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Quantum behaved binary gravitational search algorithm with random forest for twitter spammer detection

RESULTS IN ENGINEERING 2025年 25卷

作者： Sharma, Kanta Prasad Lal, Gendal Shukla, Madhu Yadav, Anupam Jayaprakash, B. Juneja, Bhanu Jagtap, Jayant Singh, Amrita Bhowmik, A. Santhosh, A. Johnson Amity Univ Amity Sch Engn & Technol Dept CSE Greater Noida Campus Greater Noida Uttar Pradesh India Vivekanand Global Univ Dept Comp Sci & Engn Jaipur India Marwadi Univ Res Ctr Fac Engn & Technol Dept Comp Engn Rajkot 360003 Gujarat India Graphic Era Hill Univ Dept Comp Sci & Engn Dehra Dun India JAIN Deemed Univ Sch Sci Dept Comp Sci & IT Bangalore Karnataka India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India NIMS Univ Rajasthan NIMS Sch Comp Sci & Artificial Intelligence Jaipur India Chandigarh Engn Coll Coll Jhanjeri Dept Comp Sci & Engn Chandigarh Grp Mohali 140307 Punjab India Chitkara Univ Chitkara Ctr Res & Dev Baddi 174103 Himachal Prades India Jimma Univ Jimma Inst Technol Fac Mech Engn Jimma Ethiopia Graphic Era Deemed Univ Dehra Dun 248002 Uttarakhand India

The emergence of social media platforms like Twitter has significantly changed the landscape of communication by increasing accessibility for widely disseminating official announcements, professional interactions, and important news in real-time. Despite these advantages, the prevalence of spammers and their spamming activities is increasing regularly. To mitigate the growing number of spammers, it is essential to develop an efficient and robust method for Twitter spammer detection. This research presents a novel QBGSRF method by combining the quantum-behaved binary gravitational search algorithm (QBGSA) with random forest (RF) for timely detection of Twitter spammers. The QBGSA algorithm adds the characteristics of quantum computing (QC) and binary gravitational search algorithm (BGSA), which enables the quantum agents to quickly determine solutions using the superposition attributes of QC and the position update via bit-flipping based on velocity probabilities of the BGSA algorithm. In the proposed QBGSRF method, the quantum agents utilize the aforementioned attributes and the principles of the RF algorithm to construct the decision trees for effectively detecting Twitter spammers. The proposed method is assessed for the datasets of 1KS-10KN and Social Honeypot. In order to access the efficacy of the proposed method, the results are also evaluated using the BGSRF method (a combination of BGSA and RF algorithm) and RF algorithm. The experimental evaluations indicate that the proposed method outperforms the aforementioned and state-of-the-art methods.

关键词： Twitter Twitter spammer detection gravitational search algorithm Quantum computing binary gravitational search algorithm Random forest Machine learning Metaheuristic

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A Multi-objective PMU Placement Method in Power System via binary gravitational search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第16期45卷 1832-1845页

作者： Singh, Satyendra Pratap Singh, Shiv Pujan Banaras Hindu Univ Varanasi Indian Inst Technol Dept Elect Engn Varanasi Uttar Pradesh India

This paper presents a binary gravitational search algorithm (BGSA) methodology for the optimal placement of Phasor Measurement Units (PMUs) to achieve full and maximum observability of the power system. The gravitational search technique has been extended in this investigation to incorporate binary variables for this purpose. The objective of proposed methodology is to minimize the total number of PMUs installed at various buses, which in turn minimize installation cost of the PMUs and improves observability redundancy by including an additional objective. Thus, the PMU placement problem has been expressed as a multi-objective problem. Besides, single PMU outage or single line outage cases in the presence of zero injection buses have been investigated. In this algorithm, the searcher agents are the collection of masses, which interact with each other using Newton's laws of gravity and motion. The proposed BGSA has been applied to the IEEE 14-bus, -30-bus, -118-bus, Northern Regional Power Grid 246-bus Indian system, and Polish 2383-bus system. Case studies reveal that the lower number of PMUs or equal number of PMUs has been produced by the proposed method compared to methods reported in the literature. In cases of equal number of PMUs, the observability produced by proposed method is higher or at least equal.

