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Adaptive Nonlinear Dimensionality Reduction with a Local Metric 23

Adaptive Nonlinear Dimensionality Reduction with a Local Met...

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12th international Symposium on Information and Communication Technology, SOICT 2023

作者： Yao, Jun Chen, Huanhuan University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400708916

Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition and machine learning. Several algorithms have been proposed to analyze the structure of high dimensional data based on the notion of manifold learning. these algorithms have been used to extract the intrinsic characteristics of different types of high dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms rely on the Euclidean metric and manually choosing a neighborhood size. they cannot recover the intrinsic geometry of data manifold automatically and accurately for some data sets. In this paper, we propose an adaptive version of ISOMAP that integrates the advantages of local and global manifold learning algorithms. Faster convergence rate of this algorithm is obtained by replacing the Euclidean metric with the arc length metric as the sum of second-order approximation to the geodesic distance. Our experiments on synthetic data as well as real world images demonstrate that our proposed algorithm can achieve better performance than that of ISOMAP and solve some harder data sets that are impossible for existing global methods. © 2023 ACM.

关键词： data mining

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Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic data 26

Towards Automatic Parsing of Structured Visual Content throu...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Schoelch, Lukas Steinhaeuser, Jonas Beichter, Maximilian Seibold, Constantin Yang, Kailun Knaeble, Merlin Schwarz, thorsten Maedche, Alexander Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. this, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. this is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.

关键词： Training Measurement Visualization Annotations Supervised learning Static VAr compensators Solids

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Design thinking Approach for Dimensionality Reduction using Ensemble Gradient Boosting Algorithm 5

Design Thinking Approach for Dimensionality Reduction using ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Selva Kumar, N. Kalaivani, D. Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350321425

Human activity recognition (HAR) has developed an essential application in the Internet of things (IoT) domain, providing a foundation for many healthcare, fitness, and security applications. However, IoT sensors' high volume of data generation can make HAR computationally challenging. Dimensionality reduction techniques have been proposed to address this challenge, however, their performance is dependent on a number of criteria, like data quality, algorithm selection, and feature space design. this paper propose a design thinking approach for dimensionality reduction in enhancing HAR in IoT using data analytics and machine learning algorithms. this research work present a comprehensive framework that incorporates user-centric design, data exploration, feature engineering, and machine learning modeling to develop the accuracy and efficiency of HAR systems. the performance of this method is assessed using a publicly available dataset for HAR and results show that our framework achieves a significant reduction in feature space with minimal loss of information, leading to improved accuracy value and efficiency of HAR systems. this research explores into the application of design thinking in addressing dimensionality reduction difficulties in HAR, which can have important implications for the development of efficient and user-friendly IoT applications. the proposed approach aims to create more efficient and user-friendly IoT applications in areas such as healthcare, fitness, and security. © 2023 IEEE.

关键词： data Analytics

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An Effective Framework for Predicting Stroke Prediction using machine learning Technique 5

An Effective Framework for Predicting Stroke Prediction usin...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Kumar Satapathy, Santosh Kondaveeti, Hari Kishan Parmar, Drashti Information and Communication Technology Pandit Deendayal Energy University Gujarat Gandhinagar India School of Computer Science Engineering VIT-AP University Vijayawada India

ISBN: (纸本)9781665493604

Today, adequately trained machine learning algorithms can be significantly used in fields such as surveillance, medicine, and data management to identify and provide solutions to problems that do not have an answer or if the current solutions are ineffective. A stroke is when blood arteries in the brain burst, harming the brain. It may also occur if the brain's supply of nutrients and blood is interrupted. the severity of a stroke can be lessened by early recognition of numerous warning symptoms. Our lifestyle has recently changed, especially in the covid era. Nowadays, Millions of people encounter illnesses, disabilities, cardiovascular diseases, being overweight, hypertension, and many more. there are more than 10 million cases per year in India only. When something stops the blood cells' flow, a person encounters a heart attack. Because of this, the tissue will start to die because of a lack of blood supply. So, we proposed a machine learning model for early detection. So, we proposed a machine learning model for early detection. the dataset used is from Kaggle, with various parameters that have been considered. In this paper, we have developed several machine learning algorithms for the early detection of the disease, taking into consideration multiple risk factors and physiological parameters that have caused the stroke. We trained our model using many machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Neural Network (NN), and then we checked its testing accuracy. We found the highest accuracy in the XGBoost Classifier with 100% accuracy and 99% accuracy in the Random Forest Classifier algorithm. We also used ensemble learning and successfully got 100% testing accuracy. © 2023 IEEE.

