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A quality of service-aware routing protocol for FANETs

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷 e5723-e5723页

作者： Malhotra, Atul Kaur, Sanmeet Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

Flying ad hoc networks (FANETs) are a class of highly dynamic mobile ad hoc networks in which flying UAVs coordinate among themselves for delivering the data to the applications. Data routing in FANETs has been a challenge due to the inherent mobile and unpredictable movement of UAV nodes. This paper proposes a quality of service-aware routing protocol (QARP) for FANETs which incorporates hybrid nature-inspired heuristics to identify optimized routes to the destination UAVs. The routing mechanism operates in two phases. In the first phase, the neighbour discovery initializes the adapted firefly algorithm to identify nodes with least packet delivery delay to the destination. In the second phase, the Gaussian quantum-behaved particle swarm optimization meta-heuristic algorithm is utilized to identify the most optimal nodes to create a route for data delivery to destination. The proposed protocol has been tested to perform better in terms of delay, routing overhead, throughput and packet delivery ratio in comparison to MA-DP-AODV, MDA-AODV and MDRMA routing protocols. This paper proposes a quality of service-aware routing protocol (QARP) for FANETs which incorporates hybrid nature-inspired heuristics to optimize routes for data delivery. The routing mechanism operates in two phases. In the first phase, the neighbour discovery initializes the adapted firefly algorithm to identify nodes with least packet delivery delay to the destination. In the second phase, the Gaussian quantum-behaved particle swarm optimization meta-heuristic algorithm identifies the most optimal nodes to create a route for data delivery. image

关键词： delay awareness firefly algorithm flying ad hoc network routing Gaussian quantum particle swarm optimization quality of service route optimization

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Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

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IEEE ACCESS 2019年 7卷 143190-143206页

作者： Zhou, Guoxiong Zhang, Wenzhuo Chen, Aibin He, Mingfang Ma, Xueshuo Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha 410004 Hunan Peoples R China

In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

关键词： Chaos theory faster R-CNN firefly algorithm Otsu threshold segmentation K-means clustering algorithm rice disease detection weighted multistage median filter

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Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期255卷

作者： Pichandi, Kavitha vellore Janarthanan, Vijayaraj Annamalai, Tamizhselvi Arumugam, Mahendran SRM Inst Sci & Technol Dept Elect & Commun Engn Vadapalani Campus Chennai Tamil Nadu India Easwari Engn Coll Dept Artificial Intelligence & Data Sci Chennai India St Josephs Coll Engn OMR Dept Informat Technol Chennai 600119 India Saveetha Inst Med & Tech Sci Saveetha Med Coll Ctr Global Hlth Res Kancheepuram India

In the modern world, due to changing lifestyles, more people are infected by deadly diseases, especially heart disorders. An early prognosis is the only possible way of increasing survival rate. One of the key elements of this context is the healthcare digitalization that is carried out through Internet of Things (IoTs) and cloud computing. However, main issue in cloud with IoT is false diagnosis, which leads to cause a major impact on patient's life. Moreover, data communication through cloud servers can be compromised by attackers due to security issues. Therefore, to overcome these issues, this paper proposes a novel secure e-healthcare system with dual objectivesaccurate disease prediction and improved cloud security. A novel diagnosis approach 'Hybrid Binary Particle firefly Optimized Extreme Learning Machine classifier (HybBPF-ELM)' to predict HD is designed, that recognize the subject with HD abnormality from the normal ones. The prediction accuracy and time efficiency of training process are improved by the adoption of a Hybrid Binary Particle firefly Optimizer (HBPFO) through fine-tuning weight and bias of Extreme Learning Machine (ELM) classifier. Additionally, a new intelligent encryption and decryption framework 'IEDF' is introduced for cloud security. It combines four encryption algorithms, including Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest- Shamir-Adleman (RSA), and Modified Blow Fish (MBF) to enhance cryptographic strength and key security. Along with this, an Automatic Sequence Encryption (ASC) for data block encryption is employed to ensure strong security and network performance. The integration of HD prediction module and cloud security framework within e-healthcare system assists healthcare experts in securely storing and transferring data for advanced diagnosis. The results show that our proposed system achieves an overall prediction accuracy of 99.36 % and less processing time for encryption and decryption of 1

关键词： Heart disease Detection Cloud security Binary Particle Swarm Optimization firefly algorithm Encryption Decryption Modified Blowfish Automated Sequential Cryptography

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Variable structure model predictive controller based gain scheduling for frequency regulation in renewable based power system

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2022年第4期35卷

作者： Sibtain, Daud Gulzar, Muhammad Majid Murtaza, Ali F. Murawwat, Sadia Iqbal, Muhammad Rasool, Imran Hayat, Aamir Arif, Arslan Univ Cent Punjab Dept Elect Engn Lahore Pakistan Lahore Coll Women Univ Dept Elect Engn Lahore Pakistan

