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An Efficient Hybrid Evolutionary Approach for Identification of Zero-Day Attacks on Wired/Wireless Network System

WIRELESS PERSONAL COMMUNICATIONS 2022年第1期123卷 1-29页

作者： Shukla, Alok Kumar VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

Attacks from network applications establish considerable security threats for computer networks and end users. Existing cyber-attack detection methods are designed primarily in signature-based approaches that can not recognize zero-day vulnerabilities. In addition, with the minimum availability of network attack data in the real world, the effectiveness of such approaches is even more limited. Today, in network security, an Intrusion Detection System (IDS) plays an important role in detecting intrusive activity. To address the existing challenges, we propose an architectural scheme through an evolutionary hybrid algorithm that incorporates teaching learning based optimization (TLBO) and Simulated Annealing (SA), called TLBOSA for IDS that extracts the most notable characteristics and eliminates the irrelevant from the high-dimensional data sets. In the proposed method, SA is integrated into the TLBO, while using it to increase the quality of the solution after each iteration of the TLBO algorithm. Support Vector Machine (SVM) is used as a fitness function in the proposed method to select the relevant attributes that can help classify the attacks accurately. For performance verification, the proposed method is used in two large datasets, such as NSL-KDD and UNSW-NB15. The empirical evaluations show that our approach outperforms the existing state-of-the-art algorithms in terms of detection rate, accuracy and false alarm rates on both NSL-KDD and UNSWNB15 data.

关键词： Intrusion detection system NSL-KDD teaching learning-based optimization Support vector machine

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Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands

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Procedia Computer Science 2025年 252卷 1-6页

作者： K. Sujatha R.S. Ponmagal Prameeladevi Chillakuru U. Jayalatsumi N. Janaki N.P.G. Bhavani Professor EEE Department Dr. MGR Educational and Research Institute Chennai-600095 India Professor CSE Department SRM Institute of Science and Technology Chennai India Asso. Professor Artificial Intelligence and Data Science Velammal Institute of Technology Chennai India Associate Professor ECE Department Dr. MGR Educational and Research Institute Chennai-600095 India Assistant Professor EEE Department Vels Institute of Science Technology & Advanced Studies Chennai India Associate Professor ECE Department Saveetha Institute of Medical and Technical Sciences Chennai India

This research presents a new combined deep learning system for effective and reliable identification of plant diseases in complicated agricultural environments. One of the most difficult jobs in agriculture is identifying plant diseases early on. Early disease detection in plants is crucial for increasing agricultural yield. With the application of machine learning and deep learning techniques, this issue has been resolved. Large crop farms can now detect plant illnesses automatically, which is advantageous as it reduces the monitoring time. The suggested approach consists of multiple important stages. To begin with, image quality of the agricultural lands is improved through preprocessing techniques like noise reduction, gamma correction and white balancing. Data augmentation is incorporated to expand the dataset and improve the generalization capacity of the model. Efficient methods such as EfficientDet and Squeeze Net, as well as color and shape based features, are included in feature extraction. The most relevant features are selected by a Hybrid optimization Algorithm (HOA), which integrates Mother optimization Algorithm (MOA), teaching learning-based optimization (TLBO) and Improved Wild Horse optimization to detect the various plant diseases like Bacterial Blight, Tungro, Blast and Brown spot. At last, a deep learning detector, which may include Recurrent Convolutional Neural Networks (R-CNNs) and Recurrent Neural Network (RNN), identifies the location and type of objects. The use of hyper parameter tuning techniques is also implemented to avoid over fitting and improve the overall generalization. This comprehensive approach depicts encouraging results in overcoming challenges in plant disease detection.

关键词： EfficientDet Squeeze Net Plant diseases Mother optimization Algorithm teaching learning-based optimization Improved Wild Horse optimization R-CNNs recurrent neural networks

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Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Shukla, Alok Kumar Dwivedi, Shubhra Singh, Deepak Singh, Sunil Kumar Tripathi, Diwakar Dewangan, Ram Kishan Thapar Inst Engn & Technol Patiala Punjab India Natl Inst Technol Raipur India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Comp Sci & Engn Manipal India Natl Inst Technol Jamshedpur India Bennett Univ SCSET Greater Noida India

Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital role in effective treatment. Previous studies have shown that existing evolutionary methods are complicated in correctly differentiating BC disease subtypes with high consistency, which seriously affects the performance of classification methods. To prevent diagnostic errors with hostile implications for patient health, in this study, we develop a new evolutionary method called SeQTLBOGA that incorporates the learner quantization before the search capability of the feature space to prevent premature falls into the local optima. In the SeQTLBOGA algorithm, quantum theory and a self-adaptive mechanism are employed to update the teaching learning-based optimization (TLBO) rule to enhance convergence search capabilities. Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. Comparative results based on optimal computing time and performance are also offered to empirically analyze the traditional algorithms. Therefore, this paper aims to evaluate the most recent quantum-inspired metaheuristic algorithms in WBCD and WDBC databases, emphasizing their advantages and disadvantages.

