bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global dynamics of a quintic Liénard system with

Z2

-symmetry I: saddle case
Authors: Chen Hebai , Tang Yilei , Xiao Dongmei 
Published: June 2021

Abstract
In the paper we deal with a quintic Liénard system of the form

x

̇

=
y
−

(

a

1

x
+

a

2

x

3

+

a

3

x

5

)

,

y

̇

=

b

1

x
+

b

2

x

3

with a

Z

2

-symmetry, where

a

1

,

a

2

,

b

1

∈
R

and a
3
b
2 ≠ 0. A complete study of this system with b
2 > 0, called the saddle case, is finished, showing that the system exhibits at most two limit cycles, and the necessary and sufficient conditions are obtained on the existence of two limit cycles and a two-saddle heteroclinic loop. We also present a global bifurcation diagram and the corresponding phase portraits of this system, including Hopf bifurcation, Bautin bifurcation, two-saddle heteroclinic loop bifurcation and double limit cycle bifurcation.
Compressible Navier–Stokes equations with heterogeneous pressure laws
Authors: Bresch Didier , Jabin Pierre-Emmanuel , Wang Fei 
Published: June 2021

Abstract
This paper concerns the existence of global weak solutions à la Leray for compressible Navier–Stokes equations with a pressure law which depends on the density and on time and space variables t and x. The assumptions on the pressure contain only locally Lipschitz assumption with respect to the density variable and some hypothesis with respect to the extra time and space variables. It may be seen as a first step to consider heat-conducting Navier–Stokes equations with physical laws such as the truncated virial assumption. The paper focuses on the construction of approximate solutions through a new regularized and fixed point procedure and on the weak stability process taking advantage of the new method introduced by the two first authors with a careful study of an appropriate regularized quantity linked to the pressure.
Adaptive Identification of Control Objects in Systems with Standard Controllers
Authors: Yakovis L.M. , Strongin P.Ya. 
Published: June 2021

Abstract
The report is devoted to the study of the problem of parametric identification of controlled dynamic systems in their normal operation mode using a model with adjustable parameters. The task of determining parameters of test signals providing the specified accuracy of identification is considered. The peculiarity of the work consists in imparting adaptive properties to the identification procedure, which makes it possible to minimize the negative effect of “swinging” of the controlled object on its output when special test signals are applied.
Designing Online Healthcare Using DDD in Microservices Architecture
Authors: Rizki M , Fajar A N , Retnowardhani A 
Published: June 2021

Abstract
Today, the system development had been transformed to service-oriented architecture. It more specific in microservices architecture to responds the agility and flexibility in software maintenance. This transformation is an important part that promoting independence, speed and security, while still kept balanced with the company’s condition through comprehensive and effective steps. Culture of the development cycle of a company highly affected by the adaptation of microservices architecture. Thus, in order to achieve flexibility, speed and security, which are the main goal of adapting microservices, changes must be deliver well and sustainably. This study focused on designing of microservices architecture for health system using domain driven design (DDD) approach.
Prognosis of Diabetes Mellitus with Transfer Learning-Based Naïve Bayes Method
Authors: Andriani A , Hartati S 
Published: June 2021

Abstract
Early detection of diabetes mellitus (DM) prognosis against several diseases cannot be done medically in the short term. Supervised Learning method can be used to classify diabetic patient data to detect the prognosis of DM. There are several problems in the classification of patient medical record data. First, medical record data is not always good, namely structured and complete. Second, DM patient medical record data from various sources do not necessarily have the same parameters. The impact of supervised learning on a dataset of DM patients is not necessarily applicable to a dataset of DM patients from different sources, so that the learning outcomes are not long-lived learning. The proposed method in this study is data classification with the Naïve Bayes method based on transfer learning by applying learning outcomes in the source domain to the target domain as a starting point for learning in the target domain. The method applied uses parameter-based transfer learning so that it can be used to overcome parameter differences in two different datasets.
Neural Network Enhancement Forecast of Dengue Fever Outbreaks in Coastal Region
Authors: Harumy THF , Ginting Dewi Sartika Br. 
Published: June 2021

