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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Healthcare Management System to Improve Data Analytics Using Support Vector machine Algorithm

Healthcare Management System to Improve Data Analytics Using...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： A. Edwin Raj V. Bini Marin C. Balakrishnan K. Elangovan R. Meenakshi M. Muthulekshmi Department of Mathematics Hindustan Institute of Technology and Science Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Dhanalakshmi Srinivasan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Knowledge discovery in databases (KDD) is concerned with progress in data analysis tools. Data mining is a crucial parts of the KDD process. Data mining is a method for discovering and extracting patterns from huge medical datasets. there are a number of benefits to using the Support Vector machine (SVM) algorithm in healthcare systems. SVM is a strong and flexible machine learning algorithm that may excel at certain healthcare analytics applications. SVM is preferred in healthcare systems because of their adaptability, ability to manage imbalanced datasets, feature interpretability, handling of high-dimensional data, handling of classification, predictive modeling tasks, handling of non-linear relationships, resistance to overfitting and provision of a distinct margin of separation. It is essential to carefully consider the unique needs and features of the healthcare application when choosing an algorithm. Healthcare systems that use SVM algorithms do so with the intention of capitalizing on SVM's capabilities to tackle domain-specific problems and tasks. In light of this development, the relevant literature on this topic with regard to approach, SVM algorithm, and outcomes are surveyed. the publications evaluated in this study have been categorized according to their relevance to the disciplines, models, tasks, and approaches discussed. Data mining is the practice of sifting through massive amounts of unstructured data for meaningful patterns and insights. Results show 90% accuracy and 98% precision and F measures of the given medical dataset.

关键词： Support vector machines machine learning algorithms Databases Medical services Prediction algorithms Classification algorithms Data mining

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Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based machine learning Techniques 22

Node Localization in Range-Free 3D-WSNs Using New DV-Hop Alg...

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proceedings of the 2022 4th international conference on Software Engineering and Development

作者： Oumaima Liouane Smain Femmam Toufik Bakir Abdessalem Ben Abdelali University of Monastir National Engineering School of Monastir Tunisia Department of Networks and Communications Haute-Alsace University France Bourgogne University France University of Monastir ISIMM of Monastirr Tunisia

ISBN: (纸本)9781450397940

In many Wireless Sensor Network (WSN) applications, location is critical. Another intriguing aspect of the acquired data is the ability to obtain exact information about sensors' locations. In order to localize multi-hop WSNs, based connectivity algorithms use their benefits, such as simplicity and acceptable accuracy, to do so. However, the localization accuracy may be limited due to the two- or three-dimensional environment (2D or 3D). Range-Free 3D-WSNs can benefit from an analytic model for hop-size quantization and an Extreme learning machine (ELM) method for localization to reduce localization errors. the additional third dimension greatly affects the accuracy of localization. Since many applications require 3D localization, it is important to develop efficient self-localization algorithms for 3D WSNs. In this paper, for a uniform distribution of sensor nodes, a new probabilistic quantization of hop size in 3D WSNs is proposed. Moreover, the extreme learning machine (ELM) which represents a new approach to WSN localization is exploited combining a conventional method (probabilistic approach) with a non-conventional method (machine learning). For a variety of conditions, our algorithms have been tested through simulation in isotropic settings. the performance of the localization model was assessed using the average localization error (LE). When compared to previous 3D-DV-Hop heuristics, the suggested localization algorithm's performance in terms of accuracy is clearly demonstrated by simulation data. With the help of the predicted hop quantization for hop-size estimation and the ELM was used for position estimation, our localization method for 3D-WSNs lowers the average localization error of nodes and has a greater location accuracy compared to its rivals.

关键词： machine learning Localization hop-quantization 3D Wireless Sensors Network

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application of Chaos theory and Data Mining to Seizure Detection of Epilepsy

Application of Chaos Theory and Data Mining to Seizure Detec...

引用

the 2012 4th international conference on machine learning and Computing（ICMLC 2012）

作者： Chien-Liang Chen Jenn-Long Liu Jhih-Yu Syu Dept.of Information Management Fortune Institute TechnologyKaohsiung 831Taiwan Dept.of Information Engineering I-Shou UniversityKaohsiung 840Taiwan Dept.of Information Management I-Shou UniversityKaohsiung 840Taiwan

this study applies Chaos theory to investigate the seizure detection of epilepsy with three groups of data （Groups H,S and E）,showing the electroencephalography （EEG） changes of *** three groups,Group H （normal state）,Group S （during seizures） and Group E （after seizures）,contain 100 series of EEG signals *** detected data are processed by chaotic theory to transform the signals to four parameters with Delay Time （τ）,Embedding Dimension （dm）,Correlation Dimension （CD） value,and Largest Lyapunov Exponent （LLE）.Furthermore,well-known classification software for data mining,termed See5 based on entropy theory,is used to find out the classification rules for the EEG signals of epilepsy patients.

