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An auto encoder as a feature-extraction tool for Raman spectroscopic compositional analysis of hydrocarbon mixtures and investigation of correlations of auto encoder-extracted - extracted variables with component concentrations

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FUEL 2025年 380卷

作者： Yang, Seung Jee Sohng, Woosuk Chung, Hoeil Hanyang Univ Dept Appl Stat Seoul 04763 South Korea Hanyang Univ Res Inst Convergence Basic Sci Seoul 04763 South Korea Hanyang Univ Dept Chem Seoul 04763 South Korea

This paper examines the ability of an auto encoder (AE) to serve as a feature-extraction tool in Raman spectroscopic compositional analysis of mixtures of 11 hydrocarbons (n n-hexane, n-heptane, n-octane, n-nonane, isooctane, cyclohexane, methyl cyclohexane, benzene, toluene, xylene, and indan) and explores its potential application to vibrational spectroscopic analysis of petroleum and petrochemicals. We found the use of AE- extracted variables enhanced analytical accuracy when determining the concentrations of the four linear hydrocarbons and xylene compared with those using the original spectra. Although the peaks of the linear hydrocarbons were relatively indistinct and similar to each other, the AE effectively extracted the relevant features from the highly overlapped spectra, improving the accuracy. To investigate how the AE variables describe variation in the concentrations of the components, correlations between the sets of AE variables and component concentrations were examined using canonical-correlation analysis. The resulting absolute R values were high, ranging from 0.936 to 0.979. This suggests that the AE variables are mutually complementary and can track spectral variation induced by changes in the concentration of the components.

关键词： Near-infrared spectroscopy Compositional analysis Hydrocarbon mixture auto encoder Feature extraction

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Spectral clustering based on extended deep ensemble auto encoder with eagle strategy

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期83卷 47085-47109页

作者： Gheytasi, Farshad Yaghoubyan, S. Hadi Rezaei, Zahra Bagherifard, Karamollah Parvin, Hamid Islamic Azad Univ Dept Comp Engn Yasooj Branch Yasuj Iran Islamic Azad Univ Young Researchers & Elite Club Yasooj Branch Yasuj Iran Islamic Azad Univ Dept Comp Engn Marvdasht Branch Marvdasht Iran Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Nourabad Mamasani Branch Young Researchers & Elite Club Nourabad Mamasani Iran

As an exploratory data analysis (EDA) process, spectral clustering (SC) reduces complex, multidimensional data sets to similar ones in rarer dimensions. Given the big challenges of high computational complexity and lack of accurate mapping in multidimensional data sets, it is essential to provide innovative solutions for SC. Against this background, the present study aims to propose a novel method based on extended Deep learning (DL), recruiting autoencoder (AE) ensembles. Here, the eagle strategy (ES) is further applied, rather than the anchor-based and landmark ones, to eliminate some complexities in SC and fill gaps in data training. As SC has a stochastic optimization structure, the ES may seem appropriate. The Fundamental Clustering and Projection Suite (FCPS) data set correspondingly represents that the proposed method has been able to surpass previous robust algorithms in terms of mean squared error (MSE), accuracy, adjusted random index (ARI), and normalized mutual information (NMI).

关键词： Spectral Clustering Deep Learning auto encoder Eagle Strategy

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Segmentation of lung on CXR images based on CXR-auto encoder segmentation with MRF

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OPTICAL AND QUANTUM ELECTRONICS 2024年第4期56卷 673-673页

作者： Kiruthika, K. Khilar, Rashmita Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept CSE Chennai 602105 Tamil Nadu India Saveetha Inst Med & Tech Sci SIMATS Saveetha Sch Engn Dept IT Chennai Tamil Nadu India

