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An Approach for Fake News Detection in Social Media Using Hybrid Classifier

CYBERNETICS AND SYSTEMS 2024年第4期55卷 894-917页

作者： Kanagavalli, N. Baghavathi Priya, S. Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai 600124 India

The users of social networks get ruined by fake news, which brings a vast effect on the offline community. A very significant aim is to improve the trust of data in online social networks by discovering fake news in an appropriately timed manner. A novel technique for identifying fake news on social media has been developed as a result. The feature extraction module receives the preprocessed output after the initial preprocessing stage on the news story. The extracted features are then analyzed using the generative adversarial network (GAN) and neural network (NN) to discover fake scores. Here, the rider shuffled shepherd optimization (RSSO), which was developed by integrating the rider optimization algorithm and the shuffled shepherd optimization algorithm, is used to train the NN classifier. Data labeling is considered an expensive task. GANs are unsupervised, so labeling of data is not required to train them. The score obtained from GAN and NN are utilized for computing the final score, which is evaluated using the weighted average model for detecting fake news. With the highest accuracy of 91.2%, the highest sensitivity of 92.1%, and the highest specificity of 88.8%, the proposed RSSO-based NN + GAN provided improved performance.

关键词： Fake detection generative adversarial network neural network rider optimization algorithm shuffled shepherd optimization algorithm

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Enhanced DV-Hop using shuffled shepherd algorithm for localizing sensor nodes in 3-D space

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WIRELESS NETWORKS 2024年第1期30卷 295-307页

作者： Gouda, Ahmed El. Abouelseoud, Yasmine Abd El-Malek, Ahmed H. Osman, Radwa Ahmed Alexandria Univ Dept Engn Math & Phys Alexandria 21544 Egypt Egypt Japan Univ Sci & Technol E JUST Elect & Commun Engn Dept Alexandria 21934 Egypt Arab Acad Sci Technol & Maritime Transport Dept Basic & Appl Sci AbuQuir CampusPOB 1029 Alexandria Egypt

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range.

关键词： DV-Hop algorithm shuffled shepherd optimization algorithm Wireless sensor networks Anchor nodes

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shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第4期35卷 e7523-e7523页

作者： Daniya, Thavasilingam Srinivasan, Vigneshwari Sathyabama Inst Sci & Technol Sch Comp Dept Comp Sci & Engn Chennai Tamil Nadu India GMR Inst Technol Dept Informat Technol Rajam Andhra Pradesh India

The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization-based deep learning (SSSO-based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak-based UNet (BLSNet), multilayer maxout, resistance spot welding-based deep recurrent neural network (RSW-based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

关键词： deep learning deep maxout network rice plant shuffled shepherd optimization algorithm social optimization algorithm

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Set Theoretical Variants of optimization algorithms for System Reliability-based Design of Truss Structures

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第3期65卷 717-729页

作者： Kaveh, Ali Hamedani, Kiarash Biabani Kamalinejad, Mohammad Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

In this paper, recently developed set theoretical variants of the teaching-learning-based optimization (TLBO) algorithm and the shuffled shepherd optimization algorithm (SSOA) are employed for system reliability-based design optimization (SRBDO) of truss structures. The set theoretical variants are designed based on a simple framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. In addition, the framework is applied to the Jaya algorithm, leading to a set-theoretical variant of the Jaya algorithm. So far, most of the reliability-based design optimization studies have focused on the reliability of single structural members. This is due to the fact that the optimization problems with system reliability-based constraints are computationally expensive to solve. This is especially the case of statically redundant structures, where the number of failure modes is so high that it is impractical to identify all of them. System-level reliability analysis of truss structures is carried out by the branch and bound method by which the stochastically dominant failure paths are identified within a reasonable time. At last, three numerical examples, including size optimization of truss structures, are presented to illustrate the effectiveness of the proposed SRBDO approach. The results indicate the efficiency and applicability of the set theoretical optimization algorithms to solve the SRBDO problems of truss structures.