关键词： Chaos theory Epilepsy Decision Tree Classification Rule

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Self-Supervised Superpixel Correspondences For Video Object Segmentation 22

Self-Supervised Superpixel Correspondences For Video Object ...

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proceedings of the 2022 4th international conference on Video, Signal and Image Processing

作者： Clement Tan Chai Kiat Yeo Cheston Tan Basura Fernando School of Computer Science and Engineering Nanyang Technological University Singapore and Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9781450397810

Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video sequence. With an attention mechanism, we train the model to reconstruct the next frame using superpixel features between adjacent frames of videos trained in a self-supervised manner. As superpixels are able to group pixels based on color and proximity, they reduce repetitive and noisy pixels that may confuse the model during tracking. In addition, the structural information provided by the superpixel features can help improve the segmentation of objects. We benchmark our method (S3CNet) against existing self-supervised pixel correspondence frameworks such as [12, 22, 24] and show that our self-supervised superpixel correspondence method performs better and is a viable alternative for visual tracking on the commonly-used DAVIS 2017 video segmentation benchmark.

关键词： Video Object Segmentation Self-Supervised learning Superpixels

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AI-Driven Alert Systems for an Intuitive Animal Monitoring System

AI-Driven Alert Systems for an Intuitive Animal Monitoring S...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： P. Latha Diana Inba Malar Y Hemavarthini C S Manjuparkavi V Sneha S Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

All animals that can sight, including humans, do so in a way that appears instinctive and natural. Creatures use their vision system early in life often even from birth to traverse their surroundings, recognize and communicate with other creatures, and learn about their surroundings. Because of this, an animal's vision system is continuously being educated and modified to enable it to do a variety of activities. In humans, for instance, this system works by way of the optic nerve, which transmits light data from the eye to the brain for interpretation and processing. this sophisticated vision system enables us to focus on objects and see the world in three dimensions, all while being able to autonomously adjust to changing light conditions. It is possible to think of computer vision, digital image processing, and digital image analysis as a combination of phrases that are frequently used to refer to related operations. the main reason for this misconception is that they are related fields that arose with the advancement of digital picture capture. thus, it's important to comprehend how these fields relate to one another, how a digital image is created, and how the various sensors that are available each best suited for a particular application differ. Since the introduction of charge- coupled devices, which signaled the beginning of digital imaging, the field has developed very quickly. In this project, a camera that records the surroundings all day long will be used to periodically monitor the entire farm. We can identify when an animal is entering with the aid of a deep learning model, and then we play the right sounds to eject the animal.

关键词： Deep learning YOLO computer vision Spirals Animals Reviews Digital images machine vision Search problems Monitoring

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SupervisedImmuneNet: Training Artificial Immune Networks using a Supervised learning Approach for Improved Multi-Class Classification 23

SupervisedImmuneNet: Training Artificial Immune Networks usi...

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proceedings of the 2023 6th international conference on Computational Intelligence and Intelligent Systems

作者： Siphesihle Philezwini Sithungu Elizabeth Marie Ehlers Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9798400709067

the most common application of artificial immune networks (AINs) is on unsupervised learning tasks. this is due to the fact that AINs are inspired by the adaptive immune system, which consists of a network of antibodies that self-organises to form a memory of external antigens. the self-organising nature of AINs makes them a natural approach for solving problems involving learning and adapting to patterns or structures present in a dataset to form an abstract representation. Training AINs in this fashion means that the dataset need not have class labels because the typical aim of the learning process is not to perform classification. However, there have been attempts to use AINs for classification tasks by considering the resulting clusters of antibodies as representative of the classes present in a dataset. this has also been done when applying AINs to the task of recognising handwritten characters. However, in all the approaches found in the literature, the common method was to leave the task of discovering classes to the AINs. Doing so is contrary to how other models are trained to do classification tasks where data samples are provided along with their class labels to guide the learning process. therefore, this paper presents a novel supervised learning approach to training AINs for multi-class classification. the proposed approach was tested on the MNIST handwritten digits dataset and achieved a classification accuracy of 99.45%.

