Home / Journals / CMC / Vol.69, No.3, 2021
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  • Open AccessOpen Access

    ARTICLE

    Implementation of Legendre Neural Network to Solve Time-Varying Singular Bilinear Systems

    V. Murugesh1, B. Saravana Balaji2,*, Habib Sano Aliy3, J. Bhuvana4, P. Saranya5, Andino Maseleno6, K. Shankar7, A. Sasikala8
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3685-3692, 2021, DOI:10.32604/cmc.2021.017836
    Abstract Bilinear singular systems can be used in the investigation of different types of engineering systems. In the past decade, considerable attention has been paid to analyzing and synthesizing singular bilinear systems. Their importance lies in their real world application such as economic, ecological, and socioeconomic processes. They are also applied in several biological processes, such as population dynamics of biological species, water balance, temperature regulation in the human body, carbon dioxide control in lungs, blood pressure, immune system, cardiac regulation, etc. Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action, the DC motor,… More >

  • Open AccessOpen Access

    ARTICLE

    Monarch Butterfly Optimization for Reliable Scheduling in Cloud

    B. Gomathi1, S. T. Suganthi2,*, Karthikeyan Krishnasamy3, J. Bhuvana4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3693-3710, 2021, DOI:10.32604/cmc.2021.018159
    Abstract Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources. The scheduling of the cloud tasks is a well-recognized NP-hard problem. The Task scheduling problem is convoluted while convincing different objectives, which are dispute in nature. In this paper, Multi-Objective Improved Monarch Butterfly Optimization (MOIMBO) algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions. Three different dispute objectives, such as makespan, reliability, and resource utilization, are deliberated for task scheduling problems.The Epsilon-fuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost… More >

  • Open AccessOpen Access

    ARTICLE

    EDSM-Based Binary Protocol State Machine Reversing

    Shen Wang1,*, Fanghui Sun1, Hongli Zhang1, Dongyang Zhan1,2, Shuang Li3, Jun Wang1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3711-3725, 2021, DOI:10.32604/cmc.2021.016562
    Abstract Internet communication protocols define the behavior rules of network components when they communicate with each other. With the continuous development of network technologies, many private or unknown network protocols are emerging in endlessly various network environments. Herein, relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection. Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols, most existing methods have proven to be time-consuming with limited efficiency, especially when applied on unknown protocol state machines. This paper proposes a… More >

  • Open AccessOpen Access

    ARTICLE

    Centralized QoS Routing Model for Delay/Loss Sensitive Flows at the SDN-IoT Infrastructure

    Mykola Beshley1, Natalia Kryvinska2,*, Halyna Beshley1, Mykhailo Medvetskyi1, Leonard Barolli3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3727-3748, 2021, DOI:10.32604/cmc.2021.018625
    (This article belongs to this Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)
    Abstract The rapidly increasing number of Internet of Things (IoT) devices and Quality of Service (QoS) requirements have made the provisioning of network solutions to meet this demand a major research topic. Providing fast and reliable routing paths based on the QoS requirements of IoT devices is very important task for Industry 4.0. The software-defined network is one of the most current interesting research developments, offering an efficient and effective solution for centralized control and network intelligence. A new SDN-IoT paradigm has been proposed to improve network QoS, taking advantage of SDN architecture in IoT networks. At the present time, most… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

    Abdullah Ali Salamai1, El-Sayed M. El-kenawy2, Ibrahim Abdelhameed3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3749-3766, 2021, DOI:10.32604/cmc.2021.018179
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm.… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling CO2 Emission of Middle Eastern Countries Using Intelligent Methods

    Mamdouh El Haj Assad1, Ibrahim Mahariq2,*, Zaher Al Barakeh2, Mahmoud Khasawneh2, Mohammad Ali Amooie3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3767-3781, 2021, DOI:10.32604/cmc.2021.018872
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract CO2 emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities. Consequently, these factors must be considered for CO2 emission prediction for seven middle eastern countries including Iran, Kuwait, United Arab Emirates, Turkey, Saudi Arabia, Iraq and Qatar. In order to propose a predictive model, a Multilayer Perceptron Artificial Neural Network (MLP ANN) is applied. Three transfer functions including logsig, tansig and radial basis functions are utilized in the hidden layer of the network. Moreover, various numbers of neurons are applied in the structure of the models. It… More >

