2690-6791ISSN (Online)
2769-786XISSN (Print)
Flower pollination algorithm (FPA) is a metaheuristic algorithm that proceeds its representation from flowers' proliferation role in plants. The optimal plant reproduction strategy involves the survival of the fittest as well as the optimal reproduction of plants in terms of numbers. These factors represent the fundamentals of the FPA and are optimization-oriented. Yang developed the FPA in 2012, which has since shown superiority to other metaheuristic algorithms in solving various real-world problems, such as power and energy, signal and image processing, communications, structural design, clustering and feature selection, global function optimization, computer gaming, and wireless sensor networking. Recently, many variants of FPA have been developed by modification, hybridization, and parameter-tuning to cope with the complex nature of optimization problems this paper provides a survey of FPA and its applications.
Read MoreDoi: https://doi.org/10.54216/JISIoT.020101
Vol. 2 Issue. 1 PP. 05-11, (2021)
The definitions of small and medium enterprises (SMEs) vary from country to country and industry to industry, each country or region has their own definition which depends on who defines it and where is utilized. SMEs play an essential role in most economies, particularly in developing countries. Many large enterprises depend on SMEs (Startups) for their supply chain; thus, SMEs need to adopt Enterprise Resources Planning (ERP) systems more and more. Since ERP system adoption is a challenging project in SMEs, the main purpose of this article is to propose an ERP implementation roadmap for SMEs. This work proposes a road map for ERP implementation in SMEs. It consists of three major stages and eight phases. The paper concludes that even though ERP is important to SMEs, its implementation is challenging, and organizations must prepare adequately to get it right.
Read MoreDoi: https://doi.org/10.54216/JISIoT.020102
Vol. 2 Issue. 1 PP. 14-25, (2020)
In this paper, applications Discrete Laguerre Wavelet Transform were used where satisfactory results were obtained, where the efficiency of our proposed theory was proved, and the examples used will prove this. Three physical samples were selected that were compressed using the proposed wavelets, and good results were obtained that prove the efficiency of the method used. Three physical samples were selected that were compressed using the proposed wavelets, and good results were obtained that prove the efficiency of the method used.
Read MoreDoi: https://doi.org/10.54216/JISIoT.020103
Vol. 2 Issue. 1 PP. 26-32, (2021)
With the growing prevalence of the Internet of Health Things (IoHT), there is an increasing need for reliable and precise categorization of electrocardiogram (ECG) indications for the early detection of cardiovascular diseases. In this research, we propose a machine learning approach for ECG classification in IoHT applications. Our solution use wavelet transforms to clean the ECG records before passing them to the model. Then, a stack of long short-term memory (LSTM) cells is built to learn the temporal interrelations in the ECG signals and make accurate predictions. We assessed the performance of our model on a publicly available dataset of ECG signals, achieving an overall accuracy of 97.5%. The experimental findings demonstrate that our models can effectively classify ECG signals in IoHT applications, providing a valuable tool for the early discovery of vascular diseases. Furthermore, our model can be certainly incorporated into IoHT systems, providing a reliable and efficient solution for ECG classification.
Read MoreDoi: https://doi.org/10.54216/JISIoT.020104
Vol. 2 Issue. 1 PP. 33-45, (2021)