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4th International Conference on Informatics and Computing, ICIC 2019

作者： Afrizal, Sandra Hakiem Hidayanto, Achmad Nizar Hakiem, Nashrul Sartono, Arief Priyambodo, Seno Eryando, Tris Faculty of Public Health Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Science and Technology Universitas Islam Negeri Syarif Hidayatullah Jakarta Indonesia Laboratory for Information and Communication Technology Services Agency for the Assessment and Application of Technology Indonesia

ISBN: (纸本)9781728122076

Antenatal Care (ANC) service performed by midwives in Primary Health Care (PHC) became more efficient through mobile health (mHealth) implementation. However, no work has yet been conducted toward the development of the technologies for integrating the service since midwives provide not only in-house clinic service but also community works such as home visit and site clinic. The objective of the study was to design a mobile health application using user-centered needs approach to consider the functionality and usability of the users. We conducted a case study in South Tangerang, an urban city of Banten Province, Indonesia using in-depth interview for prospective users and collected usability testing of mock-up mobile application through System Usability Scale (SUS) among 20 respondents. Functional features were identified namely: 1) pregnancy register such as personal identity, spouse identity, and ANC content, 2) pregnancy monitoring such as high-risk pregnancy, diagnosis, and maternal status. As conclusion, this is the first study to design a mobile Health for integrating ANC service based on UCD framework. The study indicates that UCD is found as useful guidance for designing a monitoring and registration mHealth application named SIMORI for integrating ANC service in primary health care. © 2019 IEEE.

关键词： mHealth

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Assessment of groundwater quality for the intent of irrigation in Cuttack using machine learning

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AIP Conference Proceedings 2025年第1期3224卷

作者： Ekata Gupta Mukta Goyal Mukta Makhija Madhvi Tripathi Abhishek Srivastava Guru Nanak Institute of Management Delhi India GNDIT Delhi India Department of Computer Application Integrated Academy of Management and Technology Uttar Pradesh India CSA Department Sharda University Greater NoidaUP India Faculty of Management Studies Gopal Narayan Singh University Rohtas Bihar India

The study and exploration was carried out in the Cuttack district, with 60 samples taken from the well. The pH of groundwater was considered. Electrical conductivity (EC) is one the component that was measured in several locations throughout the research vicinity. It is a major factor in determining irrigation water quality, with 16 samples, considered in light of USSL Salinity Diagram. The chemical quality of groundwater was assessed using machine learning for fourteen different strictures, *** percentage of hydrogen, EC, TDS, TH, Alkalinity, Ca, Mg, Na, K, CO3, HCO3, Cl, SO4 and Fluoride (F), according to APHA guidelinesAccording to the research, the groundwater in the study area was found suitable for agricultural purposes. Machine Learning Concepts with classification techniques have been used to predict the same.

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Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

arXiv

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arXiv 2022年

作者： Osman, Yousuf Babiker M. Li, Cheng Huang, Weijian Elsayed, Nazik Xue, Zhenzhen Zheng, Hairong Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518066 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Faculty of Mathematical and Computer Sciences University of Gezira Wad Madani Sudan

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

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Multi-verse optimization clustering algorithm for binarization of handwritten documents 1st

Multi-verse optimization clustering algorithm for binarizati...

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1st International Symposium on Signal and Image Processing, ISSIP 2017

作者： Elfattah, Mohamed Abd Hassanien, Aboul Ella Abuelenin, Sherihan Bhattacharyya, Siddhartha Faculty of Computers and Information Computer Science Department Mansoura University Dakahlia Governorate Egypt Faculty of Computers and Information Cairo University Giza Egypt Department of Computer Application RCC Institute of Information Technology Kolkata 700015 India Cairo Egypt

ISBN: (纸本)9789811088629

Binarization process of images of historical manuscripts is considered a challenge due to the different types of noise that are related to the degraded manuscripts. This paper presents an automatic clustering algorithm for binarization of handwritten documents (HD) based on multi-verse optimization. The multi-verse algorithm is used to find cluster centers in HD where the number of clusters is predefined. The proposed approach is tested on the benchmarking dataset used in the Handwritten Document Image Binarization Contest (H-DIBCO 2014). The proposed approach is assessed through several performance measures. The experimental results achieved competitive outcomes compared to the well-known binarization methods such as Otsu and Sauvola. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Clustering algorithms

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Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

arXiv

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arXiv 2023年

作者： Qi, Yaping Hu, Dan Wu, Zhenping Zheng, Ming Cheng, Guanghui Jiang, Yucheng Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Tohoku University Sendai980-8577 Japan State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis. © 2023, CC BY.

