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A novel PCA-whale optimization-based deep neural network model for classification of tomato plant diseases using GPU

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2021年第4期18卷 1383-1396页

作者： Gadekallu, Thippa Reddy Rajput, Dharmendra Singh Reddy, M. Praveen Kumar Lakshmanna, Kuruva Bhattacharya, Sweta Singh, Saurabh Jolfaei, Alireza Alazab, Mamoun Vellore Inst Technol Vellore Tamil Nadu India Dongguk Univ Dept Ind & Syst Engn Seoul South Korea Macquarie Univ Sydney NSW Australia Charles Darwin Univ Casuarina Australia

The human population is growing at a very rapid scale. With this progressive growth, it is extremely important to ensure that healthy food is available for the survival of the inhabitants of this planet. Also, the economy of developing countries is highly dependent on agricultural production. The overall economic balance gets affected if there is a variance in the demand and supply of food or agricultural products. Diseases in plants are a great threat to the yield of the crops thereby causing famines and economy slow down. Our present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis. In this work, the dataset is collected from publicly available plant-village dataset. The significant features are extracted from the dataset using the hybrid-principal component analysis-whale optimization algorithm. Further the extracted data are fed into a deep neural network for classification of tomato diseases. The proposed model is then evaluated with the classical machine learning techniques to establish the superiority in terms of accuracy and loss rate metrics.

关键词： Tomato leaf disease classification Deep neural networks Grid search hyperparameter tuning Principal component analysis whale optimization algorithm

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Optimized ensemble model for wind power forecasting using hybrid whale and dipper-throated optimization algorithms

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Alhussan, Amel Ali Farhan, Alaa Kadhim Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Khafaga, Doaa Sami Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia Univ Technol Baghdad Comp Sci Dept Baghdad Iraq Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo Egypt Shaqra Univ Coll Comp & Informat Technol Dept Comp Sci Shaqra Saudi Arabia Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura Egypt Mustaqbal Univ Coll Engn & Comp Sci Comp Engn Dept Buraydah Saudi Arabia

Introduction: Power generated by the wind is a viable renewable energy option. Forecasting wind power generation is particularly important for easing supply and demand imbalances in the smart grid. However, the biggest challenge with wind power is that it is unpredictable due to its intermittent and sporadic nature. The purpose of this research is to propose a reliable ensemble model that can predict future wind power ***: The proposed ensemble model comprises three reliable regression models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM models. To boost the performance of the proposed ensemble model, the outputs of each model are optimally weighted to form the final prediction output. The ensemble models' weights are optimized in terms of a newly developed optimization algorithm based on the whale optimization algorithm and the dipper-throated optimization algorithm. On the other hand, the proposed optimization algorithm is converted to binary to be used in feature selection to boost the prediction results further. The proposed optimized ensemble model is tested in terms of a dataset publicly available on *** and discussion: The results of the proposed model are compared to the other six optimization algorithms to prove the superiority of the proposed optimization algorithm. In addition, statistical tests are performed to highlight the proposed approach's performance and effectiveness in predicting future wind power values. The results are evaluated using a set of criteria such as root mean square error (RMSE), mean absolute error (MAE), and R-2 . The proposed approach could achieve the following results: RMSE = 0.0022, MAE = 0.0003, and R-2 = 0.9999, which outperform those results achieved by other methods.

关键词： bidirectional long short-term memory wind speed forecasting metaheuristic optimization dipper throated optimization whale optimization algorithm

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Milling chatter identification by optimized variational mode decomposition and fuzzy entropy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第9-10期121卷 6111-6124页

作者： Peng, Defeng Li, Hongkun Ou, Jiayu Wang, Zhaodong Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

Chatter is a self-excited vibration that reduces the quality of the workpiece surface and accelerates tool wear, it is a phenomenon that is difficult to monitor and control. In order to achieve high-performance manufacturing, a reliable online monitoring system for chatter needs to be developed. This paper proposes a chatter identification method based on optimized variational modal decomposition (OVMD) and fuzzy entropy to achieve online monitoring of chatter accurately. The method embeds the whale optimization algorithm (WOA) into the VMD method, and the chattering features are extracted for both simulated and experimental signals to achieve the optimal decomposition of the force signal in a rapid way. In order to solve the problem that sample entropy and approximate entropy are not sensitive to chatter detection, fuzzy entropy is introduced to monitor the chatter feature components. Experimental results show that the fuzzy entropy is particularly sensitive to the occurrence of chatter in specific embedding dimensions, and this method can monitor the dynamics of milling with higher sensitivity and stability.