Abstract
Dengue Fever is among the world’s fastest-spreading mosquito-borne illnesses. In Indonesia, more than 33 percent of the world community is in danger. The Coastal Zone is one of the region most at risk of contracting dengue fever, particularly from the social and environmental sectors, so an early diagnostic study must deal with this efficiently and effectively. This research aimed to predict and identify the coastal areas with the most severe dengue fever potential to avoid dengue fever. The methodology used is a neural network-based sensitivity Analysis and multiple linear regression. Bagan Deli, Sibolga, Tapanuli Tengah, Langkat, Medan are samples of the coastal regions used in this study. The reports used was secondary data for dengue fever patients suffering and meteorological parameters, the model used in [5-5-1] for prediction, for five years from 2014-2019. The results showed which Langkat (0.4936), Serdang Bedagai (0.4695), The Middle Of Tapanuli (0.4399), Medan (0.4313), and Sibolga (0.3133) are perhaps the most prevalent areas affected by dengue fever with a value of 89.8 percent. The Result is temperature and humidity are the conditions that most affect the transmission of dengue fever.
Thermal Analysis on Heat Pump Chili Drying Chamber with variations in the direction of air flow using Computational Fluid Dynamics
Authors: Siburian A H , Silalahi P G , Romauli N D M , Ambarita H 
Published: June 2021

Abstract
Drying chilies using a heat pump includes drying at low temperatures so that the temperature distribution and flow direction in the drying chamber need to be considered. The distribution of temperature and flow direction in the heat pump drying room with a drying room size of 1 m × 0.8 m × 0.75 m to dry 1 kg of chilies built in the Sustainable Energy and Research Center (SERC) laboratory is not good enough to make the drying time longer. The procedure has been established with the aim of evaluating the speed distribution and temperature of the drying air in the drying chamber to determine the need for redesign. Computational Fluid Dynamics (CFD) is used to analyze flow patterns that occur in the drying chamber where in this study two types of flow directions that enter the heat pump chili drying chamber are compared. CFD has replaced the classical numerical analysis method of drying processes based on experimental models. Analytical methods and experimental methods are limited in describing the distribution of temperature and air flow in the drying chamber. In this case, the finite element method is used in the form of CFD analysis where the geometry of the drying chamber is divided into several analyzed elements. After doing the CFD simulation, it is obtained that the heat pump chili drying chamber model with the flow direction from below the temperature increases faster than the heat pump chili drying chamber model with the flow direction from the side. The simulation results show that the drying room with the direction of hot air flow from below, the temperature of the dried chilies increases faster and the temperature distribution is better than the drying room with the flow direction from the side. In both models of the drying chamber, the temperature value is obtained in the timestep 600, namely in the drying chamber model with hot air flow from the side is 305,569 K and in the drying chamber model with hot air flow from below is 313,697 K. For the drying model in this heat pump chili drying room it is advisable to use the flow direction from below because it is better. This solution can also be offered to get the right drying chamber model.
Performance Analysis Similarity Matrix, Responsibility Matrix, Availability Matrix, Criterion Matrix of Affinity Propagation
Authors: Dhany Hanna Willa 
Published: June 2021

