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Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

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COMPLEX & INTELLIGENT SYSTEMS 2023年第4期9卷 3473-3485页

作者： Pustokhina, Irina V. Pustokhin, Denis A. Nguyen, Phong Thanh Elhoseny, Mohamed Shankar, K. Plekhanov Russian Univ Econ Dept Entrepreneurship & Logist Moscow 117997 Russia State Univ Management Dept Logist Moscow 109542 Russia Ho Chi Minh City Open Univ Dept Project Management Ho Chi Minh City Vietnam Amer Univ Emirates Coll Comp Informat Technol Dubai U Arab Emirates Mansoura Univ Fac Comp & Informat Mansoura Egypt Alagappa Univ Dept Comp Applicat Karaikkudi Tamil Nadu India

Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

关键词： Customer churn Customer retention Predictive modeling Machine learning rain optimization algorithm

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Efficient Cybersecurity Model Using Wavelet Deep CNN and Enhanced rain optimization algorithm

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

作者： Lavanya, V. Sekhar, P. Chandra Gandhi Inst Technol & Management Visakhapatnam 530045 Andhra Pradesh India

Cybersecurity has received greater attention in modern times due to the emergence of IoT (Internet-of-Things) and CNs (Computer Networks). Because of the massive increase in Internet access, various malicious malware have emerged and pose significant computer security threats. The numerous computing processes across the network have a high risk of being tampered with or exploited, which necessitates developing effective intrusion detection systems. Therefore, it is essential to build an effective cybersecurity model to detect the different anomalies or cyber-attacks in the network. This work introduces a new method known as Wavelet Deep Convolutional Neural Network (WDCNN) to classify cyber-attacks. The presented network combines WDCNN with Enhanced rain optimization algorithm (EROA) to minimize the loss in the network. This proposed algorithm is designed to detect attacks in large-scale data and reduces the complexities of detection with maximum detection accuracy. The proposed method is implemented in PYTHON. The classification process is completed with the help of the two most famous datasets, KDD cup 1999 and CICMalDroid 2020. The performance of WDCNN_EROA can be assessed using parameters like specificity, accuracy, precision F-measure and recall. The results showed that the proposed method is about 98.72% accurate for the first dataset and 98.64% for the second dataset.

关键词： Cyber-attacks wavelet deep convolutional neural network rain optimization algorithm wavelet pooling intrusion detection deep learning network security

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rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 195卷 107512-107512页

作者： Moazzeni, Ali Reza Khamehchi, Ehsan Amirkabir Univ Technol Tehran Polytech Dept Petr Engn Tehran Iran

With the development of powerful computers, some new methods for solving challenging problems are introduced. Some of these methods, which are called metaheuristic algorithms, such as bat algorithm or ant colony algorithm, are inspired by nature and simulate a natural phenomenon for finding the best solution. Today these algorithms with a robust approach can be used to find the solution of a complicated problem very fast although they might be trapped in the local minimums. rain optimization algorithm (ROA) is a new metaheuristic algorithm that is inspired by the raindrops, which move toward minimum points after getting to the earth. This algorithm can find global extremum as well as local extremums if its parameters are correctly tuned. After the implementation of this algorithm, we compare it with some other existing optimization algorithms such as particle swarm optimization algorithm and bat algorithm by solving 26 benchmarks and three benchmarks in various dimensions as well as a drilling optimization problem. Simulations illustrate the performance and computational time in finding the global minimum. Also, ROA can find local minimum simultaneously and it can be confidently used in optimization problems.

