Indonesian Journal of Electrical Engineering and Computer Science
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Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing
Get PDFIn wireless networks, link breaks, and restricted resources create fundamental challenges for maintaining network applications. Several wireless network routing techniques concentrate on power efficiency to expand the network lifetime, but the traffic and reliability parameters are not the primary concern. Though, these techniques are not capable of dealing with the wireless network. Hence, this paper proposes deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) in wireless networks. The wireless network is formed by clustering by the node power and selects the cluster head (CH) based on a fuzzy algorithm. The wireless node power level, node buffer space, and node reliability to consider the input parameters of the fuzzy system. Then thefuzzy algorithm gives the output for CH round length. This selected CH improves the node reliability, power efficiency with minimized network congestion. Then we use a DNN algorithm to choose an optimal relay by applying an adaptive load balance factor in the network. DNN is a machine learning algorithm, and it provides high accuracy. From the simulation results, the PTAR approach improves the network performance, such as packet received ratio, delay, residual energy, and routing overhead
Efficient and secure data transmission: cryptography techniques using ECC
Get PDFData transmission has become a crucial aspect of our daily lives in the current digital age. However, this transmission comes with the risk of security breaches, which can result in data theft and unauthorized access. This issue can be addressed by using cryptographic techniques such as elliptic curve cryptography (ECC). In comparison to other cryptosystems, ECC is a potent cryptographic tool that provides high levels of security with comparatively reduced key sizes. This paper discusses the use of ECC in efficient and secure data transmission. It provides a comprehensive overview of ECC, including its mathematical background and how it can be applied to encryption and decryption processes. The paper also presents a comparison of ECC with other cryptographic techniques and highlights its advantages, including its resistance to attacks and efficiency in resource-constrained environments. Finally, the paper discusses the implementation of ECC in real-world scenarios and its potential to revolutionize secure data transmission
Fast region based convolutional neural network ResNet-50 model for on tree Mango fruit yield estimation
Get PDFThe foundation of the Indian economy is agriculture, the amount of land available for agricultural activities has decreased due to numerous factors. To fulfill the demands of the expanding population, the maximum yield must be produced on the least amount of land that is accessible. To overcome the challenges of agriculture, many researches have been carried out to adopt technology into agriculture. As India is one of the world's top producers of Mangoes and has a vast market, and has encouraged extensive Mango farm development. Automatic yield estimation of Mangoes in the early stage is important to improve the quality and quantity of production which improves both domestic and export markets. The work proposes a fast region (FR) based convolutional neural network (CNN) residual network (ResNet)-50 model for efficient deep learning-based Mango crop yield estimation system to count the Mango fruit from the images of individual trees. A temporal Mango fruit database is used to estimate the yield of on tree Mango fruits, and a framework is provided to estimate Mango fruit yield in red, green, and blue (RGB) image. This experiment shows that the suggested FRCNN ResNet-50 model attained a better accuracy of 98.20% on the proposed dataset
Automating cloud virtual machines allocation via machine learning
Get PDFIn the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%
Forecasting water quality through machine learning and hyperparameter optimization
Get PDFForecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4%. These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring
Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm
Get PDFThere is dependably an extraordinary requirement for new types of algorithms in the population-based improvement algorithm. These algorithms improve the execution of the current algorithm. Parameter change approach assumes an essential job in improving the execution of the PSO algorithm. A new algorithm called particle acceleration-based particle swarm optimization (PA-PSO) has been proposed. In this algorithm a particle acceleration parameter is tuned. This algorithm significantly improves the performance of the PSO–time varying acceleration coefficients (PSO-TVAC) algorithm. This algorithm reduces the time varying weight of inertia and the nonlinear acceleration coefficients in the equation of the PSO-TVAC velocity vector in each iteration. Particle movements in the n-dimensional search space are governed by the kinetics of the second motion equation. Experiments demonstrate that the proposed PA-PSO algorithm outperforms the existing PSO-TVAC algorithm on five well-known reference test functions. The algorithm possesses adequate control over the local as well as global optimums
An efficient high throughput BCH module for multi-bits error correction mechanism on hardware platform
Get PDFThe bose-chaudhuri-hocquenghem (BCH) codes are a cyclic error correction codes (ECC) class. The BCH is constructed by using a polynomial over the Galois field. The BCH codes can detect and correct the multi-bits with an easy decoding mechanism. The BCH codes are used in most of the storage device's cryptography, disk drives, and satellite applications. This manuscript presents an efficient high-throughput BCH module with an encoding and decoding mechanism for multi-bit corrections. The BCH code of (15, k) is used to construct the encoder and decoder architectures. The BCH encoder decoder (ED) module with single error correction (SEC), double error correction (DEC), and triple-error correction (TEC) are discussed in detail. The BCH encoder module uses a linear feedback shift register (LFSR). The BCH decoder with SEC and DEC is constructed using the syndrome generator module (SGM) and chien search module (CSM). The BCH decoder with TEC is designed using SGM, inversion-based berlekamp-massey-algorithm (BMA), and CSMs. The BCH-ED module with SEC, DEC, and TEC utilizes <1 % chip area on Artix-7 FPGA. The BCH-ED with SEC, DEC, and TEC achieves a throughput of 7.13 Gbps, 1.2 Gbps, and 0.803 Gbps, respectively. Lastly, the BCH module is compared with existing BCH approaches with better improvement in chip area, frequency, and throughput parameters
In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform
Get PDFDigital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions
Automatic human height measurement system based on camera sensor with deep-learning and linear regression analysis
Get PDFThis study proposes a new approach for automatically measuring human height using a camera sensor with deep learning and linear regression analysis. The camera sensor is used to capture real-time images of human objects. The image is then processed with a YOLO4-based convolutional neural network (CNN) to separate the region of interest (ROI) of the human object from the background. The pixel value of the ROI vertical line is then converted into height in centimeters by the linear regression equation. The system was tested on 40 primary samples, with 20 samples used as control data and 20 samples used as test data. From the results of testing 20 control data samples, the linear regression equation was obtained as y' = 0.4034x + 24.938, which was then applied to convert the system's predicted height in centimeters for 20 test samples. The test results for 20 test samples showed that an average F1_score was 1, the R_square obtained was 0.93, the root mean square errors (RMSE) was 0.02, and the percentage of accuracy was 99.00%. The test results showed that the system was able to automatically detect human height with a very high level of correlation/similarity and accuracy between actual and predicted height
A smart emergency response system based on deep learning and Kalman filter: the case of COVID-19
Get PDFDuring an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19