Chest X-ray (CXR) images serve as a fundamental diagnostic tool in the field of medical imaging. Accurate and robust lung segmentation in CXR images is a crucial step toward prediction of age, automating disease diagnosis and monitoring. Deep learning algorithms have been fig used in the segmentation process. The low-resolution output closely resembles the original high-resolution image. However, after being resampled, the image borders become blurred and, in some cases, CXR images may have low contrast between lung tissue and surrounding structures, making it challenging for algorithms to distinguish between them accurately. To overcome the difficulties noted above, a novel approach is introduced for lung segmentation in CXR images, which combines the power of CXR-autoencoder Segmentation (CXR-AES) with Markov Random Fields (MRF) to achieve enhanced precision and performance. The CXR-AES component is responsible for feature extraction and initial segmentation, while the MRF serves as a contextual model that refines the segmentation results by considering spatial dependencies among pixels. This synergistic fusion of techniques enables the model to capture intricate lung boundaries and handle challenging cases, including pathologies and image artifacts. Finally, the above method gives a good result with dice, sensitivity, specificity, and precision performance metrics of 95.6, 89.9, 99.5, and 91.0% on segmented masks and lung, respectively. CXR-AES with MRF in lung segmentation has broad implications for clinical practice, research, and healthcare innovation. It enhances the efficiency of lung region extraction in CXR images, ultimately improving the diagnosis, treatment, and management of lung-related conditions.

关键词： auto encoder Chest X-ray Convolution Lung segmentation Marko random field

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Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from electrocardiogram signals

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SOFT COMPUTING 2023年 1-16页

作者： Vidhya, R. Bharathi Jerritta, S. Vels Inst Sci Technol & Adv Studies Dept ECE Chennai India

Several seniors and a substantial part of the general population are living in social isolation. This frequently occurs in vulnerability, isolation, and depression, which then have a poor impact on other health-related factors. A number of health problems, including a higher risk of cardio problems, are brought on by social isolation and loneliness. Electrocardiogram (ECG) usage for mental condition recognition enables accurate determination of a person's internal representation. The electrocardiogram (ECG) signals can be thoroughly analyzed to uncover hidden data that may be helpful for the precise identification of cardiac problems. ECG time-series information typically have great dimensions and complicated componentry. Using relevant information to guide training is among the main achievements of this type of learning. An ECG signal plays a significant part in the individual body's ability to manage behavior. Furthermore, loneliness identification is crucial since it has the worse effect on the circumstances that afflict persons. This study suggested an approach for detecting loneliness from an ECG signal to use a variable auto encoder-based optimization algorithm for ESN technique. The suggested approach consists of three phases for identifying a person's loneliness. Firstly, undecimated discrete wavelet transform is used to preprocess the acquired ECG data. Next, further characteristics are extracted from the precompiled signals using a variable auto encoder. For the precise categorization of loneliness in the ECG signal, a metaheuristic optimized ESN is, therefore, presented. The outcomes of the tests demonstrate that the suggested system with suitable ECG representations produces improved accuracy as well as performance.

关键词： auto encoder ESN algorithm Electrocardiogram Undecimated discrete wavelet transform Cardiac problems Social isolation

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MIXED-ATTENTION auto encoder FOR MULTI-CLASS INDUSTRIAL ANOMALY DETECTION 49

MIXED-ATTENTION AUTO ENCODER FOR MULTI-CLASS INDUSTRIAL ANOM...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu, Jiangqi Wang, Feng East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Most existing methods for unsupervised industrial anomaly detection train a separate model for each object category. This kind of approach can easily capture the category-specific feature distributions, but results in high storage cost and low training efficiency. In this paper, we propose a unified mixed-attention auto encoder (MAAE) to implement multi-class anomaly detection with a single model. To alleviate the performance degradation due to the diverse distribution patterns of different categories, we employ spatial attentions and channel attentions to effectively capture the global category information and model the feature distributions of multiple classes. Furthermore, to simulate the realistic noises on features and preserve the surface semantics of objects from different categories which are essential for detecting the subtle anomalies, we propose an adaptive noise generator and a multi-scale fusion module for the pre-trained features. MAAE delivers remarkable performances on the benchmark dataset compared with the state-of-the-art methods.

