scholarly journals Mobile Application to Detect Covid-19 Pandemic by Using Classification Techniques: Proposed System

2021 ◽  
Vol 15 (16) ◽  
pp. 34
Author(s):  
Azhar Al-zubidi ◽  
Nadia F. AL-Bakri ◽  
Rajaa K. Hasoun ◽  
Soukaena Hassan Hashim ◽  
Haider Th.Salim Alrikabi
Keyword(s):  
Mobile Application ◽  
Clustering Algorithm ◽  
Information Gain ◽  
Respiratory Infections ◽  
Back Propagation ◽  
The Public ◽  
Medical Tests ◽  
Fcm Clustering ◽  
Two Stages ◽  
Matlab Programming

<p class="0abstract">Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy.  The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing.</p>

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

IMPLEMENTASI ALGORITMA LEVENSTEIN DALAM MENGKOMPRESI TEKS PADA APLIKASI RANGKUMAN PENGETAHUAN ALAM LENGKAP

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Winda Winda ◽  
Taronisokhi Zebua
Keyword(s):  
Compression Ratio ◽  
Mobile Application ◽  
Storage Space ◽  
Compression Technique ◽  
The Public ◽  
Natural Knowledge ◽  
Database Size ◽  
Text Database

The size of the data that is owned by an application today is very influential on the amount of space in the memory needed one of which is a mobile-based application. One mobile application that is widely used by students and the public at this time is the Complete Natural Knowledge Summary (Rangkuman Pengetahuan Alam Lengkap or RPAL) application. The RPAL application requires a large amount of material storage space in the mobile memory after it has been installed, so it can cause this application to be ineffective (slow). Compression of data can be used as a solution to reduce the size of the data so as to minimize the need for space in memory. The levestein algorithm is a compression technique algorithm that can be used to compress material stored in the RPAL application database, so that the database size is small. This study describes how to compress the RPAL application database records, so as to minimize the space needed on memory. Based on tests conducted on 128 characters of data (200 bits), the compression results obtained of 136 bits (17 characters) with a compression ratio is 68% and redundancy is 32%.Keywords: compression, levestein, aplication, RPAL, text, database, mobile

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

scholarly journals Third Party Certification of Agri-Food Supply Chain Using Smart Contracts and Blockchain Tokens

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5307
Author(s):  
Ricardo Borges dos Santos ◽  
Nunzio Marco Torrisi ◽  
Rodrigo Palucci Pantoni
Keyword(s):  
Supply Chain ◽  
Mobile Application ◽  
Consumer Behaviour ◽  
Economic Incentives ◽  
Third Party ◽  
Smart Contracts ◽  
Proof Of Concept ◽  
Socially Responsible ◽  
The Public ◽  
Blockchain Technology

Every consumer’s buying decision at the supermarket influences food brands to make first party claims of sustainability and socially responsible farming methods on their agro-product labels. Fine wines are often subject to counterfeit along the supply chain to the consumer. This paper presents a method for efficient unrestricted publicity to third party certification (TPC) of plant agricultural products, starting at harvest, using smart contracts and blockchain tokens. The method is capable of providing economic incentives to the actors along the supply chain. A proof-of-concept using a modified Ethereum IGR token set of smart contracts using the ERC-1155 standard NFTs was deployed on the Rinkeby test net and evaluated. The main findings include (a) allowing immediate access to TPC by the public for any desired authority by using token smart contracts. (b) Food safety can be enhanced through TPC visible to consumers through mobile application and blockchain technology, thus reducing counterfeiting and green washing. (c) The framework is structured and maintained because participants obtain economical incentives thus leveraging it´s practical usage. In summary, this implementation of TPC broadcasting through tokens can improve transparency and sustainable conscientious consumer behaviour, thus enabling a more trustworthy supply chain transparency.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals Tackling antimicrobial resistance in the community

