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13th International Conference on Contemporary Computing, IC3 2021

作者： Kumar Pandey, Samir Prasad Shukla, Shiv Shankar Kumar Yadav, Anil Singh Rajpoot, Dharmveer Faculty of Science and Technology ICFAI University Jharkhand Ranchi India Department of Computer Science Engineering IES College of Technology M.P. Bhopal India Department of Computer Science Engineering Jaypee Institute of Information Technology U.P. Noida India

ISBN: (纸本)9781450389204

The intend of this paper is to use fuzzy mathematics to dynamically formulate an e-epidemic compartmental model in the human population. The most recent edition of individual corona virus said to be COVID-19 came out as a sudden pandemic disease within human population and in the absence of vaccination and proper treatment till date, it daunting threats heavily to human lives, infecting more than 30Mpeople and death more than 830Kpeople in more than 208 countries across the globe as on February2020, which is highly alarming. When no treatment or vaccine is available till date and to avoid COVID-19 to be transmitted in the community, to follow the minimum criteria (social distancing, using masks etc.) is the only way to prevent the disease. We have studied the ways of classical basic reproduction number as well as fuzzy basic reproduction number means at what time both differ and when matched. We have also analyzed the control strategies. For this, we have taken three cases for disease in population as: low, medium and high which tells that at what condition the disease will invade the population. © 2021 ACM.

关键词： Vaccines

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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Paradigm Shift in information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jitendra Kumar Katariya Dhananjay Yadav Gayathri C Vikas K. Kolekar Umesh M. Daivagna V. Pushparajesh Department of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Bangalore Karnataka

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research.

关键词： Knowledge engineering Deep learning Recurrent neural networks Time series analysis Network architecture Internet Forecasting

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Classification of IncRNA and mRNA using k-mers and random forest 4

Classification of IncRNA and mRNA using k-mers and random fo...

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4th International Conference on Innovative Computing, ICIC 2021

作者： Nadir, Rana M. Mateen, Hafsa Din, Saif U University of Management and Technology Department of Computer Sciences Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management and Technology Software Engineering Department Lahore Pakistan

ISBN: (纸本)9781665400916

The Long non-coding RNA is involved important biological process in our body but dark part is it also involve in some major deceases like cancer. Very few research is done on long non-coding RNA due to its structural resemblance with mRNA, which is protein coding RNA. A proper distinction model between mRNA and Long non-coding RNA is required to do further research on Long non-coding RNA and its association with ***: First, k-mers with different k values are extracted and directly tested with our model for best selection of k-mers features. Moreover, the k-mers with high accuracy score are combined in one pool and screened with the help of extra tees classifier to identify most relevant features. Furthermore short listed features are given to our random forest (RF) classifier to extract classification results for lncRna and mRna sequences of humans. Finally, over classifier is compared with other machine learning classifiers like State Vector Machine (SVM), Neural Network (NN) and K Nearest Neighbor (KNN). Results proves that our proposed model score highest accuracy, precision, recall and ***: We have established the RF classifier on the bases of combination of 2 and 3 k-mers features, which provides the accuracy of 0.9984. Thus this score is better than Convolutional Neural Network (CNN) based and all previous proposed classification approaches. © 2021 IEEE.

关键词： RNA

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Performance Evaluation of FC-RDVM and LDIWM Router Placement Methods for WMNs by WMN-PSODGA Hybrid Simulation System Considering Load Balancing and Chi-Square Distribution of Mesh Clients 1

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Barolli, Admir Sakamoto, Shinji Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University 3-7-2 Kajino-machi Koganei-shi Tokyo184-8584 Japan

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In this paper, we present WMN-PSODGA hybrid simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs). We consider Chi-square distribution of mesh clients and compare the results of a Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) with Linearly Decreacing Inertia Weight Method (LDIWM). The simulation results show that FC-RDVM has better load balancing than LDIWM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MESH networking

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Analysis of DNA Methylation Biomarkers through Liquid Biopsies for Early Breast Cancer Detection

Analysis of DNA Methylation Biomarkers through Liquid Biopsi...

