Journal of Software Engineering & Intelligent Systems

ISSN: 2518-8739 (Online)
DECEMBER 2019 | VOLUME. 4  ISSUE. 3
Title:

A SUPPORT VECTOR MACHINE BASED HEART DISEASE PREDICTION

Authors:

Tsehay Admassu Assegie

Abstract:

Disease classification problem can be solved by building machine learning models by training a machine to identify disease classes. The classification of disease is achieved by using machine learning algorithm like Support Vector machine (SVM). A SVM is a machine learning approach in which the machine uses predefined labels from the known set to determine or predict new classes of disease which has never seen before. In this paper, we used the standard kaggle heart disease dataset for classification of heart disease using a SVM learning algorithm. Finally, the accuracy of SVM is evaluated and the evaluation result shows 73.41% accuracy on heart disease classification.

Keywords: machine learning; heart disease classification; support vector machine; heart disease; prediction;
Available: A SUPPORT VECTOR MACHINE BASED HEART DISEASE PREDICTION by Tsehay Admassu Assegie is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

REVIEW ON USING AGILE METHODS IN CLOUD COMPUTING

Authors:

Mahdi Mousaei

Abstract:

Today, many cloud computing companies use agile methods to deliver software. Agile development methods and cloud computing are able to collaborate with together. Agile methods are suitable for the cloud computing environment because of their iterative approach and flexibility. The main benefits of using agile software development in cloud applications results in the improved service delivery and lesser costs. There are many advantages to using agile development with cloud computing. By using agile methods in cloud computing can overcome delays and increase the development speed of projects. Moreover, by using agile methods the resources of cloud computing may increase the speed of the projects. This paper describes the relationship between the agile methods and cloud computing.

Keywords: agile development; cloud computing; agile in cloud computing; agile and cloud development;
Available: REVIEW ON USING AGILE METHODS IN CLOUD COMPUTING by Mahdi Mousaei is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

CONVOLUTIONAL NEURAL NETWORK FOR TEXT CLASSIFICATION ON TWITTER

Authors:

Agung Triayudi

Abstract:

Natural Language Processing with a combination of Neural Network methods such as Convolutional Neural Network (CNN) that is included in the Deep Learning method and carries out a repetitive learning process to get the best representation of each word in the text. CNN Works by finding the pattern of a word among other words in the input matrix. The learning process in several convolution layers is carried out parallel and in sequence. Thus, each word is independent of other words around it. Twitter is a source of data that interests researchers to make research objects. However, the text in tweets contains many non-formal languages, abbreviations and everyday languages. Thus, it is more difficult to identify the information in it, when compared with the formal text. In this research, the Natural Language Processing method is implemented using the CNN algorithm to classify information related to the emergency-respond phase. This classification model was trained using two types of datasets, namely the crawling dataset of 1967 texts, and the dataset in the form of tweet texts from Twitter totaling 853 sentences and tested using 89 different text tweets. From the results of 3 iterations with 10 epoch training per iteration, an accuracy of 98% was obtained and a loss of 4% was obtained. Thus, it can be concluded that the algorithm functions optimally in identifying information.

Keywords: convolutional neural network; twitter; datasets; text classification; deep learning;
Available: CONVOLUTIONAL NEURAL NETWORK FOR TEXT CLASSIFICATION ON TWITTER by Agung Triayudi is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

DIAGNOSIS MODEL OF SOY BEANS DISEASES USING NEURO-FUZZY SYSTEM

Authors:

Ibrahim Rahmon, Olutayo Ajayi, Monday Eze, Jonah Joshua

Abstract:

Soybean is an important legume crop, extensively cultivated for food on which low-income population highly depend on its proteineous nutrient on daily basis for food. and oil. Soy beans consumption have been the major cheap protein-rich grain useful for treatment of malnutrition among children, for fighting against diabetes, high blood pressure, etc. Despite the nutritional and economic value of soy bean crop, a variety of pest attack such as fungi, nematode, bacteria and viruses are speedily becoming a constraint to quality and bountiful harvest. The effort of farmers to specifically identify the specific pest responsible for damaging of plantsí segment such as roots, stem, pod and leaves still remain vague and imprecise to many farmers. In this work, a neuro-fuzzy system was built with MATLAB version 8 with100 rules on six input parameter as linguistic variable or symptoms into the system to determine the disease type either as fungi or bacteria or virus and to also determine intensity rate, that is, level of damage, as the output in form of a crisp. The proposed Neuro-Fuzzy system was developed through MATLAB software using Adaptive Neuro-Fuzzy Inference System (ANFIS) box. ANFIS hybridizes the learning capacity of neural network with if-then rules of fuzzy logic to learn and design the most fitted membership function for a given set of data and thereby map the input with output. The proposed Neuro-Fuzzy System consisted of five stage: input stage, fuzzification, rule base, inference engine and defuzzification. The output of the system was to produce results for the decision maker to provide solution regarding the treatment of the infected plant for bountiful and quality harvest.

