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INDONESIA
Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2 Documents
 1 
Search results for , issue "List of Accepted Papers (with minor revisions)" : 2 Documents clear
Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms Abdali-Mohammadi, Fardin; Meqdad, Maytham N.; Kadry, Seifedine
Bulletin of Electrical Engineering and Informatics List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2716

Abstract

Internet of Things (IoT) refers to the practice of designing and modelingobjects connected to the Internet through computer networks. In the past fewyears, IoT-based health care programs have provided multidimensionalfeatures and services in real time. These programs provide hospitalization formillions of people to receive regular health updates for a healthier life.Induction of IoT devices in the healthcare environment have revitalizedmultiple features of these applications. In this paper, a disease diagnosissystem is designed based on the Internet of Things. In this system, first, thepatient's courtesy signals are recorded by wearable sensors. These signals arethen transmitted to a server in the network environment. This article alsopresents a new Hybrid Decision Making approach for diagnosis. In thismethod, a feature set of patient signals is initially created. Then thesefeatures go unnoticed on the basis of a learning model. A diagnosis is thenperformed using a neural fuzzy model. In order to evaluate this system, aspecific diagnosis of a specific disease, such as a diagnosis of a patient'snormal and unnatural pulse, or the diagnosis of diabetic problems, will besimulated.
Enhancement of the Estimation of Energy Consumption for Electric Vehicles by Using Machine Learning Cabani, Adnane; Zhang, Peiwen; Khemmar, Redouane; Xu, Jin
Bulletin of Electrical Engineering and Informatics List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2717

Abstract

Three main classes are considered of significant influence factors when predicting theenergy consumption rate of Electric Vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influ-ences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to im-prove the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EVenergy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air con-ditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimatedof energy-consumption. The proposed method is used in order to find the optimal pathbetween two points (departure-destination) in terms of energy consumption.

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