关键词： Artificial Immune Networks Immunologically Inspired Computation MNIST Handwritten Digit Recognition machine learning

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A comparative study on fetal head circumference measurement from ultrasound images using deep learning models 22

A comparative study on fetal head circumference measurement ...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Yu Wang Yudong Yao College of Medicine and Biological Information Engineering Northeastern University China Department of Electrical and Computer Engineering Stevens Institute of Technology USA

ISBN: (纸本)9781450398343

Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-time imaging and no radiation characteristics. through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). the performance of Attention U-Net is slightly worse than that of SegFormer.

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Construction and application of Talent Management Cockpit in Aerospace Enterprises 23

Construction and Application of Talent Management Cockpit in...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Qingqing Zhao Xuedong Zhang Huili Fan Qixian Liu Lanyu Gao Wenwei Gao Beijing Institute of Space Mechanics & Electricity China

ISBN: (纸本)9798400709517

With the continuous development of information technology in human resource management, it is particularly important to explore the explicit value of existing personnel information in the human resource system. this paper presents the construction method and application of talent management cockpit in aerospace enterprises. Firstly, this paper constructs a set of talent management index systems specially designed for the aerospace industry. Secondly, combining the statistical indicators commonly used in human resource systems and reports, this paper puts forward an index model based on core elements. Finally, this paper realizes a "1+3+1" talent management cockpit. the cockpit combines the main dashboard, sub-dashboard, and theme dashboard, which are systematically implemented using visual reporting tools. Users can intuitively and conveniently understand the real-time distribution and change of the enterprise's internal personnel. Business managers can make more accurate decisions and promote business development.

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application of a Data Collection Method Using Cloud Services in Smart Home 23

Application of a Data Collection Method Using Cloud Services...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Xiaoquan You Qinchun Hu Lifang Guo Jianglong Yuan School of Network and Communication Engineering Chengdu Technological University China School of Automation and Electrical Engineering Chengdu Technological University China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9798400716669

In order to meet people's demand for smart homes, we propose the design and implementation of a smart home system based on embedded ARM architecture. the data acquisition subsystem and equipment control subsystem of the system are implemented with STM32 and Raspberry Pi 3B+ as the core, respectively. Wireless data communication is achieved through Wi-Fi, and redundant NB-IOT wireless connections are included to enhance system reliability. the system's monitoring data is stored in the cloud platform, allowing for user behavior analysis and intelligent home control. System test results confirm the system's stable operation, user-friendly human-computer interaction, and excellent scalability. It is particularly suitable for the intelligent transformation of homes in older residential areas, and it has a promising market application outlook.

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Analysis Of Accounting Information Systems Based On Artificial Intelligence On Fraudulent Financial Reporting Trends In Indonesia 22

Analysis Of Accounting Information Systems Based On Artifici...

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proceedings of the 2022 4th international conference on E-Business and E-Commerce Engineering

作者： Meiryani Meiryani Vidhiya Andini Mochammad Fahlevi Winwin Yadiati Agung Purnomo Gredion Prajena Department School of Accounting Bina Nusantara University Indonesia Management Department BINUS Online Learning Bina Nusantara University Indonesia Accounting Department Faculty of Economics and Business Padjadjaran University Indonesia Entrepreneurship Department BINUS Business School Bina Nusantara University Indonesia Computer Science Department School of Computer Science Bina Nusantara University Indonesia

ISBN: (纸本)9798400700071

the accounting information system in a company functions to collect information from various entity subsystems and communicate it to the organization's information processing subsystem. Along with the development of technology and information resulting in significant changes resulting in the need for collaboration from technological developments such as artificial intelligence. By looking at the number of frauds in financial reporting, it requires organizations or companies to make changes for the good of the company related to technological advances that make Artificial Intelligence-based Accounting Information Systems become one of the important needs. this study aims to determine whether there is an effect given by the application of an Artificial Intelligence (AI)-based Accounting Information System (AIS) in detecting and preventing the tendency of fraudulent financial reporting. And to examine how big the role of the Accounting Information System (SIA) based on Artificial Intelligence (AI) in detecting and preventing the tendency of fraudulent financial reporting. this research method is a quantitative research that has a research sample of 31 respondents. the data used is primary data in the form of respondents' answers and processed using SPSS version 27. With the conclusion that the application of an artificial intelligence-based accounting information system has a positive effect on the tendency of fraudulent financial reporting based on the output of the Scatter Plot Graph. based on the coefficient of determination of 0.717 and the regression coefficient of 0.762. In conclusion, the application of accounting information systems has a significant positive effect on the prevention of fraudulent financial reporting with the effect given is 71.7%.

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