  • Open AccessOpen Access

    ARTICLE

    Container Introspection: Using External Management Containers to Monitor Containers in Cloud Computing

    Dongyang Zhan1,*, Kai Tan1, Lin Ye1,2, Haining Yu1,3, Hao Liu4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3783-3794, 2021, DOI:10.32604/cmc.2021.019432
    Abstract Cloud computing plays an important role in today's Internet environment, which meets the requirements of scalability, security and reliability by using virtualization technologies. Container technology is one of the two mainstream virtualization solutions. Its lightweight, high deployment efficiency make container technology widely used in large-scale cloud computing. While container technology has created huge benefits for cloud service providers and tenants, it cannot meet the requirements of security monitoring and management from a tenant perspective. Currently, tenants can only run their security monitors in the target container, but it is not secure because the attacker is able to detect and compromise… More >

  • Open AccessOpen Access

    ARTICLE

    Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

    Amer I. Al-Omari1, Amal S. Hassan2, Heba F. Nagy2, Ayed R. A. Al-Anzi3,*, Loai Alzoubi1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3795-3811, 2021, DOI:10.32604/cmc.2021.019061
    Abstract This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution. Assuming that the observed samples are taken from the upper record ranked set sampling (URRSS) and upper record values (URV) schemes. Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error, linear exponential and precautionary loss functions, in addition, we obtain Bayesian credible intervals. The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution. Then, the behavior of the estimates is examined at various record values. The output of the study… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

    Qasim M. Zainel1, Murad B. Khorsheed2, Saad Darwish3,*, Amr A. Ahmed4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work, our proposed framework is concerned… More >

  • Open AccessOpen Access

    ARTICLE

    Classification of Retroviruses Based on Genomic Data Using RVGC

    Khalid Mahmood Aamir1, Muhammad Bilal2, Muhammad Ramzan1,3, Muhammad Attique Khan4, Yunyoung Nam5,*, Seifedine Kadry6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3829-3844, 2021, DOI:10.32604/cmc.2021.017835
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnology Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on… More >

  • Open AccessOpen Access

    ARTICLE

    FastAFLGo: Toward a Directed Greybox Fuzzing

    Chunlai Du1, Tong Jin1, Yanhui Guo2,*, Binghao Jia1, Bin Li3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3845-3855, 2021, DOI:10.32604/cmc.2021.017697
    Abstract While the size and complexity of software are rapidly increasing, not only is the number of vulnerabilities increasing, but their forms are diversifying. Vulnerability has become an important factor in network attack and defense. Therefore, automatic vulnerability discovery has become critical to ensure software security. Fuzzing is one of the most important methods of vulnerability discovery. It is based on the initial input, i.e., a seed, to generate mutated test cases as new inputs of a tested program in the next execution loop. By monitoring the path coverage, fuzzing can choose high-value test cases for inclusion in the new seed… More >

  • Open AccessOpen Access

    ARTICLE

    Local Features-Based Watermarking for Image Security in Social Media

    Shady Y. El-mashad1, Amani M. Yassen1, Abdulwahab K. Alsammak1, Basem M. Elhalawany2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3857-3870, 2021, DOI:10.32604/cmc.2021.018660
    Abstract The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of the Slope Solute Loss Based on BP Neural Network

    Xiaona Zhang1,*, Jie Feng2, Zhiguo Yu1, Zhen Hong3, Xinge Yun1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3871-3888, 2021, DOI:10.32604/cmc.2021.020057
    Abstract The existence of soil macropores is a common phenomenon. Due to the existence of soil macropores, the amount of solute loss carried by water is deeply modified, which affects watershed hydrologic response. In this study, a new improved BP (Back Propagation) neural network method, using Levenberg–Marquand training algorithm, was used to analyze the solute loss on slopes taking into account the soil macropores. The rainfall intensity, duration, the slope, the characteristic scale of macropores and the adsorption coefficient of ions, are used as the variables of network input layer. The network middle layer is used as hidden layer, the number… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