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International computer Symposium (ICS)

作者： Xiang-Zhen Chen Chia-Yun Ou Geng-Hong Hsiao Hui-Wu Zhang Tun-Wen Pai Mee-Hoong See Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Dept. of Surgery Breast Surgery Unit Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331509071

ISBN: (纸本)9798331509088

According to World Health Organization (WHO) and World Cancer Research Fund International (WCRF International), breast cancer has become the most common cancer globally, significantly impacting women's health. This study utilized GDC and GEO public methylation chip datasets from breast cancer patients. Combined with comorbidity data analysis of these patients, critical biomarkers were identified through analyzing differential expression levels of methylation loci, serving as indicators for breast cancer risk assessment. Gene Ontology (GO) functional annotations were applied for hierarchical clustering. Functional similarities between any two genetic loci were evaluated based on their annotated GO terms, enabling the observation of candidate biomarker relationships for diagnosis biomarker selection. Furthermore, by integrating Boruta for feature selection and applying Recursive Feature Elimination (RFE) to evaluate the performance of various machine learning models, the optimal methylation biomarker combinations for breast cancer detection could be identified. The results identified eight important DNA methylation biomarkers, including CMTM5, PDCD1LG2, MIR124-3, NEFM, PTF1A, CX3CL1, PCYT2, and KCNE3 as important biomarker candidates. After functional clustering analysis, these markers exhibited excellent performance with three biomarker combinations for both tissue and liquid samples from breast cancer patients, with average prediction accuracies ranging from 0.91 to 0.93 for tissue samples and from 0.71 to 0.76 for liquid samples. Early observation of methylation differences from the suggested breast cancer biomarkers could prevent tumor formation and reduce the risk of surgical operations by early precision diagnosis and treatment.

关键词： Liquids Biopsy DNA Genomics Machine learning Biomarkers Breast cancer Cancer Tumors Immune system

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Entropy Analysis of Total Respiratory Time Series for Sepsis Detection 10

Entropy Analysis of Total Respiratory Time Series for Sepsis...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Sousa, Hugo Ribeiro, Maria Henriques, Teresa S. Faculty of Engineering University of Porto FEUP Porto Portugal Porto Portugal Computer Science Department Faculty of Sciences University of Porto Porto Portugal Centre for Health Technology and Services Research CINTESIS Faculty of Medicine University of Porto Porto Portugal Department of Community Medicine Information and Health Decision Sciences MEDCIDS Faculty of Medicine University of Porto Porto Portugal

ISBN: (纸本)9781665485579

Neonatal sepsis is characterized by the system’s extreme response to an infection and persists as one of the biggest life-threatening diseases. The gold standard treatment is administrating an antibiotic, which, unfortunately, is often made too late. The diagnosis should be easier, faster, and achieved through non-invasive methods. Recently, entropy, a non-linear feature, has been applied to different physiological signals to detect diseases having very promising results. In this study, several entropy measures were applied to the breathing cycle duration (TTot) of the respiratory signals for 20 neonates. In total, 18 distinct methods of entropy were initially applied to 30-minute segments. Using Spearman’s correlation, it was detected strong correlation similarities between some of the measures. On the other hand, bubble, attention, phase, and spectral entropies were negatively correlated with all the other measures. To detect the presence of Sepsis, the slope of the multiscale entropy index was analyzed. Also, a changing point in the slope was probed, when possible, and then was applied linear regression to two subsets of data, before and after the changing point. Effectively, the Wilcoxon Sign Rank Test showed that the results for the total slope of the Sample, Corrected Conditional, Distribution, Permutation, Fuzzy, Gridded Distribution, Incremental, and Entropy of Entropy were statistically significant to infer that entropy decreases with time. Nonetheless, further work should confirm these results with a larger dataset that includes healthy and pathological neonates. © 2022 IEEE.

关键词： Entropy

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Study of Feature Fusion Models for Human Activity Recognition on Point Clouds from mmWave Radar

Study of Feature Fusion Models for Human Activity Recognitio...

