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Search Results (16)
  • Open Access

    ARTICLE

    Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

    D. Dhinakaran1,*, P. M. Joe Prathap2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1877-1892, 2022, DOI:10.32604/iasc.2022.024509

    Abstract These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals’ crude information. We devised two unique protocols for frequent mining itemsets in horizontally partitioned… More >

  • Open Access

    ARTICLE

    DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage

    Ala Saleh Alluhaidan*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5949-5962, 2022, DOI:10.32604/cmc.2022.022028

    Abstract Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the… More >

  • Open Access

    ARTICLE

    Proposed Different Signal Processing Tools for Efficient Optical Wireless Communications

    Hend Ibrahim1, Abeer D. Algarni2,*, Mahmoud Abdalla1, Walid El-Shafai3,4, Fathi E. Abd El-Samie2,3, Naglaa F. Soliman1,2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3293-3318, 2022, DOI:10.32604/cmc.2022.022436

    Abstract Optical Wireless Communication (OWC) is a new trend in communication systems to achieve large bandwidth, high bit rate, high security, fast deployment, and low cost. The basic idea of the OWC is to transmit data on unguided media with light. This system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing (OFDM). This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection (IM/DD) system. This system requires a non-negativity constraint. The paper presents a framework for wireless optical OFDM system that comprises (IM/DD) with different forms, Direct Current biased Optical OFDM (DCO-OFDM), Asymmetrically Clipped Optical OFDM (ACO-OFDM),… More >

  • Open Access

    ARTICLE

    Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

    Suliman Aladhadh1, Hidayat Ur Rehman2, Ali Mustafa Qamar3,4,*, Rehan Ullah Khan1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724

    Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution… More >

  • Open Access

    ARTICLE

    A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

    Masoumeh Zareapoor, Jie Yang

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 721-727, 2018, DOI:10.1080/10798587.2017.1321228

    Abstract The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning… More >

  • Open Access

    ARTICLE

    Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

    Haijiang Liu1, Lianwei Cui2, Xuebin Ma1, *, Celimuge Wu3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987

    Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s… More >

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