    Fahd N. Al-Wesabi1,*, Imran Khan2, Nadhem Nemri3, Mohammed A. Al-Hagery4, Huda G. Iskander5, Quang Ngoc Nguyen6, Babar Shah7, Ki-Il Kim8
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3889-3902, 2021, DOI:10.32604/cmc.2021.016218
    Abstract The Internet of Things (IoT) is the fourth technological revolution in the global information industry after computers, the Internet, and mobile communication networks. It combines radio-frequency identification devices, infrared sensors, global positioning systems, and various other technologies. Information sensing equipment is connected via the Internet, thus forming a vast network. When these physical devices are connected to the Internet, the user terminal can be extended and expanded to exchange information, communicate with anything, and carry out identification, positioning, tracking, monitoring, and triggering of corresponding events on each device in the network. In real life, the IoT has a wide range… More >

  • Open AccessOpen Access

    ARTICLE

    Image Splicing Detection Based on Texture Features with Fractal Entropy

    Razi J. Al-Azawi1, Nadia M. G. Al-Saidi2, Hamid A. Jalab3,*, Rabha W. Ibrahim4, Dumitru Baleanu5,6,7
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3903-3915, 2021, DOI:10.32604/cmc.2021.020368
    (This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)
    Abstract Over the past years, image manipulation tools have become widely accessible and easier to use, which made the issue of image tampering far more severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is… More >

  • Open AccessOpen Access

    ARTICLE

    RSS-Based Selective Clustering Technique Using Master Node for WSN

    Vikram Rajpoot1, Vivek Tiwari2, Akash Saxena3, Prashant Chaturvedi4, Dharmendra Singh Rajput5, Mohammed Alkahtani6,7, Mustufa Haider Abidi7,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3917-3930, 2021, DOI:10.32604/cmc.2021.015826
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Wireless sensor networks (WSN) are designed to monitor the physical properties of the target area. The received signal strength (RSS) plays a significant role in reducing sensor node power consumption during data transmission. Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span. This paper introduces the RSS-based energy-efficient selective clustering technique using a master node (RESCM) to improve energy utilization using a master node. The master node positioned at the center of the network area and base station (BS) is placed outside the network area. During… More >

  • Open AccessOpen Access

    ARTICLE

    Improving Stock Price Forecasting Using a Large Volume of News Headline Text

    Daxing Zhang1,*, Erguan Cai2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302
    Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data to a higher level for… More >

  • Open AccessOpen Access

    ARTICLE

    DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

    Ziadoon K. Maseer1, Robiah Yusof1, Salama A. Mostafa2,*, Nazrulazhar Bahaman1, Omar Musa3, Bander Ali Saleh Al-rimy4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3945-3966, 2021, DOI:10.32604/cmc.2021.016074
    (This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
    Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design… More >

  • Open AccessOpen Access

    ARTICLE

    Controlled Quantum Network Coding Without Loss of Information

    Xing-Bo Pan1, Xiu-Bo Chen1,*, Gang Xu2, Haseeb Ahmad3, Tao Shang4, Zong-Peng Li5,6, Yi-Xian Yang1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3967-3979, 2021, DOI:10.32604/cmc.2021.017087
    Abstract Quantum network coding is used to solve the congestion problem in quantum communication, which will promote the transmission efficiency of quantum information and the total throughput of quantum network. We propose a novel controlled quantum network coding without information loss. The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie. Firstly, two pairs of three-particle non-maximum entangled states are pre-shared between senders and controller. By adding auxiliary particles and local operations, the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the basis.… More >

  • Open AccessOpen Access

    ARTICLE

    Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

    Saurabh Pal1, Pijush Kanti Dutta Pramanik1, Musleh Alsulami2, Anand Nayyar3,*, Mohammad Zarour4, Prasenjit Choudhury1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3981-4001, 2021, DOI:10.32604/cmc.2021.017966
    Abstract With the popularity of e-learning, personalization and ubiquity have become important aspects of online learning. To make learning more personalized and ubiquitous, we propose a learner model for a query-based personalized learning recommendation system. Several contextual attributes characterize a learner, but considering all of them is costly for a ubiquitous learning system. In this paper, a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling. A total of 208 students are surveyed. DEMATEL (Decision Making Trial and Evaluation Laboratory) technique is used to establish the validity and importance of the identified contexts and find… More >

  • Open AccessOpen Access

    ARTICLE

    Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

    Wan Hyun Cho1, Sang Kyoon Kim2, Myung Hwan Na1, In Seop Na3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758
    Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks (DNNs)… More >