关键词： rain optimization algorithm Drilling rate Drilling optimization Machine learning

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The suitability mapping of an urban spatial structure for earthquake disaster response using a gradient rain optimization algorithm (GROA)

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HELIYON 2023年第10期9卷 e20525页

作者： Aghataher, Reza Rabieifar, Hamidreza Samany, Najmeh Neysani Rezayan, Hani Islamic Azad Univ Civil Engn Fac South Tehran Branch Tehran Iran Univ Tehran Fac Geog Dept GIS & RS Tehran Iran Kharazmi Univ Fac Geog Sci Dept RS & GIS Tehran Iran

The urban spatial structure has a prominent role in the earthquake response process which should primarily be assessed in the areas that are most vulnerable to earthquake hazards. Search and rescue teams need to map and identify the appropriateness of urban infrastructures for disaster reaction after a quake to enable ease of movement and quick assistance to the casualties. The key objective of this study is to compute the appropriateness of a municipal spatial structure for crisis reaction after a destructive earthquake, with an emphasis on finding the most critical areas (those that are prone to emergency response disruption). The main contribution involves improving a geographic information system (GIS)-based earthquake-triggered hybrid framework for suitability analysis using a fuzzy analytical hierarchical process (FAHP) and gradient rain optimization algorithm (GROA). The modifying of a rain optimization algorithm (ROA) to a GROA based on gradient descent is carried out to avoid local optima, which results in optimizing the identification process of the key locations for emergency response. The planned approach has been executed in Tehran, the capital of Iran. The implementation consequences reveal the supreme crucial areas for emergency response in the study area with a demonstration of the efficiency of the GROA compared to the basic ROA. Both indicate that these sites are located in the west and southwest, while the junction degree and width of the roads are the most significant factors affecting a city's suitability for emergency response. In addition, the GROA is less sensitive to local optima and more economical than the ROA. Moreover, several rescue experts and urban planners expressed their high satisfaction (95 %) with the five-level suitability map for prioritizing the deployments of troops along with the critical area maps for preventing heavy casualties produced by the GROA.

关键词： Spatial structure Quake response Suitability analysis Fuzzy analytical hierarchical process rain optimization algorithm Gradient descent

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Automatic identification and separation of reflection patterns with the help of clustering of seismic attributes in a rain optimization meta-heuristic algorithm

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JOURNAL OF APPLIED GEOPHYSICS 2025年 237卷

作者： Soltani, Poorandokht Kahoo, Amin Roshandel Hasanpour, Hamid Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran Shahrood Univ Technol Fac Comp Engn Shahrood Iran

Seismic exploration, a key component of geophysical methods, is crucial for analyzing subsurface structures and evaluating their potential for hydrocarbon resources. However, the interpretation of geological structures based on seismic data frequently entails ambiguity and uncertainty, making it a labor-intensive endeavor that is heavily reliant on the interpreter's expertise. Seismic attributes are essential instruments for the quantitative assessment of seismic information, facilitating the identification and delineation of structural and stratigraphic elements by revealing concealed details. This paper aims to conduct a multi-attribute analysis for the automatic and unsupervised stratigraphic interpretation of two-dimensional seismic data. The research employs optimization-based clustering utilizing the rain meta-heuristic algorithm to enhance the detection of reflection patterns within the seismic data. To optimize computational efficiency and mitigate data redundancy, a subset of extracted seismic attributes was selected through the Laplacian scoring feature selection method. The results were validated against geological evidence to ensure both reliability and accuracy. The findings underscore the effectiveness of unsupervised clustering methodologies, particularly meta-heuristic optimization strategies, in enhancing the efficiency and precision of seismic interpretation. Notably, these methods automatically ascertain the optimal number of clusters, thus providing a degree of flexibility that traditional techniques, such as k-means, do not afford. The study further elucidates those meta-heuristic methods, especially the ROA method, yield superior clustering outcomes in comparison to genetic algorithms (GA) and particle swarm optimization (PSO).

