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IEEE International Conference on Big data and Smart Computing (BigComp)

作者： Arshad, Saba Kim, Gon-Woo Chungbuk Natl Univ Dept Control & Robot Engn Cheongju South Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160344

This research proposes an accurate loop closure detection method for the real-time robot navigation in changing light, viewpoint and weather conditions. It focuses on enhancing the accuracy performance of visual place recognition by integrating the semantics of image scene with handcrafted features. This method reduces the computational cost of feature matching process by segmentation of dataset. The results presented depicts that scene recognition can improve the place recognition even in the large viewpoint, weather and light changes

关键词： component visual place recognition scene recognition semantic labels feature descriptors long term autonomy visual navigation hierarchical structure data segmentation

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Image analysis of videokymographic data

Image analysis of videokymographic data

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IEEE International Conference on Image Processing, ICIP 2015

作者： Novozámsky, Adam Sedlář, Jiři Zita, Aleš Šroubek, Filip Flussef, Jan Švec, Jan G. Vydrová, Jitka Zitova, Barbará Institute of Information Theory and Automation Academy of Sciences of the Czech Republic Prague Czech Republic Voice Research Lab Department of Biophysics Faculty of Sciences Palacký University Olomouc Czech Republic Voice Centre Prague Medical Healthcom Ltd. Czech Republic

ISBN: (纸本)9781479983391

Videokymography (VKG) is a high-speed medical imaging technique used in laryngology and phoniatrics for examination of vocal fold vibrations, it offers important characteristics for diagnosis and treatment of voice disorders. VKG repeatedly scans only a single line from the scene and captures movements of vocal folds in this region of interest. This paper proposes methods for computer assisted evaluation of diagnostically important vibration features, related to movements of vocal folds and their surroundings. They are derived from existing as well as newly developed methods of digital image processing, mainly based on data segmentation and morphological operations. Performance of the developed methods is compared to expert manual assessments and it proves to be comparable with clinicians conclusions. © 2015 IEEE.

关键词： data segmentation medical imaging videokymography

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Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning 1

Window-size impact on detection rate of wearable-sensor-base...

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IEEE Life Sciences Conference (LSC)

作者： Putra, I. Putu Edy Suardiyana Vesilo, Rein Coventry Univ Fac Engn Environm & Comp Coventry CV1 5FB W Midlands England Macquarie Univ Sch Engn Sydney NSW 2109 Australia

ISBN: (纸本)9781538610305

For a supervised machine-learning based fall-detection approach, data segmentation is needed to split the data sequence into several segments. Then features are extracted from each segment, where those features are used to train and test the classifier using supervised machine-learning algorithms. A sliding window is normally used to segment a data sequence. Although the sliding window takes an important role in the training and testing processes, most existing studies in fall detection rely on the figures from previous studies, without any studies to support them. This study investigates two types of the sliding window that are usually used by existing studies: Fixed-size Non-overlapping Sliding Window (FNSW) and Fixed-size Overlapping Sliding Window (FOSW). Two publicly-accessible datasets are used in this study. Two machine-learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN), are used to train and test the classifier. The experiments show that using a window size of 2 seconds for FNSW is recommended to achieve a better F-score, where using a 25% window overlap for FOSW is recommended to get a better precision. Overall, FNSW-based machine learning approaches can achieve up to 95.1% of F-score on average, while FOSW-based machine learning approaches can achieve up to 93.3% of F-score on average.

关键词： data segmentation fall detection supervised machine learning sliding window wearable sensor

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A Fast Heart Sounds Detection and Heart Murmur Classification Algorithm

A Fast Heart Sounds Detection and Heart Murmur Classificatio...

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IEEE 11th International Conference on Signal Processing (ICSP)

作者： Ning, Taikang Ning, James Atanasov, Nikolay Hsieh, Kai-Sheng Trinity Coll Dept Engn Hartford CT 06106 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Univ Penn Dept Elect Engn Philadelphia PA USA Vet Gen Hosp Dept Pediat Kaohsiung Taiwan

ISBN: (纸本)9781467321976;9781467321969

This paper extends our previous studies and presents a fast, automatic cardiac auscultation scoring system that effectively identifies the first and second heart sounds (S-1 and S-2) and extracts clinical features of heart murmurs to assist clinical diagnosis. Using the indices derived from AR modeling, the underlying scoring system is capable of detecting and identifying S-1 and S-2, dissecting the systole and the diastole for further analysis and extracting heart murmurs features found within, such as timing, duration, loudness (intensity), pitch and shape of murmurs. To achieve a broader spectrum of application, only the relative duration difference between systole and diastole was used as the a priori information to identify S-1 and S-2. This algorithm is particularly suited for an embedded system implementation with ease of calculations while maintaining accuracy and effectiveness. The suggested has been successfully evaluated with multiple cardiac cycles, where each systole and diastole was accurately identified and isolated. The performance of the approach has met good success using clinical data from patients with a variety of systolic murmur episodes.

