Computer Science Journal (AGH University of Science and Technology, Krakow)
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476 research outputs found
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Square grid path planning for mobile anchor-based localization in wireless sensor networks
Full text linkLocalization is to provide all sensor nodes with their geographical positions. A mobile anchor-based localization in WSNs uses a mobile anchor equipped with GPS, which travels along a predetermined path. At each specified beacon point, it broadcasts its current known position to help other sensor nodes with unknown locations estimate their positions. In this paper, we analyze the determination of beacon points based on a square grid. We propose an improved path planning model named Union-curve. Our proposed model incorporates all beacon points of five previously developed paths, namely, SCAN, HILBERT, S-type, Z-curve, and -Scan on the commonly used square grid decomposition of area. Unknown sensor nodes estimate their positions using two techniques, APT and WCWCL-RSSI. Simulation results show that the proposed model has higher accuracy, with a big difference in error rate compared to the other models. In addition, this model guarantees maximum coverage with less path resolution value
Performance measurement with high performance computer of HW-GA anomaly detection algorithms for streaming data
Full text linkAnomaly detection is very important in every sector as health, education, business, etc. Knowing what is going wrong with data/digital system help peoples from every sector to take decision. Detection anomalies in real time Big Data is nowadays very crucial. Dealing with real time data requires speed, for this reason the aim of this paper is to measure the performance of our previously proposed HW-GA algorithm compared with other anomaly detection algorithms. Many factors will be analyzed which may affect the performance of HW-GA as visualization of result, amount of data and performance of computers. Algorithm execution time and CPU usage are the parameters which will be measured to evaluate the performance of HW-GA algorithm. Also, another aim of this paper is to test the HW-GA algorithm with large amount of data to verify if it will find the possible anomalies and the result to compare with other algorithms. The experiments will be done in R with different datasets as real data Covid-19 and e-dnevnik data and three benchmarks from Numenta datasets. The real data have not known anomalies but in the benchmark data the anomalies are known this is in order to evaluate how the algorithms work in both situations. The novelty of this paper is that the performance will be tested in three different computers which one of them is high performance computer
Robust Content-Based Image Retrieval using ICCV, GLCM, and DWT-MSLBP Descriptors
Full text linkThe objective of the Content-Based Image Retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependency of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors namely, Global Color Histogram and Multi-Scale Local Binary Pattern (MS-LBP). Furthermore, PCA is used for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets namely Corel-1k, Corel-5k, Corel-10k, and Ghim-10k, and results are compared based on precision and recall values at different thresholds. Euclidean distance and City Block distance are used for classification purposes. The performance study of the proposed work displays it as outperformer than the identified literature methods
FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter
Full text linkIn today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)
Finding Frequent Items: A Novel Method for Improving the Apriori Algorithm
No full textIn the current paper, we use an intelligent method for improved the Apriori algorithm in order to extract frequent itemsets. PAA (proposed Apriori algorithm) is twofold. First, it is not necessary to take only one data item at each step. In fact, all possible combinations of the items could be generated at each step. Secondly, we can scan only some transactions instead of scanning all the transactions to obtain frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC_Apriori algorithms. The results exhibit that due to the significant reduction in the number of transaction scans to obtain the itemset, the algorithm execution time is significantly reduced; as in the first experiment, the time spent to generate frequent items underwent a reduction by 52% compared to the algorithm in the first experiment. In the second experiment, the amount of time spent is equal to 65%, while in the third experiment, it is equal to 46%
Improving modified policy iteration for probabilistic model checking
Full text linkValue iteration, policy iteration and their modified versions are well-known algorithms for probabilistic model checking of Markov Decision Processes. One the challenge of these methods is that they are time-consuming in most cases. Several techniques have been proposed to improve the performance of iterative methods for probabilistic model checking. However, the running time of these techniques depends on the graphical structure of the model and in some cases their performance is worse than the performance of the standard methods. In this paper, we propose two new heuristics to accelerate the modified policy iteration method. We first define a criterion for the usefulness of the computations of each iteration of this method. The first contribution of our work is to develop and use a criterion to reduce the number of iterations in modified policy iteration. As the second contribution, we propose a new approach to identify useless updates in each iteration. This method reduces the running time of computations by avoiding useless updates of states. The proposed heuristics have been implemented in the PRISM model checker and applied on several standard case studies. We compare the running time of our heuristics with the running time of previous standard and improved methods. Experimental results show that our techniques yields a significant speed-up
An Assessment of Nature-Inspired Algorithms for Text Feature Selection
Full text linkThis paper provides a comprehensive assessment of feature selection (FS) methods that are originated from nature-inspired (NI) meta-heuristics, where two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on two different high-dimensional and real-world text datasets against the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses performed under different circumstances where classifier, feature model, and term weighting scheme are different. The results of the extensive experiments indicate that NI algorithms produce slightly different results than filter-based methods for the problem of the text FS. However, filter-based methods often provide better results by using a lower number of features and computation times
Ensemble machine learning methods to predict the balancing of ayurvedic constituents in the human body: Ensemble Machine Learning Methods to Predict
No full textAyurvedic medicines are categorized into seven constitutional forms ‘Prakriti’ which is a constituent in the Ayurvedic system of medicine to determine drought tolerance and drug responsiveness. Prakriti assessment entails a thorough physical examination as well as queries about physiological or behavioral characteristics. The prevalence of certain "doshas" is attributed by Ayurveda to the fundamental constituent of a person. Vata, pitta, and Kapha are the three main doshas mentioned. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment. In this paper, we demonstrate the result of certain machine learning methods like Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), and Adaboost algorithm for various performance characteristics to predict human body constituencies. From the observations of results it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall of 0.97, precision and F-score of 0.96, the lower RSME value obtained is 0.64. The experimental results reveal that the improved model, which is based on ensemble learning methods, outperforms traditional methods significantly. According to the findings, advancements in the proposed algorithms could give machine learning a promising future
A single-shot determination of differential gene network on multiple disease subtypes
No full text Differential gene expressional network determines the prominent genes under altered phenotypes. Traditional approach requires n(n-2)/2 comparisons for n phenotypes. We present a direct method for determining the differential network under multiple phenotypes.
We explore the non-discrete nature of gene expression as a pattern in fuzzy rough set. An edge between a pair of genes represents positive region of fuzzy similarity relation upon a phenotypic change. We apply a weight ranking formula and obtain a directed ranked network; we term it as Phenotype Interweaved Network. Nodes with large in-degree connectivity bubble up as significant genes under respective phenotypic changes.
We test the method on datasets of six diseases and achieve good corroboration with results of previous studies in two-step approach. The subgraphs of isolated genes achieve good significance upon validation through information theoretic approach. Top ranking genes determined in all our case studies have parity with genes reported by wet-lab tests
Comparative analysis of different trust metrics of user-user trust-based recommendation system
Full text linkInformation overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset