scholarly journals Penerapan Algoritma Rule Base dengan Pendekatan Hexadesimal pada Transliterasi Aksara Bima Menjadi Huruf Latin

2020 ◽  
Vol 2 (1) ◽  
pp. 130-141
Author(s):  
Arik Aranta ◽  
Fitri Bimantoro ◽  
I Putu Teguh Putrawan
Keyword(s):  
Machine Learning ◽  
Information Exchange ◽  
Learning Activities ◽  
Daily Activities ◽  
Rule Base ◽  
Community Activities ◽  
Replacement Algorithm

Aksara Bima is a text-based information exchange media that has never been lost. At this time the Bima script began to be studied again indicated, with researchers who began to explore the history and stages of the process of digitizing the characters including the Bima script. Thus providing hope for the use of characters in community activities. One problem faced when wanting to learn the characters felt by the community is the different forms of characters in Latin letters and there are fixed rules when writing characters that make the process of writing characters feel difficult. So the writing of characters in daily activities in community activities still cannot be said to be massive due to these problems. In this study, the writer will develop an algorithm that can facilitate the learning activities of characters, which is specialized in the process of reading the Bima Script. The method adopts the concept of machine learning that is applied to the process of transliteration of Bima, into Latin letters. With a string replacement algorithm that is optimized by using hexadecimal numbers. The results obtained from this transliteration process in the form of system accuracy that reaches 90.64% of 171 implemented rule base.

Machine learning based accident prediction in secure IoT enable transportation system

2021 ◽  
pp. 1-13 ◽  
Author(s):  
Bhabendu Kumar Mohanta ◽  
Debasish Jena ◽  
Niva Mohapatra ◽  
Somula Ramasubbareddy ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Information Exchange ◽  
Secure Communication ◽  
Intelligent Transportation System ◽  
Transportation System ◽  
Information And Communications Technology ◽  
Classification Models ◽  
Architecture Model ◽  
Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1957
Author(s):  
Amandine Dubois ◽  
Titus Bihl ◽  
Jean-Pierre Bresciani
Keyword(s):  
Machine Learning ◽  
Fall Prevention ◽  
Fall Risk ◽  
Learning Algorithms ◽  
Step Length ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sitting Time ◽  
Behavioral Parameters ◽  
At Home

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians’ assessment toolkit and improve fall prevention.

scholarly journals Upaya Penanggulangan Pandemi Covid-19 di Panti Asuhan Al-Falah, Padang

2020 ◽  
Vol 27 (2) ◽  
pp. 113-118
Author(s):  
Refinel Refinel ◽  
Emriadi Emriadi ◽  
Safni Safni ◽  
Mai Efdi ◽  
Syukri Syukri ◽  
...  
Keyword(s):  
Foster Children ◽  
Boarding School ◽  
Learning Activities ◽  
Daily Activities ◽  
The West ◽  
Daily Lives ◽  
West Sumatra ◽  
Rapid Transmission ◽  
Staple Foods ◽  
The Impact

The Islamic Boarding School and Orphanage Al-Falah Padang are located on Bypass Km 16, behind the West Sumatra TVRI office. Al-Falah Boarding School has 367 students, 265 of them are foster children in the Orphanage. Inadequate construction and classrooms, the students and foster children of the Islamic Boarding School and the Al-Falah Orphanage studied and lived their daily lives. The occurrence of the Covid-19 pandemic certainly affected the daily activities and learning activities of the orphanage children. Especially, the fulfillment of their basic needs. Moreover, the main problem of Covid-19 is not only about the effects by the virus on sufferers but also about its rapid transmission. Therefore, to help the crisis due to Covid-19, several lecturers and students from the Faculty of Mathematics and Natural Sciences Universitas Andalas (Unand) provided staple foods, money, masks, hand sanitizers, and disinfectants for residents of the Al-Falah Padang orphanage. It is hoped that can help the residents of the Al-Falah orphanage who certainly feel the impact of the Covid-19 pandemic.

scholarly journals Detection of FAKE NEWS on SOCIAL MEDIA using CLASSIFICATION Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3132-3138
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Information Exchange ◽  
Learning Algorithm ◽  
Daily Life ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Fake News ◽  
Other Information

In today’s world social media is one of the most important tool for communication that helps people to interact with each other and share their thoughts, knowledge or any other information. Some of the most popular social media websites are Facebook, Twitter, Whatsapp and Wechat etc. Since, it has a large impact on people’s daily life it can be used a source for any fake or misinformation. So it is important that any information presented on social media should be evaluated for its genuineness and originality in terms of the probability of correctness and reliability to trust the information exchange. In this work we have identified the features that can be helpful in predicting whether a given Tweet is Rumor or Information. Two machine learning algorithm are executed using WEKA tool for the classification that is Decision Tree and Support Vector Machine.

scholarly journals PENINGKATAN KOMPETENSI GURU TK DALAM MENYUSUN RENCANA KEGIATAN HARIAN MELALUI BIMBINGAN BERKELANJUTAN DI TK DHARMA WANITA KECAMATAN TEMBARAK TAHUN 2015

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Suharyanto Suharyanto
Keyword(s):  
Action Research ◽  
Action Observation ◽  
Kindergarten Teachers ◽  
Teacher Supervision ◽  
Kindergarten Teacher ◽  
Learning Activities ◽  
Daily Activities ◽  
Teacher Competence ◽  
The Subject ◽  
Planning Action

Result kindergarten teacher supervision is found in teaching teachers have to plan daily activities, so that learning activities are not effective. For that, it is necessary guidance to teachers in order to increase their competence in planning daily activities. The purpose of this study was to determine the increase kindergarten teacher competence in planning daily activities through ongoing guidance in the TK Dharma Wanita District of Tembarak 2015.Who is the subject of this research is all kindergarten teachers in the district Tembarak Dharma Wanita, amounting to 18 teachers. With schools, action research is expected 75% of kindergarten teachers are able to plan daily activities properly. Acts done in two cycles each cycle there are three meetings which consist of four stages, namely: planning, action, observation, and reflection. Results of the action from the first cycle to the second cycle increased aspects of kindergarten teacher competence in planning daily activities properly in TK Dharma Wanita District if the year 2015 in the first cycle there are 6 teachers or 33% increase to 15 teachers or 83%. While activity has increased from 65% in the first cycle to 79% in the second cycle. It can be concluded that through continuous guidance can improve the competence of kindergarten teachers in planning daily activities in TK Dharma WanitaTembarak District of Waterford District 2015.

Architecting IoT based Healthcare Systems Using Machine Learning Algorithms

2020 ◽  
pp. 40-66
Author(s):  
G. S. Karthick ◽  
P. B. Pankajavalli
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sensor Data ◽  
Time Data ◽  
Sensory Data ◽  
Hybrid Classification ◽  
Smart Data

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.

scholarly journals Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 645 ◽  
Author(s):  
Georgios Konstantopoulos ◽  
Elias P. Koumoulos ◽  
Costas A. Charitidis
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Portland Cement ◽  
Information Exchange ◽  
Supervised Machine Learning ◽  
Multidimensional Space ◽  
Knowledge Gain ◽  
Phase Identification ◽  
Structure Property ◽  
Local Optima

Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.

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