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Impact of SARS-CoV-2 in Online Education, Predicting and Contrasting Mental Stress of Young Students: A Machine Learning Approach

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Augmented Human Research 2021年第1期6卷 1-7页

作者： Parthiban, K. Pandey, Digvijay Pandey, Binay Kumar Department of Computer Science and Systems Engineering SreeVidyanikethan Engineering College (Autonomous) Tirupati India Department of Technical Education IET Dr. A.P.J.Abdul Kalam Technical University Uttar Pradesh Lucknow India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology US Nagar India

The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact. A complete shutdown keeps adding fresh problems for students to learn and also for educators to effectively manage the class, likely to result in the conversion of such an offline education system into a virtual class. This study investigated and described various online learning platforms, as well as teaching resource distribution technology and modern technologies used to ensure that students can study. Overall, this relates to channels that can be used to create a confidential environment in order to prevent copying in online tests. This also outlines so many difficulties in managing online teaching methods, like learners' perceptions of e-learning being more traumatic and having a negative impact on general wellbeing and social relationships. So, a technique has been proposed to provide learners with a superior online classroom teaching experience, allowing the online classroom to be as good as, if not better than, a single online classroom. This study focused on daily teaching methods that employ online learning supported by a machine teaching approach to provide an individual with a relevant stress-free solution.

关键词： COVID-19 clustering and classification Online class Respondent Stress

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Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems 14

Attributes of Big Data Analytics for Data-Driven Decision Ma...

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14th International Conference on Protection and Automation of Power Systems (IPAPS)

作者： Moradi, Jalal Shahinzadeh, Hossein Nafisi, Hamed Marzband, Mousa Gharehpetian, Gevork B. Islamic Azad Univ Khomeinishahr Branch Young Researchers & Elite Club Esfahan Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Northumbria Univ Dept Elect Engn Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781728161891

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to localscale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions.

关键词： Big data analytics Machine learning and data mining Future power systems Artificial intelligence clustering and classification Smart grids

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A cognitive technology based healthcare monitoring system and medical data transmission

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MEASUREMENT 2019年 146卷 322-332页

作者： Kumar, M. Arun Vimala, R. Britto, K. R. Aravind PSNA Coll Engn & Technol Dept IT Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept EEE Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept ECE Dindigul Tamil Nadu India

In healthcare management, the volume of patient data generated from various medical devices used for patient monitoring. Increasing healthcare data is persisted in distributed database available in cloud, for sharing among medical experts. Cloud provides most excellent methods to explore the organized and unorganized data prepared from healthcare management systems. By analyzing the healthcare data, the health conditions and the treatment methods are predicted for future treatment. Yet, the existing design could not sustain both analysis and procedure for huge quantity of multi-organized healthcare data. In this paper, it is aimed to provide a cognitive technology for efficient monitoring and transmission of medical data in a healthcare industry. To do that, a Cognitive Data Transmission Method (CDTM) is proposed for monitor, record and transmit the data patient's health related data. A highest priority is assigned in data transmission, when the health condition is critical, which can be identified from the data-packet information. The cognitive nature is obtained using Simulated Annealing method. Finally, a stochastic prophesy representation is intended to predict the future health condition of the most related patients based on their present health conditions. Performance assessment of the planned procedure is recognized through general model in the cloud surroundings, that ensures 98% accurateness of prediction and sustain 99% of CPU and bandwidth consumption to decrease the analysis time (C) 2019 Published by Elsevier Ltd.

关键词： Medical data processing Big-data Healthcare monitoring system clustering and classification

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A Comparative Study on Interior Acoustic Comfort Level of Compact Cars using Data Mining Approach

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INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING 2020年第1期17卷 7695-7708页

作者： Azuddin, K. A. Junoh, A. K. Mohamed, Z. Rahman, M. T. Abdul Univ Malaysia Perlis Inst Engn Math Kampus Pauh Putra Arau 02600 Perlis Malaysia Univ Malaysia Pahang Fac Mech Engn Pekan Pahang 26600 Malaysia Univ Malaysia Perlis Sch Mechatron Engn Kampus Pauh Putra Arau 02600 Perlis Malaysia