关键词： Support vector machines

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Classification of sEMG Biomedical Signals for Upper-Limb Rehabilitation Using the Random Forest Method 5

Classification of sEMG Biomedical Signals for Upper-Limb Reh...

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5th IEEE international conference on Advanced Systems and Emergent Technologies (IC_ASET)

作者： Briouza, Sami Gritli, Hassene Khraief, Nahla Belghith, Safya Singh, Dilbag Univ Tunis El Manar Natl Engn Sch Tunis Lab Robot Informat & Complex Syst RISC Lab LR16ES BP 37 Le Belvedere Tunis 1002 Tunisia Univ Carthage Higher Inst Informat & Commun Technol Tunis 1164 Tunisia Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Infonet Lab Gwangju South Korea

ISBN: (纸本)9781665427357

To use surface electromyography (sEMG) signals for therapy and rehabilitation purposes, we first need to tackle a fundamental problem which is the pattern recognition of these signals. Recently, machine learning (ML) techniques have drawn a lot of attention from researchers working on sEMG pattern recognition, and the usage of these techniques showed a lot of potentials and proved to be a viable option. For this work, we adopt the random forest classifier, as an ML technique, for the classification of the sEMG signals for the rehabilitation of upper limbs. Furthermore, to be able to test its performance, we considered and tested different combinations of five different time-domain features, namely MAV, WL, ZC, SSC, and finally RMS. thus, and via experimental results on the adopted dataset, we show how the choice of features influences the quality of classification.

关键词： Rehabilitation Human Arm sEMG Random Forest machine learning Feature Selection

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Research on Grid Material Analysis for Inventory Management Based on data mining Technology 5

Research on Grid Material Analysis for Inventory Management ...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Liu, Hailong Bai, Hui An, Ming Jiang, Maijie Shi, Liang Chen, Jiayi Chen, Haoran Wang, Tiezhen State Grid Beijing Electric Power Company State Grid Beijing Electric Power Company Material Branch Beijing China

ISBN: (纸本)9798350357738

With the continuous expansion of the construction scale of power grid projects, the turnover and backlog of materials are constantly increasing. In addition, the maintenance and emergency repair activities of distribution networks in various regions are also related to many factors such as natural aging and disasters, and their material needs exhibit seasonality and periodicity. To better match materials and reduce the management costs and disposal risks of power grid companies, this paper uses the ReliefF-PCA method to extract features from inventory materials. then, hierarchical clustering is performed on inventory materials, and a pattern recognition method based on the BPF neural network is used to achieve multi-dimensional automatic classification;Using the multi-source data analysis method based on the SCA to extract the association rules between inventory matching and backlog inventory materials, and corresponding inventory material circuit management references are provided. From case analysis, it can be proven that this model can achieve multi-dimensional automatic classification of inventory materials, break through the data barriers between protocol inventory matching and material management business, improve the processing efficiency of backlog inventory materials, and provide data support for the refined management of power grid inventory materials. © 2023 IEEE.

关键词： data mining

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A Computational Investigation on Precision Autism and Metabolic Disorders: Predictive machine learning for Hepatic Ailment Classification 23

A Computational Investigation on Precision Autism and Metabo...

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5th international conference on Image, Video and Signal Processing, IVSP 2023

作者： Jacob, Shomona Gracia Bennet, Bensujin Sulaiman, Majdi Engineering Department University of Technology and Applied Sciences Nizwa Oman Assistant Dean-Academics University of Technology and Applied Sciences Salalah Oman