关键词： teaching learning-based optimization Breast Cancer Feature selection Genetic algorithm Quantum Support vector machine

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Effective Minimization of Side Lobe Level of Sparse Thinned Planar Array Antenna in Multiple Planes with Constraints

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ELECTROMAGNETICS 2021年第5期41卷 303-314页

作者： Dutta, Kailash Pati Mahanti, G. K. Panda, G. Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 India CV Raman Global Univ Dept Elect & Telecommun Engn Bhubneswar India

In this paper, the authors propose to design thinned sparse planar array antenna in multiple planes using meta-heuristic optimization algorithm such as teaching learning-based optimization (TLBO) algorithm. The objective is to achieve the minimum value of side lobe level (SLL) in the radiation pattern in two vertical planes as well as in all vertical planes. The optimal positions of the isotropic antennas in the array are obtained having multiple optimization constraints such as total number of antennas in the array, aperture area and minimum inter-element spacing. The simulation results demonstrate that the proposed method is found to be relatively simpler but promising compared to the results reported using other competitive methods.

关键词： Pattern synthesis sparse thinned array side lobe level (SLL) teaching learning-based optimization

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Hybrid SFO and TLBO optimization for biodegradable classification

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SOFT COMPUTING 2021年第24期25卷 15417-15443页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal 148106 Sangrur India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Sangrur India

In this paper, a hybrid sunflower-teaching learning-based optimization (SFO-TLBO) algorithm has been proposed, integrating two well-known algorithms, namely sunflower optimization, and teaching-learning-based optimization. The proposed hybrid SFO-TLBO algorithm has been incorporated based on the ratio of the fitness value of the search agent and the average fitness value of the population. The SFO has more tendency of global exploration but has a problem of slow convergence speed. On the other hand, the convergence of the TLBO algorithm is fast, but it may get stuck in local optima. In this work, we have hybridized SFO with TLBO to take advantage of both the algorithms and also to balance the trade-off between exploitation and exploration. The proposed hybrid SFO-TLBO has been first applied to nineteen test benchmark functions. The outcomes have been compared with twelve state-of-the-art algorithms. In seventeen benchmark test functions, better performance has been achieved by the proposed hybrid SFO-TLBO. Also, the proposed hybrid SFO-TLBO algorithm has been used to solve an Internet of Vehicles (IoV) optimization problem and performance has been compared with fifteen state-of-the-art algorithms. In solving the IoV problem, hybrid SFO-TLBO has been achieved better performance in comparison with all fifteen considered algorithms which showcases the applicability of hybrid SFO-TLBO algorithm in resolving large-scale, real-world problems. In addition, for feature selection problems, a binary version of hybrid SFO-TLBO has been proposed, in which a wrapper approach is used to decide an optimal subset of features with a k-nearest neighbor classifier. The proposed binary method also determines an optimal value of k for the k-nearest neighbor classifier. The binary hybrid SFO-TLBO algorithm has been applied to the QSAR biodegradation dataset for classification, and for evidencing its efficiency, its performance has been compared with the other state-of-the-art algorithms.

关键词： Biodegradable molecules IoV k-Nearest neighbor classifier Quantitative structure-activity relationship Sunflower optimization teaching learning-based optimization

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optimization of Electrodischarge Machining Parameters Using Non-traditional optimization Techniques 2nd

Optimization of Electrodischarge Machining Parameters Using ...

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2nd International Conference on Recent Advances in Manufacturing, RAM 2021

作者： Agarwal, Kaushik Joshi, Shishir Asudani, Divyansh Savani, Dixit Patel, Deep Prajapati, Ashish R. Desai, Keyur P. Dave, Harshit K. Department of Mechanical Engineering SVNIT Gujarat Surat India

ISBN: (纸本)9789811699511

Electrodischarge machining (EDM) is a popular non-conventional material removal process because it can machine conducting materials. EDM processing conditions are vital for the response parameters such as overcut and surface roughness. Various EDM parameters that are important are voltage, current, spark frequency, spark gap, pulse duration and machining time. However, the selection of EDM processing parameters for better machining of a workpiece is challenging as the variation of some process parameters can decrease the overcut, but at the same time, it will lead to higher surface roughness. Therefore, the selection of an optimum combination of EDM process parameters is essential. For achieving the best level of process parameters, an effective optimization technique is very vital. An optimization algorithm is an iterative technique that compares different solutions until an optimum, or satisfactory solution is found. Nowadays, these optimization techniques are solely dependent on computerized programs. Particle swarm optimization (PSO), teaching learning-based algorithm (TLBO), Rao algorithm (RA), Jaya algorithm (JA) and firefly are some of the non-traditional optimization techniques. In the present work, an attempt is made to study the non-traditional optimization techniques and use the same to attain single and multiple objective optimizations of surface roughness and overcut values. In addition to that, the performance of different optimization techniques in terms of prediction accuracy and convergence characteristics is compared. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrodischarge machining Firefly algorithm Jaya algorithm Material removal rate Particle swarm optimization Process parameter optimization Rao algorithm teaching learning-based optimization

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Measurement Interval Effect on Photovoltaic Parameters Estimation