Vehicle acoustic comfort is one of the ergonomic measurement criteria that are essential for car occupants. Furthermore, interior cabin noise of a car may affect the driver's concentration when driving. This study is to investigate the noise comfort level of car interior on several compact cars. The objective is to measure interior cabin noise for all three cars and then to compare their acoustic comfort level using subfield data mining approach. A deduction was made to rate the best car among the three in term of acoustic comfort. The interior cabin noise was obtained for the cases where engine speed is varied while the cars are in stationary and moving condition. The noise was assessed according to pre-determined subjective and objective criteria. The sound quality parameters was assessed by regression analysis. In subjective assessment, the recorded noise was evaluated based on jury assessment. Then, the data mining approach was implemented to illustrate the noise level. The collected noise data were divided into five clusters through hierarchical clustering method. To assess the accuracy of noise data clusters, the method of k-nearest neighbours was performed and the results show a high accuracy rate (> 95%). Finally, the interior noise of the three cars was compared by using the analysis of variation. The vehicle acoustic comfort index was produced for the three cars tested in this study. In addition, the acoustic quality among the three cars is presented using anova. Annoyance index of the three cars was generated using data mining method. From the results, Axia car model has the best acoustic comfort compared to the other two cars by objective evaluation. By subjective evaluation, Axia car model recorded the lowest level of annoyance.

关键词： Acoustical noise Automotive clustering and classification

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Learning to Predict Population-Level Label Distributions 19

Learning to Predict Population-Level Label Distributions

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World Wide Web Conference (WWW)

作者： Liu, Tong Venkatachalam, Akash Bongale, Pratik Sanjay Homan, Christopher M. Rochester Inst Technol Rochester NY 14623 USA Gleason Corp Rochester NY USA

ISBN: (纸本)9781450366755

Machine learning problems are often subjective or ambiguous. That is, humans solving the same problems might come to legitimate but completely different conclusions, based on their personal experiences and beliefs. In supervised learning, particularly when using crowdsourced training data, multiple annotations per data item are usually reduced to a single label representing ground truth. This hides a rich source of diversity and subjectivity of opinions about the labels. Label distribution learning associates for each data item a probability distribution over the labels for that item, thus can preserve the diversity that conventional learning hides or ignores. We introduce a strategy for learning label distributions with only five-to-ten labels per item by aggregating human-annotated labels over multiple, semantically related data items. Our results suggest that specific label aggregation methods can help provide reliable representative semantics at the population level.

关键词： Subjectivity in crowdsourcing label distribution learning clustering and classification social media

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High-resolution Self-Organizing Maps for advanced visualization and dimension reduction

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NEURAL NETWORKS 2018年 105卷 166-184页

作者： Saraswati, Ayu Van Tuc Nguyen Hagenbuchner, Markus Tsoi, Ah Chung Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Self-Organizing Map Artificial neural network clustering and classification

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Hair Modeling and Simulation by Style

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COMPUTER GRAPHICS FORUM 2018年第2期37卷 355-363页

作者： Jung, Seunghwan Lee, Sung-Hee Korea Adv Inst Sci & Technol Grad Sch Culture Technol Daejeon South Korea

As the deformation behaviors of hair strands vary greatly depending on the hairstyle, the computational cost and accuracy of hair movement simulations can be significantly improved by applying simulation methods specific to a certain style. This paper makes two contributions with regard to the simulation of various hair styles. First, we propose a novel method to reconstruct simulatable hair strands from hair meshes created by artists. Manually created hair meshes consist of numerous mesh patches, and the strand reconstruction process is challenged by the absence of connectivity information among the patches for the same strand and the omission of hidden parts of strands due to the manual creation process. To this end, we develop a two-stage spectral clustering method for estimating the degree of connectivity among patches and a strand-growing method that preserves hairstyles. Next, we develop a hairstyle classification method for style-specific simulations. In particular, we propose a set of features for efficient classifications and show that classifiers trained with the proposed features have higher accuracy than those trained with naive features. Our method applies efficient simulation methods according to the hairstyle without specific user input, and thus is favorable for real-time simulation.

