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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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Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

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DATA TECHNOLOGIES AND APPLICATIONS 2022年第1期56卷 103-131页

作者： Vinolin, V. Sucharitha, M. Noorul Islam Ctr Higher Educ Thuckalay India Malla Reddy Coll Engn & Technol Maisammaguda India

Purpose With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images. Design/methodology/approach This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola-Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images. Findings Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy. Originality/value The Taylor-ROA is developed by integra

关键词： Image forgery detection Deep learning network rider optimization algorithm Taylor series Spliced images

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Stock Market Prediction Using Optimized Deep-ConvLSTM Model

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BIG DATA 2020年第1期8卷 5-24页

作者： Kelotra, Amit Pandey, Prateek Jaypee Univ Engn & Technol Guna 473226 Madhya Pradesh India

Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed rider-based monarch butterfly optimization (rider-MBO) algorithm. The proposed rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.

关键词： rider optimization algorithm monarch butterfly optimization stock forecast deep learning

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Optimal deep recurrent neural network for sentiment grade classification

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2021年第6期17卷 557-579页

作者： Jasti, Sireesha Gandhi Inst Technol & Management VNRVJIET Comp Sci & Engn Visakhapatnam Andhra Pradesh India

Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam wo

关键词： Sentiment classification Feature fusion Feedback artificial tree algorithm rider optimization algorithm Natural language processing Deep RNN

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Optimal location of UPQC for power quality improvement: novel hybrid approach

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2020年第6期18卷 1519-1541页

作者： Gaddala, Kaladhar Raju, P. Sangameswara St Anns Coll Engn & Technol Chirala India Siddhartha Inst Sci & Technol Narayanavanam India

Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness. Design/methodology/approach This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections. Findings Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA);0.09 per cent better than GMDA and WOA;and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models. Originality/value This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors' knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.

关键词： Power quality enhancement UPQC Voltage stability index Optimal placement rider optimization algorithm

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Big Data Classification in IOT Healthcare Application Using Optimal Deep Learning

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2023年第1期17卷 33-58页

作者： Akhtar, Md Mobin Ahamad, Danish Shatat, Abdallah Saleh Ali Shatat, Ahmad Saleh Ali Riyadh Elm Univ Dept Basic Sci Riyadh Saudi Arabia Atheeb Intergraph Saudi Co AISC Riyadh Saudi Arabia Appl Sci Univ Coll Adm Sci Dept Management Informat Syst Eker Bahrain Appl Sci Univ Coll Adm Sci MIS Dept Eker Bahrain

The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly rider Competitive Swarm optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly rider optimization algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly algorithm (DA) and rider optimization algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feed

关键词： Mapreduce framework Dragonfly algorithm competitive swarm optimization deep residual network rider optimization algorithm

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Frequent itemset-based feature selection and rider Moth Search algorithm for document clustering

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第4期34卷 1098-1109页

作者： Yarlagadda, Madhulika Rao, K. Gangadhara Srikrishna, A. JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVRJC Coll Engn Dept Informat Technol Chowdavaram Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of informa-tion retrieval technologies. This paper introduces Modsup-based frequent itemset and rider optimization-based Moth Search algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on fre-quent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining rider optimization algorithm (ROA), and the Moth Search algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed doc-ument clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Document clustering Moth Search algorithm rider optimization algorithm Frequent itemset Stop word removal

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Predicting the settlement of pile based on a hybrid form of the model by considering Least Square Support Vector Regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 529-542页

作者： Chen, Qiang Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

Understanding the vertical settlement is crucial when designing the pile and foundation type used in real estate, specifically regarding the pile settlement (Sp). This issue is of utmost importance due to the numerous variables involved in designing piles that penetrate rock. Despite multiple efforts, clear and precise theoretical explanations regarding the interactions between soil and piles are currently unclear. As a result, many studies have opted to employ artificial intelligence techniques for determining the subsidence rate of piles over time under different loading conditions. This study presents a machine learning (ML) that effectively predicts the values of Sp, namely Least Square Support Vector Regression (LSSVR). In addition, the proposed model coupled with three meta-heuristic algorithms, including the Flow Direction algorithm (FDA), Chimp optimization algorithm (ChOA), and rider optimization algorithm (ROA), to improve the performance and obtain the optimal results as a framework of hybrid. As a result, LSFD determined the most suitable effects with R2 and RMSE values equal to 0.2503 and 0.9952, respectively. Overall, using LSSVR with FDA, ChOA, and ROA can improve the accuracy and robustness of the model in predicting pile settlement, making it a valuable tool for geotechnical engineers designing foundation systems.

关键词： Pile settlement Least Square support vector regression Flow direction algorithm Chimp optimization algorithm rider optimization algorithm

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Intelligent Recommendation System for E-Learning using Membership Optimized Fuzzy Logic Classifier 3

Intelligent Recommendation System for E-Learning using Membe...

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3rd IEEE-Pune-Section International Conference (PuneCon)

作者： Das, Anupam Al Akour, Mohammad Cotton Univ Gauhati Assam India Al Sharqyat Univ Ibra Oman

ISBN: (纸本)9781728196008

Technology-enhanced learning provides various communication and information technologies for learning and teaching. The teachers also feel comfortable with the Open Educational Resources repositories that learn the existing learning materials when a new course is developed. Yet, this exists as a non-trivial problem because the learning environments should be flexible for the students to learn on the basis of their situations and characteristics. Hence, the main aim of this paper is to provide personalized dynamic and continuous recommendations for online learning systems. This paper plans to implement the novel recommendation system for online learning using intelligent techniques. The main steps of the proposed model are (a) Data collection, (b) Feature extraction, and (c) classification. Initially, the data are collected locally from the Ekhool learning application. Then the feature extraction techniques, such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Principle Component Analysis (PCA) are used for selecting the most relevant features. Further, the classifier termed as Fuzzy Logic Classifier is adopted as the recommendation system, where the improvement is made in the membership limits by optimizing it with the rider optimization algorithm (ROA). The superiority of the proposed method is proved by the performance analysis in terms of various performance measures.

关键词： E-learning Recommendation system t-Distributed Stochastic Neighbour Embedding Principle Component Analysis Fuzzy Logic Classifier rider optimization algorithm Membership optimization

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Study on Comparison of Various optimization algorithms for Minimizing THD of the Hybrid Wind Solar Power Generation System 5

Study on Comparison of Various Optimization Algorithms for M...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Ravikumar, S. Vennila, H. Deepak, R. Nurul Islam Ctr Higher Educ Dept EEE Kumaracoil Tamil Nadu India Coll Engn Dept EEE Adoor Kerala India

ISBN: (数字)9781728146850

ISBN: (纸本)9781728146850

Electricity plays an indispensable role in human lives. Due the increasing need for electricity in domestic, commercial and industrial applications and the deletion of conventional sources,the power generation system is switched on to systems with renewable energy sources. Therefore the power quality problems arises and research has been going on to improve the power quality. This paper is a study about the various power quality improving algorithms applied to the hybrid wind solar power generation with multilevel inverters. in comparison with various optimization algorithms, more control parameters with a new algorithm called rider optimization algorithm (ROA) is suggested.

关键词： rider optimization algorithm hybrid power system multilevel inverters Total harmonics distortion (THD)

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