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ENERGIES 2023年第18期16卷 6460-6460页

作者： Mesbahi, Oumaima Afonso, Daruez Tlemcani, Mouhaydine Bouich, Amal Janeiro, Fernando M. Univ Evora Dept Mechatron P-7000671 Evora Portugal Inst Earth Sci Instrumentat & Control Lab P-7000671 Evora Portugal Univ Politecn Valencia Inst Design & Mfg IDF Dept Appl Phys Valencia 46000 Spain Univ Lisbon Inst Telecomunicacoes Inst Super Tecn P-1049001 Lisbon Portugal

Recently, the estimation of photovoltaic parameters has drawn the attention of researchers, and most of them propose new optimization methods to solve this problem. However, the process of photovoltaic parameters estimation can be affected by other aspects. In a real experimental setup, the I-V characteristic is obtained with IV tracers. Depending on their technical specifications, these instruments can influence the quality of the I-V characteristic, which in turn is inevitably linked to the estimation of photovoltaic parameters. Besides the uncertainties that accompany the measurement process, a major effect on parameters estimation is the size of the measurement interval of current and voltage, where some instruments are limited to measure a small portion of the characteristic or cannot reach their extremum regions. In this paper, three case studies are presented to analyse this phenomenon: different characteristic measurement starting points and different measurement intervals. In the simulation study the parameters are extracted from 1000 trial runs of the simulated I-V curve. The results are then validated using an experimental study where an IV tracer was built to measure the I-V characteristic. Both simulation and experimental studies concluded that starting the measurements at the open circuit voltage and having an interval spanning a minimum of half of the I-V curve results in an optimal estimation of photovoltaic parameters.

关键词： photovoltaic parameters measurements interval instruments optimization teaching learning-based optimization Nelder-Mead

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Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

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Computer Systems Science & Engineering 2023年第10期47卷 721-739页

作者： Mohamed Esmail Karar Amged Sayed A.Mahmoud College of Computing and Information Technology(CCIT) Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportP.O.Box 12577Smart VillageSmart Village-B 2401Egypt

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability *** paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status *** infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good *** developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)*** networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs *** avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F *** simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy ***,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

关键词： Intelligent medical systems telemedicine fuzzy control teaching learning-based optimization

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A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Gautam, Anurag Ibraheem Sharma, Gulshan Kumawat, Manoj Ahmer, M. Faraz Department of Electrical Engineering Jamia Millia Islamia University New Delhi India Department of Electrical Engineering Technology University of Johannesburg Johannesburg South Africa Department of Electrical Engineering National Institute of Technology New Delhi India Department of EEE Mewat Engineering College Haryana India

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the teaching-learning-based optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf optimization (GWO) and Particle Swarm optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets. © 2024

关键词： Congestion mitigation Deregulated power system Line utilization factor teaching learning-based optimization Thyristor controlled series capacitor

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optimization of Laminar Boundary Layers in Flow over a Flat Plate Using Recent Metaheuristic Algorithms

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ENERGIES 2022年第14期15卷 5069页

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Manisa Celal Bayar Univ Comp Programming Dept TR-45140 Manisa Turkey Sakarya Univ Software Engn Dept TR-54050 Serdivan Turkey Amity Univ Dubai Comp Sci & Engn Dept Dubai 345019 U Arab Emirates Sakarya Univ Comp Engn Dept TR-54050 Serdivan Turkey Rochester Inst Technol Dubai Comp Secur Dept Dubai 341055 U Arab Emirates

Heat transfer is one of the most fundamental engineering subjects and is found in every moment of life. Heat transfer problems, such as heating and cooling, where the transfer of heat between regions is calculated, are problems that can give exact solutions with parametric equations, many of which were obtained by solving differential equations in the past. Today, the fact that heat transfer problems have a more complex structure has led to the emergence of multivariate models, and problems that are very difficult to solve with differential equations have emerged. optimization techniques, which are also the subject of computer science, are frequently used to solve complex problems. In this study, laminar thermal boundary layers in flow over a flat plate, a sub-problem of heat transfer, is solved with recent metaheuristic algorithms. teaching learning-based optimization (TLBO), sine cosine optimization (SCO), gray wolf optimization (GWO), whale optimization (WO), salp swarm optimization (SSO), and Harris hawk optimization (HHO) algorithms are used in the study. In the optimization problem, the laminar boundary layer thickness, heat flow, and distance from the leading edge are determined. These three models' minimum, maximum, and target values are found under the specified design variables and constraints. In the study, 540 optimization models are run, and it is seen that HHO is the most suitable optimization technique for heat transfer problems. Additionally, SSO and WO algorithms gave results close to HHO. Other algorithms also set model targets with an average of less than 0.07% and acceptable error rates. In addition, the average problem solution time of all optimization algorithms and all models was 0.9 s. To conclude, the recent metaheuristic algorithms are found to be powerful and fast in solving heat transfer problems.

关键词： heat transfer optimization laminar flow laminar boundary layers flat plate teaching learning-based optimization sine cosine optimization gray wolf optimization whale optimization salp swarm optimization Harris hawk optimization

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