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· One min read

Dear Colleagues,

Currently, we are in the process of editing a forthcoming book entitled "Metaverse Applications for New Business Models and Disruptive Innovation", to be published by IGI Global, an international publisher of progressive academic research.

We would like to take this opportunity to cordially invite you all to submit your work for consideration in this book. We are certain that your contribution on this topic and/or other related research areas would make an excellent addition to this book. Please visit https://www.igi-global.com/publish/call-for-papers/call-details/5848 for more details regarding this book and to submit your work. You may kindly use the following link to submit a chapter: https://www.igi-global.com/publish/call-for-papers/submit/5848

If you have any questions or concerns, please do not hesitate to contact us. Thank you very much for your consideration of this invitation, and I hope to hear from you by 4th April 2022.

Best wishes,
Muhammad Anshari (Universiti Brunei Darussalam, #bruneidarussalam)
Muhammad Syafrudin (Sejong University, #korea)
Ganjar Alfian (Universitas Gadjah Mada (UGM), #indonesia)
Editors

· 2 min read

Abstract

Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model’s features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.

Published in: Biocybernetics and Biomedical Engineering
DOI: 10.1016/j.bbe.2020.10.004

· 2 min read
Dear Colleagues,

I am pleased to let you know that I have been appointed as Editorial Board of ComTech and kindly invite you to submit your works to journal ComTech. ComTech is a semi-annual journal, published the issue every June and December. The journal invites professionals in education, research, and entrepreneurship to participate in disseminating ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics. ComTech has been accredited by Directorate General of Higher Education, Ministry of Education and Culture, Republic of Indonesia (DIKTI) under the decree number 3/E/KPT/2019 (SINTA 2) and indexed by CrossRef, ASEAN Citation Index, Directory of Open Access Journals (DOAJ), Science and Technology Index 2 (SINTA2), Garda Rujukan Digital (Garuda), Microsoft Academic Search, Google Scholar, etc. There will be an article-processing charge (APC) for the accepted papers to publish the paper under Open Access license (Creative Commons Attribution-ShareAlike 4.0 International License). The APC fee is Rp. 2.000.000,00 (IDR) and the author will receive a complimentary hard copy of our journal. However, the APC fee is FREE-of-CHARGE for international authors.

Please consider contributing to this journal and thank you for your consideration.

Dr. Muhammad Syafrudin

Editorial Board
Journal link: ComTech

· One min read

Abstract

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

Published in: Mathematics
DOI: 10.3390/math8091620