Abstract
Each process in classifying data into several clusters or grouping so that the data in one cluster has a maximum similarity level and between clusters has a minimum similarity is called clustering. Clustering is divided into 2 approaches in its development, namely the partitioning and hierarchical approach to clustering[1]. The Water Quality Status dataset has 8 attributes, 4 classes and 120 instances, Class distribution is good condition (30 instances), lightly polluted (30 instances), medium polluted (30 instances) and heavily polluted (30 instances). 70% of the data will be used as training data and 30% of the data will be used as randomized test data. The simplify the process of completing the performance calculation of the clustering model, the research implementation was carried out using the MATLAB function. That iteration is carried out with the number of clusters generated from 100 to 2,500 iterations with the results of the number of clusters as many as 10 clusters. In the experiment, iteration amounted to 5000 and there was a change in the results of the number of clusters by 9 clusters. After re-testing using the number of iterations of 10,000-50,000 iterations, but the number of clusters produced did not change anything at all. So that the conclusion in testing the AP method produces the most optimal number of clusters of 10 clusters.
Research Review on Big Data Usage for Learning Analytics and Educational Data Mining: A Way Forward to Develop an Intelligent Automation System
Authors: Yunita A , Santoso H B , Hasibuan Z A 
Published: June 2021

Abstract
Digitalization and the development of information technology, especially Artificial Intelligence, have been embraced in all fields. At the same time, data has grown mostly from the digital footprints or any technology information system. The development of technology and big data offers enormous opportunities to conduct big data analytics in any field, including education. This study aims to review current research related to big data analytics in education and explain future research direction. Using Kitchenham’s technique, we selected and clustered the literature into the types of data, methods, type of data analytics and learning analytics application used. The results show that research of big data learning analytics generally aims to improve the learning process, analyze learner behaviour for student profiling, improve student retention and evaluate student feedback in the context of MOOCs and Learning Management System. Several future directions for this topic are: 1) building a big open dataset including data pre-processing and addressing the problem of imbalanced dataset, 2) process mining for learning log activity to gain knowledge and insights from online behaviour, not only from the perspective of the learner but also from the activities of the teacher, 3) designing an automated framework which uses big data and allows descriptive, predictive, prescriptive analytical learning to be carried out. To summarize, embracing big data to learning analytics and educational data mining is an open research area that seems very powerful in education.
Adhd Classification From FMRI Data Using Fine Tunining in SVM
Authors: Anitha S , Thomas Geroge S 
Published: June 2021

Abstract
ML (Machine learning) is a subset of AI and also improved learning technique has different performance in result over the conventional ML determining the complexity in structures of dimensional data. ADHD is one of the most important neurological disorders and it is represented by different symptoms and we can extract useful the information from FMRI time series. In this paper the ADHD identification and classification is obtained by machine learning techniques. This paper explores an artificial intelligence in unsupervised learning is appropriate to learn features from raw data. The proposed system presented with two stage approaches for ADHD diagnosis which associated SoftMax Regression and SVM fine tuning approach. In the implementation part used FMRI brain images are data sets. The two stage approach shows the high accuracy in performance by using the learning techniques.
Side Effects of Mercury Based Clinical Thermometer and India’s Trade Performance to Select Countries – an Analysis
Authors: Kalpana Sai B. , Senith S. , Anthony Raj S. 
Published: June 2021

Abstract
A clinical thermometer is a device that tests the temperature of a person’s body. Thermometers are now commonplace not only in hospitals and clinics, but even in offices, institutes, and even several households. Though the invention of digital thermometer and other temperature measuring have been launched, sold in the market, and shrink the local and global demand, still Galileo’s Mercurian is wanted by a specific market. In this article, a detailed analysis is performed about India’s export and import of clinical thermometer for select countries.
Literature Review on Estimation of Drug Parameters Using Physiologically-Based Pharmacokinetic Modeling
Authors: Ambikadevi B. , Subha Hency Jose P. 
Published: June 2021

AbstractPhysiologically based pharmacokinetic (PBPK) modelling is a computing technique used to identify the pharmacokinetic properties of humans and other animal species. This technique is used to identify various drug parameters. One of the parameters is bioavailability. Bioavailability is defined as the fraction of substances being absorbed by the body. Contrasts in bioavailability, realizing whether drug formulations are identical is necessary. The area under the plasma concentration time curve which is AUC determines the bioavailability of the drug. PBPK model is a significant modelling to determine the pharmacokinetic parameters. The goal of this paper is to review various literature which uses PBPK model in different pharmaceutical applications in different populations evaluating, verifying, predicting and identifying different pharmacokinetic and drug parameters.
Pre-Diabetic Retinopathy identification using hybridGenetic Algorithm-Neural Network classifier
Authors: Mohamed Jebran P , Gupta Shweta 
Published: June 2021

