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    Wyświetlanie 1-6 z 6
    Tytuł:
    FPGA-based secure and noiseless image transmission using lea and optimized bilateral filter
    Autorzy:
    Hebbale, Sunil B.
    Akula, V.S. Giridhar
    Baraki, Parashuram
    Powiązania:
    https://bibliotekanauki.pl/articles/27312891.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    lightweight encryption algorithm
    bilateral filter
    whale optimization algorithm
    discrete wavelet transform
    Opis:
    In today’s world, the transmission of secured and noiseless images is a difficult task. Therefore, effective strategies are important for securing data or secret images from attackers. Besides, denoising approaches are important for obtaining noise-free images. For this, an effective crypto-steganography method that is based on a lightweight encryption algorithm (LEA) and the 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 the image transmission, a secret image is encrypted by the LEA algorithm and embedded into the cover image using discrete wavelet transform (DWT) and MLSB techniques. After the image transmission, an extraction process is performed in order to recover the secret image. Finally, a bilateral WOA filter 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 results in performance that is superior to conventional bilateral and Gaussian filters in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
    Źródło:
    Computer Science; 2022, 23 (4); 451--466
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimal sliding mode controller design based on whale optimization algorithm for lower limb rehabilitation robot
    Autorzy:
    Sabah, Noor
    Hameed, Ekhlas
    Al-Huseiny, Muayed S
    Powiązania:
    https://bibliotekanauki.pl/articles/1956062.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Optimal Sliding Mode Controller
    Whale Optimization Algorithm
    lower limb
    rehabilitation robot
    kończyna dolna
    robot rehabilitacyjny
    Opis:
    The Sliding Mode Controllers (SMCs) are considered among the most common stabilizer and controllers used with robotic systems due to their robust nonlinear scheme designed to control nonlinear systems. SMCs are insensitive to external disturbance and system parameters variations. Although the SMC is an adaptive and model-based controller, some of its values need to be determined precisely. In this paper, an Optimal Sliding Mode Controller (OSMC) is suggested based on Whale Optimization Algorithm (WOA) to control a two-link lower limb rehabilitation robot. This controller has two parts, the equivalent part, and the supervisory controller part. The stability assurance of the controlled rehabilitation robot is analyzed based on Lyapunov stability. The WO algorithm is used to determine optimal parameters for the suggested SMC. Simulation results of two tested trajectories (linear step signal and nonlinear sine signal) demonstrate the effectiveness of the suggested OSMC with fast response, very small overshoot, and minimum steady-state error.
    Źródło:
    Applied Computer Science; 2021, 17, 3; 47-59
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid binary whale optimization algorithm based on taper shaped transfer function for software defect prediction
    Hybrydowy, binarny algorytm WOA oparty na transmitancji stożkowej do prognozowania defektów oprogramowania
    Autorzy:
    Alnaish, Zakaria A. Hamed
    Hasoon, Safwan O.
    Powiązania:
    https://bibliotekanauki.pl/articles/27315468.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    feature selection
    binary whale optimization algorithm
    taper-shaped transfer function
    software defect prediction
    wybór cech
    algorytm optymalizacji binarnej
    transmitancja stożkowa
    przewidywanie defektów oprogramowania
    Opis:
    Reliability is one of the key factors used to gauge software quality. Software defect prediction (SDP) is one of the most important factors which affectsmeasuring software's reliability. Additionally, the high dimensionality of the features has a direct effect on the accuracy of SDP models.The objective of this paper is to propose a hybrid binary whale optimization algorithm (BWOA) based on taper-shape transfer functions for solving feature selection problems and dimension reduction with a KNN classifier as a new software defect prediction method. In this paper, the values of a real vector that representsthe individual encoding have been converted to binary vector by using the four types of Taper-shaped transfer functionsto enhance the performance of BWOA to reduce the dimension of the search space. The performance of the suggestedmethod (T-BWOA-KNN)was evaluatedusing eleven standard software defect prediction datasets from the PROMISE and NASA repositories depending on the K-Nearest Neighbor (KNN) classifier. Seven evaluation metrics have been used to assess the effectiveness of the suggested method. The experimental results have shownthat the performanceof T-BWOA-KNNproduced promising results compared to other methods including ten methods from the literature, four typesof T-BWOAwith the KNN classifier. In addition, the obtained results are compared and analyzed with other methods from the literature in termsof the average numberof selected features (SF) and accuracy rate (ACC) using the Kendall W test. In this paper, a new hybrid software defect prediction methodcalledT-BWOA-KNNhas been proposed which is concerned with the feature selection problem. The experimental results have provedthatT-BWOA-KNN produced promising performance compared with other methods for most datasets.