关键词： phasor measurement unit law of gravity law of motion binary gravitational search algorithm observability optimal placement contingency

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A new approach for unit commitment problem via binary gravitational search algorithm

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APPLIED SOFT COMPUTING 2014年 22卷 249-260页

作者： Yuan, Xiaohui Jia, Bin Zhang, Shuangquan Tian, Hao Hou, Yanhong Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

This paper proposes a new gravitational search algorithm to solve the unit commitment (UC) problem, which is integrated binary gravitational search algorithm (BGSA) with the Lambda-iteration method. The proposed method is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. Furthermore, local mutation strategies are applied to improve the performance of BGSA. The implementation of the proposed method for UC problem consists of three stages. Firstly, the BGSA based on priority list is applied for solution unit scheduling when neglecting minimum up/down time constraints. Secondly, heuristic search strategies are used to handle minimum up/down time constraints and decommit excess spinning reserve units. Thirdly, local mutation strategies are raised to avoid premature convergence of the algorithm and prevent it from trapping into local optima. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. The feasibility and effectiveness of the proposed method is verified by the systems with the number of units in the range of 10-100 and the results are compared with those of other methods reported in literatures. The results clearly show that the proposed method gives better quality solutions than other methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： binary gravitational search algorithm Unit commitment Economic load dispatch Heuristic strategy Local mutation

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Enhancing Time Series Forecasting With an Optimized binary gravitational search algorithm for Echo State Networks

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IEEE ACCESS 2023年 11卷 79466-79479页

作者： Ahmad, Zohaib Mahmood, Tariq Alam, Teg Rehman, Amjad Saba, Tanzila Sahara Univ Dept Comp Sci & Informat Technol Narowal 51600 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Prince Sattam bin Abdulaziz Univ Coll Engn Dept Ind Engn Al Kharj 11942 Saudi Arabia Azad Inst Engn & Technol Chandrawal via Bangla Bazar & BijnourNear CRPF Ca Lucknow 226002 India

The echo state network (ESN) is a cutting-edge reservoir computing technique designed to handle time-dependent data, making it highly effective for addressing time series prediction tasks. ESN inherits the more precise design of standard neural networks and the relatively simple learning process and has a strong computing capacity for solving nonlinear problems. It can disseminate low-dimensional information cues to high-dimensional areas enabling extracting data. However, this study has proven that not all reservoir output dimensions directly impact model generalization. This study desires to enhance the ESN model's generalization abilities by decreasing the redundant reservoir output feature. A remarkable hybrid model is proposed that optimizes the ESN output association through feature selection. This model is called the binary improved gravitational search algorithm (BIGSA) echo state network (BIGSA-ESN). BIGSA's feature selection approach complements the ESN output connection architecture. In this study, evaluation was performed using root mean square error (RMSE). The experimental findings on the Lorenz and Mackey-Glass benchmark time-series datasets demonstrate that the proposed technique outperforms conventional evolutionary methods. Moreover, empirical findings on predicting a significant water quality parameter from the wastewater treatment process (WWTP) dataset demonstrate that the proposed ensemble of BIGSA models performs very well in real-world scenarios.

关键词： Network optimization time series forecasting binary gravitational search algorithm root mean square error echo state network

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Descriptor selection evaluation of binary gravitational search algorithm in quantitative structure-activity relationship studies of benzyl phenyl ether diamidine's antiprotozoal activity and Chalcone's anticancer potency

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2018年 182卷 31-40页

作者： Baghgoli, Tayebeh Mousavi, Mehdi Bababdani, Behnam Mohseni Shahid Bahonar Univ Kerman Dept Chem Fac Sci POB 76175-133 Kerman Iran