关键词： teaching-learning-based optimization algorithm shuffled shepherd optimization algorithm Jaya algorithm truss structure structural optimization structural reliability theory

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Enhancement of video demosaicing techniques using optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第8期35卷

作者： Alphonse, Babitha Sylam, Boyed Wesley Alfred Manonmaniam Sundaranar Univ Nesamony Mem Christian Coll Dept Comp Sci Thirunelveli India

Demosaicing and denoising are the most significant steps in the imaging process for both images and videos. The correlation noise corrupts the image quality, such that an accurate interpretation of the image is avoided. Thus, demosaicing is utilized to restructure the full-color image from imperfect color samples. In this article, cat shuffled shepherd optimization-based local polynomial approximation and intersection of confidence intervals (Cat SSO-based LPA-ICI) approach is developed for video demosaicing. The LPA-ICI model is employed for frame demosaicing in which the second-order polynomial interpolation filter coefficients are produced using the devised optimization algorithm, named cat SSO technique. Similarly, generative adversarial network classifier is also applied for frame demosaicing. In addition, initialization and flow estimation is performed for the demosaicing process. Besides, spatio temporal demosaicing model is utilized in order to get a noise-free sequence. However, the developed cat SSO technique is newly devised by the incorporation of the shuffled shepherd optimization algorithm and cat swarm optimization technique. The created video demosaicing model outperformed with peak signal-to-noise ratio, root mean square error, and second derivative-like measure of enhancement values of 56.85 dB, 0.46, and 60.62, respectively.

关键词： cat swarm optimization color interpolation generative adversarial network shuffled shepherd optimization algorithm video demosaicing

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shuffled shepherd political optimization-based deep learning method for credit card fraud detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第10期35卷

作者： Ganji, Venkata Ratnam Chaparala, Aparna Sajja, Radhika Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India RVR & JC Coll Engn Comp Sci & Engn Guntur Andhra Pradesh India RVR & JC Coll Engn Mech Engn Guntur Andhra Pradesh India

Digital transactions based on credit cards are gradually increasing concept due to expediency. The amount of fraudulent transactions has intensely enlarged in modern days, because of the fast development of e-services, namely e-finance, mobile payments, and e-commerce as well as the promotion of credit cards. Criminal fraud behaviors and user's payment behaviors are frequently varying, thus performance improvement of the fraud identification method and its stability are more challenging processes. The shuffled shepherd Political optimization-based Deep Residual network (SSPO-based DRN) scheme is established for credit card fraud identification in this research. The SSPO is developed by merging the Political optimization (PO) and shuffled shepherd optimization algorithm (SSOA). The quantile normalization model is an effective preprocessing technique, which normalizes the data for effective detection. Moreover, fisher score and class information gain effectively select the required features. Data augmentation is employed for increasing the data size, thereby the detection performance is improved. The Deep Residual Network (DRN) is employed for credit card fraud recognition, which is trained by devised SSPO algorithm. The SSPO-based DRN approach achieved enhanced performance with testing sensitivity of 0.9279, specificity of 0.9023, and accuracy of 0.9120.

关键词： deep residual network political optimization approach quantile normalization shuffled shepherd optimization algorithm

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Boundary Strategy for optimization-based Structural Damage Detection Problem using Metaheuristic algorithms

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第1期65卷 150-167页

作者： Kaveh, Ali Hosseini, Seyed Milad Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the shuffled shepherd optimization algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

关键词： structural damage detection Boundary Strategy metaheuristic optimization algorithms shuffled shepherd optimization algorithm vibration data

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GAN-FuzzyNN: optimization Based Generative Adversarial Network and Fuzzy Neural Network Classification for Change Detection in Satellite Images

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SENSING AND IMAGING 2023年第1期24卷 1-1页

作者： Gite, K. R. Gupta, Praveen Chatrapati Shivaji Maharaj Univ Dept Comp Engn Old Mumbai-Pune Expressway Panvel Navi Mumbai 410221 Maharashtra India Chatrapati Shivaji Maharaj Univ Dept CS IT Old Mumbai Pune Expressway Navi Mumbai 410221 Maharashtra India