    Niezawodność jest jednym z kluczowych czynników stosowanych do oceny jakości oprogramowania.Przewidywanie defektów oprogramowania SDP (ang. Software Defect Prediction) jest jednym z najważniejszych czynników wpływających na pomiar niezawodności oprogramowania. Dodatkowo, wysoka wymiarowość cech ma bezpośredni wpływ na dokładność modeli SDP.Celemartykułu jest zaproponowanie hybrydowego algorytmu optymalizacji BWOA (ang. Binary Whale Optimization Algorithm) w oparciu o transmitancję stożkową do rozwiązywania problemów selekcji cech i redukcji wymiarów za pomocą klasyfikatora KNN jako nowej metody przewidywania defektów oprogramowania.W artykule, wartości wektora rzeczywistego, reprezentującego indywidualne kodowanie zostały przekonwertowane na wektor binarny przy użyciu czterech typów funkcji transferu w kształcie stożka w celu zwiększenia wydajności BWOA i zmniejszenia wymiaru przestrzeni poszukiwań.Wydajność sugerowanej metody (T-BWOA-KNN) oceniano przy użyciu jedenastu standardowych zestawów danych do przewidywania defektów oprogramowania z repozytoriów PROMISE i NASA w zależności od klasyfikatora KNN. Do oceny skuteczności sugerowanej metody wykorzystano siedemwskaźników ewaluacyjnych. Wyniki eksperymentów wykazały, że działanie rozwiązania T-BWOA-KNN pozwoliło uzyskaćobiecujące wyniki w porównaniu z innymi metodami, w tym dziesięcioma metodami na podstawie literatury, czterema typami T-BWOA z klasyfikatorem KNN. Dodatkowo, otrzymane wyniki zostały porównanei przeanalizowane innymi metodami z literatury pod kątem średniej liczby wybranych cech (SF) i współczynnika dokładności (ACC), z wykorzystaniem testu W.Kendalla. W pracy, zaproponowano nową hybrydową metodę przewidywania defektów oprogramowania, nazwaną T-BWOA-KNN, która dotyczy problemu wyboru cech. Wyniki eksperymentów wykazały, że w przypadku większości zbiorów danych T-BWOA-KNN uzyskała obiecującą wydajnośćw porównaniu z innymi metodami.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 4; 85--92
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimal tuning procedure for FOPID controller of integrated industrial processes with deadtime
    Autorzy:
    Anuja, R.
    Sivarani, T.S.
    Germin Nisha, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173529.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    industrial process integrated with dead time
    tuning of FOPID
    whale optimization algorithm
    proces przemysłowy zintegrowany z czasem martwym
    strojenie FOPID
    algorytm optymalizacji wielorybów
    Opis:
    Industrial processes such as batch distillation columns, supply chain, level control etc. integrate dead times in the wake of the transportation times associated with energy, mass and information. The dead time, the cause for the rise in loop variability, also results from the process time and accumulation of time lags. These delays make the system control poor in its asymptotic stability, i.e. its lack of self-regulating savvy. The haste of the controller’s reaction to disturbances and congruence with the design specifications are largely influenced by the dead time; hence it exhorts a heed. This article is aimed at answering the following question: “How can a fractional order proportional integral derivative controller (FOPIDC) be tuned to become a perfect dead time compensator apposite to the dead time integrated industrial process?” The traditional feedback controllers and their tuning methods do not offer adequate resiliency for the controller to combat out the dead time. The whale optimization algorithm (WOA), which is a nascent (2016 developed) swarm-based meta-heuristic algorithm impersonating the hunting maneuver of a humpback whale, is employed in this paper for tuning the FOPIDC. A comprehensive study is performed and the design is corroborated in the MATLAB/Simulink platform using the FOMCON toolbox. The triumph of the WOA tuning is demonstrated through the critical result comparison of WOA tuning with Bat and particle swarm optimization (PSO) algorithm-based tuning methods. Bode plot based stability analysis and the time domain specification based transient analysis are the main study methodologies used.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e139954, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Aspect-based sentiment classification model employing whale-optimized adaptive neural network
    Autorzy:
    Balaganesh, Nallathambi
    Muneeswaran, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173622.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    aspect-based sentiment analysis
    whale optimization algorithm
    artificial neural network
    opinion mining
    analiza nastrojów oparta na aspektach
    algorytm optymalizacji wielorybów
    sztuczna sieć neuronowa
    eksploracja opinii
    Opis:
    Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137271
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Aspect-based sentiment classification model employing whale-optimized adaptive neural network
    Autorzy:
    Balaganesh, Nallathambi
    Muneeswaran, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2128172.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    aspect-based sentiment analysis
    whale optimization algorithm
    artificial neural network
    opinion mining
    analiza nastrojów oparta na aspektach
    algorytm optymalizacji wielorybów
    sztuczna sieć neuronowa
    eksploracja opinii
    Opis:
    Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137271, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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