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Fast Linde-Buzo-Gray (FLBG) algorithm for Image Compression through Rescaling Using Bilinear Interpolation

JOURNAL OF IMAGING 2024年第5期10卷 124-124页

作者： Bilal, Muhammmad Ullah, Zahid Mujahid, Omer Fouzder, Tama Univ Technol Dept Informat Engn Technol Nowshera 24170 Pakistan Pak Austria Fachhochschule: Inst Appl Sci & Techno Dept Elect & Comp Engn Mang Haripur Haripur 22621 Pakistan Univ Girona Inst Informat & Aplicac Girona 17003 Spain Daffodil Int Univ Dept Elect & Elect Engn Dhaka 1216 Bangladesh

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde-Buzo-Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.

关键词： computational time codebook firefly algorithm bat algorithm image compression Linde-Buzo-Gray peak signal to noise ratio vector quantization

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A novel algorithm for energy market risk forecasting

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 99卷 107813-107813页

作者： Wang, Xiaofei Pei, Pei Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

This paper proposes a novel artificial intelligence based approach for the accurate forecasting of the market cost incorporating the error risk. The proposed model makes use of a hybrid evolving solution based on firefly algorithm and support vector regression (SVR) for getting to the highest accuracy and precision. In contrast to the artificial neural network models which face the over fitting problem, the SVR would keep a limit on the forecast model complexity and thus would escape from the overfitting concerns. In order to get into the maximum performance of the SVR, one need to adjust the hyperparameters optimally. This article proposes the firefly algorithm which mimics the social behavior of these insects in their colony. In addition, a new modification method is introduced which would add up the algorithm population diversity and thus enhance the search results. The appropriate performance of the proposed hybrid AI base model is assessed on the typical market price datasets.

关键词： Artificial intelligence firefly algorithm Forecasting Market price risk Optimization

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2-D DOA Estimation in a Cuboid Array Based on Metaheuristic algorithms and Maximum Likelihood

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International Journal of Communications, Network and System Sciences 2020年第8期13卷 121-137页

作者： Gilberto Lopes Filho Ana Cláudia Barbosa Rezende Lucas Fiorini Cruz Flávio Henrique Teles Vieira Rodrigo Pinto Lemos Universidade Federal de Goiás (UFG) Goiánia Brazil

This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.

关键词： Metaheuristic algorithms Genetic algorithm firefly algorithm DOA Estimation Maximum Likelihood

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A Framework for Detecting Phishing Websites using GA based Feature Selection and ARTMAP based Website Classification

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Procedia Computer Science 2020年 171卷 1083-1092页

作者： Priya Saravanan Selvakumar Subramanian Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli-620 015 India

Nowadays, Phishing attack has gained more attention among all the other attacks existing in online social media. The fraudulent E-mail sent form the fake website that looks like the legitimate website is the initial carter for launching the phishing attacks. This is a kind of social engineering attack in which, the user is targeted for stealing the personal information, viz., user name, password, and banking credentials for committing the financial crimes. The existing phishing website detection methods suffer from two issues in terms of feature selection scheme that does not consider the right set of features for detection and the classifier which is trained with the poor hyper parameters. In this paper, the phishing websites are detected by extracting the various features from the collection of phishing and legitimate websites obtained from PhishTank and starting point directory service. This constructed feature vector is further processed by the proposed feature selection module GenFea to obtain the reduced set of features. This reduced feature vector is further processed by the proposed phishing detection module PhiDec to predict the type of a website. The performance of the proposed approach is compared with the existing machine learning classifiers and neural network classifiers. From the experimental results, it is observed that the proposed approach outperformed the other existing classifiers for detecting the phishing websites.

关键词： Phishing websites Online security Machine learning classifiers Adaptive Resonance Theory Mapping Genetic algorithm firefly algorithm

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Optimisation of CaO-Al2O3-SiO2-CaSO4-based catalysts performance for methanolysis of waste lard for biodiesel production using response surface methodology and meta-heuristic algorithms

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FUEL PROCESSING TECHNOLOGY 2022年 226卷 107066-107066页

作者： Ajala, E. O. Ehinmowo, A. B. Ajala, M. A. Ohiro, O. A. Aderibigbe, F. A. Ajao, A. O. Univ Ilorin Dept Chem Engn Ilorin Kwara State Nigeria Univ Lagos Dept Chem & Petr Engn Lagos Nigeria Univ Michigan Dept Chem Engn Ann Arbor MI 48109 USA

The CaO-Al2O3-SiO2-CaSO4-based solid catalysts developed from calcium carbide residue (CCR) was investigated for biodiesel production using waste lard in optimisation and regeneration studies. The catalysts were synthe-sized by calcination of the CCR at the temperature of 500, 700 and 900 degrees C and sulphonation, to give Cat500, Cat700 and Cat900 respectively. The catalysts were studied to optimise the biodiesel yield from waste lard using combinations of response surface methodology (RSM) and meta-heuristic algorithms such as particle swarm optimisation (PSO), genetic algorithm (GA) and firefly algorithm (FA). The process parameters investigated were methanol: oil molar ratio (6-12 w/w), reaction temperature (50-60 degrees C), reaction time (1-4 h), catalyst quantity (5-15 % (w/w)) and catalyst type (Cat500, Cat700 and Cat900). The study revealed that the 12:1 MeOH: oil molar ratio, 59.97 degrees C reaction temperature, 1 h reaction time, 5% (w/w) catalyst quantity and Cat500 catalyst type gave biodiesel yield of 96.35%. The performance of the meta-heuristic algorithms based on the optimisation output compared well with that of the RSM. This study concludes that the catalyst developed from the CCR can be regenerated after the ninth cycle of usage and re-utilised for efficient biodiesel production.

