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Search Results (71)
  • Open Access

    ARTICLE

    Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

    A. S. Harish*, C. Malathy

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 589-600, 2023, DOI:10.32604/iasc.2023.032030

    Abstract Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The segmentation could be on various… More >

  • Open Access

    ARTICLE

    Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

    Shakunthala Masi*, Helenprabha Kuttiappan

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 733-744, 2023, DOI:10.32604/iasc.2023.025919

    Abstract In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is a cerebral vascular disease causes… More >

  • Open Access

    ARTICLE

    Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

    Aqsa Aqdus1, Rashid Amin1,*, Sadia Ramzan1, Sultan S. Alshamrani2, Abdullah Alshehri3, El-Sayed M. El-kenawy4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719

    Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

  • Open Access

    ARTICLE

    Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

    I. Vallirathi1,*, S. Ebenezer Juliet2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2349-2360, 2023, DOI:10.32604/csse.2023.024864

    Abstract Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node… More >

  • Open Access

    ARTICLE

    MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data

    Jyoti Arora1, Meena Tushir2, Keshav Sharma1, Lalit Mohan1, Aman Singh3,*, Abdullah Alharbi4, Wael Alosaimi4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4801-4817, 2022, DOI:10.32604/cmc.2022.025960

    Abstract Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known… More >

  • Open Access

    ARTICLE

    Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

    Neetika Bhandari1,*, Payal Pahwa2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2043-2055, 2023, DOI:10.32604/iasc.2023.028368

    Abstract Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems. This data can be very important and useful if it is harnessed carefully and correctly. Useful information can be extracted from this massive data using the Data Mining process. The information extracted can be used to make vital decisions in various industries. Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group. Clustering methods are of various types. Many parameters and indexes exist… More >

  • Open Access

    ARTICLE

    Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

    Anuj Sharma1, Deepak Prashar2, Arfat Ahmad Khan3, Faizan Ahmed Khan4, Settawit Poochaya3,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3629-3648, 2022, DOI:10.32604/cmc.2022.030879

    Abstract Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells (WBC), and it is also called a blast blood cell. In the marrow of human bones, leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC, and if any cell gets blasted, then it may become a cause of death. Therefore, the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives. Subsequently, in terms of detection, image segmentation techniques play a vital role, and they turn out to be the important image processing… More >

  • Open Access

    ARTICLE

    Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

    S. Sivanantham1,*, V. Mohanraj2, Y. Suresh2, J. Senthilkumar2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1617-1631, 2023, DOI:10.32604/csse.2023.025893

    Abstract In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a… More >

  • Open Access

    ARTICLE

    P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

    Ayman Altameem1, Ramesh Chandra Poonia2, Ankit Kumar3, Linesh Raja4, Abdul Khader Jilani Saudagar5,*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 553-566, 2023, DOI:10.32604/iasc.2023.027579

    Abstract Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to… More >

  • Open Access

    ARTICLE

    Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm

    R. Meena1,*, V. Thulasi Bai2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1643-1658, 2022, DOI:10.32604/iasc.2022.025305

    Abstract The Covid-19 outbreak has an unprecedented effects on people's daily lives throughout the world, causing immense stress amongst individuals owing to enhanced psychological disorders like depression, stress, and anxiety. Researchers have used social media data to detect behaviour changes in individuals with depression, postpartum changes and stress detection since it reveals critical aspects of mental and emotional diseases. Considerable efforts have been made to examine the psychological health of people which have limited performance in accuracy and demand increased training time. To conquer such issues, this paper proposes an efficient depression detection framework named Improved Chimp Optimization Algorithm based Convolution… More >

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