Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

A novel mobile epilepsy warning system

Abstract: This paper presents a new design of mobile epilepsy warning system for medical application in telemedical environment. Mobile Epilepsy Warning System (MEWS) consists of a wig with a cap equipped with sensors to get Electroencephalogram (EEG) signals, a collector which is used for converting signals to data, Global Positioning System (GPS), a Personal Digital Assistant (PDA) which has Global System for Mobile (GSM) module and execute Artificial Neural Network (ANN) software to test current patient EEG data with pre-learned data, and a calling center for patient assistance or support. The system works as individual sensors obtain EEG signals from patient who has epilepsy and establishes a communication between the patient and Calling Center (CC) in case of an epileptic attack. MEWS learning process has artificial neural network classifier, which consists of Multi Layered Perceptron (MLP) neural networks structure and back-propagation training algorithm.

Pandemics and domestic violence during covid-19

Abstract: The year 2020 met us with the COVID-19 pandemic. The covid-19 pandemic has gone past a mere health challenge. Its effect can be felt in the economy and society in general. Women form a large chunk of the response efforts geared at flattening the curve of the COVID-19 scourge. As the first point of contact, caregivers, medical personnel, volunteers, logistics facilitators, researchers and scientists and other professionals critical to the fight against the virus, women are making profound contributions in the fight against the spread of the outbreak. Most of the caregivers found in our homes and communities today are women. Furthermore, women stand a higher risk of infection and loss of their sources of livelihood, and as the outbreak continues to spread, there is all likelihood that they may not be able to access programs vital to their reproductive and sexual health. There is also a rise in cases of domestic violence against women in this crisis period. This study will be exploring a wide range of literature about pandemics that have happened in the past and previous public health emergencies and crisis, to enable it to ascertain patterns by which pandemics can further heighten the different kinds of violence against women. Evidence gathered from this study will be used to make recommendations to governments, civil society organizations, community-based agencies, and international donor agencies to help make women and children’s health priority, keeping them safe and preparing them adequately for another possible pandemic.The year 2020 met us with the COVID-19 pandemic. The covid-19 pandemic has gone past a mere health challenge. Its effect can be felt in the economy and society in general. Women form a large chunk of the response efforts geared at flattening the curve of the COVID-19 scourge. As the first point of contact, caregivers, medical personnel, volunteers, logistics facilitators, researchers and scientists and other professionals critical to the fight against the virus, women are making profound contributions in the fight against the spread of the outbreak. Most of the caregivers found in our homes and communities today are women. Furthermore, women stand a higher risk of infection and loss of their sources of livelihood, and as the outbreak continues to spread, there is all likelihood that they may not be able to access programs vital to their reproductive and sexual health. There is also a rise in cases of domestic violence against women in this crisis period. This study will be exploring a wide range of literature about pandemics that have happened in the past and previous public health emergencies and crisis, to enable it to ascertain patterns by which pandemics can further heighten the different kinds of violence against women. Evidence gathered from this study will be used to make recommendations to governments, civil society organizations, community-based agencies, and international donor agencies to help make women and children’s health priority, keeping them safe and preparing them adequately for another possible pandemic.

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

Abstract: To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

Distance and Energy Consumption Minimization in Electric Traveling Salesman Problem with Time Windows

Abstract: As global pollution caused by transportation increases, the need for cleaner energy becomes more significant each day. For this reason, one of the recent global technological and scientific tendencies is to develop and include electric vehicles in transportation. In this paper, an Electric Traveling Salesman Problem with Time Windows was studied by considering two objectives: minimizing the total distance and minimizing the total energy consumption. As a solution method, the well-known Simulated Annealing algorithm was hybridized with a constructive heuristic and a local search heuristic. This algorithm was executed on a set of well-known benchmark instances from the literature separately for the two objectives and the results were presented.

A General Variable Neighborhood Search for the NoIdle Flowshop Scheduling Problem with Makespan Criterion

Abstract: This paper proposes a novel general variable neighborhood search (GVNS) algorithm to solve the no-idle flowshop scheduling problem with the makespan criterion. The initial solution of the GVNS is generated using the FRB5 heuristic. In the outer loop, insert and swap operations are employed to shake the permutation. In the inner loop of variable neighborhood descent procedure, two effective algorithms, namely, Iterated Greedy (IG) and Variable Block Insertion Heuristic (VBIH) algorithms are used. Note that, an effective referenced insertion scheme is employed in these IG and VBIH algorithms. The proposed GVNS algorithm is compared with the standard IG algorithm using the benchmark instances. The computational experiments show that the GVNS performs much better than the standard IG. Furthermore, the results of the standard IG and GVNS algorithms are compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS algorithm improves some of the current best-known solutions in the literature. Consequently, it can be said that the GVNS is very effective for the no-idle flowshop scheduling problem with the makespan criterion.