关键词： Chatter identification Optimized variational modal decomposition Fuzzy entropy whale optimization algorithm

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An enhanced whale optimized fractional order pi^λ controller based power quality improvement for sensorless brushless dc motor under dynamic operating conditions

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 2647-2666页

作者： Prakash, R. Ayyar, K. Sree Sastha Inst Engn & Technol Elect & Elect Engn Chennai 600123 Tamil Nadu India SRM Valliammai Engn Coll Elect & Instrumentat Engn Kanchipuram Tamil Nadu India

This paper presents an Enhanced whale optimization algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI lambda) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, namely (i) Enhanced whale optimization (EWO), (ii) Invasive Weed optimization (IWO), and (iii) Social Spider optimization (SSO) for fine-tuning of the FOPI(lambda )controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI lambda controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI lambda controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation.

关键词： BLDC motor drives fractional order PID controller (FOPI lambda) whale optimization algorithm sensorless speed control techniques

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Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 13107-13123页

作者： Faridmehr, Iman Shariq, Mohd Plevris, Vagelis Aalimahmoody, Nasrin South Ural State Univ Lenin Prospect 76 Chelyabinsk 454080 Russia Aligarh Muslim Univ ZH Coll Engn & Technol Dept Civil Engn Aligarh 202001 Uttar Pradesh India Qatar Univ Coll Engn Dept Civil & Architectural Engn Doha Qatar Islamic Azad Univ Dept Elect Engn Yazd Branch Yazd Iran

This study investigates a Novel Hybrid Informational model for the prediction of creep and shrinkage deflection of reinforced concrete (RC) beams containing different percentages of ground granulated blast furnace slag (GGBFS) at different ages, varying from 1 to 150 days. The percentage of cement replacement by GGBFS varies from 20 to 60%. In order to examine the effects of the applied load and tensile reinforcement on creep behavior, the magnitude of two-point loading was varied from 200 kg to a maximum of 350 kg while the percentage of tensile reinforcement (rho) was selected as either 0.77% or 1.2%. The current situation about short-term and long-term deflections due to creep and shrinkage available in the international standards, including ACI, BS and Eurocode 2, is discussed. The results indicate that RC beams containing GGBFS have larger deflections than the ones with conventional concrete (i.e., ordinary Portland cement concrete). After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. Several statistical metrics, including the root mean square error and the coefficient of variation, revealed that the generalized model achieved the most reliable and accurate prediction of the concrete beam's deflection in comparison with international standards and other models. This novel informational model can simplify the design processes in computational intelligence structural design platforms in future.

关键词： GGBFS Creep and shrinkage deflection Neural networks whale optimization algorithm

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Optimized Blade Angle Controller to Enhance Wind Speed Variability Impacts

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第6-7期49卷 612-623页

作者： El-Sharkawy, Mahmoud M. Attia, Mahmoud A. Abdelaziz, Almoataz Y. Ain Shams Univ Fac Engn Dept Elect Power & Machines Cairo Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

-This paper discusses the negative impact of wind farm penetration on the grid. This bad impact in this study is caused by wind farms due to wind speed variability. As wind turbines depend on wind speed to capture energy, this wind energy is assumed to be unpredicted due to the stochastic nature of wind. The paper focuses on that problem, wind speed variability, showing the most common solutions to overcome it. The paper presents pitch angle control as one of the most practical techniques experienced to overcome the wind speed variability challenge. Also, it illustrates the PID controller based on both Harmony Search optimization and whale optimization algorithms. First, the paper compares the effect of applying optimized PID control on blade angles with both HSA and WOA in one season of the year. Then;it studies the most effective algorithm among them and applies it to the rest of the year seasons.

关键词： wind turbine efficiency doubly fed induction generator pitch angle controller Harmony Search algorithm whale optimization algorithm PID

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Network Security Situation Assessment Based on Improved WOA-SVM

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IEEE ACCESS 2022年 10卷 96273-96283页

作者： Zhang, Ran Liu, Min Pan, Zhihan Yin, Yifeng Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450001 Henan Peoples R China

Network security situation assessment is an important means of understanding the current network security situation to provide a basis for taking security measures. To address the problem that the accuracy of existing network security situation assessment methods needs to be improved, this paper proposes a network security situation assessment method based on support vector machine (SVM) optimized by whale optimization algorithm (WOA) that is improved by adaptive weight (AW) combined with simulated annealing algorithm (SA). In this method, the SVM is embedded into the fitness function calculation of the improved WOA, and the global optimization characteristics of WOA are used to determine the optimal penalty parameter c and kernel function parameter g of the SVM. To solve the problem of the WOA being prone to falling into local extremum and slow convergence when solving large and complex data problems, an adaptive weight is used to adjust the whale position update coefficient, and a simulated annealing algorithm (SA) is used to increase random search factors to avoid falling into local extremum, so as to improve the global optimization ability. The experimental results show that this method is feasible, can assess the network security situation more accurately, and has better convergence than other assessment algorithms based on an improved SVM.