Nowadays, change detection with satellite images plays an essential role in urban planning, resources survey, and understanding global environmental changes. However, numerous satellite images are persistently acquired each and every second and they possess a significant source of data for the assessment of the spatiotemporal case. Moreover, obtaining reference data associated with satellite images dealing with land cover changes still remains a major challenging issue. Besides, traditional techniques for change detection are not valuable because of complex texture features. To cope up with such limitations, an effective mechanism is proposed for change detection by exploiting Fuzzy Neural Network (FNN) classification, which is an integration of the Fuzzy concept with Neural Network (NN), and also segmentation is done using Taylor shuffled shepherd optimization (TSSO)-based Generative Adversarial Network (GAN). The proposed TSSO is obtained by incorporating the Taylor series and shuffled shepherd optimization (SSO) and the proposed approach achieved a maximum overall accuracy of 0.932, minimum overall error of 0.0704, and maximum kappa coefficient of 0.911. The accuracy of the devised TSSO-based GAN + Fuzzy NN is 2.28%, 4.78%, 0.33%, and 13.26% improved than the Kernel Principal Component Analysis Convolutional Mapping Network (KPCA-MNet), Multiclass Support Vector Machine (MSVM), Patchlevel and pixel-level change detection network (PPCNET), and Image Fusion Network (IFN), respectively, for Image-1.

关键词： Change detection Taylor series Generative adversarial network shuffled shepherd optimization algorithm Fuzzy neural network

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shuffled shepherd SOCIAL POLITICAL OPTIMIZED DEEP LEARNING FOR RICE LEAF DISEASE CLASSIFICATION AND SEVERITY PERCENTAGE PREDICTION

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2024年第4期36卷

作者： Geetha, M. Ramaswamy, Velumani Kumar, K. Suresh Daniya, T. SA Engn Coll Dept Informat Technol Chennai Tamil Nadu India Gayatri Vidya Parishad Coll Engn A Dept Comp Sci & Engn Madhurawada Andhra Pradesh India Saveetha Engn Coll Dept Informat Technol Chennai 602105 Tamil Nadu India GMR Inst Technol Dept Informat Technol Rajam 532127 Andhra Pradesh India

Rice is the most commonly consumed food in the world and several diseases affect the rice plants easily resulting in huge economic losses and decreased yield. Thus, the early stage of identification is necessary to control and alleviate the influences of pest attacks. The common disease affecting in rice is brown spot (BS). Most of the previous methods used image recognition techniques and machine-driven disease diagnosis systems to detect the crop diseases. However, these techniques are not feasible to process lots of images, time-consuming, inaccurate, and expensive. Hence, an effective approach, named shuffled shepherd social political optimization algorithm (SSSPOA) based deep learning is developed for rice leaf infection categorization and severity percentage detection. The developed SSSPOA is the merging of shuffled shepherd social optimization (SSSO) and political optimizer (PO). Here, the input image is pre-processed by using the RoI extraction method to eliminate the unwanted noise from the image. Then, the segmentation process is done by using the DFC technique. Deep maxout network (DMN) is adopted for grading the leaf diseases into blast, bacterial blight, tungro, and BS where the training step of DMN is conducted utilizing designed SSSPOA. In addition, forecasting of severity percentage takes place using deep long short-term memory (LSTM) by taking segmented values such that the tuning mechanism of deep LSTM is done utilizing the same SSSPOA. Therefore, the presented strategy outperformed different conventional models and achieved efficient performance with a higher testing accuracy of 0.954, a sensitivity of 0.987, a specificity of 0.965, a lower mean square error (MSE) of 0.076, and a lower root mean square error (RMSE) of 0.275, respectively.

关键词： Rice leaf Deep maxout network Deep long short-term memory Political optimizer shuffled shepherd optimization algorithm

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FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第4期24卷

作者： Prasad, Rajesh S. Prasad, Jayashree Rajesh Chaudhari, Bhushan S. Ranjan, Nihar M. Srivastava, Rajat MIT Art Design & Technol Univ Dept Comp Sci & Engn Pune India SVKMs Inst Technol Dept Informat Technol Dhule Maharashtra India Rajarshi Shahu Coll Engn Dept Informat Technol Pune India Natl Univ Sci & Technol Coll Engn Muscat Oman

Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-shuffled shepherd optimization algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus optimization (ACVO) and shuffled shepherd optimization algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.

关键词： Satellite imagery flood detection anti-corona virus optimization shuffled shepherd optimization algorithm deep learning

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