In this work, the effect of various gravitational constant functions, G(t), were evaluated on the performance of the newly proposed binary gravitational search algorithm (BGSA) as a feature selection method for chemical systems. To fulfill this aim, linear, exponential, logarithmic and square root functions were studied. These functions were implemented in the binary gravitational search algorithm computer code sequentially and their performances were evaluated in a quantitative structure-activity relationship (QSAR) study for selection of the most informative descriptors. In the QSAR study, sixty cationic benzyl phenyl ether diamidine derivatives, which had been synthesized and evaluated against acute infection of Trypanosoma brucei rhodesiense (T.b. rhodesiense), were examined. The number and the kind of descriptors, which were selected by the BGSA, were highly dependent on the applied G(t) function. The results of internal and external validation tests indicate that the exponential function was superior to the other gravitational constant functions for applying in the binary gravitational search algorithm. A general model was established using seven descriptors for the ten training and validation sets. Regardless of subsetting, the selected descriptors and generated model can successfully describe experimental variation of antiprotozoal activity of benzyl phenyl ether diamidine derivatives. In addition, in another QSAR study, anticancer potency of a series of 87 Chalcone derivatives was satisfactorily modeled by using the BGSA-BRANN method. Comparison of BGSA results with those obtained by genetic algorithm (GA) indicates superiority of the BGSA.

关键词： binary gravitational search algorithm Feature selection in QSAR gravitational constant function Antiprotozoal activity Benzyl phenyl ether diamidine derivatives Anticancer potency of chalcone derivatives

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IBGSS: An Improved binary gravitational search algorithm based search strategy for QoS and ranking prediction in cloud environments

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APPLIED SOFT COMPUTING 2020年第0期88卷 105945-000页

作者： Somu, Nivethitha Raman, Gauthama M. R. Kaveri, Akshya Rahul, Akshay K. Krithivasan, Kannan Sriram, Shankar V. S. Indian Inst Technol SEIL Mumbai 400076 Maharashtra India Singapore Univ Technol & Design iTrust Ctr Res Cyber Secur Singapore Singapore Tata Consultancy Serv Chennai 600113 Tamil Nadu India Full Creat Chennai 600113 Tamil Nadu India SASTRA Deemed Univ Dept Math DMRL Thanjavur 613401 Tamil Nadu India SASTRA Deemed Univ Sch Comp CISH Thanjavur 613401 Tamil Nadu India

Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS based service ranking prediction is an NP-Complete problem which examines the order of ranked service sequence with respect to the unique QoS requirements. To address the NP-Complete problem, greedy and optimization-based strategies such as CloudRank and PSO have been widely employed in service oriented environments. However, they pose several challenges with respect to the similarity measure based QoS prediction, trap at local optima, and near optimal solution. Hence, this paper presents Improved binary gravitational search Strategy (IBGSS), an optimization based search strategy to address the challenges in the state-of-the-art QoS value prediction and service ranking prediction techniques. IBGSS employs improved cosine similarity measure, and Newton-Raphson inspired binary gravitational search algorithm (NR-BGSA) for accurate QoS value prediction and optimal service ranking prediction respectively. The effectiveness of IBGSS over the state-of-the-art QoS value prediction and ranking prediction techniques was validated using two real world QoS datasets, namely WSDream#1 and web service QoS dataset in terms of various statistical measures (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Precision Correlation (APC)). (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cloud computing QoS prediction Ranking prediction Cosine similarity binary gravitational search algorithm

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Generation of suprasegmental information for speech using a recurrent neural network and binary gravitational search algorithm for feature selection

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APPLIED INTELLIGENCE 2014年第4期40卷 772-790页

作者： Sheikhan, Mansour Islamic Azad Univ Fac Engn Dept Elect Engn South Tehran Branch Tehran Iran