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Tonkie-Crine ◽  
L Abel ◽  
O Van Hecke ◽  
K Wang ◽  
C Butler
Keyword(s):  
Antibiotic Resistance ◽  
Community Care ◽  
Respiratory Infections ◽  
Antibiotic Prescription ◽  
Future Research ◽  
Antibiotic Prescribing ◽  
Acute Respiratory Infections ◽  
Community Settings ◽  
The Public ◽  
Need Support

Abstract Antibiotic prescription is a major driver of antibiotic resistance. The majority of antibiotic prescribing occurs in community care settings, often for respiratory infections. A substantial proportion of prescriptions are issued not according to guidelines, particularly for acute respiratory infections which can be self-limiting. Prescribers in these settings need support to prescribe antibiotics more prudently. Patients and the public also need support to manage infections which are self-limiting. This presentation presents a summary of how antimicrobial stewardship (AMS) activities are being used in community settings. Firstly, types of community-level interventions are discussed, including those aimed at clinicians, patients and the public. Next, we assess interventions based on their effectiveness at reducing antibiotic prescriptions or use and their cost-effectiveness. Finally, we discuss directions for future research and consider how the research to date could influence policy.

scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

Improved Clustering Algorithm of Spatial Data Structure Based on Matlab Simulink

2014 ◽  
Vol 608-609 ◽  
pp. 98-102
Author(s):  
Shan Mei Xiong ◽  
Ru Lian Wu ◽  
Hui Wang
Keyword(s):  
Spatial Data ◽  
Clustering Algorithm ◽  
Geographical Location ◽  
Calculation Model ◽  
Space Structure ◽  
Urban Tourism ◽  
Programming Algorithm ◽  
Tourism Destination ◽  
Consumption Structure ◽  
Matlab Programming

This paper has introduced the clustering algorithm into the model of urban tourism destination consumption structure, and has used MATLAB programming algorithm to improve the calculation model of consumption structure for tourism destination, which has obtained the spatial data model of the consumption structure. The model roundly considers the influence of geographical location, cultural factors, political factors and economic factors, and it establishes new clustering algorithm model with four coefficients, and has realized the algorithm by the use of MATLAB programming. Finally, the consumption structure of the same destination in different provinces is calculated by using the spatial system model, which has obtained the calculation curve of consumption space structure and the clustering results, and has provided technical reference for the research on consumption of urban tourism destination.

scholarly journals Metalearning approach for leukemia informative genes prioritization

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Vânia Rodrigues ◽  
Sérgio Deusdado
Keyword(s):  
High Performance ◽  
Information Gain ◽  
Pearson Correlation ◽  
Microarray Gene Expression Data ◽  
Microarray Gene Expression ◽  
The Public ◽  
Leukemia Diagnosis ◽  
Chi Squared ◽  
Logistic Regression Method ◽  
Single Attribute

AbstractThe discovery of diagnostic or prognostic biomarkers is fundamental to optimize therapeutics for patients. By enhancing the interpretability of the prediction model, this work is aimed to optimize Leukemia diagnosis while retaining a high-performance evaluation in the identification of informative genes. For this purpose, we used an optimal parameterization of Kernel Logistic Regression method on Leukemia microarray gene expression data classification, applying metalearners to select attributes, reducing the data dimensionality before passing it to the classifier. Pearson correlation and chi-squared statistic were the attribute evaluators applied on metalearners, having information gain as single-attribute evaluator. The implemented models relied on 10-fold cross-validation. The metalearners approach identified 12 common genes, with highest average merit of 0.999. The practical work was developed using the public datamining software WEKA.

Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

2022 ◽  
pp. 1-9
Author(s):  
P. Vijayalakshmi ◽  
K. Muthumanickam ◽  
G. Karthik ◽  
S. Sakthivel
Keyword(s):  
Clustering Algorithm ◽  
Water Drop ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Uterine Wall ◽  
Health Issues ◽  
Testing Dataset ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Difference

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

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