关键词： heart sounds heart murmurs AR modeling data segmentation

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Artificial Intelligence-Based Customer Behavioral Analysis: Techniques, Applications, and Business Insights

Artificial Intelligence-Based Customer Behavioral Analysis: ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Aneja, Aseem Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319200

Artificial Intelligence (AI) has revolutionized the way businesses get their client behavior, empowering a more profound understanding of shopper inclinations and impelling the advancement of focused showcasing techniques. This paper centers on the application of the K-means clustering calculation inside AI for client behavioral investigation. The point is to investigate the procedures, applications, and commerce bits of knowledge determined from this strategy. The paper starts by presenting the elemental concepts of K-means clustering and its part in fragmenting expansive datasets into unmistakable clusters based on comparable characteristics. At that point digs into the different strategies utilized in pre-processing and includes extraction to optimize the algorithm's execution, guaranteeing exact and proficient examination of client behavior. Moreover, this consideration exhibits the wide-ranging applications of K-means in client behavioral investigation, emphasizing its noteworthiness in client division, personalized promoting, and item suggestion frameworks. By viably categorizing clients into particular bunches based on their inclinations and buy history, businesses can tailor their showcasing endeavors, move forward in client engagement, and boost general sales. Moreover, the paper highlights the urgent part of K-means in giving important trade experiences. By analyzing client clusters, businesses can distinguish rising patterns, anticipate future buyer behaviors, and refine their item offerings to meet advancing requests. Moreover, the integration of K-means with other AI procedures, such as normal dialect handling and assumption examination, empowers a comprehensive understanding of client input and opinion and encourages improving client fulfillment and brand dependability. This investigation contributes to the developing body of information on AI-based client behavioral investigation, emphasizing the significance of K-means in encouraging data-driven deci

关键词： Artificial Intelligence (AI) Customer Behavior Analysis data segmentation etc K-means Clustering

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Mango Fruit Disease Detection by Integrating MobileNetV2 and Long Short-Term Memory

Mango Fruit Disease Detection by Integrating MobileNetV2 and...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Alekhya, Jandhyam Lakshmi Nithin, Palli Sai Enosh, Parimi Devika, Yenuga Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350378092

Mango, a prized fruit in tropical regions, ranks as the third most traded tropical fruit globally, following pineapple and avocado. Despite its economic significance, the quality of mangoes is often compromised by various factors, including diseases. This paper introduces an innovative approach to disease detection in mangoes, departing from traditional Convolutional Neural Networks models. Instead, we propose a hybrid model that incorporates MobileNetV2 for feature extraction, LSTM for capturing feature relationships, and dense layers for accurate disease classification. The model is trained on the MangoFruit-DDS dataset, comprising over 1700 images. To optimize training, the dataset undergoes augmentation and segmentation processes, enhancing the model's ability to recognize and classify diseases accurately. This novel combination of MobileNetV2, LSTM, and dense layers enables the model to discern intricate patterns and dependencies within the mango data. Our goal is to achieve a higher level of accuracy in disease detection, contributing to improved mango production quality. The proposed hybrid model not only addresses the challenges posed by mango diseases but also underscores the potential of advanced models in agricultural applications. By leveraging the MangoFruitDDS dataset and innovative model architecture, this research aims to advance disease detection capabilities, fostering healthier and more lucrative mango harvests. © 2024 IEEE.

关键词： data augmentation data segmentation LSTM mango fruit disease MobileNetV2 transfer learning

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Liver Tumor Detection Using Deep Learning Techniques 7

Liver Tumor Detection Using Deep Learning Techniques

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Deokate, Sarika T. Pede, Shailaja Dhotre, Kalyani Sawant, Aarti Dusunge, Chaitrali Kirve, Mrudula Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

segmentation and categorization of Liver CT scan images is very crucial phase, many algorithms are used to detect the tumor from CT scan images.. We designed a model for liver tumor detection using U-net architecture. U-net is a convolutional neural network (CNN) which is generally used for image segmentation method, such as medical image analysis. The U-Net model is very much efficient for a multiple image segmentation assignments, and has been employed in a various types of applications, including medical image segmentation and biological image segmentation. It uses many convolutional layers to extract the features automatically. We have used 3DIRCADB dataset and we have got the accuracy around 98.61 % and dice coefficient is 75.97%. The accuracy is showing the efficiency of U-net architecture, but it requires big size dataset and high processing head. So we have used Thepades sorted block truncation coding (TSBTC) algorithm on the 3dircadb dataset and extracted the features of the dataset till 8ary. Then we have used different classifier families like function(Logistic), Bayes(BayesNet), Lazy(IB1, Kstar), Tree(RandomForest). We got highest accuracy of97.82% by using BayesNet classifier with TSBTC 'Septanary' version. Still work is going on to improve our results using the ensemble based classifier with TSBTC. © 2023 IEEE.