This paper presents a load frequency control (LFC) design using the variable structure model predictive controller (VSMPC) for gain scheduling (GS) of PI controller in PV connected thermal for multi-area system. Due to the increasing impact of renewable energy sources into the grid system, the system frequency deteriorates. The proposed PV connected thermal system is evaluated under varying load conditions under increased penetration of the PV source. The combination of renewable energy into the thermal power system aggravates the frequency and it is indispensable to mitigate such a problem by introducing an optimal controller. Perturbation in load greatly affects the system frequency and this problem is addressed by using VSMPC based gain scheduling knowing the effect of proportional and integral in transient and steady-state conditions. The oscillations in frequency are controlled by a proposed controller and the impact of frequency under penetration of PV is monitored. Further verification of the proposed technique is accomplished through communication delay in the governor and turbine. The Hardware in the loop (HIL) which validates the accuracy and real time performance of the controller. Finally, the validation of the proposed controller is compared with MPC to tune PI controller and renowned evolutionary tuned techniques like particle swarm optimization (PSO), genetic algorithm (GA) and firefly algorithm (FA) to optimize PI controller. The VSMPC with GS of PI controller shows promising results.

关键词： firefly algorithm gain scheduling genetic algorithm load frequency control model predictive controller particle swarm optimization variable structure system

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Nature inspired algorithm based design of near ideal fractional order low pass Chebyshev filters and their realization using OTAs and CCII

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INTEGRATION-THE VLSI JOURNAL 2024年 97卷

作者： Daryani, Ritu Aggarwal, Bhawna Netaji Subhas Univ Technol Elect & Commun Engn Dept New Delhi India

Fractional order filters offer greater freedom of design and a precise control over stopband attenuation in electronic circuits and systems. This paper presents the design of a fractional order low pass Chebyshev filter (FOLCF) that achieves near -ideal response characteristics. The methodology introduced utilizes metaheuristic optimization methods, including particle swarm optimization, firefly algorithm, and grey wolf optimization. These techniques are employed to precisely adjust the filter coefficients for the orders (1+alpha), (2+alpha), and (3+alpha). The adjustment is carried out by comparing the desired behaviour of the FOLCF with generalized fractional order low pass transfer functions. Throughout these instances, the parameter alpha is varied within the range of (0, 1). The designed filters are then tested and compared on the basis of various factors. Simulation results demonstrate that the designed filters closely follow the behaviour of an ideal Chebyshev filter with maximum passband and stopband magnitude errors being -31.93 dB and -52.74 dB respectively for (1+alpha) order filters. These values for (2+alpha) and (3+alpha) order FOLCF have been observed to be -30.04 dB and -55.91 dB;-17.72 dB and -49.52 dB respectively. Furthermore, it has been observed that the proposed work outperforms existing state-of-the-art approaches in various aspects, including magnitude error, stopband attenuation, and cut-off frequency. The stability of the designed filters has been verified through stability analysis. Additionally, practical feasibility of the proposed FOLCF is demonstrated through SPICE simulations for alpha = [0.2,0.5,0.8] using second generation current conveyor (CCII) and operational transconductance amplifier (OTA) based topologies while approximating the constant phase element using fifth order continued fraction expansion. The SPICE implementations closely follow the behaviour of ideal filter with -48.67 dB and -62.8 dB as mean square errors for CCII

关键词： Fractional order filters Chebyshev filter Nature inspired algorithms Particle swarm optimization firefly algorithm Grey wolf optimization

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Control of electric machines using flower pollination algorithm based fractional order PID controller

Global Transitions Proceedings

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Global Transitions Proceedings 2021年第2期2卷 227-232页

作者： Tirumalasetty Chiranjeevi Naladi Ram Babu Ankit Yadav Vijay Kumar Das Shyama Charan Prasad Amardeep Sonkar Sachin Kumar Verma REC Sonbhadra Uttar Pradesh India NIT Silchar Assam India

In this work, fractional order proportional integral derivative controller (FOPIDC) is proposed and implemented for speed control of electric machines using nature-inspired metaheuristic optimization algorithm (MOA) named as flower pollination algorithm (FPA). Investigations are performed by considering the second-order transfer function of D.C Motor. FOPIDC constants K P , K I , K D , λ , and μ are optimized by considering FPA. The desired response of the electric machine is obtained by using tuned parameters of FOPIDC. After that, FOPIDC response using FPA is compared with PIDC response using FPA in the sense of time-domain specifications and performance indices (PI) like ‘integral of square error (ISE), integral of time-weighted square error (ITSE), integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE)’. Finally, the desired response of electric machine using FPA based FOPIDC is compared with desired response of electric machine using firefly algorithm (FA) and particle swarm optimization (PSO) algorithm based FOPIDC.

