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  • Open Access


    Developing a Secure Framework Using Feature Selection and Attack Detection Technique

    Mahima Dahiya*, Nitin Nitin

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430

    Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based… More >

  • Open Access


    Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

    Mohammad Alamgeer1, Amal Al-Rasheed2, Ahmad Alhindi3, Manar Ahmed Hamza4,*, Abdelwahed Motwakel4, Mohamed I. Eldesouki5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163

    Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN)… More >

  • Open Access


    Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

    Subramanian Kanageswari1, D. Gladis2, Irshad Hussain3,*, Sultan S. Alshamrani4, Abdullah Alshehri5

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 415-428, 2023, DOI:10.32604/iasc.2023.032053

    Abstract One of the leading cancers for both genders worldwide is lung cancer. The occurrence of lung cancer has fully augmented since the early 19th century. In this manuscript, we have discussed various data mining techniques that have been employed for cancer diagnosis. Exposure to air pollution has been related to various adverse health effects. This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer. We have introduced data mining in lung cancer to air pollution, and our approach includes preprocessing, data mining, testing… More >

  • Open Access


    Research on Electricity Consumption Model of Library Building Based on Data Mining

    Jiaming Dou1, Hongyan Ma1,2,3,*, Rong Guo1

    Energy Engineering, Vol.119, No.6, pp. 2407-2429, 2022, DOI:10.32604/ee.2022.019654

    Abstract With the exponential development of Chinese population, the massive energy consumption of buildings has recently become an interest subject. Although much research has been conducted on residential buildings, heating ventilation and air conditioning (HVAC), little research has been conducted on the relationship between student’s behavior, campus buildings, and their subsystems. Using classical seasonal decomposition, hierarchical clustering, and apriori algorithm, this paper aims to provide an empirical model for consumption data in campus library. Smart meter data from a library in Beijing, China, is adopted in this paper. Building electricity consumption patterns are investigated on an hourly/daily/monthly basis. According to the… More >

  • Open Access


    Data Mining Based Integrated Electric-Gas Energy System Multi-Objective Optimization

    Zhukui Tan1,*, Yongjie Ren1, Hua Li1, Weili Ren2, Xichao Zhou2, Ming Zeng1

    Energy Engineering, Vol.119, No.6, pp. 2607-2619, 2022, DOI:10.32604/ee.2022.019550

    Abstract With the proposal of carbon neutrality, how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy industry. Based on the idea of multi-energy complementarity, a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas (P2G) technology and gas turbine in this paper. Furthermore, a multi-objective optimization model with economic improvement, carbon emission reduction and peak-load shifting as objectives is proposed, and solved by BSO algorithm. Finally, a typical power-gas coupling system is selected… More >

  • Open Access


    Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

    Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2, Fahd Althukair3

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3727-3741, 2023, DOI:10.32604/iasc.2023.031442

    Abstract Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order. This process is a probability density distribution with adequate training data that can converge to the input density curve. In this paper, we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling. A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling. The solution uses data mining techniques to link datum in groups by linking their components. The learning techniques are novel presentations of Gaussian modelling that use prior knowledge of the representation hyper-parameters and… More >

  • Open Access


    Automatic Clustering of User Behaviour Profiles for Web Recommendation System

    S. Sadesh1,*, Osamah Ibrahim Khalaf2, Mohammad Shorfuzzaman3, Abdulmajeed Alsufyani3, K. Sangeetha4, Mueen Uddin5

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3365-3384, 2023, DOI:10.32604/iasc.2023.030751

    Abstract Web usage mining, content mining, and structure mining comprise the web mining process. Web-Page Recommendation (WPR) development by incorporating Data Mining Techniques (DMT) did not include end-users with improved performance in the obtained filtering results. The cluster user profile-based clustering process is delayed when it has a low precision rate. Markov Chain Monte Carlo-Dynamic Clustering (MC2-DC) is based on the User Behavior Profile (UBP) model group’s similar user behavior on a dynamic update of UBP. The Reversible-Jump Concept (RJC) reviews the history with updated UBP and moves to appropriate clusters. Hamilton’s Filtering Framework (HFF) is designed to filter user data… More >

  • Open Access


    Web Page Recommendation Using Distributional Recurrent Neural Network

    Chaithra1,*, G. M. Lingaraju2, S. Jagannatha3

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 803-817, 2023, DOI:10.32604/csse.2023.028770

    Abstract In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the… More >

  • Open Access


    Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

    B. Murugeshwari1,*, S. Rajalakshmi1, K. Sudharson2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074

    Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while… More >

  • Open Access


    Prediction Model for a Good Learning Environment Using an Ensemble Approach

    S. Subha1,*, S. Baghavathi Priya2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2081-2093, 2023, DOI:10.32604/csse.2023.028451

    Abstract This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is used to validate the… More >

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