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Cyber Security and Applications 2025年 3卷

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha Bansal, Shavi Attar, Razaz Waheeb Alhomoud, Ahmed Psannis, Konstantinos Department of Computer Science and Information Engineering Asia University Taiwan Taichung 413 China School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India University of Economics and Human Science Warsaw Poland Ronin Institute Montclair NJ United States Hong Kong Metropolitan University (HKMU) Hong Kong Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Insights2Techinfo India UCRD Chandigarh University Chandigarh India Management Department College of Business Administration Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia University of Macedonia Greece

The rise in mobile malware risks brought on by the explosion of Android smartphones required more efficient detection techniques. Inspired by a cascade of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, optimized using the earthworm optimization algorithm (EOA), this study presents an android malware detection model. The paper used random forest model for feature selection. With a 99% accuracy and the lowest loss values, the proposed model performs better than conventional models including GRU, LSTM, RNN, Logistic Regression, and SVM. The findings highlight the possibility of proposed method in improving Android malware detection, thereby providing a strong answer in the changing scene of cybersecurity. © 2024

关键词： Android earthworm optimization algorithm GRU LSTM Malware detection

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A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering

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SOFT COMPUTING 2020年第20期24卷 15529-15549页

作者： Rad, Mina Hosseini Abdolrazzagh-Nezhad, Majid Islamic Azad Univ Dept Comp Engn Birjand Branch Birjand Iran Bozorgmehr Univ Qaenat Fac Engn Dept Comp Engn Qaen Iran

Data aggregation from different databases into a data warehouse creates multidimensional data such as data cubes. With regard to the 3D structure of data, data cube clustering has significant challenges to perform on data cube. In this paper, new preprocessing techniques and a novel hybridization of DBSCAN and fuzzy earthworm optimization algorithm (EWOA) are proposed to solve the challenges. Proposed preprocessing consists of an assigned address to each cube cell and dimension move to create a related 2D data from the data cube and new similarity metric. The DBSCAN algorithm, as a density-based clustering algorithm, is adopted based on both Euclidean and newly proposed similarity metric, which are called DBSCAN1 and DBSCAN2 for the related 2D data. A new hybridization of the EWOA and DBSCAN is proposed to improve the DBSCAN, and it is called EWOA-DBSCAN. Also, to dynamically tune parameters of EWOA, a fuzzy logic controller is designed with two fuzzy group rules of Mamdani (EWOA-DBSCAN-Mamdani) and Sugeno (EWOA-DBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN's parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the proposed algorithms are compared with DBSCAN1 and GA-DBSCAN1, GA-DBSCAN1-Mamdani and GA-DBSCAN1-Sugeno. The experimental results, consisting of 20 runs, indicate that the proposed ideas achieved their targets.

关键词： Data cube Dimension move DBSCAN clustering Fuzzy logic controller Dynamic tuning parameters earthworm optimization algorithm

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Application of earthworm optimization algorithm for solution of Optimal Power Flow

Application of Earthworm Optimization Algorithm for solution...

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International Conference on Opto-Electronics and Applied Optics (Optronix)

作者： Ghosh, Ishita Roy, Provas Kumar Univ Engn & Management Dept Elect Engn Kolkata India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India

ISBN: (纸本)9781728100708

Economic and efficient generation of electricity is one of the main concern for power system engineers. To achieve this objective "optimal power flow" or "OPF" problem needs to be solved efficiently in a fast manner. Better quality of electricity is to be produced with required voltage level and reduced cost while satisfying all the system constraints. Modern trend is to apply several population based optimization algorithm in solving "OPF" problem. These optimization algorithms give better results in comparison to the conventional approaches. In this paper a recently evolved soft computing technique namely "earthworm optimization algorithm" (EWA) is used to get the solution of "OPF" of "IEEE 300-bus test system". Performance of "EWA" in solving "OPF" problems has been evaluated and compared with some of the well-established optimization algorithms. The results obtained using "EWA" seems to be satisfactory in all respect.

