Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT

Priyanka; Ruchi

doi:https://doi.org/10.54216/FPA.140104

International Journal of Neutrosophic Science | 2023

On Some Results About The n-Potent Fuzzy Groups and Anti-Fuzzy Groups

In this paper, we have studied for the first time the concept of n-potent fuzzy groups and n-potent anti-fuzzy groups. Many related properties will be proved such as the intersection of n-potent fuzzy groups, the product of n-potent anti-fuzzy groups, and the factor groups formed by these structures.

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Walter C. Toapanta mail -

Jorge Lenín A. Espinoza mail -

Silvio M. Vivar mail -

Tania L. Vizcaíno Cárdenas mail

link https://doi.org/10.54216/IJNS.220409

Volume & Issue

Vol. Volume 22 / Iss. Issue 4

Details open_in_new

International Journal of BIM and Engineering Science | 2023

Net-Zero Energy Building Using Solar Photovoltaic Energy and Modeling within BIM Environment: Case Study of Al-Ajraf Elementary School in Quneitra

This research focuses on transforming Al-Ojraf Primary School in Quneitra Governorate into a zero-energy building by securing its energy source using a photovoltaic solar system. The Building Information Modeling (BIM) environment was utilized to create the necessary electrical plans and determine the available surface areas required for implementing this system. The existing electrical loads in the school (for lighting and fans) were initially calculated, followed by determining the suitable photovoltaic system to meet these loads, including the number of solar panels and the surface area needed for their installation. The calculated capacity of this system amounted to 12.510 kilowatts, composed of 12 solar panels, requiring an installation area of 32 square meters. The required capacity of the photovoltaic system was recalculated after replacing the school's electrical equipment with devices operating on direct current (DC), resulting in a system capacity of 7.260 kilowatts, providing a savings of 5.25 kilowatts, i.e., by 42%. The number of panels required to power the school's loads decreased to 6 panels, reducing the necessary installation area to 16 square meters, i.e., by 50%. This replacement also eliminated the need for batteries and expensive inverters, resulting in significant cost savings and a substantial reduction in electricity consumption from the main grid.In Sweida Governorate، there are a total of 253 elementary schools. If these schools were transformed into zero-energy buildings following the method outlined in this research, it would lead to a daily saving of 1328.25 kilowatts from the main grid.

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Reema Mrad mail -

Doha Jdeed mail -

Sonia Ahmed mail

link https://doi.org/10.54216/IJBES.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2023

Effects of Applying BIM on Facility Management for Existing Buildings

Building Information Modelling (BIM) is increasingly being used construction projects, and it demonstrates its ability to improve the construction industry's performance, However, its application in facility management still moderate and has not yet reached the potential and expected full use. The most common problem facing facility managers is the ability to access and manage the information. Information is the key to operate existing buildings and most importantly is the ability to collect, analyze, and handle it in an appropriate manner to be used for the facility management phase and the entire building life. However, there is insufficient understanding of the correct standards, processes and policies to be followed in the submission and management of such data, a significant lack of professionals and lack of knowledge of their software. This study aims to explore the value of BIM and the challenges affecting its application in FM, as well as address the information required for effective facilities management in existing buildings and the challenges to maintain a continuous update of BIM information in FM. The research methodology is based on analytical method: Using a questionnaire to a sample of staff and engineers in facilities management to detect the effects of applying BIM to facility management. The research demonstrated the importance of creating a BIM model for existing buildings and its effects to improve operations and maintenance, the need to increase BIM practices in engineering organizations. and indicated the most important benefits of the BIM for facility management application as: increase the efficiency of operation and maintenance staff's access to data, improve future operation design and preventive maintenance, facilitate decision-making throughout the operation and maintenance phase, and finally reduce costs and time while increase the quality of procedures.

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Rania Salman mail -

Rana Maya mail

link https://doi.org/10.54216/IJBES.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

A Tagging Model using Segmentation Proposal Network

This paper presents a tagging model used the Segmentation map as reference regions. The suggested model leverages an encoder-decoder architecture combined with a proposal layer and dense layers for accurate object tagging and segmentation. The proposed model utilizes a pre-trained VGG16 encoder to extract high-level features from input images, followed by a decoder network that reconstructs the image. A proposal layer generates a binary map indicating the presence or absence of objects at each location in the image. The proposal layer is integrated with the decoder output and further refined by a convolutional layer to produce the final segmentation. Two dense layers are employed to predict object classes and bounding box coordinates. The model is trained using a custom loss function that combines categorical cross-entropy loss and means squared error loss. Experimental results demonstrate the effectiveness of the proposed model in achieving accurate object tagging and segmentation.