关键词： Anomaly detection Industrial defect segmentation auto encoder

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An improved gated recurrent unit based on auto encoder for sentiment analysis

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International Journal of Information Technology (Singapore) 2024年第1期16卷 587-599页

作者： Zulqarnain, Muhammad Alsaedi, Ahmed Khalaf Zager Sheikh, Rubab Javid, Irfan Ahmad, Maqsood Ullah, Ubaid Faculty of Computing The Islamia University Bahawalpur Punjab Bahawalpur Pakistan Physic Department College of Science University of Misan Amarah Iraq Department of Computer Science and IT University of Poonch Rawalakot AJK Poonch Pakistan College of Computing Riphah International University Faisalabad Campus Faisalabad Pakistan

Sentiment analysis is a particularly common task for determining user thoughts and has been widely used in Natural Language Processing (NLP) applications. Gated Recurrent Unit (GRU) was already effectively integrated into the NLP process with comparatively excellent results. GRU networks outperform traditional recurrent neural networks in sequential learning tasks and solve gradient vanishing and explosion limitations of RNNs. This paper introduces a new method called Normalize auto-Encoded GRU (NAE-GRU) to address data dimensionality reduction using an auto-encoder and to improve performance through batch normalization. Empirically, we demonstrate that with slight adjustments to hyperparameters and optimization of statistic vectors, the proposed model achieves excellent results in sentiment classification on benchmark datasets. The developed NAE-GRU approach outperformed other various traditional approaches in terms of accuracy and convergence rate. The experimental results showed that the developed NAE-GRU approach accomplished better sentiment analysis accuracy of 91.32%, 82.27%, 87.43%, and 84.49% on IMDB, SSTb, Amazon review, and Yelp review datasets respectively. Furthermore, experimental results have shown that the developed approach is proficient in reducing the loss function and capturing long-term relationships with an effective design that achieved excellent results compared to state-of-the-art methods. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： auto encoder Deep learning Gated recurrent unit Recurrent neural network Sentiment analysis

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auto encoder Based = Abnormal Fluctuations Monitoring Method

Auto Encoder Based = Abnormal Fluctuations Monitoring Method

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International Conference on Big Data, Information and Computer Network (BDICN)

作者： Jin, Liuluofeng Liu, Zishuo Tang, Yifan Beijing Univ Posts & Telecommun Elect Informat Engn Century Coll Beijing 102101 Peoples R China Hong Kong Baptist Univ Div Sci & Technol Hong Kong 999077 Peoples R China Capital Univ Econ & Business Overseas Chinese Coll Beijing 100070 Peoples R China

ISBN: (纸本)9781665484763

A variety of factors affect the changes of stock prices, and the data sets have large time scales and are closely correlated. Accurate anomaly detection in the stock market can help investors evaluate and detect stock prices in different areas of various problems. In order to help stockholders to be more strategic and productive in stock capital, we are prose an algorithm that is both concise and effective. However, we found that a group of people make profits by manipulating the market to influence the stock price. This is just one of many factors that affect stock prices. However, based on previous studies, we find that many anomaly analyses are based on some existing dead rules, making it difficult to improve the accuracy of the process. Moreover, in the stock market, the outlier detection model is not suitable for dynamic high-latitude target detection, we must consider various factors of node variables. Therefore, in this paper, we firstly obtained high-frequency trading data and trained a model. The comparison between the algorithm we proposed, and the other three baselines experimental results show that our proposed algorithm is more accurate than others, reducing the discriminant value of sequence fluctuation from 15% to 3%, and the screening result is closest to NYSE Rule 48, which has higher explanatory power and is more accurate.

关键词： auto encoder Abnormal Fluctuations Monitoring Stock Market Anomaly detection

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Integrating a Neural Network Model based on LSTM and auto encoder into the Travel and Tourism Industry 2

Integrating a Neural Network Model based on LSTM and Auto En...

