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International Journal of Reconfigurable and Embedded Systems (IJRES)
Published by Institute of Advanced Engineering and Science
ISSN : 20894864     EISSN : 27222608     DOI : -
Core Subject : Economy,
Economics, Econometrics & Finance
The centre of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable core in system-on-chip (SoC). Reconfigurable and Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons etc. The aim of IJRES is to provide a vehicle for academics, industrial professionals, educators and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable and embedded systems. The scope of the IJRES addresses the state of the art of all aspects of reconfigurable and embedded computing systems with emphasis on algorithms, circuits, systems, models, compilers, architectures, tools, design methodologies, test and applications.
Arjuna Subject : -
Articles 456 Documents
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Machine learning based education data mining through student session streams Hanumanthappa, Shashirekha; Prakash, Chetana
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp383-394

Abstract

Recently, significant growth in using online-based learning stream (i.e., elearning systems) have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students' performance. Data mining (DM) employing machine learning (ML) techniques have been employed in the e-learning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemblebased model does not incorporate feature importance into the student performance prediction model. Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely modified XGBoost (MXGB). The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGBabased student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model.
Design of access control framework for big data as a service platform Sharma, Santosh Kumar; Pratap, Ajay; Dev, Harsh
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp151-159

Abstract

Big data as a service (BDaaS) platform is widely used by various organizations for handling and processing the high volume of data generated from different internet of things (IoT) devices. Data generated from these IoT devices are kept in the form of big data with the help of cloud computing technology. Researchers are putting efforts into providing a more secure and protected access environment for the data available on the cloud. In order to create a safe, distributed, and decentralised environment in the cloud, blockchain technology has emerged as a useful tool. In this research paper, we have proposed a system that uses blockchain technology as a tool to regulate data access that is provided by BDaaS platforms. We are securing the access policy of data by using a modified form of ciphertext policy-attribute based encryption (CP-ABE) technique with the help of blockchain technology. For secure data access in BDaaS, algorithms have been created using a mix of CP-ABE with blockchain technology. Proposed smart contract algorithms are implemented using Eclipse 7.0 IDE and the cloud environment has been simulated on CloudSim tool. Results of key generation time, encryption time, and decryption time has been calculated and compared with access control mechanism without blockchain technology.
Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data Pratama, Thomas Oka; Sunarno, Sunarno; Wijatna, Agus Budhie; Haryono, Eko
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp577-585

Abstract

In 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.
Accurate plant species analysis for plant classification using convolutional neural network architecture Patil, Savitha; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp160-170

Abstract

Recently, plant identification has become an active trend due to encouraging results achieved in plant species detection and plant classification fields among numerous available plants using deep learning methods. Therefore, plant classification analysis is performed in this work to address the problem of accurate plant species detection in the presence of multiple leaves together, flowers, and noise. Thus, a convolutional neural network based deep feature learning and classification (CNN-DFLC) model is designed to analyze patterns of plant leaves and perform classification using generated fine-grained feature weights. The proposed CNN-DFLC model precisely estimates which the given image belongs to which plant species. Several layers and blocks are utilized to design the proposed CNN-DFLC model. Fine-grained feature weights are obtained using convolutional and pooling layers. The obtained feature maps in training are utilized to predict labels and model performance is tested on the Vietnam plant image (VPN-200) dataset. This dataset consists of a total number of 20,000 images and testing results are achieved in terms of classification accuracy, precision, recall, and other performance metrics. The mean classification accuracy obtained using the proposed CNN-DFLC model is 96.42% considering all 200 classes from the VPN-200 dataset.
An efficient floating point adder for low-power devices Narayanappa, Manjula; Yellampalli, Siva S.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp253-261

Abstract

With an increasing demand for power hungry data intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks.
Smart farming based on IoT to predict conditions using machine learning Widianto, Mochammad Haldi; Setiawan, Yovanka Davincy; Ghilchrist, Bryan; Giovan, Gerry
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp595-603