关键词： earthworm optimization algorithm Optimal power flow Meta-heuristic Evolutionary algorithm

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Performance Analysis of Fractional earthworm optimization algorithm for Optimal Routing in Wireless Sensor Networks

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2021年第32期8卷 1-10页

作者： Pasupuleti, Venkat Rao Balaswamy, Ch Lakireddy Bali Reddy Coll Engn Dept Elect & Commun Engn Mylavaram Andhra Pradesh India Gudlavalleru Engn Coll Dept ECE Gudlavalleru India

In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models. Hence to increase Network lifetime, optimized clustering and energy-efficient routing techniques are required. In all applications of WSN, not only energy-efficient but also delay and throughput of the network are important for efficient transmission of data to the destination. This paper analyses optimized clustering by selecting cluster heads based on fractional calculus earthworm optimization algorithm (FEWA). The route from cluster heads to sink node is selected based on the fit factor. This paper's main intention is to provide an extensive comparative study of the FEWA with all standard optimization-based clustering and routing techniques. This method's performance is compared with existing optimized clustering methods like GA, PSO, ACO, DE and EWO in terms of the number of energy, delay, and throughput. At the end of 1000 iterations, the analysis shows that the FEWA outperforms existing methods with maximum average remaining energy of the nodes as 0.216J, the minimum average delay of 0.208 sec and maximum average throughput of 88.57% for 100 nodes.

关键词： clustering routing optimization earthworm optimization algorithm fractional calculus

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Modified earthworm optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

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IEEE ACCESS 2023年 11卷 35089-35096页

作者： Alrowais, Fadwa Negm, Noha Khalid, Majdi Almalki, Nabil Marzouk, Radwa Mohamed, Abdullah Al Duhayyim, Mesfer Alneil, Amani A. Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Riyadh 12372 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca 21955 Saudi Arabia King Saud Univ Coll Educ Dept Special Educ Riyadh 12372 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11845 Egypt Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 16273 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16273 Saudi Arabia

Among the most prominent field in the human-computer interface (HCI) is emotion recognition using facial expressions. Posed variations, facial accessories, and non-uniform illuminations are some of the difficulties in the emotion recognition field. Emotion detection with the help of traditional methods has the shortcoming of mutual optimization of feature extraction and classification. Computer vision (CV) technology improves HCI by visualizing the natural world in a digital platform like the human brain. In CV technique, advances in machine learning and artificial intelligence result in further enhancements and changes, which ensures an improved and more stable visualization. This study develops a new Modified earthworm optimization with Deep Learning Assisted Emotion Recognition (MEWODL-ER) for HCI applications. The presented MEWODL-ER technique intends to categorize different kinds of emotions that exist in the HCI applications. To do so, the presented MEWODL-ER technique employs the GoogleNet model to extract feature vectors and the hyperparameter tuning process is performed via the MEWO algorithm. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. Finally, the quantum autoencoder (QAE) model is implemented for the identification and classification of emotions related to the HCI applications. To exhibit the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is performed. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with maximum accuracy of 98.91%.

关键词： Feature extraction Human computer interaction Emotion recognition Brain modeling Convolutional neural networks Computational modeling Deep learning Human-computer interaction artificial intelligence deep learning emotion recognition earthworm optimization algorithm

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An optimization-based deep belief network for the detection of phishing e-mails

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第4期54卷 529-549页

作者： Arshey, M. Viji, Angel K. S. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Thuckalay India Coll Engn Kottayam Kerala India

Purpose Phishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods. Design/methodology/approach The primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally. Findings The accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed me

关键词： E-mail phishing optimization Fractional concept Deep learning Spam mails earthworm optimization algorithm Deep belief network Bhattacharya distance

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Improving convergence in swarm algorithms by controlling range of random movement

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NATURAL COMPUTING 2021年第3期20卷 513-560页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

Swarm intelligence algorithms are stochastic algorithms, i.e. they perform some random movement. This random movement imparts the algorithms with exploration capabilities and allows them to escape local optima. Exploration at the start of execution helps with thorough inspection of the search/solution space. However, as the algorithm progresses, the focus should ideally shift from exploration to exploitation. This shift would help the algorithm to enhance existing solutions and improve its convergence capabilities. Hence if the range of random movement is not kept in check, it may limit an algorithm's convergence capabilities and overall efficiency. To ensure that the convergence of an algorithm is not compromised, an improved search technique to reduce range of uniform random movement was recently proposed for bat algorithm. Uniform distribution and levy distribution are the most commonly used random distributions in swarm algorithms. In this paper, the applicability of the improved search technique over different swarm algorithms employing uniform and levy distributions, as well as Cauchy distribution has been studied. The selected algorithms are firefly algorithm, cuckoo search algorithm, moth search algorithm, whale optimization algorithm, earthworm optimization algorithm and elephant herding optimization algorithm. The resultant variants of each of these algorithms show improvement upon inclusion of the improved search technique. Hence results establish that the improved search technique has positive influence over swarm algorithms employing different random distributions.

