Home / Journals / IASC / Vol.30, No.3, 2021
  • Open Access

    ARTICLE

    Performance Comparison of PoseNet Models on an AIoT Edge Device

    Min-Jun Kim1, Seng-Phil Hong2, Mingoo Kang1, Jeongwook Seo1,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 743-753, 2021, DOI:10.32604/iasc.2021.019329
    Abstract In this paper, we present an oneM2M-compliant system including an artificial intelligence of things (AIoT) edge device whose principal function is to estimate human poses by using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. Although MobileNet v1 is generally known to be much faster but less accurate than ResNet50, it is necessary to analyze the performances of whole PoseNet models carefully and select one of them suitable for the AIoT edge device. For this reason, we first investigate the computational complexity of the models about their neural network layers and parameters and then compare their performances… More >

  • Open Access

    ARTICLE

    Early Detection of Lung Carcinoma Using Machine Learning

    A. Sheryl Oliver1, T. Jayasankar2, K. R. Sekar3,*, T. Kalavathi Devi4, R. Shalini5, S. Poojalaxmi5, N. G. Viswesh5
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 755-770, 2021, DOI:10.32604/iasc.2021.016242
    Abstract Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through preprocessing strategies, ordinal to cardinal… More >

  • Open Access

    ARTICLE

    Machine Learning-based Detection and Classification of Walnut Fungi Diseases

    Muhammad Alyas Khan1, Mushtaq Ali1, Mohsin Shah2, Toqeer Mahmood3, Muneer Ahmad4, NZ Jhanjhi5, Mohammad Arif Sobhan Bhuiyan6,*, Emad Sami Jaha7
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 771-785, 2021, DOI:10.32604/iasc.2021.018039
    Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization.… More >

  • Open Access

    ARTICLE

    Radio Labeling Associated with a Class of Commutative Rings Using Zero-Divisor Graph

    Azeem Haider1,*, Ali N.A. Koam1, Ali Ahmad2
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 787-794, 2021, DOI:10.32604/iasc.2021.019391
    Abstract Graph labeling is useful in networks because each transmitter has a different transmission capacity to send or receive wired or wireless links. An interference of signals can occur when transmitters that are close together receive close frequencies. This problem has been modeled mathematically in the radio labeling problem on graphs, where vertices represent transmitters and edges indicate closeness of the transmitters. For this purpose, each vertex is labeled with a unique positive integer, and to minimize the interference, the difference between maximum and minimum used labels has to be minimized. A radio labeling for a graph is a function from… More >

  • Open Access

    ARTICLE

    Association between Temperature and Relative Humidity in Relation to COVID-19

    Ansari Saleh Ahmar1,*, M. A. El Safty2, Samirah Al Zahrani2, R. Rusli3, Abdul Rahman3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 795-803, 2021, DOI:10.32604/iasc.2021.016868
    Abstract The aim of this study is to determine the association between temperature and humidity in relation to COVID-19 above 3°C. This was carried out in the cities of Bandung and Surabaya which have temperatures of about 22°C to 31°C. Data was analyzed using descriptive analysis and the Pearson and Spearman correlation for normally and abnormally distributed data. The results showed that there was no association between people under monitoring (ODP)/close contact, patients under surveillance (PDP)/suspect, and COVID-19 confirmed cases in relation to the temperature and humidity in Bandung and Surabaya. Furthermore, there was no relationship between temperature and humidity with… More >

  • Open Access

    ARTICLE

    Computational Methods for Non-Linear Equations with Some Real-World Applications and Their Graphical Analysis

    Amir Naseem1, M.A. Rehman1, Thabet Abdeljawad2,3,4,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 805-819, 2021, DOI:10.32604/iasc.2021.019164
    (This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
    Abstract In this article, we propose some novel computational methods in the form of iteration schemes for computing the roots of non-linear scalar equations in a new way. The construction of these iteration schemes is purely based on exponential series expansion. The convergence criterion of the suggested schemes is also given and certified that the newly developed iteration schemes possess quartic convergence order. To analyze the suggested schemes numerically, several test examples have been given and then solved. These examples also include some real-world problems such as van der Wall’s equation, Plank’s radiation law and kinetic problem equation whose numerical results… More >

  • Open Access

    ARTICLE

    Sentiment Analytics: Extraction of Challenging Influencing Factors from COVID-19 Pandemics