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Smart World Congress (SWC), IEEE

作者： Lin Kong Houpu Zhou Guochen Zhang Runhe Huang Yanbo Ma Yunlong Luo Yun Su Faculty of Computer and Information Sciences Hosei University Tokyo Japan Pontosense Inc. Canada School of Information Science and Technology Southwest Jiaotong University Chengdu China College of Computer Science and Engineering Northwest Normal University Lanzhou Gansu

Human Activity Recognition (HAR) has gained significant attention as a technological response to the ever-increasing global need to monitor the health of elderly populations. This research focused on millimeter-wave (mmWave) radar technology for HAR because of its comparative advantages over competing technologies in privacy protection, superior penetration, and lack of a requirement for ambient light. This research proposes a feature fusion model for detecting activities that incorporates both voxel and Doppler information embedded in pixels. The proposed model achieves high accuracy of 95.70% for recognizing human activities, which is a significant improvement in performance over the performance using a single model, particularly in distinguishing between challenging activity categories. This paper provides an overview of the HAR system based on mmWave radar, including the design of two submodels and the composition of the feature fusion model, as well as an accuracy analysis of the proposed model through experimentation. The study also identifies several challenges that need to be addressed in future research, such as the improvement of dataset categories, the pre-processing of point cloud data, and the development of models with greater accuracy in recognizing similar activity categories.

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Uncovering Brain Network Modules in Major Depression During Naturalistic Music Perception with Block Term Decomposition

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Zhu, Yongjie Hao, Yuxing Liu, Jia Cong, Fengyu Dalian Univ. of Tech. School of Biomedical Engineering China Univ. of Helsinki Dept. of Computer Science Finland Aalto University Dept. of Neuroscience and Biomedical Engineering Finland Dalian University of Technology School of Biomedical Engineering Faculty of Medicine Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland Dalian Univ. of Tech. School of Biomedical Engineering Faculty of Medicine China

Major depressive disorder (MDD) has been linked to altered brain networks and might be relieved by music therapy. Yet, the neurophysiological basis, especially the functional network mechanism, of music therapy on depression remains poorly understood. Here, we apply a novel dynamic module detection method based on block term decomposition (BTD) to examine the reorganization of time-varying topological network structures during music listening in MDD using electroencephalography (EEG). Specifically, temporal adjacency matrices generated using a sliding-window technique form a three-way tensor. The multilinear rank-(L, L, 1) BTD is applied to directly derive hidden network modules with specific time evolution from the temporally concatenated tensors for each frequency band. After temporal correlation analysis with musical features extracted from music stimuli, we identify several frequency-dependent network modules with specific temporal patterns modulated by musical features. These modular networks encompass subnetworks of default mode, frontoparietal, language, and sensorimotor networks involved in the delta, alpha, and beta bands, exhibiting significantly different modulations by music between healthy control and MDD groups. These results implicate that the altered oscillatory modular networks might affect the dynamic processing of musical features for MDD patients, which could offer valuable perspectives on revealing the neural mechanisms of music therapy for MDD. © 2016 IEEE.

关键词： Tensors

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The Electrical Properties of a-IGZO TFT Prediction Based on Variational Autoencoder Model

The Electrical Properties of a-IGZO TFT Prediction Based on ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shih-Shin Hu Kean-Thye Bea Wei-Hsuan Lin Da-Zheng Lin Xiu-Zhi Chen Ting-Ru Lin Hsin-Hui Hu Yen-Lin Chen Kun-Ming Chen Wai Khuen Cheng Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Taiwan Semiconductor Research Institute Hsinchu Taiwan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia

In this study, we integrated the Polynomial Regression module with a Variational Autoencoder (PolyReg-VAE) model to foster the inference of transfer curves before further deriving the electrical properties of a-IGZO TFT. This approach is able to predict the electrical characteristics of a-IGZO-TFT with different process parameters in a short time, reducing the cost and the time of developing new technology.

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