关键词： Network security situation assessment support vector machine whale optimization algorithm adaptive weight simulated annealing algorithm

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An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106227-106227页

作者： Zhu, Wei Liu, Lei Kuang, Fangjun Li, Lingzhi Xu, Suling Liang, Yingqi Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Ningbo Univ Affiliated Hosp Med Sch Dept Dermatol Ningbo 315020 Zhejiang Peoples R China Wenzhou Med Univ Wenzhou 325035 Zhejiang Peoples R China

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set de-termines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimi-zation. However, different algorithms have different performances for different medical disease images. For example, the whale optimization algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

关键词： Image segmentation Skin cancer whale optimization algorithm Levy operator Chaotic random mutation strategy Kapur ?s entropy

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Hybrid binary whale with harris hawks for feature selection

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NEURAL COMPUTING & APPLICATIONS 2022年第21期34卷 19377-19395页

作者： Alwajih, Ranya Abdulkadir, Said Jadid Al Hussian, Hitham Aziz, Norshakirah Al-Tashi, Qasem Mirjalili, Seyedali Alqushaibi, Alawi Univ Teknol PETRONAS Ctr Res Data Sci CeRDaS Comp Informat Sci Dept Seri Iskandar Perak Malaysia Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77030 USA Univ Albaydha Fac Adm & Comp Sci Albaydha Yemen Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

A tremendous flow of big data has come from the growing use of digital technology and intelligent systems. This has resulted in an increase in not just the dimensional issues that big data encounters, but also the number of challenges that big data faces, including redundancies and useless features. As a result, feature selection is offered as a method for eliminating unwanted characteristics. This study introduces the BWOAHHO memetic technique, which combines the binary hybrid whale optimization algorithm (WOA) with Harris Hawks optimization (HHO). A transfer function to transfer continuous characteristics to binary to fulfill the feature selection nature condition. The efficiency of the selected attributes is assessed using a wrapper k-Nearest neighbor (KNN) Classifier. About 18 benchmark datasets obtained from UCI repository were utilized to measure the proposed method's proficiency. The performance of the novel hybrid technique was evaluated by comparing to that of WOA, HHO, Particle Swarm optimization (PSO), the Genetic algorithm (GA), and the WOASAT-2. With the new hybrid feature selection method, the WOA algorithm's efficiency was improved. Classification accuracy, average fitness, average selected attributes, and computational time were all used as performance indicators. In terms of accuracy, the proposed BWOHHO algorithm compared with 5 similar metaheuristic algorithms. The BWOAHHO had a classification accuracy of 92% in the 18 datasets, which was higher than BWOA (90%), BPSO (82%), and BGA (82%). (83%), the fitness measures of the BWOHHO algorithm are 0.08, which is lower than the average fitness of BWOA, BPSO, and BGA., in terms of selected attribute size compare the proposed BWOAHHO algorithm to the results obtained by the other five techniques The average selected feature sizes for BWOAHHO, BWOA, BHHO, BGA, and WOASAT-2 were 18.07, 20.12, 22.3, 22.32, 22.40, and 15.99, respectively, and computing time for the proposed BWOHHO was 7.36 in second which wa

关键词： Machine learning Classification Feature (variable) selection whale optimization algorithm Hybrid feature algorithm Harris hawks optimization

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Detection of DDOS Attack using Deep Learning Model in Cloud Storage Application

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期127卷 419-439页

作者： Agarwal, Ankit Khari, Manju Singh, Rajiv Banasthali Vidyapith Dept Comp Sci Tonk Rajasthan India Netaji Subhas Univ Technol East Campus New Delhi India

In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network security. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve efficient detection with a small number of false alarms. In this case, deep learning techniques are appropriate and effective algorithm to categorize both normal and attacked information. Hence, a novel a feature selection-whale optimization algorithm-deep neural network (FS-WOA-DNN) method is proposed in this research article to mitigate DDoS attack in effective manner. Initially, pre-processing step is carried out for the input dataset where min-max normalization technique is applied to replace all the input in a specified range. Later on, that normalized information is fed into the proposed FS-WOA to select the optimal set of features for ease the classification process. Those selected features are subjected to deep neural network classifier to categorize normal and attacked data. Further to enhance the security of proposed model, the normal data are secure with the help of homomorphic encryption and are securely stored in the cloud. The proposed algorithm will be simulated using the MATLAB tool and tested experimentally that shows 95.35% accuracy in detecting DDoS attack.

关键词： DDoS attack Deep learning Feature selection Min– max normalization whale optimization algorithm Deep neural network Homomorphic encryption

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