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  • Open Access

    ARTICLE

    Fine-Grained Bandwidth Estimation for Smart Grid Communication Network

    Jingtang Luo1, Jingru Liao2, Chenlin Zhang3, Ziqi Wang4, Yuhang Zhang2, Jie Xu2,*, Zhengwen Huang5

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1225-1239, 2022, DOI:10.32604/iasc.2022.022812

    Abstract Accurate estimation of communication bandwidth is critical for the sensing and controlling applications of smart grid. Different from public network, the bandwidth requirements of smart grid communication network must be accurately estimated in prior to the deployment of applications or even the building of communication network. However, existing methods for smart grid usually model communication nodes in coarse-grained ways, so their estimations become inaccurate in scenarios where the same type of nodes have very different bandwidth requirements. To solve this issue, we propose a fine-grained estimation method based on multivariate nonlinear fitting. Firstly, we use linear fitting to calculate the… More >

  • Open Access

    ARTICLE

    Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

    S. Ushasukhanya*, M. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654

    Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective conservation system. Such a framework… More >

  • Open Access

    ARTICLE

    Modeling of Anthrax Disease via Efficient Computing Techniques

    Ali Raza1,2, Dumitru Baleanu3,4, Muhammad Yousaf2, Naeem Akhter2, Syed Kashif Mahmood2, Muhammad Rafiq5,*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1109-1124, 2022, DOI:10.32604/iasc.2022.022643

    Abstract Computer methods have a significant role in the scientific literature. Nowadays, development in computational methods for solving highly complex and nonlinear systems is a hot issue in different disciplines like engineering, physics, biology, and many more. Anthrax is primarily a zoonotic disease in herbivores caused by a bacterium called Bacillus anthracis. Humans generally acquire the disease directly or indirectly from infected animals, or through occupational exposure to infected or contaminated animal products. The outbreak of human anthrax is reported in the Eastern Mediterranean regions like Pakistan, Iran, Iraq, Afghanistan, Morocco, and Sudan. Almost ninety-five percent chances are the transmission of… More >

  • Open Access

    ARTICLE

    Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

    P. Jayapriya*, K. Umamaheswari

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1293-1308, 2022, DOI:10.32604/iasc.2022.022583

    Abstract Automated biometric authentication attracts the attention of researchers to work on hand-based images to develop applications in forensics science. Finger Knuckle Print (FKP) is one of the hand-based biometrics used in the recognition of an individual. FKP is rich in texture, less in contact and known for its unique features. The dimensionality of the features, extracted from the image, is one of the main problems in pattern recognition. Since selecting the relevant features is an important but challenging task, the feature subset selection is an optimization problem. A reduced number of features results in enhanced classification accuracy. The proposed FKP… More >

  • Open Access

    ARTICLE

    Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features

    P. Arunachalam1, N. Janakiraman1,*, Arun Kumar Sivaraman2, A. Balasundaram3, Rajiv Vincent2, Sita Rani4, Barnali Dey5, A. Muralidhar2, M. Rajesh2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1241-1259, 2022, DOI:10.32604/iasc.2022.022573

    Abstract Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency sub-bands… More >

  • Open Access

    ARTICLE

    Analyzing the Big Data Security Through a Unified Decision-Making Approach

    Abdulaziz Attaallah1, Hassan Alsuhabi2, Sarita Shukla3, Rajeev Kumar3,*, Bineet Kumar Gupta3, Raees Ahmad Khan4

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1071-1088, 2022, DOI:10.32604/iasc.2022.022569

    Abstract The use of cloud services, web-based software systems, the Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), and other wireless sensor devices in the health sector has resulted in significant advancements and benefits. Early disease detection, increased accessibility, and high diagnostic reach have all been made possible by digital healthcare. Despite this remarkable achievement, healthcare data protection has become a serious issue for all parties involved. According to data breach statistics, the healthcare data industry is one of the major threats to cyber criminals. In reality, healthcare data breaches have increased at an alarming rate in recent years.… More >

  • Open Access

    ARTICLE

    A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes

    Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1153-1165, 2022, DOI:10.32604/iasc.2022.022359

    Abstract People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique… More >

  • Open Access

    ARTICLE

    Crow Search Algorithm with Improved Objective Function for Test Case Generation and Optimization

    Meena Sharma, Babita Pathik*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1125-1140, 2022, DOI:10.32604/iasc.2022.022335

    Abstract Test case generation and optimization is the foremost requirement of software evolution and test automation. In this paper, a bio-inspired Crow Search Algorithm (CSA) is suggested with an improved objective function to fulfill this requirement. CSA is a nature-inspired optimization method. The improved objective function combines branch distance and predicate distance to cover the critical path on the control flow graph. CSA is a search-based technique that uses heuristic information for automation testing, and CSA optimizers minimize test cases generated by satisfying the objective function. This paper focuses on generating test cases for all paths, including critical paths. The control… More >

  • Open Access

    ARTICLE

    A Secure Encrypted Classified Electronic Healthcare Data for Public Cloud Environment

    Kirupa Shankar Komathi Maathavan1,*, Santhi Venkatraman2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 765-779, 2022, DOI:10.32604/iasc.2022.022276

    Abstract The major operation of the blood bank supply chain is to estimate the demand, perform inventory management and distribute adequate blood for the needs. The proliferation of big data in the blood bank supply chain and data management needs an intelligent, automated system to classify the essential data so that the requests can be handled easily with less human intervention. Big data in the blood bank domain refers to the collection, organization, and analysis of large volumes of data to obtain useful information. For this purpose, in this research work we have employed machine learning techniques to find a better… More >

  • Open Access

    ARTICLE

    Quality Prediction of Wearable Apps in the Google Play Store

    Shifa Siddiqui1, Muhammad Shahzad Faisal1, Shahzada Khurram2, Azeem Irshad3, Mohammed Baz4, Habib Hamam5, Naeem Iqbal6, Muhammad Shafiq7,*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 877-892, 2022, DOI:10.32604/iasc.2022.022266

    Abstract Play Store reviews play an important role in demonstrating that decisions are made from the user’s perspective, and contain a wealth of knowledge that can be used to understand quality issues and help developers build higher-quality mobile applications. Even for very important information, it can ensure the authenticity of user-generated content. In Play Store, wearable applications were recently launched, and are always open and easy to use, and are gradually being welcomed by users. Driven by popularity and self-interest, profit-incentive developers are developing low-quality applications and hiring robots to exaggerate ratings, reviews, or install counts. This is how spam in… More >

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