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Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images

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The Journal of Photographic Science 2024年第8期72卷

作者： Abirami S Lanitha B Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie firefly optimization algorithm (BBCFO). The BBCFO is the incorporation of Border Collie optimization (BCO), the firefly optimization algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.

关键词： Shepard convolutional neural network bald eagle search algorithm border collie optimization magnetic resonance imaging firefly optimization algorithm U-Net++ Shepard Convolutional Neural Network feature extraction

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Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA

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THEORETICAL AND APPLIED CLIMATOLOGY 2019年第3-4期138卷 1471-1480页

作者： Aghelpour, Pouya Mohammadi, Babak Biazar, Seyed Mostafa Bu Ali Sina Univ Fac Agr Dept Water Engn Hamadan Hamadan Iran Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Jiangsu Peoples R China Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change;therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with firefly optimization algorithm (SVR-FA) as a meta-innovative model, in long-term forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during 1951-2011, for training (75%) and testing (25%). After selecting the best models, the average monthly temperature has been forecasted for the period 2012-2017. The results showed that the models had better performances in Extra-Arid and Warm (Abadan) and after that Extra-Arid and Cold (Isfahan) climate, in long-term forecasting. The weakest performances of the models were reported in Semi-Arid and Cold climate, including Mashhad and Tabriz. Also, despite the use of the non-linear SVR model and its meta-innovative type, SVR-FA, the results showed that, in the climates of Iran, the linear and classical SARIMA model still offers a more appropriate performance in temperature long-term forecasting. So that it could forecast the average monthly temperature of Abadan with root mean square error (RMSE) = 1.027 degrees C, and Isfahan with RMSE = 1.197 degrees C for the 6 years ahead. The SVR and SVR-FA models also had good performances. The results of this checking also report the effectiveness of the merging SVR model with the firefly optimization algorithm in temperature forecasting in Iran's climates, so, compared with the SVR model, it is suggested to use SVR-FA for temperature forecasting.

关键词： Average monthly temperature SVR firefly optimization algorithm SARIMA Iran

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Malware Detection: Harmony of Fuzzy and firefly (FF-MD)

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2020年第5-6期35卷 585-607页

作者： Kilic, Vahide Nida Esiz, Esra Sarac Adana Alparslan Turkes Sci & Technol Univ Comp Engn Adana Turkey

Android Operating System an (OS) is open-source, easy to use, and user-friendly mobile OS. In this way, it is very preferred. As a result, it becomes the target of malicious people. Applications installed on the Android OS from the Google Play Store or by third-party application providers, also known as Android Package Files (APKs), may contain malicious software. So far, a variety of analyzes and detections have been made to detect such malware. While detecting malware, good results have been obtained with various methods, but malicious people have developed methods of hiding themselves against these methods. We propose a new feature selection method based on firefly optimization algorithm (FOA) with the Fuzzy Set-Based (FSB) weighting method. The proposed method performs better than traditional feature selection methods with fewer features. The experimental results of this study proved that FOA is an acceptable optimization algorithm for feature selection to detect malware in terms of classification performance and classification runtime. In addition, experimental evaluation of TF-IDF and FSB weighting methods indicates the effectiveness of the FSB weighting with a full feature set.

关键词： Malware detection android operating system fuzzy set-based weighting firefly optimization algorithm feature selection data mining

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Efficient Deployment and Mission Timing of Autonomous Underwater Vehicles in Large-Scale Operations 15th

Efficient Deployment and Mission Timing of Autonomous Underw...

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15th International Conference on Advanced Data Mining and Applications (ADMA)

作者： MahmoudZadeh, Somaiyeh Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia

ISBN: (纸本)9783030352318;9783030352301

This study introduces a connective model of routing- local path planning for Autonomous Underwater Vehicle (AUV) time efficient maneuver in long-range operations. Assuming the vehicle operating in a turbulent underwater environment, the local path planner produces the water-current resilient shortest paths along the existent nodes in the global route. A re-routing procedure is defined to re-organize the order of nodes in a route and compensate any lost time during the mission. The firefly optimization algorithm (FOA) is conducted by both of the planners to validate the model's performance in mission timing and its robustness against water current variations. Considering the limitation over the battery lifetime, the model offers an accurate mission timing and real-time performance. The routing system and the local path planner operate cooperatively, and this is another reason for model's real-time performance. The simulation results confirms the model's capability in fulfilment of the expected criterion and proves its significant robustness against underwater uncertainties and variations of the mission conditions.

关键词： Autonomy firefly optimization algorithm Local path planning Mission routing Mission time management

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Generating Prioritized Test Sequences Using firefly optimization Technique 1

Generating Prioritized Test Sequences Using Firefly Optimiza...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Panthi, Vikas Mohapatra, D. P. Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 Odisha India

ISBN: (纸本)9788132222088;9788132222071

The aim of this study is to propose an algorithm for generating minimal test sequences by applying firefly optimization technique. In this study, we use state machine diagram for the behavioral specification of software. This paper generates the important test sequences for composite states in the state machine diagram under consideration. The generated test sequences are then prioritized based on a software coverage criterion. The use of firefly technique results in efficient prioritization of the generated test sequences.