Abstract
Diabetic retinopathy (DR) is one of the main prevalent diabetes problems, causing blurry vision and degeneration amongst adults of working age. The first symptoms of DR include Microaneurysms (MA). A Genetic Algorithm-Artificial Neural Network (GA-NN) technique is developed for early diagnosis of DR. There are five steps of the proposed framework. Image pre-processing is achieved using r-polynomial transformation. In the extraction, the K-means algorithm is used to segment blood vessels, and candidate patches were generated. Shape attributes, GLCM and LBP features have been derived from excluded blood vessel image and from patches separately. To achieve independent classification, GA-NN classifiers is employed. The ultimate decision system projects the MA or non-MA class labels by plurality voting for eachclassifier. This methodology was tested on two databases: e-Ophtha-MA and DIARETDB1. The e-ophtha-MA and DIARETDB1 datasets had AUCs of 0.89 and 0.87, respectively, on the receiver operating characteristic (ROC) curve.
Automatic Indoor Air Quality Control of COVID -19 Patient Facilities Using Type-2 Fuzzy Controller
Authors: Naskar Indrajit , Pal A K 
Published: June 2021

Abstract
Big facilities can be used to set up cohort wards for severe and critical COVID-19 patients. The purpose of carbon dioxide and humidity control is to provide healthy air for breathing by both diluting the pollutants originating in the building and removing the pollutants from it. In this paper, for proper mechanical ventilation to maintain indoor air quality (IAQ), a Self-tuned Interval Type-2 Fuzzy PI controller (SIT2FPIC) is proposed. 49 fuzzy if-then rules are used to tune the Interval Type-2 Fuzzy PI controller (IT2FPIC) for proper functioning and to reduce the parameter variations. Correlations of CO2 concentration and air humidity are studied to maintain the air quality in COVID-19 facilities. Mathematical models of CO2 concentration and air humidity in hospitals or temporary COVID-19 facilities are developed considering the presence of COVID-19 patients, doctors, nurses, etc in the room. The control and monitoring of room air quality are very much essential for any infectious diseases like coronavirus. To maintain the air quality in terms of humidity and carbon dioxide, the proposed SIT2FPIC is applied to the mathematical model of humidity and carbon dioxide for the simulation study. The effectiveness of the proposed controller in mechanical ventilation in the treatment facilities is shown by a comparative study of different transient parameters. Effects of load variations in the model are also studied to check the usefulness of the proposed scheme.
Lipid Data Acquisition for devices Treatment of Coronary Diseases Health stuff on the Internet of Medical Things
Authors: Pradeep Raj M S , Manimegalai P , Ajay P , Amose John 
Published: June 2021

Abstract
Recently, the widespread deployment of smart phones globally, biomedical diagnostics. Smartphone-based devices are expected to be commonly used Intelligent Health Tracking Point-of-Care (PoC) Internet of Medical Things (IoMT) applications. As a result, this paper presents a smartphone-based blood lipid data acquisition dongle for measuring blood lipid levels such as TC, HDL-C, and triglycerides (TG). Blood loss in the fingertip to manage coronary artery disease. A thin photochemical during detection. The test strip, composed of LEDs and detectors, is plugged into a small dongle where the colour switches. The intensities of the calculated reflective coefficient of the lipide determinant are indicated. The product of chromogenics. Such photochemical data acquisition data dongle was focused on smart phones. Validated and achieved a correlation coefficient above 0.843 by reference to a clinical analyzer and summed. 5.017 per unit variance coefficient (CV) for 93 blood lipid patients assessment. The photochemistry Therefore, dongle is promising for the potential treatment of IoMT chronic diseases.
A Noval Method for Gait And Tremor Quantification In Parkinson’s Disease Patients
Authors: Sai Sri Lakshmi M , Mridula Rukmini , Borgohain Rupam 
Published: June 2021