Abstract

Smart farming is a type of technology that utilizes the internet of things (IoT) to provide information on agricultural and environmental conditions as well as perform automation. Some of these ecological conditions can be used and analyzed in machine learning (ML) data management. This study focuses on utilizing ML algorithms to find the best prediction; typically used methods include linear regression, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). In the application of smart farming, research on IoT and artificial intelligence (AI) is still uncommon since most IoT cannot make predictions like AI. Because basically, some IoT can't make predictions as AI does. In this Study, predictions were made by looking at the regression results in the form of root mean square error (RMSE) and absolute error. The results show a strong and weak correlation between features (positive or negative). The best prediction results are obtained by XGBoost when predicting temperature (RMSE 6.656 and absolute error 3.948) and (soil moisture 17.151 and absolute error 11.269). However, using different parameters (RMSE RF and absolute error DT) on RF and DT resulted in good and distinct results. Linear regression, on the other hand, produced unsatisfactory and poor result.
Implementation of first order statistical processor on FPGA for feature extraction Hadiyoso, Sugondo; Ramdani, Ahmad Zaky; Irawati, Indrarini Dyah; Wijayanto, Inung
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp234-243

Abstract

Statistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%.
Portable neonatus incubator based on global positioning system Salahuddin, Nur Sultan; Sari, Sri Peornomo; Musyaffa, Aqilla Rahman
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp735-747

Abstract

The role of baby incubator is crucial in assisting premature babies to adjust to their new surroundings. However, the current baby incubator causes challenges when used for emergency first aid. The challenge is often because of its cumbersome size, which makes transportation to referral hospitals difficult. To address this issue, portable neonate incubator based on the global positioning system (GPS) was developed. The results of implementation testing showed that the incubator system effectively monitored longitude and latitude coordinates, as well as the temperature and humidity of the incubator room, and the body temperature of neonates. Weighing approximately 5.8 kg, this incubator was versatile, compatible with both AC and DC voltage power sources, and came equipped with a carrying bag for easy transportation by midwives or medical personnel. Consequently, this development marked an innovative advancement in neonate incubator medical equipment, facilitating the swift tracking of the neonate incubator's coordinate position in case of unexpected events on the way to the hospital.
Design and development of control and monitoring hydroponic system Mujtahidin, Muhammad Hanafi; Shah, Ahmad Feirdaous Mohd; Jais, Ahmadul Sayyidi Amin; Annuar, Khalil Azha Mohd; Sapiee, Mohd Razali Mohamad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp41-51

Abstract

The global agriculture system faces significant challenges in meeting the growing demand for food production, particularly given projections that the world's population will reach 70% by 2050. Hydroponic farming is an increasingly popular technique in this field, offering a promising solution to these challenges. This paper will present the improvement of the current traditional hydroponic method by providing a system that can be used to monitor and control the important element in order to help the plant grow up smoothly. This proposed system is quite efficient and user-friendly that can be used by anyone. This is a combination of a traditional hydroponic system, an automatic control system and a smartphone. The primary objective is to develop a smart system capable of monitoring and controlling potential hydrogen (pH) levels, a key factor that affects hydroponic plant growth. Ultimately, this paper offers an alternative approach to address the challenges of the existing agricultural system and promote the production of clean, disease-free, and healthy food for a better future.
Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning Muthusamy, Rajendiran; Kannan, Charulatha; Mani, Jayarathna; Govindharajan, Rathinasabapathi; Ayyasamy, Karthikeyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp414-423

Abstract

With the increase in the computation power of devices wireless communication has started adopting machine learning (ML) techniques. Intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic wave propagation by changing the electric and magnetic values of its surface. State-of-the-art ML especially on deep learning (DL)-based IRS-enhanced communication is an emerging topic. Yet while integrating IRS with other emerging technologies possibilities of adversarial data creaping is high. Threats to security, their mitigation, and complexes for AI-powered applications in next generation networks are continuously emerging. In this work the ability of an IRS enhanced wireless network in future-generation networks to prevent adversarial machinelearning attacks is studied. The artificial intelligence (AI) model is used to minimize the susceptibility of attacks using defense distillation mitigation technique. The outcome shows that the defensive distillation technique (DDT) increases the strength and performance by around 22% of the AI method under an adversarial attack.

Page 33 of 46 | Total Record : 456

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