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  • Open Access

    ARTICLE

    Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

    Abdulwahab Ali Almazroi, Mohamed Meselhy Eltoukhy*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3729-3741, 2023, DOI:10.32604/cmc.2023.036020

    Abstract Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user’s identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users’ touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an… More >

  • Open Access

    ARTICLE

    A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

    Sameh Abd El-Ghany1,2, A. A. Abd El-Aziz1,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4633-4648, 2023, DOI:10.32604/cmc.2023.035779

    Abstract Coronary artery disease (CAD) is one of the most authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of human-trained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned random forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk… More >

  • Open Access

    ARTICLE

    A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

    Md. Moddassir Alam1, Md Mottahir Alam2, Muhammad Moinuddin2,3, Mohammad Tauheed Ahmad4, Jabir Hakami5, Anis Ahmad Chaudhary6, Asif Irshad Khan7, Tauheed Khan Mohd8,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4553-4571, 2023, DOI:10.32604/cmc.2023.032118

    Abstract Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based… More >

  • Open Access

    ARTICLE

    IOT Based Smart Parking System Using Ensemble Learning

    Walaa H. Elashmawi1,3, Ahmad Akram2, Mohammed Yasser2, Menna Hisham2, Manar Mohammed2, Noha Ihab2, Ahmed Ali4,5,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3637-3656, 2023, DOI:10.32604/iasc.2023.035605

    Abstract Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication and automated payments are handled… More >

  • Open Access

    ARTICLE

    Predicting the Thickness of an Excavation Damaged Zone around the Roadway Using the DA-RF Hybrid Model

    Yuxin Chen1, Weixun Yong1, Chuanqi Li2, Jian Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714

    Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key basis for roadway stability discrimination and support structure design, and it is of great engineering significance to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters… More >

  • Open Access

    ARTICLE

    Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

    Yu Zhang, Zhihua Xiong*, Zhuoxi Liang, Jiachen She, Chicheng Ma

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 447-469, 2023, DOI:10.32604/cmes.2023.022699

    Abstract A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample… More >

  • Open Access

    ARTICLE

    Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

    Madona B. Sahaai*, G. R. Jothilakshmi, E. Praveen, V. Hemath Kumar

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1507-1521, 2023, DOI:10.32604/iasc.2023.032256

    Abstract Magnetic Resonance Imaging (MRI) is one of the important resources for identifying abnormalities in the human brain. This work proposes an effective Multi-Class Classification (MCC) system using Binary Robust Invariant Scalable Keypoints (BRISK) as texture descriptors for effective classification. At first, the potential Region Of Interests (ROIs) are detected using features from the accelerated segment test algorithm. Then, non-maxima suppression is employed in scale space based on the information in the ROIs. The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour (kNN), Support Vector Machine (SVM) and Random Forest (RF). An MCC system… More >

  • Open Access

    ARTICLE

    Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

    E. Bharath1,*, T. Rajagopalan2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102

    Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy… More >

  • Open Access

    ARTICLE

    Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

    Mutiullah Jamil1, Hafeez ul Rehman1, SaleemUllah1, Imran Ashraf2,*, Saqib Ubaid1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5545-5557, 2023, DOI:10.32604/cmc.2023.033449

    Abstract Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular wheat species are identified by… More >

  • Open Access

    ARTICLE

    Social Engineering Attack Classifications on Social Media Using Deep Learning

    Yichiet Aun1,*, Ming-Lee Gan1, Nur Haliza Binti Abdul Wahab2, Goh Hock Guan1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4917-4931, 2023, DOI:10.32604/cmc.2023.032373

    Abstract In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of… More >

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