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Suha Dh. Athab mail -

Abdulamir A. Karim mail

link https://doi.org/10.54216/FPA.130212

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Fusion-Based Econometric Analysis: Assessing Investment Project Efficacy and Business Decision Making

In this fusion-driven study, a comprehensive examination of investment projects' effectiveness in the unique economic context of Uzbekistan unfolds, employing econometric analysis to unveil the consequential relationship between economic indicators and business performance. The research employs a confluence of descriptive statistics, panel data regression models, and time-series analysis to unravel the intricate correlation matrix that binds various dimensions of investment outcomes within the country's distinct economic climate. Emphasizing the singular nature of the Uzbek economic environment, the study aims to provide a granular understanding of investment efficacy, offering strategic insights to guide economic policymakers and entrepreneurs in making informed decisions. Notably, Uzbekistan witnessed $2.5 billion in foreign direct investment inflows in 2022, making the knowledge gained from this detailed investigation particularly valuable. Set against the backdrop of a complex macro and micro-economic landscape, characterized by abundant natural resources, and pressing developmental challenges, the dynamic interplay between investment efficacy and diverse influencing factors comes to the fore. As a result, the study envisions its insights contributing to the formulation of strategies that harness Uzbekistan's investment climate potential, ultimately driving economic development and fostering business growth.

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Muhammad Eid Balbaa mail -

Astanakulov O. Tashtemirovich mail

link https://doi.org/10.54216/FPA.130213

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhanced Non-Invasive Blood Glucose Monitoring System Employing Wearable Optical Technology

Diabetes presents significant health risks globally, necessitating precise blood glucose monitoring to prevent serious repercussions including blindness, renal illness, kidney failure, heart disease, and even death from hyperglycemia or hypoglycemia, it is imperative to maintain normal blood glucose levels. However, regular blood glucose monitoring can be difficult for diabetics, and current non-invasive techniques sometimes do not assess blood sugar levels accurately or directly. In order to solve this problem, this study suggests a wearable optical system that is affordable and low-complexity. In this study, a wearable optical system has been proposed which can address the challenges in the accuracy and convenience in existing methods. This system used an Arduino Nano as a central control unit and a laser-transmitted module for blood glucose measurement. Light Dependent Resistors (LDRs) is used to detect and measure the intensity of laser light passing through the skin and impressed by blood glucose levels. The results are displayed on Organic Light Emitting Diode (OLED). During one weak trial, the system achieved average error present of 7.6% and 3.9% for before and after meal blood glucose concentration. The aim of this study is to enhance the lifestyle of diabetic patients by providing user-friendly technology for convenient blood glucose monitoring. It focuses on the potential benefits of non-invasive approaches and concentrates on the importance of the proposed wearable optical system in improving healthcare outcomes.

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Mohammad Abid Al-Hashim mail -

Wameedh Raad Fathel mail -

Hiba Dhiya Ali mail -

Marwa Mawfaq Mohamedsheet Al-Hatab mail

link https://doi.org/10.54216/FPA.190101

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Comparison Between ARIMA and EEMD+ARIMA Models in Forecasting Electricity Consumption

Accurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA) denoted by (EEMD+ARIMA), to know which model is better performing a historical US monthly electricity consumption from DEC-2000 to SEP-2022 were used. The data were divided into training set (90%) and testing set (10%) to insure the model accuracy. The mean absolute square error, root mean square error, mean absolute error and mean absolute percentage error measurements were used to test the ARIMA and hybrid EEMD+ARIMA performance, the results show that the hybrid EEMD+ARIMA outperforms ARIMA model with the lowest RMSE, MAE, MPE, MAPE, MASE. For the best model, Akaike Information Criterion and Bayesian Information Criterion were applied to choose the best. The results show that the AIC and BIC of the EEMD+ARIMA were lower than the ARIMA model, which indicates that the EEMD+ARIMA is better than the single ARIMA in forecasting of electricity consumption. The conclusion reveals that the hybrid EEMD+ARIMA provides more accurate forecasting and performs significantly better than the ARIMA in forecasting of electricity.