Suprasegmental (prosody) features of discourse provide a vehicle by which speakers reflect their mental purposes to listeners. Generating suitable prosody information is critical to expressing messages and improving the intelligibility and naturalness of synthetic speech. Generic prosody generators should provide information about pitch frequency (F (0)) contours, energy levels, word durations, and inter-word pause durations for speech synthesizers. The present study used a recurrent neural network (RNN) for prosody generation. The inputs of this RNN were word-level and syllable-level linguistic features. To provide data efficiently for the RNN-based prosody generator in the training, validation, and test phases, automatic segmentation and labeling of phonemes were performed. The number of inputs to the RNN was reduced by employing a binary gravitational search algorithm (BGSA) for feature selection (FS). The proposed prosody generator provided 12 output prosodic parameters for the current syllable for representing pitch contour, log-energy contour, inter-syllable pause duration, duration of syllable, duration of the vowel in the syllable, and vowel onset time. Experimental results demonstrated the success of the RNN-based prosody generator in synthesizing the six prosodic elements with acceptable root mean square error (RMSE). By using a BGSA-based FS unit, a lighter neural model was achieved with a 53 % reduction in the number of weight connections, producing RMSEs with acceptable degradation over the no-FS unit prosody generator. The performance of the BGSA-based FS method was compared with a binary particle swarm optimization (BPSO) algorithm, and the BGSA showed slightly better results. A modified mean opinion score scale was used to evaluate the intelligibility and naturalness of synthesized speech using the proposed method.

关键词： Prosody generation Recurrent neural network Feature selection binary gravitational search algorithm binary particle swarm optimization Modified MOS scale

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Feature subset selection using improved binary gravitational search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第3期26卷 1211-1221页

作者： Rashedi, Esmat Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Feature selection is one of the important activities in various fields such as computer vision and pattern recognition. In this paper, an improved version of the binary gravitational search algorithm (BGSA) is proposed and used as a tool to select the best subset of features with the goal of improving classification accuracy. By enhancing the transfer function, we give BGSA the ability to overcome the stagnation situation. This allows the search algorithm to explore a larger group of possibilities and avoid stagnation. To evaluate the proposed improved BGSA (IBGSA), classification of some well known datasets and improving the accuracy of CBIR systems are experienced. Results are compared with those of original BGSA, genetic algorithm (GA), binary particle swarm optimization (BPSO), and electromagnetic-like mechanism. Comparative results confirm the effectiveness of the proposed IBGSA in feature selection.

关键词： Feature selection binary gravitational search algorithm dataset classification content-based image retrieval

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Facing the classification of binary problems with a hybrid system based on quantum-inspired binary gravitational search algorithm and K-NN method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第10期26卷 2424-2430页

作者： Han, XiaoHong Quan, Long Xiong, XiaoYan Wu, Bing Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan 030024 Peoples R China

Since given classification data often contains redundant, useless or misleading features, feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms to decrease the dimensional number of features involved. Removing irrelevant features in the feature space and identifying relevant features correctly is the primary objective, which can increase classification accuracy. In this paper, a novel QBGSA-K-NN hybrid system which hybridizes the quantum-inspired binary gravitational search algorithm (QBGSA) with the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) is proposed. The main aim of this system is to improve classification accuracy with an appropriate feature subset in binary problems. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The experimental results show that the proposed method is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Feature selection binary classification gravitational search algorithm binary gravitational search algorithm Quantum computing K-nearest neighbor

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Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems

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SOFT COMPUTING 2017年第9期21卷 2307-2324页

作者： Bostani, Hamid Sheikhan, Mansour Islamic Azad Univ South Tehran Branch Dept Comp Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Commun Engn Tehran Iran

Intrusion detection systems (IDSs) play an important role in the security of computer networks. One of the main challenges in IDSs is the high-dimensional input data analysis. Feature selection is a solution to overcoming this problem. This paper presents a hybrid feature selection method using binary gravitational search algorithm (BGSA) and mutual information (MI) for improving the efficiency of standard BGSA as a feature selection algorithm. The proposed method, called MI-BGSA, used BGSA as a wrapper-based feature selection method for performing global search. Moreover, MI approach was integrated into the BGSA, as a filter-based method, to compute the feature-feature and the feature-class mutual information with the aim of pruning the subset of features. This strategy found the features considering the least redundancy to the selected features and also the most relevance to the target class. A two-objective function based on maximizing the detection rate and minimizing the false positive rate was defined as a fitness function to control the search direction of the standard BGSA. The experimental results on the NSL-KDD dataset showed that the proposed method can reduce the feature space dramatically. Moreover, the proposed algorithm found better subset of features and achieved higher accuracy and detection rate as compared to the some standard wrapper-based and filter-based feature selection methods.

关键词： Feature selection Intrusion detection system Anomaly detection binary gravitational search algorithm Mutual information Hybrid model

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