediction based on rough set and support vector machine approach 1st

Indian stock market prediction based on rough set and suppor...

引用

1st International Conference on Intelligent and Cloud Computing, ICICC 2019

作者： Nayak, Rudra Kalyan Tripathy, Ramamani Mishra, Debahuti Burugari, Vijay Kumar Selvaraj, Prabha Sethy, Abhisek Jena, Biswaranjan GunturAndhra Pradesh India Department of Master of Computer Application United School of Business Management BhubaneswarOdisha India University BhubaneswarOdisha India Department of Computer Science and Engineering VIT-AP University AmaravatiAndhra Pradesh India Tata Consultancy Services BhubaneswarOdisha India

ISBN: (纸本)9789811562013

In recent trends, the stock market prediction has been treated as a challenging task for every people who have been associated with the financial market. Forecasting is the activity of historic data to set up the direction of future trends. It is a very difficult task to predict stock prices because, in every second, the market price is fluctuating. Most of the investors have an interest in doing research on prediction of price financial products such as gold, mutual fund, crude oil, currency exchange, and minerals using varieties of machine learning and data mining techniques. On the basis of different parameters of data like opening price, closing price, date, high and low, stock market price has been predicted. Basically for Indian markets, the two indices such as Sensex for BSE (Bombay Stock Exchange) and nifty for NSE (National Stock Exchange) are the benchmark indices used for forecasting the market price. Day by day huge volumes of people are paying their keen interest in money trading and they want to become profit makers instantly. Here we proposed a novel rough set—Support Vector Machine (R-SVM) approach to predict the Indian stock market data. The R-SVM method is found to be prominent when compared with rough set based algorithms such as decision tree, Naïve Bayes, and artificial neural network in terms of prediction accuracy and complexity. © Springer Nature Singapore Pte Ltd 2021.

关键词： Financial markets

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Pre-trained Language Model based Medical Named Entity Recognition

Pre-trained Language Model based Medical Named Entity Recogn...

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2021 China Automation Congress, CAC 2021

作者： Cheng, Zheng Wu, Jiaju Ji, Bin Liu, Huijun School of Automation University of Electronic Science and Technology of China Mianyang China Institute of Computer Application China Academy of Engineering Physics Mianyang China College of Civil Aviation Nanjing University of Aeronautics and Astronautic Nanjing China College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781665426473

Medical named entity recognition is an important part of structuring Chinese electronic medical records and construction of medical knowledge graph. The CCKS2019 conference organized a medical named entity recognition evaluation task to extract six types of medical entities from unstructured Chinese electronic medical records. Based on the data set of this evaluation task, pre-trained language model based entity recognition approaches are studied First, select the BiLSTM-CRF model based on random initialized word embedding as the baseline system;secondly, apply wordlvec to the baseline system;thirdly, apply ELMo to the baseline system. Experimental results show that the pre-trained language model is comparable to the best approach of this evaluation task, and the context-related pre-trained language model performs better. © 2021 IEEE

关键词： Knowledge graph

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BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised applications

arXiv

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

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TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation

arXiv

引用

arXiv 2023年

作者： Jiang, Yunliang Yan, Lili Zhang, Xiongtao Liu, Yong Sun, Danfeng The Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources Huzhou University Huzhou313000 China The School of Information Engineering Huzhou University Huzhou313000 China The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science Hangzhou Dianzi University Hangzhou310018 China

One-shot image generation (OSG) with generative adversarial networks that learn from the internal patches of a given image has attracted world wide attention. In recent studies, scholars have primarily focused on extracting features of images from probabilistically distributed inputs with pure convolutional neural networks (CNNs). However, it is quite difficult for CNNs with limited receptive domain to extract and maintain the global structural information. Therefore, in this paper, we propose a novel structure-preserved method TcGAN with individual vision transformer to overcome the shortcomings of the existing one-shot image generation methods. Specifically, TcGAN preserves global structure of an image during training to be compatible with local details while maintaining the integrity of semantic-aware information by exploiting the powerful long-range dependencies modeling capability of the transformer. We also propose a new scaling formula having scale-invariance during the calculation period, which effectively improves the generated image quality of the OSG model on image super-resolution tasks. We present the design of the TcGAN converter framework, comprehensive experimental as well as ablation studies demonstrating the ability of TcGAN to achieve arbitrary image generation with the fastest running time. Lastly, TcGAN achieves the most excellent performance in terms of applying it to other image processing tasks, e.g., super-resolution as well as image harmonization, the results further prove its superiority. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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