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2nd International Conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Ambashtha, Kanahaiya Lal Vijayalakshmi, N.S. Aarif, Mohd Jeevalatha, R. Kuchipudi, Ramu Reddy, Thokala Sai Krishna Faculty of Information Technology Gopal Narayan Singh University Bihar Sasaram India Department Economics Nehru Memorial College Karnataka Sullia India GRN Publications Greater Noida India Department of Chemistry Panimalar Engineering College Chennai City Campus Tamilnadu Chennai India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Agriculture SR University Telangana Warangal India

ISBN: (纸本)9798350340235

The travel and tourism sector is one of the world’s most important economic drivers. This study analyzes the growth and transformation of the worldwide tourism sector. What keeps the economy going, what generates employment, what contributes to social stability, and what drives societal development. The industry is crucial to the global economy, supporting the livelihoods of hundreds of millions of people. When it comes to economic activity, tourism often serves as the only game in town on many islands. The ultimate goal of the role is to encourage the development of sustainable economies. From the greatest global travel companies to the smallest tour operators or hostel proprietors, the travel and tourism business employs millions of people worldwide. It can achieve real changes in the political and social systems through our work together. Preprocessing, feature selection, and model training are the three main components of the proposed method. Preprocessing is performed to clean the data. The model's efficacy is measured using LSTM-AE after undergoing a filter-based and recursive feature selection process. © 2023 IEEE.

关键词： auto encoder Long Short Term Memory Recursive Feature Selection

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An auto encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations 22

An Auto Encoder-based Dimensionality Reduction Technique for...

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Laskar, Md Tahmid Rahman Chen, Cheng Johnston, Jonathan Fu, Xue-Yong Bhushan, Shashi T. N. Corston-Oliver, Simon Dialpad Canada Inc Vancouver BC Canada

ISBN: (纸本)9781450387323

An entity linking system links named entities in a text to their corresponding entries in a knowledge base. In recent years, building an entity linking system that leverages the transformer architecture has gained lots of attention. However, deploying a transformerbased neural entity linking system in industrial production environments in a limited resource setting is a challenging task. In this work, we present an entity linking system that leverages a transformer-based BERT encoder (the BLINK model) to connect the product and organization type entities in business phone conversations to their corresponding Wikipedia entries. We propose a dimensionality reduction technique via utilizing an auto encoder that can effectively compress the dimension of the pre-trained BERT embeddings to 256 from the original size of 1024. This allows our entity linking system to significantly optimize the space requirement when deployed in a resource limited cloud machine while reducing the inference time along with retaining high accuracy.

关键词： Entity Linking BLINK Dimensionality Reduction auto encoder BERT Elasticsearch

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Multi-layer tree liquid state machine recurrent auto encoder for thyroid detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第12期80卷 17773-17783页

作者： Saktheeswari, M. Balasubramanian, T. Dept Comp Sci & Applicat Uthangarai Tamil Nadu India Periyar Univ Dept Comp Sci Salem 636011 11 Tamil Nadu India Sri Vidya Mandir Arts & Sci Coll Dept Comp Sci & Applicat Uthangarai Tamil Nadu India

The proposed work presents thyroid detection strategy by addressing the various challenges faced when raw data is applied to complex neural network like structure. The authors present a Multi-Layer Tree Liquid State Machine Recurrent auto encoder for the detection of the thyroid nodules. The tree based architecture prevents the loss of original information from dataset when applied to machine learning models like neural network. Liquid State Machine (LSM) prevents the loss of temporal feature of the data from the dataset. The multi layered architecture of the proposed system helps to classify the thyroid stage accurately. The classification rate of the proposed strategy increased when compared to other techniques where the aspect of dataset is not considered.

关键词： Liquid state machine Spiking neural network auto encoder Multi class imbalance data

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