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  • Open Access

    ARTICLE

    Analyzing the Data of Software Security Life-Span: Quantum Computing Era

    Hashem Alyami1, Mohd Nadeem2, Wael Alosaimi3, Abdullah Alharbi3, Rajeev Kumar4,*, Bineet Kumar Gupta4, Alka Agrawal2, Raees Ahmad Khan2

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 707-716, 2022, DOI:10.32604/iasc.2022.020780

    Abstract Software or web application security is the main objective in the era of Information Technology (IT) and Artificial Intelligence (AI). Distinguishing proof of security at the initial stage produces significant results to comprehend the administration of security relics for best potential outcomes. A security alternative gives several methods and algorithms to ensure the software security. Security estimation is the vital factor in assessing, administrating, controlling security to improve the nature of security. It is to be realized that assessment of security at early stage of development helps in identifying distinctive worms, dangers, weaknesses and threats. This paper will talk about… More >

  • Open Access

    ARTICLE

    Constructing a Deep Image Analysis System Based on Self-Driving and AIoT

    Wen-Tsai Sung1, Sung-Jung Hsiao2,*, Chung-Yen Hsiao1

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1223-1240, 2022, DOI:10.32604/iasc.2022.020746

    Abstract This research is based on the system architecture of Edge Computing in the AIoT (Artificial Intelligence & Internet of Things) field. In terms of receiving data, the authors proposed approach employed the camera module as the video source, the ultrasound module as the distance measurement source, and then compile C++ with Raspberry Pi 4B for image lane analysis, while Jetson Nano uses the YOLOv3 algorithm for image object detection. The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load driving. The load drive has… More >

  • Open Access

    ARTICLE

    Competitive Risk Aware Algorithm for k-min Search Problem

    Iftikhar Ahmad1,*, Abdulwahab Ali Almazroi2, Mohammed A. Alqarni3, Muhammad Kashif Nawaz1

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1131-1142, 2022, DOI:10.32604/iasc.2022.020715

    Abstract In a classical k-min search problem, an online player wants to buy k units of an asset with the objective of minimizing the total buying cost. The problem setting allows the online player to view only a single price quotation at each time step. A price quotation is the price of one unit of an asset. After receiving the price quotation, the online player has to decide on the number of units to buy. The objective of the online player is to buy the required k units in a fixed length investment horizon. Online algorithms are proposed in the literature… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Rice Plant Disease Detection Model

    R. P. Narmadha1,*, N. Sengottaiyan2, R. J. Kavitha3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679

    Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant… More >

  • Open Access

    ARTICLE

    Breast Cancer Detection Through Feature Clustering and Deep Learning

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

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1273-1286, 2022, DOI:10.32604/iasc.2022.020662

    Abstract In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to combine segments together to identify… More >

  • Open Access

    ARTICLE

    Computation of Aortic Geometry Using MR and CT 3D Images

    Maryam Altalhi1, Sami Ur Rehman2, Fakhre Alam2, Ala Abdulsalam Alarood3, Amin ur Rehman2, M. Irfan Uddin4,*

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 961-969, 2022, DOI:10.32604/iasc.2022.020607

    Abstract The proper computation of geometric parameters of the aorta and coronary arteries are very important for surgery planning, disease diagnoses, and age-related changes observation in the vessels. The accurate knowledge about the geometry of aorta and coronary arteries is required for the proper investigation of heart related diseases. The geometry of aorta and coronary arteries includes the diameter of the ascending and descending aorta and coronary arteries, length of the coronary arteries, branching angles of the coronary arteries and branching points. These geometric parameters from arteries can be computed from the 3D image data. In this paper, we propose an… More >

  • Open Access

    ARTICLE

    Computational Approach via Half-Sweep and Preconditioned AOR for Fractional Diffusion

    Andang Sunarto1,*, Praveen Agarwal2,3,4, Jumat Sulaiman5, Jackel Vui Lung Chew6

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1173-1184, 2022, DOI:10.32604/iasc.2022.020542

    Abstract Solving time-fractional diffusion equation using a numerical method has become a research trend nowadays since analytical approaches are quite limited. There is increasing usage of the finite difference method, but the efficiency of the scheme still needs to be explored. A half-sweep finite difference scheme is well-known as a computational complexity reduction approach. Therefore, the present paper applied an unconditionally stable half-sweep finite difference scheme to solve the time-fractional diffusion equation in a one-dimensional model. Throughout this paper, a Caputo fractional operator is used to substitute the time-fractional derivative term approximately. Then, the stability of the difference scheme combining the… More >

  • Open Access

    ARTICLE

    Automated Deep Learning of COVID-19 and Pneumonia Detection Using Google AutoML

    Saiful Izzuan Hussain*, Nadiah Ruza

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1143-1156, 2022, DOI:10.32604/iasc.2022.020508

    Abstract Coronavirus (COVID-19) is a pandemic disease classified by the World Health Organization. This virus triggers several coughing problems (e.g., flu) that include symptoms of fever, cough, and pneumonia, in extreme cases. The human sputum or blood samples are used to detect this virus, and the result is normally available within a few hours or at most days. In this research, we suggest the implementation of automated deep learning without require handcrafted expertise of data scientist. The model developed aims to give radiologists a second-opinion interpretation and to minimize clinicians’ workload substantially and help them diagnose correctly. We employed automated deep… More >

  • Open Access

    ARTICLE

    Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

    Ahmad A. A. Alkhatib*, Qusai Abed-Al

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

    Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

  • Open Access

    ARTICLE

    Investigation of Techniques for VoIP Frame Aggregation Over A-MPDU 802.11n

    Qasem M. Kharma*, Abdelrahman H. Hussein, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 869-883, 2022, DOI:10.32604/iasc.2022.020415

    Abstract The widespread and desirable features of IP and IEEE 802.11 networks have made these technologies a suitable medium for carrying voice over IP (VoIP). However, a bandwidth (BW) exploitation obstacle emerges when 802.11 networks are used to carry VoIP traffic. This BW exploitation obstacle is caused by the large 80-byte preamble size of the VoIP packet and a waiting time of 765 μs for each layer 2 VoIP frame. As a solution, IEEE 802.11n was consequently designed with a built-in layer 2 frame aggregation feature, but the adverse impact on the VoIP performance still needed to be addressed. Subsequent VoIP… More >

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