关键词： Transesterification Vegetable oil Animal fat Genetic algorithm Particle swarm optimisation firefly algorithm Fatty acid methyl esters

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Enhancing Brain Tumor Assessment: A Comprehensive Approach using Computerized Diagnostic Tool and Advanced MRI Techniques

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Procedia Computer Science 2024年 235卷 3350-3368页

作者： Saleh Alaraimi Imad Al Naimi Suresh Manic Naserya Al Hinai Samiya Al Shukaili National University of Science & Technology Muscat PO 620 PC 130 Sultanate of Oman

Assessment of brain tumour using Three-Dimensional Magnetic Resonance Imaging (3D MRI) is computationally multifaceted task. Currently, hospitals employ 2D MRI scans, followed by manual evaluation by experienced doctors, aided by a Computerized Diagnostic Tool (CDT). This research aims to develop an advanced CDT to significantly enhance the accuracy of brain tumor assessment. The CDT presented in this study evaluates Axial-View (AV), Coronal-View (CV), and Sagittal-View (SV) MRI images. It encompasses a comprehensive pipeline, including pre-processing, post-processing, feature extraction, feature selection, and categorization phases. Various tumor segmentation techniques, including active contour, level-set, watershed, and region growing, are thoroughly explored. Additionally, a comparative analysis of classification methods such as SVM, ANFIS, k-NN, Random Forest, and Adaboost is conducted. Experimental validation using the BRATS 2016 dataset and real-time 2D MRI data demonstrates that the proposed CDT consistently achieves an average classification accuracy exceeding 95% in tumor-based categorization. This research represents a significant advancement in brain tumor assessment, leveraging machine learning and advanced MRI techniques to improve diagnostic precision.

关键词： Machine learning Brain Tumor 3D MRI firefly algorithm

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An optimal angle control strategy for switched reluctance motor drive in EV applications

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JOURNAL OF CONTROL AND DECISION 2024年第2期11卷 271-282页

作者： Shahakar, Deepak A. Warkad, Sanjay B. PR Pote Patil Coll Engn & Management Dept Elect Engn Amravati India PR Pote Patil Coll Engn & Management Dept Elect Engn Kathora Rd Amravati India

In this study, a hybrid method of the Harris-Hawk optimisation method is used to simultaneously manage the speed of a switched reluctance motor and minimise the torque ripple. A proportional integral (PI) speed controller is used in the outer loop of the three-phase, six-phase switching reluctance motor, and a PI current controller is used in the inner loop. Turn-on and turn-off angles are also controlled. In order to decrease current parameter error measures, reduce torque ripples, and increase efficiency, the task of finding the turn-on and turn-off angles is treated as an optimisation problem. Using the suggested optimisation technique, a model with adjustable turn-on and turn-off angles under different load torques is produced. When contrasted to current optimisation methodologies, the suggested model is demonstrated to be efficient for torque ripple suppression and speed augmentation simultaneously.

关键词： SRM path finder algorithm firefly algorithm turn-on and turn-off angles torque ripple speed regulation PI controller

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GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Zhou, Peng Chen, Wei Wang, Jing Wang, Huan Zhang, Yunfeng Cao, Bingyu Sun, Shan He, Lina Xinjiang Univ Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China North China Inst Aerosp Engn Sch Comp Langfang 065000 Hebei Peoples R China

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine-cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

关键词： Agricultural Internet of Things Wireless sensor network Clustering routing firefly algorithm Energy consumption Network lifetime

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using firefly-algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging firefly algorithm Feature optimization Classification

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Performance Evaluation of Different Optimization algorithms for Power Demand Forecasting Applications in a Smart Grid Environment

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Procedia Computer Science 2012年 12卷 320-325页

作者： Ashraf Ul Haque Paras Mandal Julian Meng Ricardo L. Pineda Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El Paso TX 79968 USA

This paper presents an in-depth performance evaluation of three different optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO), and firefly (FF) algorithm for power demand forecasting in a deregulated electricity market and smart grid environments. In this framework, this paper proposes a hybrid intelligent algorithm for power demand forecasts using the combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network that is optimized by using FF optimization algorithm. The effectiveness and accuracy of the proposed hybrid WT+FF+FA model is trained and tested utilizing the data obtained from ISO-NE electricity market.

关键词： Electricity market firefly algorithm fuzzy ARTMAP genetic algorithm load demand forecasting neural networks particle swarm optimization smart grid

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