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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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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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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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Despise Detection through LOA Using Fs with 1D-CNN in Public Forum 5

Despise Detection through LOA Using Fs with 1D-CNN in Public...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Gunapriya, B. Kavin, Balasubramanian Prabhu Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Loyola College Department of Computer Science Chennai India Shiv Nadar University Delhi India Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350383522

Hate speech is any speech intended to incite hatred in others. Hate speech is escalating quickly and unpredictably in this digital age where everyone is connected via social media. Due to a lack of understanding of the distinction among hate speech numerous people do not realise they are engaging in hate speech when they criticise somewhat on social media. Victims experience a sense of social alienation as a result, and those who propagate it frequently end up in legal trouble. To combat people's ignorance, it is crucial to detect any instances of hate speech in the sentences. A machine learning technique is frequently used to help classify each sentence in order to detect such sentences. Additionally, it is a difficult duty because to the enormous volume of rising hate speech on social media from diverse sources. This work offers a lion optimisation algorithm (LOA) with Processing Based Hate Speech Detection (HSD) and Classification model in response to this objective. The main goal of the proposed LOA-based HSD model is to recognise and categorise instances of hate speech on social media platforms. The described model uses data preprocessing at numerous phases, such as tokenization, vectorization, etc., to achieve this. In addition, the organisation of social media text into three categories-neutral, offensive, and hate language-is done using a convolutional neural network (ID-CNN) model. The proposed model is experimentally validated, and the outcomes are assessed from several angles. The results of the experiment showed that the LOA-ID-CNN model achieved favourably compared to current state-of-the-art techniques © 2024 IEEE.

关键词： Convolutional Neural Network Hate Speech Finding lion optimization algorithm social media Twitter. Natural Language Processing

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Charging station placement optimization based on the location significance prediction

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 12218-12239页

作者： Matkovic, Daria Matijasevic, Terezija Capuder, Tomislav Univ Zagreb Fac Elect & Comp Engn Unska 3 Zagreb 10000 Croatia

One of the key challenges in charging infrastructure planning is ensuring optimal charging station placement. To address this issue, we introduce a novel approach to optimize the placement of electric vehicle charging stations, integrating a novel location-based charging station significance prediction model with a lion optimization algorithm (LOA) where the significance is defined as the combination of charging energy and the number of sessions. First, the data recorded on the existing charging stations are analyzed and preprocessed. Subsequently, we introduced the modified support vector regression (SVR) model for significance prediction and compared it with eight existing models showing its superiority over others. The SVR modification is related to the kernel function, where the standard Gaussian kernel is adapted to better suit location-based significance predictions. Following this, we utilize LOA to optimize the placement of additional charging stations in established charging infrastructure based on the prediction model trained on data congregated at the existing charging stations. The optimization is conducted for Zagreb and Split to evaluate the performance on small and large datasets. The results are assessed using significance prediction and pseudo-simulation. The new charging stations in Zagreb have a significance prediction of 10.53% greater than the calculated significance of existing charging stations. Furthermore, the significance prediction for new stations in Split is 0.42% greater than the calculated significance for existing stations. Pseudo-simulation proves that new charging stations have 38.15% greater significance than the existing stations in Zagreb and 31.24% in Split. Both methods confirm that infrastructure significance, in terms of charging energy and session count, is improved.

关键词： charging station charging station placement Electric vehicles lion optimization algorithm support vector regression

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Energy Efficient Data Migration Concerning Interoperability Using Optimized Deep Learning in Container-Based Heterogeneous Cloud Computing

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ADVANCES IN ENGINEERING SOFTWARE 2023年第1期183卷

作者： Hiremath, Tej. C. Rekha, K. S. Natl Inst Engn Dept Comp Sci & Engn Mysuru 570008 India

Cloud computing generates a proper computing platform and facilitates optimizing with the utilization of infrastructure resources, increases flexibility, and decreases deployment time. Interoperability is one of the major challenges to be studied for ensuring seamless access and sharing of services and resources. Containers have developed into the most dependable and lightweight platform for virtualization to deliver cloud services that offer flexible sorting, scalability, and portability. This paper presents energy-efficient data migration approach using hybrid optimized deep learning in a heterogeneous cloud. Simulation of the cloud is carried out with Physical Machines (PM), container, and Virtual Machines (VM) in the cloud. Migration application is done with proposed Taylor lion-based Poor and Rich optimization (Taylor lion-based PRO), wherein load is found by Actor Critic Neural Network (ACNN). Moreover, objective functions utilized are agility, migration time, predicted load, demand, transmission cost, resource capacity, energy consumption, as well as reputation. Here, Taylor lion-based PRO is formed by hybridization of the Taylor series along lion optimization algorithm (LOA), and Poor and Rich optimization (PRO). Furthermore, the performance of data migration concerning interoperability is carried out with three performance metrics, like load, resource capacity, and energy consumption of 0.006, 0.364, and 0.281.

关键词： Poor and Rich optimization Taylor series Actor Critic Neural Network Predicted load lion optimization algorithm

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Deer hunting optimization technique for clustering unsupervised data in data mining

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2023年第1期14卷

作者： Azeez, Hayder Hussein Southern Tech Univ Basra Iraq

Unsupervised data clustering investigation is a standout among the most valuable tools and is an informative task in data mining that looks to characterize similar articles' gatherings. One of the eminent algorithms for the clustering field is K-means clustering. Scholars recommended enhancing the nature of K-means, and optimization algorithms were hybridized. In this study, a heuristic calculation, deer hunting optimization algorithm (DHOA), was adjusted for K-means data clustering by altering the fundamental parameters of DHOA calculation, which are propelled from the characteristic enlivened calculations. During this work, a new human-based descriptive DHOA has been developed following a human deer hunting strategy. In order to attack the fawn, hunters update their positions based on the movement of the leader and backward movement while also considering the angle of the deer. In this work, the DHOA was hybridized with K-means clustering and the performance of the proposed approach is tested against UCI repository data with different algorithms.

关键词： Clustering K-means deer hunting optimization lion optimization algorithm cuckoo search optimization

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Safe and secured routing using multi-objective fractional artificial lion algorithm in WSN

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第21期34卷 e7098-e7098页

作者： Prakash, Perumalla Suman Kavitha, Dwaram Reddy, Pakanati Chenna Jawaharlal Nehru Technol Univ Dept CSE Ananthapuramu 515002 Andhra Pradesh India G Pulla Reddy Engn Coll Comp Sci & Engn Kurnool Andhra Pradesh India

The routing process in WSN needs to be more secure and safe, thus a secure routing process is carried out in this research, where the source node transmits packet simultaneously to destination. WSN nodes are simulated initially, and then the optimal cluster head (CH) from the entire nodes are selected. Here, CH selection is done with the developed Fractional Artificial lion algorithm (FAL) method. The proposed FAL approach is introduced by combining fractional Calculus, lion optimization algorithm (LOA), and the Artificial Bee Colony (ABC). Moreover, the fitness parameters, like delay, energy, distance, and link lifetime (LLT) where the minimal distance is chosen as the best path. Consequently, route maintenance is done after the secure routing. Moreover, the performance of devised FAL is compared with various performance metrics, like several alive nodes, energy, distance, and throughput. The developed FAL approach obtains a maximal alive node of 45, a minimal distance of 9.29 m, maximal energy of 0.14 J, and maximal throughput of 92.25%.

关键词： Artificial Bee colony fractional calculus link lifetime lion optimization algorithm WSN

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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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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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