  • Open AccessOpen Access

    ARTICLE

    A Compact Size 5G Hairpin Bandpass Filter with Multilayer Coupled Line

    Qazwan Abdullah1,2,*, Ömer Aydoĝdu2, Adeeb Salh3, Nabil Farah4, Md Hairul Nizam Talib4, Taha Sadeq5, Mohammed A. A. Al-Mekhalfi3, Abdu Saif6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4025-4042, 2021, DOI:10.32604/cmc.2021.018798
    Abstract The multilayer structure is a promising technique used to minimize the size of planar microstrip filters. In the flexible design and incorporation of other microwave components, multilayer band-pass filter results in better and enhanced dimensions. This paper introduces a microstrip fifth-generation (5G) low-frequency band of 2.52–2.65 GHz using a parallel-coupled line (PCL) Bandpass filter and multilayer (ML) hairpin Bandpass filter. The targeted four-pole resonator has a center frequency of 2.585 GHz with a bandwidth of 130 MHz. The filters are designed with a 0.1 dB passband ripple with a Chebyshev response. The hairpin-line offers compact filter design structures. Theoretically, they… More >

  • Open AccessOpen Access

    ARTICLE

    Application of Grey Model and Neural Network in Financial Revenue Forecast

    Yifu Sheng1, Jianjun Zhang1,*, Wenwu Tan1, Jiang Wu1, Haijun Lin1, Guang Sun2, Peng Guo3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4043-4059, 2021, DOI:10.32604/cmc.2021.019900
    Abstract There are many influencing factors of fiscal revenue, and traditional forecasting methods cannot handle the feature dimensions well, which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend. The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso. It can reduce the dimensionality of the original data, make separate predictions for each explanatory variable, and then use neural networks to make multivariate predictions, thereby making up for the… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Layered Deep Learning Features Fusion for Human Action Recognition

    Sadia Kiran1, Muhammad Attique Khan1, Muhammad Younus Javed1, Majed Alhaisoni2, Usman Tariq3, Yunyoung Nam4,*, Robertas Damaševičius5, Muhammad Sharif6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800
    (This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is… More >

  • Open AccessOpen Access

    ARTICLE

    Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

    Ahmed B. Khoshaim1, Saleem Abdullah2, Shahzaib Ashraf3,*, Muhammad Naeem4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4077-4094, 2021, DOI:10.32604/cmc.2021.016973
    (This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

    Mohamed Esmail Karar1,2, Omar Reyad1,3,*, Abdel-Haleem Abdel-Aty4, Saud Owyed5, Mohd F. Hassan6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4095-4111, 2021, DOI:10.32604/cmc.2021.019059
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    Abstract Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases

    Hanyu Shi1, Weiguang Qu1,2,*, Tingxin Wei2,3, Junsheng Zhou1, Yunfei Long4, Yanhui Gu1, Bin Li2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4113-4127, 2021, DOI:10.32604/cmc.2021.019518
    Abstract In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture… More >

  • Open AccessOpen Access

    ARTICLE

    Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

    Diaa Salam Abd Elminaam1,2,*, Nabil Neggaz3, Ibrahim Abdulatief Ahmed4,5, Ahmed El Sawy Abouelyazed4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4129-4149, 2021, DOI:10.32604/cmc.2021.019047
    Abstract At present, the immense development of social networks allows generating a significant amount of textual data, which has facilitated researchers to explore the field of opinion mining. In addition, the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem. This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence (SI)-based algorithm, Harris hawks algorithm, to select the most relevant terms. The experimental study has been tested on two datasets: Arabic Jordanian General Tweets and Opinion Corpus for Arabic. In terms of accuracy and number… More >

  • Open AccessOpen Access

    ARTICLE

    Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

    Vinay Arora1, Karun Verma1, Rohan Singh Leekha2, Kyungroul Lee3, Chang Choi4,*, Takshi Gupta5, Kashish Bhatia6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Machine Learning Technique with Effective Heart Disease Prediction System

    Mohammad Tabrez Quasim1, Saad Alhuwaimel2,*, Asadullah Shaikh3, Yousef Asiri3, Khairan Rajab3, Rihem Farkh4,5, Khaled Al Jaloud4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984
    (This article belongs to this Special Issue: AI 2.0-Enabled Next Generation Intelligence of Things for Smart Enterprise Systems)
    Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured… More >

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