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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Ambekar, Rahul K. Kolekar, Uttam D. Pacific Acad Higher Educ & Res Univ Udaipur 313003 Rajasthan India AP Shah Inst Technol Thana 400615 Maharashtra India

ISBN: (纸本)9781509032433

Mobile Ad-hoc Network (MANET) becomes the facilitating and emerging trends for data transmission. Due to the nodes are freely connected with each other, the challenging is to provide the secure transmission. To attain this objective, the Adaptive Fractional lion TOpology-HIding multipath routing protocol (AFL-TOHIP) is proposed. The TOHIP protocol constitutes route request phase, route reply phase and route probe phase, to acquire the multiple numbers of disjoint paths in MANET. Consequently, the proposed algorithm is developed to select the optimal routing path. Here, the fractional calculus is utilized to integrate with the lion optimization algorithm. Then, the fractional lion algorithm is incorporated into the TOHIP protocol, named AFL-TOHIP. In addition to improve the solution searching, the fractional lion algorithm is mathematically integrated with the TOHIP protocol. In the proposed algorithm, the fractional derivative is adapted according to the generation of the solution. Finally, the optimal routing path is obtained, used to transmit the packets from source to destination node. The extensive experimental results are evaluated using MATLAB implementation. Then, the performance is analysed compared to existing algorithms. Thus, the novel AFL-TOHIP optimization algorithm achieves 0.603 throughputs and 0.572 less delay, which enhances the transmission efficiency and robustness.

关键词： MANET routing path lion optimization algorithm Fractional Calculus TOHIP multipath routing protocol

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CC-LA: determining optimal switching angles in a cascaded H-bridge multilevel inverter with the aid of binary cat cubpool-based lion algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第9期29卷 12399-12413页

作者： Jain, Pragya Deshmukh, Suresh Pandurang Atharva Coll Engn Mumbai Maharashtra India Inst Chem Technol Mumbai Maharashtra India

A multilevel inverter (MLI) is a power electronic device that includes the capability to offer the preferred voltage level (alternating) in the output. Accordingly, selective harmonic elimination (SHE) or pulse width modulation (PWM) methodologies were deployed widely. However, these techniques are not appropriate in a certain situation which involves a huge number of switching angles if an excellent primary guess is not obtainable. Hence, this paper intends to determine the optimum switching angles of a cascaded H-bridge multilevel inverter (CH-MLI) using a hybrid lion optimization algorithm (LOA) and binary cat swarm algorithm (BCSO). The objective of optimizing the switching angle is to generate the needed fundamental voltage and minimize the harmonic content. This is done by resolving the transcendental equations characterizing the harmonic content. The switching angles, i.e., alpha(1), alpha(2).... alpha(m), should be tuned in such a way that it satisfies the condition 0 < alpha(1), alpha(2) ....< alpha(m) < pi/2 Here, the optimal tuning of switching angles is done by the proposed binary cat cubpool-based lion algorithm (BCC-LA). In addition, the analysis is done for the proposed method over the state-of-the-art models in terms of total harmonic distortion (THD), and the impact of varying loads is also examined for the proposed and traditional models, and thus, the superior performance of the proposed model is validated.

关键词： Multilevel inverter Cascaded H-bridge multilevel inverter Optimal switching angles lion optimization algorithm Cat swarm algorithm

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MLP-LOA: a metaheuristic approach to design an optimal multilayer perceptron

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SOFT COMPUTING 2019年第23期23卷 12331-12345页

作者： Bansal, Priti Gupta, Shakshi Kumar, Sumit Sharma, Shubham Sharma, Shreshth Netaji Subhas Inst Technol Dept Informat Technol New Delhi India KIET Grp Inst Dept Informat Technol Ghaziabad UP India

Designing an ANN is a complex task as its performance is highly dependent on the network architecture as well as the training algorithm used to select proper synaptic weights and biases. Choosing an optimal design leads to greater accuracy when the ANN is used for classification. In this paper, we propose an approach multilayer perceptron-lion optimization algorithm (MLP-LOA) that uses lion optimization algorithm to find an optimum multilayer perceptron (MLP) architecture for a given classification problem. MLP-LOA uses back-propagation (BP) for training during the optimization process. MLP-LOA also optimizes learning rate and momentum as they have a significant role while training MLP using BP. LOA is a population-based metaheuristic algorithm inspired by the lifestyle of lions and their cooperative behavior. LOA, unlike other metaheuristics, uses different strategies to search for optimal solution, performs strong local search and helps to escape from worst solutions. A new fitness function is proposed to evaluate MLP based on its generalization ability as well as the network's complexity. This is done to avoid dense architectures as they increase chances of overfitting. The proposed approach is tested on different classification problems selected from University of California Irvine repository and compared with the existing state-of-the-art techniques in terms of accuracy achieved during testing phase. Experimental results show that MLP-LOA performs better as compared to the existing state-of-the-art techniques.

