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Deep features based convolutional neural network to detect and automatic classification of white blood cells

Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9191-9205页

作者： Yentrapragada, Divyateja Student of Postgraduate Program in Data Science and Machine Learning Graham School University of Chicago and Jigsaw Academy ChicagoIL60637 United States

White blood cells (WBCs) play an important role in the human body's immune system and it widely presented in the human body. Recently, the incidence of blood diseases related to WBC increases in the human body. In the research application, optimal WBC is a popular field for the diagnosis of blood disease, the optimal WBC count gives useful information. The Deep Features Based Convolutional Neural Network (DFCNN) is created in this research to detect the count of WBC and automated categorization using CNN from the picture database. With three phases, the proposed method working are feature extraction, feature selection, and classification phases. In the extraction phase, the Combined CNN structure is designed which is a combination of AlexNet, GoogLeNet, and ResNet-50 respectively. The combined CNN architecture is utilized to extract 3000 essential features from the image database. In the feature selection phase, the hybrid Mayfly Algorithm with Particle Swarm Optimization (HMA-PSO) is designed for selecting the essential feature from the feature sets. In the HMA-PSO algorithm, with the help of PSO algorithm, the velocity updating of the mayfly is achieved. The proposed classifier named Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), collects the selected features. This classifier is utilized to classify the WBC types such as Neutrophils, Eosinophils, Monocytes, and Lymphocytes respectively. The proposed method is implemented in the platform of MATLAB and performances are evaluated by statistical measurements such as precision, recall, specificity, accuracy, and F_Measure. The results of the proposed method are compared with the other existing methods such as MA-RNN and PSO-RNN respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Impact of Immersive Technology on Physiological Parameters in Psychiatric Disorders 13

Impact of Immersive Technology on Physiological Parameters i...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kataria, Anjali Singh, Gurjinder Sandhu, Jasminder Chitkara Institute of Engineering and Technology Punjab India Iilm University Department of Machine Learning and Data Science Noida India

ISBN: (纸本)9798350380583

The brain is an essential component that regulates the general functioning of the body. The brain consists of millions of neurons that govern human behavior in response to sensory stimuli. To understand the mental functioning of the brain, it is important to assess its neural signals. Presently, the exponential expansion of prescription medications and substances, including alcohol and drugs, is exerting a significant influence on human psychology. The rising prevalence of different health disorders can be attributed to the increasing number of cases. The sensorimotor cortex, which in humans is in charge of coordinating both physical and cognitive activities, can be used to visualize brain signals. The present research describes an empirical investigation that employs Virtual Reality therapy to examine the affective condition of individuals suffering from drug addiction. The study utilized the Emotiv Insight 5-channel EEG headset to collect EEG data from 50 participants, and the Emotiv Pro software was employed for data analysis. The study findings indicate that Virtual Reality treatment has a substantial positive impact on the stress levels and concentration of persons suffering from drug addiction. © 2024 IEEE.

关键词： Electroencephalography

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Using Generative Models to Improve Fire Detection Efficiency 10

Using Generative Models to Improve Fire Detection Efficiency

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Andriyanov, Nikita Kim, Alexandr Fao, Xenin Data Analysis And Machine Learning Department Financial University Under The Government Of The Russian Federation Moscow Russia Data Science Department Huazhong University Of Science And Technology Huazhong China

ISBN: (纸本)9798350372939

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1-2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models. © 2024 IEEE.

关键词： Generative adversarial networks

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Urban Land Cover Classification with Efficient Hybrid Quantum machine learning Model 13

Urban Land Cover Classification with Efficient Hybrid Quantu...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Fan, Fan Shi, Yilei Zhu, Xiao Xiang Data Science in Earth Observation Munich Germany Wessling Germany Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (纸本)9798350308365

Urban land cover classification aims to derive crucial information from earth observation data and categorize it into specific land uses. To achieve accurate classification, sophisticated machine learning models trained with large earth observation data are employed, but the required computation power has become a bottleneck. Quantum computing might tackle this challenge in the future. However, representing images into quantum states for analysis with quantum computing is challenging due to the high demand for quantum resources. To tackle this challenge, we propose a hybrid quantum neural network that can effectively represent and classify remote sensing imagery with reduced quantum resources. Our model was evaluated on the Local Climate Zone (LCZ)-based land cover classification task using the TensorFlow Quantum platform, and the experimental results indicate its validity for accurate urban land cover classification. © 2024 IEEE.