关键词： CCS Concepts •Computing methodologies → Physical simulation clustering and classification Shape analysis

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Combining classification and regression for improving shear wave velocity estimation from well logs data

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 182卷 106260-000页

作者： Du, Qizhen Yasin, Qamar Ismail, Atif Sohail, Ghulam Mohyuddin China Univ Petr East China Key Lab Deep Oil & Gas Qingdao 266580 Shandong Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266071 Shandong Peoples R China Univ Engn & Technol Dept Geol Engn Lahore Pakistan Univ Saskatchewan Dept Civil Geol & Environm Engn Saskatoon SK Canada

A shear wave is a critical property that helps to constrain rock properties such as lithology, pore fluid, and pore pressure. Nevertheless, valid and reliable estimation of shear wave velocity in highly heterogeneous reservoirs is still considered a challenge to be faced. In this paper, we propose a strategy for improving shear wave velocity estimation based on the combination of clustering and classification plus regression using conventional logs. The approach includes two steps. In step one, data clustering algorithms are applied to build a classifier model for identifying the distinct electrofacies (EF) based on similarity of well log responses. In step two, a distinct and specialized regression model is selected from more sophisticated methods (empirical models, statistical regression, virtual intelligence, and rock physics modeling) for determining the final shear wave velocity profile. The effectiveness of this proposed strategy is validated over a dataset from offset wells and their respective measured shear wave velocity data. The final results revealed that the proposed strategy that combines data mining task of clustering and classification plus regression, led to more uniform and better predictive performance of shear wave velocity (R-2 = 0.97) in comparison with the use of stand-alone rock physics model (R-2 = 0.70), virtual intelligence (R-2 = 0.82), and statistical regression (R-2 = 0.91). The silent features of this proposed strategy is that field-specific regression models are built based on log derived EF and geological information.

关键词： Shear wave velocity clustering and classification Multivariate regression Rock physics modeling Neural network

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Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data

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JOURNAL OF MEDICAL INTERNET RESEARCH 2019年第2期21卷 e11757页

作者： Park, Jaram Kim, Jeong-Whun Ryu, Borim Heo, Eunyoung Jung, Se Young Yoo, Sooyoung Seoul Natl Univ Off eHlth Res & Business Bundang Hosp 173Beon Gil 82 Seongnam 13620 South Korea Seoul Natl Univ Dept Otorhinolaryngol Bundang Hosp Seongnam South Korea Seoul Natl Univ Dept Otorhinolaryngol Coll Med Seoul South Korea Seoul Natl Univ Dept Family Med Bundang Hosp Seongnam South Korea

Background: Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension. Objective: Our goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level. Methods: We used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients' medical history across all health care providers in Korea (N= 51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics. Results: Machine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=. 772, external test F1-score=. 613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of feat

关键词： health risk appraisal risk hypertension chronic disease clustering and classification decision support systems

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GATE: classification and clustering of text for Semi-vowel /j/-Morphophonemic approach

GATE: Classification and Clustering of text for Semi-vowel /...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Reddy, K. Sairam Sasanka, K. Prasanna, S. Venkatesan, R. SRC Sastra Univ CSE Kumbakonam India

ISBN: (纸本)9781509012770

In recent years, many successful machine learning applications have been developed. classification & clustering is one such. This application is cross-disciplinary, now that it is based on data mining algorithms on the technical side and on graphemes and morphophonemic on the linguistic side. It will thus map the correspondence between grapheme < y > and related phonemes via morphemes in a given context. The grapheme < y > is often realized as an approximant /j/ (a consonant phoneme), as in < yacht >;or as vowel / I / as in < racy >, or a diphthong / aI / as in < sky >, etc. The objective, that is to say, of this text analysis is to map the various articulators//phonemic realizations of the grapheme < y >. This experiment will thus help the study the occurrence of < y > in different positions in words, word initially, finally or elsewhere, as in these examples. The training data (or corpus) chosen for this experimentation is a set of English literary texts [Harry Potter part 1, part 2, The complete works of William Shakespeare By William Shakespeare, The Adventures of Sherlock Holmes By Arthur Conan Doyle, (A Christmas carol) By Charles Dickens's. As for the tools, the alphabet recognizer is used for retrieving information and categorizing the data as noun, adjective, etc. Then, GATE Developer, the Text engineering tool is used to analyze the dataset and to derive the output with statistical data of global distribution < y > as in all the input documents. This project is particularly relevant because it offers, as will be demonstrated, a solution to problems due to lack of one-to-one correspondence between spelling and pronunciation in English, as for instance, in the context of language pedagogy. Glossary of technical terms (of both linguistics & computing) is appended, now that the paper is inter-disciplinary.

关键词： clustering and classification machine learning Text mining GATE graphane morphme phoneme morphophonemic mapping NLP

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