关键词： Multilayer perceptron lion optimization algorithm Classification Back-propagation

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Multi-classification of kidney abnormalities in sonography using the LOA-MFO and long-term recurrent convolutional network

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第5期83卷 13577-13612页

作者： Mactina, F. Josephine Nijofi Neduncheliyan, S. Bharath Inst higher Educ & Res Chennai 600126 Tamil Nadu India

CKD is a medical condition that affects people all around the world and is a major health concern. It increases the risk of developing cardiovascular and cerebrovascular diseases, which can lead to serious illness and even death. Ultrasound imaging is typically the initial and most widely used diagnostic technique for individuals at risk for CKD. The existing methods are restricted by features with high dimensions, computational hurdles, and extended processing times. To address these issues, this article proposes the development of an enhanced deep-learning model with an optimum selection of features for the accurate diagnosis of CKD. The proposed technique begins with pre-processing, involving image filtering and contrast enhancement. Then, presented an improved Otsu's algorithm for segmenting kidney masses, followed by a stage of feature extraction. A hybrid lion optimization algorithm (LOA) and Moth flame optimization (MFO) is a novel contributions to improve the convergence rate of the MFO algorithm. The hybrid FS algorithms select the optimal subset of features for disease classification. Finally, a novel Long-Term Recurrent Convolutional Network (LRCN) is introduced for detecting kidney impairment. The models are developed and validated utilizing a database of ultrasonography (US) images including four classes of Chronic kidney images: stone, cyst, tumor, and normal. The efficiency of the framework is assessed based on its of accuracy, precision, recall, F1-score, and specificity of 98.7%, 96.6%, 96.4%, 97.9%, and 96.2% respectively. In addition, testing results show that the framework obtains the best overall performance when compared to existing methods for the classification of ultrasound images.

关键词： Deep learning Chronic kidney disease Feature selection Classification Ultrasound images Long-term recurrent convolutional network Feature extraction lion optimization algorithm Moth flame optimization

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Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller optimization Techniques

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IEEE ACCESS 2022年 10卷 12742-12752页

作者： Rao, Thamatapu Eswara Sundaram, Elango Chenniappan, Sharmeela Almakhles, Dhafer Subramaniam, Umashankar Bhaskar, M. S. Coimbatore Inst Technol Dept Elect & Elect Engn Coimbatore 641014 Tamil Nadu India Anna Univ Coll Engn Dept Elect & Elect Engn Chennai 600025 Tamil Nadu India Prince Sultan Univ Coll Engn Dept Commun & Networks Renewable Energy Lab Riyadh 11586 Saudi Arabia

The main aim of this paper is to introduce a framework for the design and modelling of a photovoltaic (PV)-wind hybrid system and its control strategies. The purpose of these control techniques is to regulate continuous changes in the operational requirements of the hybrid system;currently, in power system networks, the distribution of energy plays a major role in maintaining power reliability in distribution systems. In this study, the proposed hybrid system was incorporated with a combined PV and wind energy system. Maximum power point tracking (MPPT) methods have been proposed to achieve maximum efficiency from the designed system. In addition, this study focused on improving the stability of the hybrid system. To improve the power quality and transient stability of the proposed system, we introduce a novel control strategy called the distributed power flow controller (DPFC) implementation with an optimization technique called the lion optimization algorithm(LOA)technique. This LOA control technique was developed for the first time in the application of a DPFC controller in a grid-connected system. The control technique was developed using signals from the system parameters, that is, voltage and current. To tune these parameters, this study used fuzzy logic and lion optimization techniques. The proposed system with controllers was tested in MATLAB/Simulink and the results were compared.

关键词： optimization Wind turbines Renewable energy sources Wind energy Voltage measurement Current measurement Control systems Distributed power flow controller fuzzy logic controller grid interconnected lion optimization algorithm PV system and wind energy system

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A nature inspired optimization algorithm for VLSI fixed-outline floorplanning

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2020年第1期103卷 173-186页

作者： Shunmugathammal, M. Columbus, C. Christopher Anand, S. Cape Inst Technol Tirunelveli 627114 Tamil Nadu India PSN Coll Engn & Technol Dept Comp Sci Engn Tirunelveli 627152 Tamil Nadu India Rajagiri Sch Engn & Technol Dept Elect & Commun Kochi 682039 Kerala India