Abstract
Tremor and gait disturbances are one of the key features in Parkinson’s Disease. There is an unmet need to evaluate the complex motor disturbances, especially tremor, gait and balance disturbances, that manifest and progress with the duration of disease. Till date there exists no validated system which can quantify gait and tremor, the values of which can be used clinically to assess patients. To analyze the tremor, we used MEMS based triaxial accelerometers attachable to the region of interest, mainly knuckles. And for gait assessment, we used snug fit shoes attached with switches at prefixed points. Data was collected and analyzed offline in MATLAB. With this instrument we were able to successfully measure gait and tremor in the patients. Standardization with complete removal of human intervention in this whole process of assessment must be studied further.
Smart Shopping Trolley System using IoT
Authors: Muralidharan J. , Muthukumaran N. , Ranjith Kumar R. , Rubika M. 
Published: June 2021

Abstract
Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today’s life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds. When there are exclusive sales and discounts, the urgency is intensified. Here we will use IOT based technology for people buy various things and place them in a trolley. When they have completed their order, they must pay at the billing desk. The Bills generated by a RFID reader which is a time-consuming procedure that leads to long queues at billing counters. The key objective is to build a framework that can be used in shopping malls to fix the issue described above. Both trolleys would be fitted with the system. It will require an RFID reader; RFID tags would be found in many of the mall’s goods. When a consumer places items in the trolley, the code is read, and the price of certain items is saved in the admin portal. When we add items to the cart, the device depicts the item’s name, number, expiration date, and rate, which is then applied to the overall bill. As a result, billing will take place within the trolley itself. The complete bill data will be passed to online portal Wi-Fi Module with trolley id, and we will be able to pay the balance either online or at bill counter by scanning the RFID tag placed in the trolley. In-season inventory reductions are often applied in transactions through admin, and item stock may be recorded. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.
Stage of lung cancer and toxic content determination in the lung walls using X-Ray image
Authors: Kumaraguru S , Deepa AR , Anil Kumaar K M , Bhuvaneswari M. 
Published: June 2021

Abstract
Massive global, developing nations depend earlier detection of disease. Automated analysis of both the lung domains is a vital part in the machine diagnosis of digital Chest radiography. In this report, using image sequence patient specific adaptive lung structures that identify lung parameters, outpacing nation performance, we introduce a semi verification comprehensive lungs segmentation method. The goal of this study is to examine an effective and efficient image segmentation technique to calculate the analysis of computerized tomography (CT) application of analytical by clinicians. Current methodologies of diagnostic imaging produce massive images that are incredibly unpleasant to manually analyse. The consequences of segmentation algorithms improve the accuracy and also the duration of converge. There seems to be a strong need anyway period and develop to apply new developmental algorithms for solving problems. Image classification segmentation associated with. Lung cancer among men is the most commonly diagnosed cancer globally. In order to protect human lives, premature diagnosis of lung cancer changes to felicitous therapy.
The role of EMI/EMC for the medical devices according to IEC – 60601-1 standards
Authors: Siddeeq Mohammad Abubakar , Mythili A , Ahamed Syed Junaid 
Published: June 2021

Abstract
This paper gives information about the important role of the EMI/EMC for the medical devices according to the standard IEC-60601-1. PRE-Compliance testing is mandatory for all medical devices. The main objective of pre-compliance tests is all about whether the device is affected by electromagnetic interference. Electromagnetic interference is the disruption of an electronic device when in the vicinity of electromagnetic energy of an external source either man-made or naturally occurring