关键词： Seismic reflection pattern Metaheuristic algorithms Unsupervised learning rain optimization algorithm K -means Clustering

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A novel ROA optimized Bi-LSTM based MPPT controller for grid connected hybrid solar-wind system

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第2期42卷 378-401页

作者： Raj, Dharma T. Kumar, C. Subramaniam, G. Dhanesh, T. Jasper, J. VV Coll Engn Dept Elect & Elect Engn Tisaiyanvilai India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur India Cape Inst Technol Dept Elect & Elect Engn Rajakrishnapuram India Ponjesly Coll Engn Dept Elect & Elect Engn Nagercoil India

Purpose Renewable energy sources such as solar photovoltaic (PV) and wind are ubiquitous because of their lower environmental impact. Output from solar PV and wind turbines is unstable;hence, this article aims to propose an effective controller to extract maximum available power. Design/methodology/approach By focusing on the varying nature of solar irradiance and wind speed, the paper presents the maximum power point tracking (MPPT) technique for renewable energy sources, and power regulation is made by the novel inverter design. Moreover, a DC-DC boost converter is adopted with solar PV, and a doubly fed induction generator is connected with the wind turbine. The proposed MPPT technique is used with the help of a rain optimization algorithm (ROA) based on bi-directional long short-term memory (Bi-LSTM) (ROA_Bi-LSTM). In addition, the sinusoidal pulse width modulation inverter is used for DC-AC power conversion. Findings The proposed MPPT technique has jointly tracked the maximum power from solar PV and wind under varying climatic conditions. The power flow to the transmission line is stabilized to protect the load devices from unregulated frequency and voltage deviations. The power to the smart grid is regulated by three-level sinusoidal pulse width modulation inverter. Originality/value The methodology and concept of the paper are taken by the author on their own. They have not taken a duplicate copy of any other research article.

关键词： Dual diode solar PV Wind energy conversion system rain optimization algorithm Bi-directional long short-term memory Doubly-fed induction generator Full-wave rectifier Battery Sinusoidal pulse width modulation inverter Power electronic simulation Photonics applications

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Developing machine learning models with metaheuristic algorithms for droplet size prediction in a microfluidic microchannel

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Eslami, Faezeh Kamali, Reza Shiraz Univ Sch Mech Engn Shiraz *** Iran

Droplet-based microfluidics holds promise for advanced biomedical applications due to its enhanced accuracy, throughput, rapid reaction time, and minimal reagent consumption. However, achieving droplets with the desired radius often entails costly design iterations. In this paper, we propose a novel approach that combines machine learning algorithms with metaheuristic algorithms to leverage their respective strengths in speed and accuracy. Specifically, we demonstrate the effectiveness of the rain optimization algorithm (ROA) in enhancing the accuracy of five machine learning models. Additionally, we introduce a hybrid model, optimized by ROA, for predicting droplet generation in a cross-junction microfluidic channel, achieving a coefficient of determination (R 2 ) of 0.937, reflecting a 3.6 % increase in accuracy. We employ symbolic regression to validate the hybrid model, yielding an R 2 value of 0.987, indicating strong agreement with real data. Furthermore, we utilize the SHAP method in Explainable AI to interpret the hybrid model ' s performance. Notably, the proposed hybrid model requires only four seconds to solve the prediction problem, significantly reducing computational time compared to the numerical model and experimental method. This approach facilitates the study of each input parameter ' s impact on droplet size prediction, offering otherwise challenging and costly insights to obtain experimentally.

关键词： Droplet-based microfluidic Metaheuristic algorithm rain optimization algorithm Particle swarm optimization Symbolic regression Explainable AI

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Symbol question conversion in structured query language using fuzzy with deep attention based rain LSTM

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期81卷 32323-32349页

作者： Tuli, Preeti Patra, Jyoti Prakash Shri Shankaracharya Inst Profess Management & Tec Comp Sci & Engn Dept Raipur Madhya Pradesh India