VLSI floorplan optimization problem aim to minimize the following measures such as, area, wirelength and dead space (unused space) between modules. This paper proposed a method for solving floorplan optimization problem using Genetic algorithm which is named as 'lion optimization algorithm' (LOA). LOA is developed for non-slicing floorplans having soft modules with fixed-outline constraint. Although a number of GAs are developed for solving VLSI floorplan optimization problems, they are using weighted sum approach with single objective optimization and crossover between two B*tree structure is not yet attempted. This paper explains, power of B*tree crossover operator for multiobjective floorplanning problem. This operator introduces additional perturbations in initial B*tree structure to create two new different B*tree structures compared with classical GA approach. Simulation results on Microelectronics Center of North Carolina and Gigascale Systems Research Center benchmarks indicate that LOA floorplanner achieves significant savings in wirelength and area minimization also produces better results for dead space minimization compared to previous floorplanners.

关键词： B*tree crossover lion optimization algorithm LOA Genetic algorithm VLSI Multiobjective optimization

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Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1385-1400页

作者： Devagnanam, J. Elango, N. M. Bharathiar Univ Dept Comp Sci Coimbatore Tamil Nadu India VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India

Cloud computing is one of the new age technologies which has great prominent factor in the development of the enterprises and markets. The major exertion in the cloud computing is related to the resource being allocated. The optimal resource allocation is one which allocates the best suitable cluster resources for the task to execute with consideration of the different parameters, such as time, cost, and scalability, makespan, reliability, availability, throughput, resource utilization and so on. In this paper, a resource allocation optimization method in the cloud computing based on the exponential lion algorithm is proposed. The exponential lion based resource allocation for cloud computing taken into account saves the execution time, run time, and improves the revenue for the cloud provider. The proposed E-lion based resource allocation approaches are compared with the PSO, SL-PSO, and lion using the performance measures profit, CPU utilization rate, and memory utilization rate. The simulations of the experiments show that the algorithm in this paper has improved the algorithm performance efficiently with profit maximal profit of 38.74 and minimal CPU and memory utilization rate of 0.00031, and 0.00036 respectively.

关键词： Cloud computing Infrastructure as a service Resource allocation Exponential theory lion optimization algorithm Virtual machine

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Parallel architecture for cotton crop classification using WLI-Fuzzy clustering algorithm and Bs-lion neural network model

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IMAGING SCIENCE JOURNAL 2017年第8期65卷 438-456页

作者： Ramesh, P. S. Letitia, S. Anna Univ Madras 600025 Tamil Nadu India Directorate Tech Educ DOTE Dept Elect & Commun Engn Madras Tamil Nadu India

The application of remote sensory images in crop monitoring has been increasing in the recent years due to its high classification accuracy. In this paper, a novel parallel classification methodology is proposed using a new clustering and classification concept. A novel neural network model with the Bs-lion training algorithm is developed by integrating the Bayesian regularization training with the lion algorithm. Here, two levels of parallel processing are performed, namely parallel WLI-Fuzzy clustering and parallel BS-lion neural network classification. The experimentation of the proposed parallel methodology is carried out using satellite images obtained from the Indian remote sensing satellite IRS-P6. The performance of the proposed system is compared with the existing techniques using validation measures accuracy, sensitivity and specificity. The experimentations resulted in promising results with an accuracy of 0.8994, sensitivity of 0.8682 and specificity of 0.8739, which favour the performance of the proposed parallel architecture in the classification.

关键词： Remote sensing imagery Bayesian regularization neural network lion optimization algorithm WLI-Fuzzy clustering

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MAC-Leonets-A novel optimized hybrid convolutional neural networks for the segmentation and diagnosis of Edema diseases using retinal OCT images

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10605-10620页

作者： Sudha, V. Shanmugam, Sathiya Priya Anitha, D. Raja, R. Sona Coll Technol Salem Dept ECE Tamil Nadu India Panimalar Engn Coll Dept ECE Chennai Tamil Nadu India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur Tamilnadu India Muthayammal Engn Coll Autonomous Dept EEE Rasipuram Tamilnadu India

An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients' life quality. But, these are liable to more errors and less performance, which requires further improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using lion optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively.

关键词： Machine learning deep learning retinal optical coherance tomography images convolutional layers lion optimization algorithm

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Automatic text classification using BPlion-neural network and semantic word processing

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IMAGING SCIENCE JOURNAL 2018年第2期66卷 69-83页

作者： Ranjan, Nihar M. Prasad, Rajesh S. Sinhgad Inst Technol & Sci Pune Maharashtra India NBN Sinhgad Sch Engn Pune Maharashtra India

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation lion algorithm (BP lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPlion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPlion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.

关键词： Text classification back propagation algorithm lion optimization algorithm semantic words hyponymy

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