International Journal of Reconfigurable and Embedded Systems (IJRES)
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Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction
Get PDFIn this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics
Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data
Get PDFIn the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud radon data. We present an analysis of the tele-monitoring system, data collection methods, and the application of regression-based machine learning algorithms. Utilizing a comprehensive dataset spanning 30 training instances and 105 test instances, the study evaluates multiple metrics to ascertain the efficacy of the prediction models. Our findings reveal that the linear regression approach yields the best earthquake magnitude prediction method, with the lowest values across multiple evaluation metrics: standard deviation 0.40, mean absolute error (MAE) 0.30, mean absolute percentage error (MAPE) 6%, root mean square error (RMSE) 0.52, mean squared error (MSE) 0.28, symmetric mean absolute percentage error (SMAPE) 0.06, and conformal normalized mean absolute percentage error (cnSMAPE) 0.97. Additionally, we discuss the implications of the research results and the potential applications in enhancing existing earthquake prediction methodologies
Affordable digital electronics for building a hybrid dynamic marker structure with infrared illumination light patterns
Get PDFThis work deals with the integration of low-cost electronic devices that were integrated into constructing a dynamic maker that allows the triggering of augmented reality events. A hybrid structure was developed to combine the most favorable aspects of fiducial markers and dynamic markers. The lighting infrared patterns are effectively modifiable through the programming of an ESP8266 microcontroller card. To test the system, an infrared lighting pattern generated was detected through a digital camera, and an augmented reality application was implemented using a web page for displaying text. Electronic shift registers were used for the temporal storage of the infrared illumination pattern. The infrared illumination marker can’t be detected by human eyes, but it is easily recognized due to the inner black square shape embedded into a white wooden structure
Design of a linear motor-based magnetic levitation train prototype
Get PDFThis study explores the modelling of a magnetic levitation train and its implementation using a microcontroller. Magnetic levitation (maglev) is a technology that enables vehicles to levitate and move without wheels. Maglev research has been conducted globally, but maglev trains haven't received much attention. Due to the sophisticated linear motor technology for contactless transit, building a maglev train requires enormous investments. This paper is crucial for understanding the linear motor technologies necessary for levitation and propulsion. The primary objectives of this study include creating a model of the maglev train using a linear motor circuit, investigating the maglev effect concerning different coil and magnet types, and monitoring the train's propulsion and levitation using a microcontroller. This work constructs a linear motor system for the maglev train, comprising a mechanical structure with a permanent magnet for levitation and electromagnets for propulsion. A microcontroller is employed to sense the magnetic field, produced by the permanent magnet and electromagnets. In summary, this paper successfully designed a maglev train prototype using a linear motor circuit to establish the repulsive mechanism for both levitation and propulsion, with levitation~1 cm from the track and demonstrated the ability to move along a 30 cm track
Reconfigurable linear feedback shift register for wireless communication and coding
Get PDFLinear feedback shift register (LFSR) is the basic building block of the communication system used in different coding, error detection and correction codes, such as gold, low-density parity check (LDPC), polar, and turbo codes. There are simple shift register-based n-bit counters with a few XOR gates that behave pseudo-randomly. The LFSR is used in chip hardware for high-speed operations, error control, and the generation of pseudo-random numbers. The hardware chip design and performance estimation of the LFSR is the problem for specific communication system. The motivation of the work is to generate the Gold code sequence by the integration of two LFSR. The article proposes the hardware chip design and simulation of two 5-bit LFSR modules used for the gold sequence generator applicable for the communication systems. The novelty of the work is that the design is scalable and can be extended based on the requirements of the systems which is synthesized and experimentally verified on the Zynq-7000 field programmable gate array (FPGA) board. The concept of this design is programmable and can be extended to n-bit based on the applications. The work is supported, and formulated using very high speed integrated circuit hardware description language (VHDL) programming in Xilinx ISE 14.7 software
Automation irrigation system using arduino for smart crop field productivity
Get PDFAgriculture is essential to the prosperity of agricultural countries like India. Thus, the suggested strategy is to use automation and internet of thing (IoT) technology to make agriculture smart. Applications enabled by the IoTs include irrigation decision assistance, crop growth monitoring and selection, and more. an Arduino-powered technology that boosts agricultural productivity. This study's main goal is to find the least quantity of water necessary to grow crops. Most farmers squander a lot of time on the fields rather than concentrating on the water that plants have access to at the right moment. The suggested system determines the required amount of water based on the data obtained from the sensors. Two sensors provide data on the soil's temperature, humidity, amount of sunlight each day, and soil temperature to the base station. The suggested systems must determine the amount of water required for irrigation based on these criteria. The system's main benefit is the use of precision agriculture (PA) in conjunction with cloud computing, which will maximise the use of water fertilisers while maximising crop yields and also assist in determining field weather conditions
Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system
Get PDFBrain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods
Custom administering attention module for segmentation of magnetic resonance imaging of the brain
Get PDFTaking into account how brain tumors and gliomas are notorious forms of cancer, the medical field has found several methods to diagnose these diseases, with many algorithms that can segment out the cancer cells in the magnetic resonance imaging (MRI) scans of the brain. This paper has proposed a similar segmenting algorithm called a custom administering attention module. This solution uses a custom U-Net model along with a custom administering attention module that uses an attention mechanism to classify and segment the glioma cells using long-range dependency of the feature maps. The customizations lead to a reduction in code complexity and memory cost. The final model has been tested on the BraTS 2019 dataset and has been compared with other state-of-the-art methods for displaying how much better the proposed model has performed in the category of enhancing, non-enhancing and peritumoral gliomas
Effects of wood texture and color on aesthetic pleasure: two experimental studies
Get PDFThe texture and color on wood are key factors that influence an individual's perception of it. However, little research has been done to confirm what kind of pattern in textures and colors are more likely to evoke individual aesthetic pleasure. Therefore, twenty-four decorative wood from northern China were selected, identified, and quantified in their colors with a CR-5 Colorimeter. We picked out eight kinds of wood with optimal texture characteristics through feature-fusion wood grain recognition (FWGR) and enhanced the texture features with photoshop in VR space. The result show that, in the color dimension, woods in the hue range of 20-25 and saturation of 65-75 were considered beautiful for individual perception of aesthetic. In the texture dimension, the size of the space affects the individual's preference for texture. When the pattern of the wood ray is continuous and clear, the individual's perception of its fluidity is enhanced; while for the fuzzy and interrupted pattern of the wood ray, the individual does not follow the fluidity of the line but focuses on the overall uniformity
Development of electrocardiogram intelligent and wearable monitoring system-assisting in care
Get PDFThe primary factor contributing to the high mortality rate in our country is coronary heart disease, which affects almost 50% of people from rural regions. Internet of things (IoT) contributes effectively to the development of point of care (POC) gadgets that support the medical upkeep of an expanding agricultural population. An electrocardiogram test is crucial for analysing cardiac disorders. Therefore, we must develop a POC piece of hardware to assess the health of the heart in an affordable manner and to design it for the patients without interfering with their daily regular procedure in order to monitor the patient's coronary heart disease. As a result, we must design an uninterrupted workbench, which consists of three main integrated parts. The first is a mobile Bluetooth low energy device that has 5-lead electrocardiogram (ECG) surveillance equipment and the smallest form factor. The smart phone Android application that inherits, resolves, and maps the data sent from the ECG device comes next. The patient's information and report details are then compiled on a cloud server for the doctor's future attribution needs