关键词： Test sequence firefly optimization algorithm State machine Composite model

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Comparing Cuckoo Search, Bee Colony, firefly optimization, and Electromagnetism-Like algorithms for Solving the Set Covering Problem 15th

Comparing Cuckoo Search, Bee Colony, Firefly Optimization, a...

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15th International Conference on Computational Science and Its Applications (ICCSA)

作者： Soto, Ricardo Crawford, Broderick Galleguillos, Cristian Barraza, Jorge Lizama, Sebastian Munoz, Alexis Vilches, Jose Misra, Sanjay Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Univ Cent Chile Santiago Chile Univ San Sebastian Santiago Chile Atilim Univ Ankara Turkey Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319214047;9783319214030

The set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, firefly optimization, and Electromagnetism-Like algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley's OR-Library.

关键词： Combinatorial optimization Set covering problem Cuckoo search algorithm Bee colony algorithm firefly optimization algorithm Electromagnetism-Like algorithm

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Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

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APPLIED SOFT COMPUTING 2015年 36卷 506-518页

作者： Sivakumar, S. Venkatesan, R. PSG Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India PSG Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, Bat optimization algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and firefly optimization algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches. (C) 2015 Published by Elsevier B.V.

关键词： Meta-heuristics NP-hard problems Mobile Anchor Positioning Bat optimization algorithm Modified Cuckoo Search firefly optimization algorithm

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A new approach based on firefly algorithm for vision-based railway overhead inspection system

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MEASUREMENT 2015年 74卷 43-55页

作者： Aydin, Ilhan Firat Univ Dept Comp Engn TR-23119 Elazig Turkey

The maintenance of the pantograph-catenary system is a crucial problem in railway inspection systems. The quality of current collection depends on maintaining good contact between a moving pantograph and an overhead contact wire. Problems in the current collection system lead to the damage and disruption of railway traffic. Pantograph arcing occurs between the pantograph and the overhead wire because of the wear of the contact strip. This paper proposes an automatic inspection system for detecting the pantograph and arcs that occur using a firefly optimization algorithm. The proposed method is able to localize the pantograph as a rectangular region and detect the burst of arcing under different illumination and weather conditions simultaneously. The firefly algorithm based Otsu method is used to detect arcs that occur in this region. The proposed approach can serve as an automatic monitoring system and can detect the occurrence of arcing due to loss of contact. The proposed method was applied to four real pantograph videos and efficient results were obtained. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： firefly optimization algorithm Image thresholding Pantograph-catenary system Arc detection

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firefly Based Approach for Optimal Power System Black-Start Restoration

Firefly Based Approach for Optimal Power System Black-Start ...

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15th International Scientific Conference on Electric Power Engineering (EPE)

作者： El-Zonkoly, Amany Desouki, Hussein Farghaly, Ahmed Arab Acad Sci & Technol Dept Elect & Control Engn Alexandria Egypt

ISBN: (纸本)9781479938070

The restoration of power system after a global blackout is a very challenging problem. The power system restoration should be accomplished as soon as possible. To achieve a faster restoration process, an optimal schedule for the black-start (BS) units to crank the non-black start (NBS) units is required. In this paper, the firefly optimization algorithm (FA) is used to find both the optimal final sequence of NBS units restoration and the optimal transmission paths. The objective is to minimize the overall restoration time which lead to the maximization of the power generation capability of the system. The proposed algorithm is applied successfully to the IEEE 39 bus system.

关键词： Power system restoration Black-start Generation capability firefly optimization algorithm

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A Novel Hybrid Approach Using Wavelet, firefly algorithm, and Fuzzy ARTMAP for Day-Ahead Electricity Price Forecasting

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IEEE TRANSACTIONS ON POWER SYSTEMS 2013年第2期28卷 1041-1051页

作者： Mandal, Paras Ul Haque, Ashraf Meng, Julian Srivastava, Anurag K. Martinez, Ralph Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99163 USA Univ Texas El Paso CERM El Paso TX 79968 USA

This paper presents a novel hybrid intelligent algorithm utilizing a data filtering technique based on wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network in order to forecast day-ahead electricity prices in the Ontario market. A comprehensive comparative analysis with other soft computing and hybrid models shows a significant improvement in forecast error by more than 40% for daily and weekly price forecasts, through the application of a proposed hybrid WT + FF + FA model. Furthermore, low values obtained for the forecast mean square error (FMSE) and mean absolute error (MAE) indicate high degree of accuracy of the proposed model. Robustness of the proposed hybrid intelligent model is measured by using the statistical index (error variance). In addition, the good forecast performance and the rapid adaptability of the proposed hybrid WT + FF + FA model are also evaluated using the PJM market data.

关键词： Electricity market firefly optimization algorithm fuzzy ARTMAP network short-term price forecasting wavelet transform

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