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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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AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ 12

AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graph...

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12th International Conference on learning Representations, ICLR 2024

作者： Belouadi, Jonas Lauscher, Anne Eger, Steffen Natural Language Learning Group Bielefeld University Germany Data Science Group University of Hamburg Germany Natural Language Learning Group University of Mannheim Germany

Generating bitmap graphics from text has gained considerable attention, yet for scientific figures, vector graphics are often preferred. Given that vector graphics are typically encoded using low-level graphics primitives, generating them directly is difficult. To address this, we propose the use of TikZ, a well-known abstract graphics language that can be compiled to vector graphics, as an intermediate representation of scientific figures. TikZ offers human-oriented, high-level commands, thereby facilitating conditional language modeling with any large language model. To this end, we introduce DaTikZ, the first large-scale TikZ dataset consisting of 120k TikZ drawings aligned with captions. We fine-tune LLaMA on DaTikZ, as well as our new model CLiMA, which augments LLaMA with multimodal CLIP embeddings. In both human and automatic evaluation, CLiMA and LLaMA outperform commercial GPT-4 and Claude 2 in terms of similarity to human-created figures, with CLiMA additionally improving text-image alignment. Our detailed analysis shows that all models generalize well and are not susceptible to memorization. GPT-4 and Claude 2, however, tend to generate more simplistic figures compared to both humans and our models. We make our framework, AutomaTikZ, along with model weights and datasets, publicly available. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Modeling languages

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Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and L...

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1st International Conference on Artificial Intelligence Trends and Pattern Recognition, ICAITPR 2022

作者： Marusani, Jagadeesh Sudha, B.G. Darapaneni, Narayana Pes University Data Science and Machine Learning Bengaluru India Data Science and Machine Learning Great Learning Bengaluru India Artificial Intelligence and Machine Learning Great Learning Bengaluru India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection. © 2022 IEEE.

关键词： Object detection

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

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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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Revolutionizing Stock Market Intelligence: A Deep Dive into Machine learning for IT Sector Price Predictions 4

Revolutionizing Stock Market Intelligence: A Deep Dive into ...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mishra, Parth Parashar, Gaurav Chaudhary, Vaibhav Sehgal, Ishant Data Science & Deep Learning Lab Kiet Group of Institutions Ghaziabad India Kiet Group of Institutions Ghaziabad India

ISBN: (纸本)9798350307757

This study challenges the efficient market hypothesis by presenting a diverse approach to stock price prediction, incorporating statistical, machine learning, and deep learning models with daily stock data. Demonstrating effectiveness in capturing volatile patterns, the framework integrates artificial intelligence with traditional time-series analysis methods, including autoregressive integrated moving average (ARIMA), long short- term memory (LSTM), and Facebook Prophet. A noteworthy advancement is the exceptional forecasting accuracy of the predictive toolkit, enabling users to create and share personalized stock portfolios based on advanced, unique forecasts. In the realm of business intelligence, the framework employs a novel machine- learning approach for predicting stock price movements in the information technology sector. Emphasizing the significance of accurate stock price prediction, the paper advocates for continuous exploration of diverse machine learning techniques. In conclusion, the review highlights the synergy between traditional time-series analysis and modern machine-learning techniques with substantial potential for navigating the complexities of financial markets and empowering users in making well-informed investment decisions. © 2024 IEEE.

关键词： Time series analysis

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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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DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material 23

DOC-DICAM: Domain Aware One Class Defect Identification in C...

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23rd IEEE International Conference on Machine learning and Applications, ICMLA 2024

作者： Yunker, Austin Kettimuthu, Rajkumar Kral, Zachary Argonne National Laboratory Data Science and Learning Division Lemont United States Spirit AeroSystems Wichita United States

ISBN: (纸本)9798350374889

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. We show that our method can effectively identify defects and requires minimal training and inference time. © 2024 IEEE.

关键词： Ultrasonic testing

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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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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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