Effective communication between human and machine is achieved using Natural Language Processing (NLP). However, the users are not aware about the Structured Query Language (SQL) of the normal database (DB). Therefore, to make users more aware of SQL, a deep attention based rain LSTM (Long Short-Term Memory) is introduced in proposed process for developing the SQL query model. The input query of user is entered in natural language form which is initially pre-processed. Different steps are included in pre-processing they are Lower case conversion, Tokenization, Escape Word Removal and Parts Of speech (POS) tagger. After pre-processing stage, a novel fuzzy approach is introduced with the help of rain LSTM method. With the help of the feature matrix technique, the words of the pre-processed query are given a certain rank. The LSTM-ROA (rain optimization algorithm) method is used for categorizing the words into relations, attributes and clauses on the basis of tagged elements. After removing ambiguous attributes, a final query is generated based on the extracted elements. The proposed method is implemented in the Python platform and the performances are analysed in terms of Accuracy, Precision, Recall and Error Rate. The proposed DA-RLSTM achieved 95.74% accuracy which illustrates that the proposed work has attained better result than the existing methods.

关键词： Natural language processing Structured query language Feature matrix Fuzzy logic rain optimization algorithm Long short-term memory

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An efficient adaptive feature selection with deep learning model-based paddy plant leaf disease classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 22639-22661页

作者： Dubey, Ratnesh Kumar Choubey, Dilip Kumar Indian Inst Informat Technol Bhagalpur Dept Comp Sci & Engn Bhagalpur Bihar India

Agriculture is the essential source of national income for some nations including India. Infections in crops/plants are serious causes of reduced quantity and quality of production, resulting in economic loss. Therefore, the detection of diseases in crops is very essential. Plant disease symptoms are evident in different parts of plants. However, plant leaves are commonly used to diagnose infection. Therefore, in this paper, we focus on automatic leaf disease detection using the deep learning model. The presentedmodel consists of four phases namely, pre-processing, feature extraction, feature selection, and classification. At first, the captured paddy leaf images are converted into an RGB color modelthe median filter is used to remove the noise present in the green band. Then, the texture and color features are extracted from the green band. After the feature extraction, important features are selected using a combination of machine learning and optimization algorithm. Here, initially, the features are selected using support vector machine-recursive feature elimination (SV-RFE) and an adaptive rain optimization algorithm (ARO). Then, the common features are selected. The selected features are given to the adaptive bi-long short-term memory (ABi-LSTM) classifier to classify an image as Blast disease, Bacterial Leaf Blight disease, Tungro, or normal image. The efficiency of the presented technique is estimatedbased on the accuracy, sensitivity, specificity, and performance compared with state-of-the-art works.

关键词： Paddy leaf plant Deep learning Median filter SV-RFE ABi-LSTM rain optimization algorithm

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CNN-OLSTM: Convolutional Neural Network with Optimized Long Short-Term Memory Model for Twitter based Sentiment Analysis

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IETE JOURNAL OF RESEARCH 2024年第3期70卷 2558-2569页

作者： Vanam, Harika Raj, Jeberson Retna Sathyabama Inst Sci & Technol Comp Sci & Engn Chennai India

Social Networking continues the growth of web users, with people sharing their ideas and opinions daily in the form of texts, pictures, videos, and speech. Text classification is still an important issue because these large texts were derived from diverse sources and different thinking people. The shared concept must be incomplete, random, noisy, and in the form of different languages. To solve this issue, in this paper, Convolutional Neural Network with Optimized Long Short Term Memory Model (CNN-OLSTM) based sentimental analysis is proposed. The presented approach contains pre-processing, word2vec conversion, and prediction. Initially, data are pre-processed by using tokenization, stop word removal, and stemming. After the pre-processing, the skip-gram model (SGM) based word2vec conversion is performed. Then, the extracted vectors are given to the CNN-OLSTM classifier to classify a tweet as positive or negative polarity. In this, the CNN model effectively reduces the dimension of the input vector using max-pooling layers and convolutional layers. Also, the LSTM model is capable of catching long-term dependencies between word sequences. To enhance the LSTM performance, the rain optimization algorithm (ROA) is effectively used. The results show that the suggested CNN-OLSTM approach outperforms conventional deep neural networks in terms of accuracy.

关键词： Convolution neural network long short-term memory rain optimization algorithm sentimental analysis skip-gram model

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