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  • Open Access

    ARTICLE

    An Optimized Feature Selection and Hyperparameter Tuning Framework for Automated Heart Disease Diagnosis

    Saleh Ateeq Almutairi*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609 - 28 July 2023

    Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then,… More >

  • Open Access

    ARTICLE

    Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

    Mrim M. Alnfiai1,2, Nabil Almalki1,3, Fahd N. Al-Wesabi4,*, Mesfer Alduhayyem5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2639-2652, 2023, DOI:10.32604/iasc.2023.034069 - 15 March 2023

    Abstract Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers… More >

  • Open Access

    ARTICLE

    Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

    A. Ramachandran1,*, K. Gayathri2, Ahmed Alkhayyat3, Rami Q. Malik4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2177-2194, 2023, DOI:10.32604/csse.2023.034438 - 09 February 2023

    Abstract Cyber-physical system (CPS) is a concept that integrates every computer-driven system interacting closely with its physical environment. Internet-of-things (IoT) is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds. Since the complexity level of the CPS increases, an adversary attack becomes possible in several ways. Assuring security is a vital aspect of the CPS environment. Due to the massive surge in the data size, the design of anomaly detection techniques becomes a challenging issue, and domain-specific knowledge can be applied to resolve it. This article develops an… More >

  • Open Access

    ARTICLE

    Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

    Nidhi Agarwal1,2, M. Gokilavani3, S. Nagarajan4, S. Saranya5, Hadeel Alsolai6, Sami Dhahbi7,*, Amira Sayed Abdelaziz8

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 141-152, 2023, DOI:10.32604/cmc.2023.030074 - 22 September 2022

    Abstract In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes More >

  • Open Access

    ARTICLE

    A Novel Aquila Optimizer Based PV Array Reconfiguration Scheme to Generate Maximum Energy under Partial Shading Condition

    Dong An1, Junqing Jia1, Wenchao Cai1, Deyu Yang1, Chao Lv1, Jiawei Zhu2, Yingying Jiao3,*

    Energy Engineering, Vol.119, No.4, pp. 1531-1545, 2022, DOI:10.32604/ee.2022.019284 - 23 May 2022

    Abstract This paper develops a real-time PV arrays maximum power harvesting scheme under partial shading condition (PSC) by reconfiguring PV arrays using Aquila optimizer (AO). AO is based on the natural behaviors of Aquila in capturing prey, which can choose the best hunting mechanism ingeniously and quickly by balancing the local exploitation and global exploration via four hunting methods of Aquila: choosing the searching area through high soar with the vertical stoop, exploring in different searching spaces through contour flight with quick glide attack, exploiting in convergence searching space through low flight with slow attack, and More > Graphic Abstract

    A Novel Aquila Optimizer Based PV Array Reconfiguration Scheme to Generate Maximum Energy under Partial Shading Condition

  • Open Access

    ARTICLE

    Optimized Generative Adversarial Networks for Adversarial Sample Generation

    Daniyal M. Alghazzawi1, Syed Hamid Hasan1,*, Surbhi Bhatia2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3877-3897, 2022, DOI:10.32604/cmc.2022.024613 - 29 March 2022

    Abstract Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet,… More >

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