关键词： D. C Motor speed control firefly algorithm Flower pollination algorithm FOPID controller performance indices PSO

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An evaluation of different bio-inspired feature selection techniques on multivariate calibration models in spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 246卷 119042-119042页

作者： El-Zeiny, Mohamed B. Zawbaa, Hossam M. Serag, Ahmed Modern Univ Technol & Informat MTI Analyt Chem Dept Fac Pharm Cairo Egypt Beni Suef Univ Fac Comp & Informat Bani Suwayf Egypt Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Al Azhar Univ Pharmaceut Analyt Chem Dept Fac Pharm Cairo 11751 Egypt

Herein, two new swarm intelligence based algorithms namely;grey wolf optimization (GWO) and antlion opti-mization (ALO) algorithms were presented, for the first time, as variable selection tools in spectroscopic data analysis. In order to assess the performance of these algorithms, they were applied along with the recently introduced firefly algorithm (FFA) and the well-established genetic algorithm (GA) and particle swarm optimization (PSO) algorithm on four different spectroscopic datasets of varying sizes and nature (UV and IR). Partial least squares (PLS) regression models were built using the selected variables by these algorithms along with the full spectral data as the reference models. The obtained results prove that the ALO and GWO optimization algorithms select variables in most cases less than GA and PSO while keeping the PLS performance almost the same. Accordingly, these algorithms can be successfully used for variable selection in spectroscopic data analysis. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Antlion optimization Grey wolf optimization firefly algorithm Genetic algorithm Feature selection Spectroscopy

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Adversarial example-based test case generation for black-box speech recognition systems

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SOFTWARE TESTING VERIFICATION & RELIABILITY 2023年第5期33卷 e1848-e1848页

作者： Cai, Hanbo Zhang, Pengcheng Dong, Hai Grunske, Lars Ji, Shunhui Yuan, Tianhao Hohai Univ Coll Comp & Informat Nanjing Peoples R China RMIT Univ Sch Comp Technol Melbourne Australia Humboldt Univ Berlin Germany

Test case generation techniques based on adversarial examples are commonly used to enhance the reliability and robustness of image-based and text-based machine learning applications. However, efficient techniques for speech recognition systems are still absent. This paper proposes a family of methods that generate targeted adversarial examples for speech recognition systems. All are based on the firefly algorithm (F), and are enhanced with gauss mutations and / or gradient estimation (F-GM, F-GE, F-GMGE) to fit the specific problem of targeted adversarial test case generation. We conduct an experimental evaluation on three different types of speech datasets, including Google Command, Common Voice and LibriSpeech. In addition, we recruit volunteers to evaluate the performance of the adversarial examples. The experimental results show that, compared with existing approaches, these approaches can effectively improve the success rate of the targeted adversarial example generation. The code is publicly available at .

关键词： adversarial example firefly algorithm Gauss mutation gradient estimation method speech recognition test case generation

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MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents

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DRONES 2022年第7期6卷 181-181页

作者： Viviana Campo, Liseth Ledezma, Agapito Carlos Corrales, Juan Univ Cauca Telemat Engn Grp St 54-70 Popayan 190003 Colombia Univ Carlos III Madrid Control Learning & Optimizat Grp Madrid 28911 Spain

Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the firefly algorithm generated coverage paths.

关键词： area partitioning connectivity coverage mission firefly algorithm GAMA platform heterogeneity Micro Aerial Vehicles Voronoi-tessellation

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RSS-LSTM: A Metaheuristic-Driven Optimization Approach for Efficient Text Classification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 296-310页

作者： Nasir, Muhammad Samsudin, Noor Azah Khalid, Shamsul Kamal Ahmad Baowidan, Souad Arshad, Dr. Humaira Sharif, Wareesa Univ Tun Hussein Onn UTHM Fac Comp Sci & Informat Technol Johor Baharu Malaysia King Abdulaziz Univ Fac Comp & IT Jeddah Saudi Arabia Islamia Univ Bahawalpur IUB Dept Comp Sci Bahawalpur Pakistan Islamia Univ Bahawalpur IUB Dept Artificial Intelligence Bahawalpur Pakistan

The digital data consumed by the average user daily is huge now and is increasing daily all over the world, which requires sophisticated methods to automatically process data, such as retrieving, searching, and formatting the data, particularly for classifying text data. Long Short-Term Memory (LSTM) is a prominent deep learning model for text classification. Several metaheuristic approaches, such as the Genetic algorithm (GA), Particle Swarm Optimization (PSO), and firefly algorithm (FF), have also been used to optimize Deep Learning (DL) models for classification. This study introduced an improved technique for text classification, called RSS-LSTM. The proposed technique optimized the hyperparameters and kernel function of LSTM through the Ringed Seal Search (RSS) algorithm to enhance simplification and learning ability. This work was also compared and evaluated against state-of-the-art techniques such as GALSTM, PSO-LSTM, and FF-LSTM. The results showed significantly better results using the proposed techniques, with an accuracy of 96%, recall of 96%, precision of 96%, and 95% fmeasure on the Reuters-21578 dataset. In addition, it showed an accuracy of 77%, recall of 77%, precision of 78%, and f-measure of 76% on the 20 Newsgroups dataset, while it achieved accuracy, recall, precision, and f-measure of 91%, 91%, 94%, and 90%, respectively, using the AG News dataset.

关键词： Deep learning text classification Long Short-Term Memory Ringed Seal Search metaheuristic algorithms Part Swarm Optimization Genetic algorithm firefly algorithm hyperparameter optimization

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