关键词： Quantum computers

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Functional linear non-Gaussian acyclic model for causal discovery

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Behaviormetrika 2024年第2期51卷 567-588页

作者： Yang, Tian-Le Lee, Kuang-Yao Zhang, Kun Suzuki, Joe Graduate School of Engineering Science Osaka University Osaka Japan Department of Statistics Operations and Data Science Temple University Philadelphia United States Machine Learning Department Carnegie Mellon University Pittsburgh United States Machine Learning Department MBZUAI Abu Dhabi United Arab Emirates

In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a linear non-Gaussian acyclic model (LiNGAM), encompassing both the causal ordering of variables and their respective connection strengths. However, LiNGAM can only deal with the finite-dimensional case. To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the functional linear non-Gaussian acyclic model (Func-LiNGAM). Our motivation stems from the desire to identify causal relationships in brain-effective connectivity tasks involving, for example, fMRI and EEG datasets. We demonstrate why the original LiNGAM fails to handle these inherently infinite-dimensional datasets and explain the availability of functional data analysis from both empirical and theoretical perspectives. We establish theoretical guarantees of the identifiability of the causal relationship among non-Gaussian random vectors and even random functions in infinite-dimensional Hilbert spaces. To address the issue of sparsity in discrete time points within intrinsic infinite-dimensional functional data, we propose optimizing the coordinates of the vectors using functional principal component analysis. Experimental results on synthetic data verify the ability of the proposed framework to identify causal relationships among multivariate functions using the observed samples. For real data, we focus on analyzing the brain connectivity patterns derived from fMRI data. © The Behaviormetric Society 2024.

关键词： Causal discovery Darmois–Skitovich theorem Functional data Gaussian process LiNGAM Non-Gaussian

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Reducing sequential change detection to sequential estimation 41

Reducing sequential change detection to sequential estimatio...

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41st International Conference on machine learning, ICML 2024

作者： Shekhar, Shubhanshu Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We consider the problem of sequential change detection under minimal assumptions on the distribution generating the stream of observations. Formally, our goal is to design a scheme for detecting any changes in a parameter or functional θ of the data stream distribution that has small detection delay, but guarantees control on the frequency of false alarms in the absence of changes. We describe a simple reduction from sequential change detection to sequential estimation using confidence sequences (CSs): begin a new level-(1 − α) CS at each time step, and proclaim a change as soon as the intersection of all active CSs becomes empty. We prove that the average run length of our scheme is at least 1/α, resulting in a change detection scheme with minimal structural assumptions (thus allowing for possibly dependent observations, and nonparametric distribution classes), but strong guarantees. We also describe an interesting parallel with Lorden's reduction from change detection to sequential testing and connections to the recent "e-detector" framework. Copyright 2024 by the author(s)

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Improving Hotel Search Autocomplete in Online Travel Agent (OTA) Mobile Apps Using Elasticsearch and learning to Rank 11

Improving Hotel Search Autocomplete in Online Travel Agent (...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Pratama, Yoga Aliarham Siswanto, Elisafina Budi, Setia Liono, Jonathan Qodari, Arif Arief-Ang, Irvan B. PT. Global Tiket Network Machine Learning Engineering Indonesia PT. Global Tiket Network Data Science Indonesia PT. Global Tiket Network Data Science Singapore

ISBN: (纸本)9798350389654

The use of smart search autocomplete for accommodation discovery within Online Travel Agent (OTA) platforms is essential, as it significantly influences the user experience when utilizing these services. This paper presents a method to improve search autocomplete for accommodation discovery in OTA mobile apps using a learning to Rank (LTR) model. The LTR model is implemented as the core module in Elasticsearch's Query Auto Completion (QAC) service. The proposed method uses a two-step process for generating autocomplete results, involving candidate filtering and final result generation based on the LTR prediction score. This study demonstrates the effectiveness of the proposed method in enhancing the average click-through position by 7.74% for queries with two to four characters, resulting in a 6.46 % increase in the number of items clicked in the first position and a reduction of the average time required for users to click on autocomplete results by 33 %. The findings that we conclude in this extensive study could be used to inform and provoke the work of researchers and engineers in the field of search autocomplete by providing insights on how to improve user experiences through the implementation of LTR techniques in Elasticsearch. © 2024 IEEE.

关键词： learning to rank

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Intelligent Assistant for Multivariant Analysis 26

Intelligent Assistant for Multivariant Analysis

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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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Automated Quality Assessment Using Appearance-Based Simulations and Hippocampus Segmentation on Low-Field Paediatric Brain MR Images 1st

Automated Quality Assessment Using Appearance-Based Simulati...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Sundaresan, Vaanathi Dinsdale, Nicola K Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031830105

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%, thus ranking in the 1st place in the LISA2024 Challenge. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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