关键词： Raman spectroscopy

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

arXiv

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arXiv 2020年

作者： Wei, Shoulin Luo, Kaida Wang, Feng Deng, Hui Mei, Ying Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China Center for Astrophysics Guangzhou University Guangzhou510006 China

The Square Kilometre Array (SKA) would be the worlds largest radio telescope with eventually over a square kilometre of collecting area. However, there are enormous challenges in its data processing. The using of modern distributed computing techniques to solve the problem of massive data processing in SKA is one of the most important challenges. In this study, basing on the Dask distribution computational framework, and taking the visibility function integral processing as an example, we adopt a multi-level parallelism method to implement distributed averaging over time and channel. Dask Array was used to implement super large matrix or arrays with supported parallelism. To maximize the usage of memory, we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance mechanism. The validity of the proposed pattern was also verified by using the Common Astronomy Software application (CASA), wherein we analyze the smearing effects on images reconstructed from different resolution visibilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Visibility

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Adaptive Data Replication Optimization Based on Reinforcement Learning

Adaptive Data Replication Optimization Based on Reinforcemen...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chee Keong Wee Richi Nayak Digital Application Services Business Applications Technology Services eHealth Queensland Queensland Australia School of Electrical Engineering & Computer Science Science & Engineering Faculty Queensland University of Technology Brisbane Queensland Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.

关键词： Software Business Throughput Reinforcement learning Tuning Machine learning algorithms Adaptation models

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Sentiment classification method for Chinese and Vietnamese bilingual news sentence based on convolution neural network

Sentiment classification method for Chinese and Vietnamese b...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2018

作者： He, Xiaoxu Gao, Shengxiang Yu, Zhengtao Liu, Shulong Hong, Xudong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Key Lab of Computer Technologies Application of Yunnan Province Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9783030002138

Sentiment classification of the Chinese and Vietnamese news is important to analysis public opinion of Chinese and Vietnamese. For cross-lingual sentiment analysis, this paper studies the method of sentiment analysis for Chinese and Vietnamese bilingual news sentence based on convolution neural network (CNN). Firstly, collect Chinese and Vietnamese news data from Internet, use the data collected to train word vector. Secondly, employ the pre-trained word vector and bilingual lexicon to project the Chinese news labeled data to bilingual word vector space, the problem of the lack of annotated corpus in Vietnamese is solved in a certain level. Finally, using convolution neural network to make cross-lingual sentiment classification. Experiment result shows that the F score of Vietnamese news classification has reached 86%. Compared with the traditional methods, the proposed method does not need to construct sentiment lexicon and perform feature extraction, and the performance of cross-lingual sentiment classification is also remarkable. © Springer Nature Switzerland AG 2019.

关键词： Sentiment analysis

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Ordinal Preferences Driven Reputation Measurement for Online Services with User Incentive

Ordinal Preferences Driven Reputation Measurement for Online...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaodong Fu Li Liu Lijun Liu Yong Feng Kun Yue Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.

关键词： Measurement Web services Conferences Aggregates Performance analysis Optimization

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3D object recognition based on data fusion at feature level via principal component analysis 1st

3D object recognition based on data fusion at feature level ...

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1st International Symposium on Signal and Image Processing, ISSIP 2017

作者： Ibrahim, Abdelhameed Ella Hassanien, Aboul Bhattacharyya, Siddhartha Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Computers and Information Cairo University Cairo Egypt Department of Computer Application RCC Institute of Information Technology Kolkata 700 015 India Giza Egypt

ISBN: (纸本)9789811088629

In computer vision applications, recognizing a 3D object is considered an effective research area. The recognition of 3D objects can be accomplished by building accurate 3D models of these objects. The 3D object reconstruction is the creation of 3D models from a set of images. Different reconstruction techniques can be used to obtain 3D models using calibrated and uncalibrated images of the required object. In this work, a method for evaluating the effectiveness of using 3D models produced from different 3D modeling techniques is presented. Different feature extraction, matching, and fusion techniques of average, addition, principal component analysis, and discrete wavelet transform are used to get multiple models. The targeted object models are then used as input to an artificial neural network which is used to compute the recognition error value. Experiments use real-world images for modeling to evaluate the proposed method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Object recognition

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