关键词： Exploitation Improved search Moth search algorithm Cuckoo search algorithm Firefly algorithm Whale optimization algorithm earthworm optimization algorithm Elephant herding optimization algorithm

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A discrete bio-inspired metaheuristic algorithm for efficient and accurate image matting

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MEMETIC COMPUTING 2019年第1期11卷 53-64页

作者： Cai, Zhao-Quan Lv, Liang Huang, Han Liang, Yi-Hui Coll Huizhou Huizhou 516007 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

With the development of digital multimedia technologies, image matting has become one of the most popular research problem in academic field and been widely applied in industrial communities. The key challenge of image matting is how to extract the foreground region (target region) from a given image accurately. Sampling-based image matting technology implements matting by sampling some foreground pixels and background pixels from known regions and finding the best foreground-background sample pair for every undetermined pixel. The best foreground-background sample pair represents the true foreground and background colors of the corresponding undetermined pixel and they can estimate the region of this undetermined pixel accurately. Therefore, the quality of matting depends on whether the best sample pair can be found. This search process can be regarded as a combinational optimization problem. In this paper, in order to obtain more accurate matting result, we applied a bio-inspired metaheuristic algorithm to solve this problem, which is based on the promising earthworm optimization algorithm (EWA). By analyzing the property of this optimization problem, we upgrade two reproductions and the cauchy mutation of EWA to discrete calculations. The proposed algorithm is called as the discrete earthworm optimization algorithm (D-EWA). By comparing with existing optimization algorithms on a standard benchmark dataset, the experimental results show that the proposed D-EWA can obtain more accurate matting results on both visual effect and quantitative metric.

关键词： Image matting Sample optimization problem earthworm optimization algorithm Discrete calculation

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Optimal hierarchical attention network-based sentiment analysis for movie recommendation

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SOCIAL NETWORK ANALYSIS AND MINING 2022年第1期12卷 1-16页

作者： Roy, Deepjyoti Dutta, Mala Assam Town Univ Dept Comp Sci & Engn Gauhati India

In recent days, recommender systems are extremely widespread applications for handling information overload issues by offering personalized recommendations, and also, the researchers began to utilize the sentiment analysis-based movie recommendation system. This paper devises a novel technique for sentimental classification, which helps to recommend the positive reviewed movies. Here, the water cycle earthworm optimization (WCEWO) is newly devised by incorporating water cycle algorithm and earthworm optimization algorithm. The procedure is started by providing the input to the matrix calculation stage, which then builds the matrix based on the user's preferences. Here, the service grouping is performed using ROCK algorithm. Thereafter, the group matching is performed by Cosine similarity for achieving best group from all other groups. Once the group matching is done, the matching is carried out between the query and the visitor binary sequence using Kendall Tau distance measure for identifying best visitor sequence. Finally, sentimental classification is done using hierarchical attention networks (HAN), where the training process of HAN is performed using WCEWO algorithm and hence the suitable movie recommendation is achieved by offering appropriate positive reviewed movie recommendations to the users. Mean square error (MSE), root-mean-square error (RMSE), and accuracy are used as performance indicators to assess the effectiveness of the established approach. The created WCEWO-based HAN gives maximum accuracy of 89.81%, minimum MSE of 0.185, and minimum RMSE of 0.430.

关键词： Sentiment analysis Movie recommendation Water cycle algorithm earthworm optimization algorithm Hierarchical attention networks

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Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 58卷

作者： Vijay, M. Indumathi, G. VSB Engn Coll Karur 639111 Tamil Nadu India Mepco Schlenk Engn Coll Sivakasi India

Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological characteristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively.

关键词： Multi-modal Bio-metric system Chicken Swarm optimization earthworm optimization algorithm Deep Belief Network Multi-SVNN

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