ISBN: (纸本)9781450398381

Artificial intelligence is an emerging area of modern research that aims at infusing machine intelligence through computational techniques. Precision Autism is the term coined to view Autism Spectrum Disorders (ASD) through a physiological lens than a purely psychological one. Recent research on ASD has revealed the unfavorable heterogeneous gamut to which this neurocognitive degeneration holds sway. Genes linked to autism in the SFARI database are also associated with genomic information that is linked to 25 other non-neurological diseases. Most ASD individuals were found to suffer from Obesity, gastrointestinal ailments, diabetes, hypertension, and other metabolic disorders. this research work aims at exploring the impact of conventional, deep, and Automated machine learning techniques in hepatic ailment classification as the initial investigation step before applying machine learning techniques on genetic data to unearth the relationship between ASD and hepatic ailments. this article places focus on two different datasets comprising of more than 900 patient records acquired from the University of California, Irvine, machine learning Repository. A predictive data mining framework that compares the performance of supervised learning classifiers in hepatic disorder classification based on Mathew's Correlation Coefficient and Balanced Accuracy is presented here. To the best of our knowledge, this is the first attempt to explore automated machine learning in liver disorder classification and to employ standard statistical measures for unbalanced data in rating classifier performance. Deep Neural Networks yielded the highest performance in classification with balanced accuracy ∼74% and an MCC of 0.46 on the BUPA data. © 2023 ACM.

关键词： data mining

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Investigation on the Bi-direction Fusion of data and Knowledge in Prognostic and Health Management 5

Investigation on the Bi-direction Fusion of Data and Knowled...

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5th international conference on System Reliability and Safety Engineering, SRSE 2023

作者： Zhang, Tong Liu, Zhihao Huang, Yuanxing Cui, Bofeng Zhu, Xin Tao, Laifa Marine Design and Research Institute of China China State Ship-Building Corporation Limited Shanghai China Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798350305944

data and knowledge fusion are hot topics of machine learning, and the important working direction of Prognostic and Health Management technology (PHM). In order to crack up the schismatically situation of data and knowledge in industrial community, we propose the bi-direction fusion of data and knowledge in PHM including knowledge mining and knowledge embedding. We present an overall framework and conduct exploratory studies through actual project cases. In terms of knowledge mining, a KMAHP (knowledge mining analytic hierarchy process) test system decision method for structural health monitoring is proposed. And for knowledge embedding, a physical-based health baseline construction method is proposed for critical telemetry health status monitoring of aircraft. the results of the two cases are in line with expectations, which show that the proposed fusion framework and the concrete implementation plan are both effective exploration research and engineering practice for PHM. © 2023 IEEE.

关键词： Aircraft

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5th international conference on Real Time Intelligent Systems, RTIS 2023

5th International Conference on Real Time Intelligent System...

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5th international conference on Real Time Intelligent Systems, RTIS 2023

ISBN: (纸本)9783031558474

the proceedings contain 41 papers. the special focus in this conference is on Real Time Intelligent Systems. the topics include: the Application of Artificial Intelligence Technology in Dance Teaching;design of a Cabin English Automatic Broadcasting Detection System Based on Deep learning;application of Improved Clustering Algorithms in Modern Education Technology’s Blended learning;an IoT Blockchain to Improve the Traceability on Agri-Food Applications;process Analysis of Depression Expression and Action Changes and their Psychological Integration Based on Deep learning Algorithms;moderating the Role of Real-Time Intelligence Systems in the Relationship Between Emotional Intelligence and Employee Performance;optimization of Logistics Distribution Route Based on Improved Genetic Algorithm;carrier Adjustment Algorithm for Mobile Communication Network Based on Deep Reinforcement learning;diabetic Retinopathy Detection Using Amalgamated Deep learning Algorithm;evaluation Method of College Students’ Education Based on Artificial Neural Networks;research on Intelligent Algorithm-Based Swimming Athlete Pose recognition and Correction Method;study on a Web-Based Remote Classroom English Writing Practice System Using Intelligent Model;coordination Training and Testing of Upper and Lower Limbs in Aerobics Under Neural Networks;vocal Melody Extraction Based on Sparse Autoencoding Neural Networks;Football Players’ Physical Parameter Simulation Training Based on AI Intelligent Simulation;Total Cost of Ownership Optimization for ERP System Implementation Project Using Advanced Technologies;design of Neural Network-Based Intelligent Robot-Assisted English Translation System;english Language Translation machine System Based on Human-Computer Interaction;early Warning of Subjective Perception of Students Based on Big data Analysis Techniques in Growth Environment;towards an Autonomous Response System. Case: Denial of Service Attacks.

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object recognition and pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

the identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. this finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. this dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. this research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. the LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. the approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. the recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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