Keywords: neuro-fuzzy system; crisp; matlab; fuzzification; de-fuzzification;
Available: DIAGNOSIS MODEL OF SOY BEANS DISEASES USING NEURO-FUZZY SYSTEM by Ibrahim Rahmon, Olutayo Ajayi, Monday Eze, Jonah Joshua is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

SYSTEMIC LUPUS ERITHEMATOSUS DISEASE DETECTION EXPERT SYSTEM IN IMMUNOLOGY USING WEB-BASED DEMPSTER-SHAFER METHOD

Authors:

Amelia Fadjrin KamalL, Agung Triayudi, Ira Diana Sholihati

Abstract:

Autoimmune is a disease caused by healthy cells or human immune system being attacked. Thus, the body must be able to distinguish between its own antigens and foreign antigens. There are also several types of autoimmune diseases, one of which is lupus. Lots of people underestimate their health, and because of the lack of information about the disease, an application in the form of an expert system is needed to diagnose symptoms and provide information about Lupus. In this study, the Dempster-shafer method is used because it is believed to be able to combine separate pieces of information to be able to calculate the likelihood of an event. And the results of this study indicate that the system that works to diagnose Lupus has a level of accuracy of; 80% with the system testing of 15 data conducted by direct experts.

Keywords: immunology; expert system; systemic lupus erythematotus; dempster-shafer; web based;
Available: SYSTEMIC LUPUS ERITHEMATOSUS DISEASE DETECTION EXPERT SYSTEM IN IMMUNOLOGY USING WEB-BASED DEMPSTER-SHAFER METHOD by Amelia Fadjrin KamalL, Agung Triayudi, Ira Diana Sholihati is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

SYSTEM PERFORMANCE ANALYSIS OF THE MINISTRY OF TOURISMíS ICT DEPARTMENT USING COBIT 4.1 AND IT BALANCED SCORECARD

Authors:

Aulia Putri Sakinah, Yuhilza Hanum

Abstract:

Information Communication and Technology (ICT) Department is one of the department in the ministry of tourism. Information system governance directs and controls an organization in achieving organizational goals by balancing the risks and benefits of the information system and its processes. COBIT 4.1 was developed by the IT Governance Institute (ITGI) which provides business process oriented guidelines to assist in optimizing investment in information systems by providing a measure of system performance assessment. Recommendations for improvement based on the results of the level of maturity from COBIT are formulated with the IT Balanced Scorecard using Critical Success Factor (CSF) and Key Performance Indicator (KPI). The research focuses on aligning strategic objectives in the ICT Department of Ministry of Tourism and information systems performance analysis methods which is COBIT 4.1 and recommendations for improvement using the IT Balanced Scorecard perspectives. The result of performance measurement will be the basis of the recommendations proposed in this research are expected to assist in the improvement and development of performance of Information System in ICT Department of Ministry of Tourism.

Keywords: COBIT 4.1; critical success factor; ICT; IT balanced scorecard; key performance indicator;
Available: SYSTEM PERFORMANCE ANALYSIS OF THE MINISTRY OF TOURISMíS ICT DEPARTMENT USING COBIT 4.1 AND IT BALANCED SCORECARD by Aulia Putri Sakinah, Yuhilza Hanum is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Title:

APPLICATIONS OF MACHINE LEARNING IN EDUCATION AND HEALTH SECTOR: AN EMPIRICAL STUDY

Authors:

Komal Hafeez, Qanetah Ahmed

Abstract:

In todayís world machine learning has been incorporated in almost every system/application/algorithm whether it is a hospital management system or a learning management system. This paper lays emphasis on the role of machine learning in everyday life, understanding the many ways of selecting the right technique of ML for your system/application and discussing the various social networking applications that make use of machine learning in their back-end algorithm. The purpose of this research paper is to understand machine learning alongside its usage in the fields of education and health.

Keywords: machine learning; health sector; education; empirical study; algorithms;
Available: APPLICATIONS OF MACHINE LEARNING IN EDUCATION AND HEALTH SECTOR: AN EMPIRICAL STUDY by Komal Hafeez, Qanetah Ahmed is available under cc by-nc 4.0 License on 31st December 2019. Permissions beyond the scope of this license may be available at JSEIS.
Download PDF

Archive

Volume 5:
Issue 2   New
Issue 1
Volume 4:
Issue 3
Issue 2
Issue 1
Volume 3:
Issue 3
Issue 2
Issue 1
Volume 2:
Issue 3
Issue 2
Issue 1
Volume 1:
Issue 2
Issue 1
Journal PicsCaomei Publishers © 2016-2020
f t