关键词： CNN(Convolutional Neural Network) data augmentation data segmentation Hepatocellular Carcinoma Thepades sorted block truncation coding U-net

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Website Information Extraction Method and Algorithms Based on Big data Technology

Website Information Extraction Method and Algorithms Based o...

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4th International Workshop on Materials Engineering and Computer Sciences (IWMECS)

作者： Zhang Guoming Nanchang Inst Sci & Technol Nanchang 330108 Jiangxi Peoples R China

ISBN: (纸本)9781912407408

Based on the rich experience of teaching practice and theoretical analysis, it scientifically explained the cognitive semantic theory in vocabulary teaching methods, and apply them to the practice, in order to test the effectiveness of new vocabulary teaching method. This paper has conducted the empirical research combined vocabulary teaching empirical analysis with questionnaire survey. The results show that, English vocabulary teaching in higher vocational colleges under the guidance of the cognitive semantics theory helps to strengthen the students' vocabulary cognitive thinking, to construct complete and systematic vocabulary semantic network, greatly stimulate students' vocabulary learning interest and initiative, improve the students' vocabulary learning efficiency, to improve students' vocabulary level in a maximum degree.

关键词： data segmentation Path algorithm Big data technology Web site information extraction

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Massively-parallel column-level segmentation of depth images

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JOURNAL OF COMPUTATIONAL SCIENCE 2021年 50卷

作者： Cebrian, P. Moure, J. C. Univ Autonoma Barcelona UAB Comp Architecture & Operating Syst Dept Barcelona Spain

Column-level segmentation of depth images is an energy-efficient strategy to perform 3D perception in autonomous-driving systems. These systems must perform 3D perception in real time through a pipeline of multiple tasks, which benefits from proposals that prioritize low complexity and short execution time over high levels of accuracy. For many years, column-level segmentation of depth images has been solved with the Stixels proposal, which uses an optimization algorithm with O(n(2)) computational complexity. This manuscript is an extended version of the ICCS paper "GPU-accelerated RDP Algorithm for data segmentation"(Cebrian and Moure, 2020). We present an alternative column-level segmentation proposal based on the RDP split-and-merge strategy, which has O(*** n) computational complexity. The qualitative results obtained with the KITTI and Synthia image datasets evidence that our proposal can generate depth representations with greater compression and accuracy than the Stixels proposal. More importantly, we engineered a massively parallel design optimized for the low-power, GPU-accelerated embedded systems typically used for autonomous driving applications. For the datasets above, our proposal runs on a low-power NVIDIA Volta GPU 22 to 68 times faster than Stixels GPU-accelerated code. Additionally, our code achieves higher performance speedups as the computational capabilities and size of depth images increase.

关键词： GPU acceleration data segmentation Segmented parallel reduction

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User-friendly nonlinear nonparametric estimation framework for vibro-acoustic industrial measurements with multiple inputs

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 145卷

作者： Csurcsia, Peter Zoltan Peeters, Bart Schoukens, Johan Siemens Ind Software NV Interleuvenlaan 68 B-3001 Leuven Belgium Vrije Univ Brussel Dept Engn Technol Pl Laan 2 B-1050 Elsene Belgium

This paper introduces a user-friendly estimation framework for industrial measurements of vibro-acoustic systems with multiple inputs. Many mechanical structures are inherently nonlinear and there is no unique solution for modeling nonlinear systems. This is especially true when multiple-input, multiple-output (MIMO) systems are considered. This paper addresses the questions related to the user-friendly semi-automatic processing of MIMO measurements with respect to the design of experiment and the analysis of the measured data. When the proposed framework is used, with a minimal user interaction, it is easily possible a) to decide, if the underlying system is linear or not, b) to decide if the linear framework is still accurate (safe) enough to be used, and c) to tell the inexperienced (non-expert) user how much can be gained using an advanced nonlinear framework. The proposed nonparametric industrial framework is illustrated on various real-life measurements. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： MIMO systems Nonparametric estimation Nonlinear system identification User-friendly methods Automatic processing data segmentation Transient elimination Direct field acoustic excitation Ground vibration testing

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