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  • Open Access


    Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

    Fadwa Alrowais1, Ahmed S. Almasoud2, Radwa Marzouk3, Fahd N. Al-Wesabi4,5, Anwer Mustafa Hilal6,*, Mohammed Rizwanullah6, Abdelwahed Motwakel6, Ishfaq Yaseen6

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2783-2795, 2022, DOI:10.32604/cmc.2022.025204

    Abstract Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling… More >

  • Open Access


    Churn Prediction Model of Telecom Users Based on XGBoost

    Hao Chen*, Qian Tang, Yifei Wei, Mei Song

    Journal on Artificial Intelligence, Vol.3, No.3, pp. 115-121, 2021, DOI:10.32604/jai.2021.026851

    Abstract As the cost of accessing a telecom operator’s network continues to decrease, user churn after arrears occurred repeatedly, which has brought huge economic losses to operators and reminded them that it is significant to identify users who are likely to churn in advance. Machine learning can form a series of judgment rules by summarizing a large amount of data, and telecom user data naturally has the advantage of user scale, which can provide data support for learning algorithms. XGBoost is an improved gradient boosting algorithm, and in this paper, we explore how to use the algorithm to train an efficient… More >

  • Open Access


    Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

    B. Srikanth1,*, Swarajya Lakshmi V. Papineni2, Gutta Sridevi3, D. N. V. S. L. S. Indira4, K. S. R. Radhika5, Khasim Syed6

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 21-34, 2022, DOI:10.32604/iasc.2022.022423

    Abstract In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography,… More >

  • Open Access


    An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

    T. Gopalakrishnan1, Periakaruppan Sudhakaran2, K. C. Ramya3, K. Sathesh Kumar4, Fahd N. Al-Wesabi5,6,*, Manal Abdullah Alohali7, Anwer Mustafa Hilal8

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 305-320, 2022, DOI:10.32604/cmc.2022.020914

    Abstract Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for the future. In this scenario,… More >

  • Open Access


    Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory

    Saman Taheri1, Behnam Talebjedi2,*, Timo Laukkanen2

    Energy Engineering, Vol.118, No.6, pp. 1577-1594, 2021, DOI:10.32604/EE.2021.017795

    Abstract Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs).… More >

  • Open Access


    Stock-Price Forecasting Based on XGBoost and LSTM

    Pham Hoang Vuong1, Trinh Tan Dat1, Tieu Khoi Mai1, Pham Hoang Uyen2, Pham The Bao1,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685

    Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we… More >

  • Open Access


    Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost

    Mohamed Ezz1,2,*, Murtada K. Elbashir1,3, Hosameldeen Shabana4,5

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2077-2092, 2021, DOI:10.32604/cmc.2021.018155

    Abstract It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients’ admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions. We use the… More >

  • Open Access


    Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization

    Tian Liwei1,2,*, Feng Li1, Sun Yu3, Guo Yuankai4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

    Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide it in a training set… More >

  • Open Access


    Suggestion Mining from Opinionated Text of Big Social Media Data

    Youseef Alotaibi1,*, Muhammad Noman Malik2, Huma Hayat Khan3, Anab Batool2, Saif ul Islam4, Abdulmajeed Alsufyani5, Saleh Alghamdi6

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3323-3338, 2021, DOI:10.32604/cmc.2021.016727

    Abstract Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The… More >

  • Open Access


    XGBoost Algorithm under Differential Privacy Protection

    Yuanmin Shi1,2, Siran Yin1,2, Ze Chen1,2, Leiming Yan1,2,*

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

    Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data. More >

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