groups

Abdulsalam Elnaeem Balila mail -

Ani Bin Shabri mail

link https://doi.org/10.54216/FPA.140101

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

AI-based model for fraud detection in bank systems

Due to the very high direct or indirect costs of fraud, banks and financial institutions seek to accelerate the recognition of the activities of fraudsters. The reason for this is its direct effect on serving the customers of these institutions, reducing operating costs and remaining as a reliable and valid financial service provider. On the other hand, in recent years, with the development of information and communication technology, electronic banking has become very popular. In the meantime, it is inevitable to use fraud detection techniques to prevent fraudulent actions in banking systems, especially electronic banking systems. In this paper, a method has been developed that leads to the improvement of fraud detection in information security and cyber defense systems. The main purpose of fraud detection systems is to predict and detect false financial transactions and improve the intrusion detection system using information classification. In this regard, the genetic algorithm, which is known as one of the stochastic optimization methods, is used. At the end, the results of the genetic algorithm have been compared with the results of the decision tree classification and the regression tree. The simulation results show the effectiveness and superiority of the proposed method.  

groups

Ahmed Al-Fatlawi mail -

Ahmed A. Talib Al-Khazaali mail -

Sajjad H. Hasan mail

link https://doi.org/10.54216/FPA.140102

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Design and Implementation of IoT-Based Weather Monitoring System

Due to advancement in technology, various fields have boosted the development of systems that improve people’s life quality, contributing to the welfare of the community by providing relevant and pertinent information for decision-making. On the Internet of Things (IoT), the systems demand measuring and monitoring several environmental variables. The heterogeneity of the captured data and the measuring instruments used to hinder the interoperability among the different components of the IoT. The problems are raised an interest in the development of methods and tools that support the heterogeneity of the data from the sensors, the measurements, and the measuring devices. Some existing tools have resolved some of these interoperability problems.  However, it forces to IoT developers to use sensors from specific brands, limiting their generalized use in the community. Furthermore, it is required to solve the challenge of integrating different protocols in a same IoT project. Besides, by generating alerts, it may help making decisions daily, considering the data provided by the sensors. it is required to solve the challenge of integrating different protocols in a same IoT project. To overcome the limitations of the existing glitches, there is need to develop a framework based on network of sensors via software that allows communication-using protocols in a specific environment to monitor the quality of air and to alarm users about this. In this paper, a prototype of proposal is mentioned about the architecture, list of hardware, software and different APIs are utilized to gather data in a systematic way so as users can visualize data in a semantic view. The visualization is shown later by using Matplotlib, Seaborn tools of Machine Learning (ML) and Deep Learning (DL) to plot the temperature along with humidity in a historical span. The result shows that accuracy obtained via Machine Learning Classifier is 87% in the context of Weather Prediction.

groups

Sushant Kumar mail -

Saurabh Mukherjee mail -

Richa Gupta mail

link https://doi.org/10.54216/FPA.140103

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT

Internet-of-Things (IoT)-based heart disease prediction is a complex task and processing the real collected data directly for remote patient monitoring suffers from the limitations due to the irrelevant data features, affecting the prediction accuracy and raising the security concerns. Hence, the efficient Adaptive ensembled deep Convolution neural network –Bidirectional Long Short Term Memory (Adaptive ensembled deep CNN-BiLSTM ) classifier model is proposed via the fusion of interactive hunt-based CNN and Whale on Marine optimization (WoM)-based deep BiLSTM. The Adaptive optimization developed from the standard hybrid characteristics such as random searching, seeking, attack prohibition, following, and waiting characteristics optimized the fusion parameters of the developed classifier for attaining high detection accuracy. Additionally, the modified Elliptic Curve Cryptography (ECC) based Diffi-Huffman encryption algorithm provides the authentication and security of sensitive patient data in heart disease prediction. The developed model is evaluated with other competent methods in terms of accuracy, sensitivity, specificity as well as F-measure, which are reported as 97.573%, 98.012%, 97.592%, and 97.705% respectively.

groups

Priyanka Dhaka mail -

Ruchi Sehrawat mail

link https://doi.org/10.54216/FPA.140104

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new