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  • 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


    Vision-Aided Path Planning Using Low-Cost Gene Encoding for a Mobile Robot

    Wei-Cheng Wang, Chow-Yong Ng, Rongshun Chen*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 991-1006, 2022, DOI:10.32604/iasc.2022.022067

    Abstract Path planning is intrinsically regarded as a multi-objective optimization problem (MOOP) that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops a novel optimized approach using the genetic algorithm (GA) to drive the multi-objective evolutionary algorithm (MOEA) for the path planning of a mobile robot in a given finite environment. To represent the positions of a mobile robot as integer-type genes in a chromosome of the GA, a grid-based method is also introduced to relax the complex environment to a simple grid-based map. The system architecture is composed of a mobile robot, embedded with… More >

  • Open Access


    Epilepsy Radiology Reports Classification Using Deep Learning Networks

    Sengul Bayrak1,2, Eylem Yucel2,*, Hidayet Takci3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3589-3607, 2022, DOI:10.32604/cmc.2022.018742

    Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More >

  • Open Access


    Deep Neural Networks Based Approach for Battery Life Prediction

    Sweta Bhattacharya1, Praveen Kumar Reddy Maddikunta1, Iyapparaja Meenakshisundaram1, Thippa Reddy Gadekallu1, Sparsh Sharma2, Mohammed Alkahtani3, Mustufa Haider Abidi4,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2599-2615, 2021, DOI:10.32604/cmc.2021.016229

    Abstract The Internet of Things (IoT) and related applications have witnessed enormous growth since its inception. The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain. Although the applicability of these applications are predominant, battery life remains to be a major challenge for IoT devices, wherein unreliability and shortened life would make an IoT application completely useless. In this work, an optimized deep neural networks based model is used to predict the battery life of the IoT systems. The present study uses the Chicago Park Beach dataset collected from the publicly available data… More >

  • Open Access


    Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection

    Ruikun Li1,*, Yun Li2, Wen He1,3, Lirong Chen1, Jianchao Luo1

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 381-398, 2021, DOI:10.32604/cmes.2021.016264

    Abstract Anomaly detection is an important method for intrusion detection. In recent years, unsupervised methods have been widely researched because they do not require labeling. For example, a nonlinear autoencoder can use reconstruction errors to attain the discrimination threshold. This method is not effective when the model complexity is high or the data contains noise. The method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal data. However, compressed features may… More >

  • Open Access


    Bit Rate Reduction in Cloud Gaming Using Object Detection Technique

    Daniyal Baig1, Tahir Alyas1, Muhammad Hamid2, Muhammad Saleem3, Saadia Malik4, Nadia Tabassum5,*, Natash Ali Mian6

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3653-3669, 2021, DOI:10.32604/cmc.2021.017948

    Abstract The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the… More >

  • Open Access


    Efficient Three-Dimensional Video Cybersecurity Framework Based on Double Random Phase Encoding

    Osama S. Faragallah1,*, Walid El-Shafai2, Ashraf Afifi1, Ibrahim Elashry3, Mohammed A. AlZain1, Jehad F. Al-Amri1, Ben Soh4, Heba M. El-Hoseny5, Hala S. El-Sayed6, Fathi E.Abd El-Samie2

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 353-367, 2021, DOI:10.32604/iasc.2021.016865

    Abstract With the rapidly increasing rate of using online services and social media websites, cybercriminals have caused a great deterioration in the network security with enormous undesired consequences. Encryption techniques may be utilized to achieve data robustness and security in digital multimedia communication systems. From this perspective, this paper presents an optical ciphering framework using Double Random Phase Encoding (DRPE) for efficient and secure transmission of Three-Dimensional Videos (3DVs). Firstly, in the DRPE-based 3DV cybersecurity framework proposed in the paper, an optical emitter converts each frame of the transmitted 3DV into an optical signal. Then, the DRPE technique encrypts the obtained… More >

  • Open Access


    Adaptive Binary Coding for Scene Classification Based on Convolutional Networks

    Shuai Wang1, Xianyi Chen2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2065-2077, 2020, DOI:10.32604/cmc.2020.09857

    Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method… More >

  • Open Access


    QDCT Encoding-Based Retrieval for Encrypted JPEG Images

    Qiuju Ji1, Peipeng Yu1, Zhihua Xia1, *

    Journal on Big Data, Vol.2, No.1, pp. 33-51, 2020, DOI:10.32604/jbd.2020.01004

    Abstract Aprivacy-preserving search model for JPEG images is proposed in paper, which uses the bag-of-encrypted-words based on QDCT (Quaternion Discrete Cosine Transform) encoding. The JPEG image is obtained by a series of steps such as DCT (Discrete Cosine Transform) transformation, quantization, entropy coding, etc. In this paper, we firstly transform the images from spatial domain into quaternion domain. By analyzing the algebraic relationship between QDCT and DCT, a QDCT quantization table and QDTC coding for color images are proposed. Then the compressed image data is encrypted after the steps of block permutation, intra-block permutation, single table substitution and stream cipher. At… More >

  • Open Access


    An Amorphous 2-Dimensional Barcode

    Han Jin1, Shi Jin2, *, Junfeng Wu2

    Journal of Cyber Security, Vol.2, No.1, pp. 37-48, 2020, DOI:10.32604/jcs.2020.07209

    Abstract Most existing 2-dimensional barcodes are designed with a fixed shape and clear area. Having a fixed shape and clear area makes the barcode difficult to lay out with other text and pictures. To solve this problem, an amorphous 2-dimensional barcode is presented in this paper. The barcode uses encoding graph units to encode data. There are two key points in a 2-dimensional barcode: One is the encoding graph unit, the other is its encoding rules. Because encoding graph units of a 2-dimensional barcode are surrounded by other graphics, the units should be self-positioned and distinguished from other units. This paper… More >

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