    Mahmoud Oglah Al Hasan Baniata*, Sohail Asghar
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 821-836, 2021, DOI:10.32604/iasc.2021.018612
    Abstract The advancement in electronic devices and communication technologies in social media have introduced major changes in today’s communication and people have accepted such communicational habits at a rapid pace. The changes involve the way people started interacting with each other, and modern mean of discovering new groups of people, and individuals with similar mindsets, mutual interests, and ideas to share with. As far as the communities are concerned, there are so many social drives (such as “Say No to Plastic”) that need to be discussed on a certain platform for their promotion. Although, it’s quit is challenging, but with the… More >

  • Open Access

    ARTICLE

    Robust Optimal Proportional–Integral Controller for an Uncertain Unstable Delay System: Wind Process Application

    Rihem Farkh1,2, Yasser Fouad1,*, Haykel Marouani1
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 837-851, 2021, DOI:10.32604/iasc.2021.018214
    Abstract In industrial practice, certain processes are unstable, such as different types of reactors, distillation columns, and combustion systems. To ensure greater maneuverability and improve the speed of response command, certain systems in the military and aviation fields are purposely configured to be unstable. These systems are often more difficult to control than stable systems and are of particular interest to designers and control engineers. Despite all advances in process control over the past six decades, the proportional–integral–derivative (PID) controller is still the most common. The main reasons are the simplicity, robustness, and successful applications provided by PID-based control structures. The… More >

  • Open Access

    ARTICLE

    A Two-Step Approach for Improving Sentiment Classification Accuracy

    Muhammad Azam1, Tanvir Ahmed1, Rehan Ahmad2, Ateeq Ur Rehman3, Fahad Sabah1, Rao Muhammad Asif4,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 853-867, 2021, DOI:10.32604/iasc.2021.019101
    Abstract Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is… More >

  • Open Access

    ARTICLE

    Image Denoising Using a Nonlinear Pixel-Likeness Weighted-Frame Technique

    P. Vinayagam1,*, P. Anandan2, N. Kumaratharan3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 869-879, 2021, DOI:10.32604/iasc.2021.016761
    Abstract Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hardware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a nonlinear modified pixel-likeness weighted-frame technique. The proposed architecture works in two stages.… More >

  • Open Access

    ARTICLE

    Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm

    Muhammad Shoukat Aslam1, Taher M. Ghazal2,3, Areej Fatima4, Raed A. Said5, Sagheer Abbas1, Muhammad Adnan Khan6,7,*, Shahan Yamin Siddiqui1,8, Munir Ahmad1
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 881-888, 2021, DOI:10.32604/iasc.2021.017920
    Abstract The real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appropriate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent… More >

  • Open Access

    ARTICLE

    Adversarial Examples Generation Algorithm through DCGAN

    Biying Deng1, Ziyong Ran1, Jixin Chen1, Desheng Zheng1,*, Qiao Yang2, Lulu Tian3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 889-898, 2021, DOI:10.32604/iasc.2021.019727
    Abstract In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM and establish a generation model,… More >

  • Open Access

    ARTICLE

    Development of a Web-Based Telemedicine System for Covid-19 Patients

    Morshedul Bari Antor1, A. H. M. Shafayet Jamil1, Maliha Mamtaz1, Mohammad Monirujjaman Khan1,*, Sultan S. Alshamrani2, Mehedi Masud3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 899-915, 2021, DOI:10.32604/iasc.2021.018914
    Abstract In the Covid-19 pandemic, people have been very concerned about the safety and are avoiding crowded places like hospitals. An online telemedicine web-based technology can help to overcome this situation. This paper presents an online telemedicine system that helps to promote collaboration between doctors, hospitals, and patients. The system allows doctors to serve patients from remote areas. The system also allows both doctors and patients to communicate through video calls or text messages. Patients using the system can store information about their health, search for doctors, and consult medical professionals using text messages and video calls. Doctors can also register… More >

  • Open Access

    ARTICLE

    A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease

    Mounita Ghosh1, Md. Mohsin Sarker Raihan1, M. Raihan2, Laboni Akter1, Anupam Kumar Bairagi3, Sultan S. Alshamrani4, Mehedi Masud5,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 917-928, 2021, DOI:10.32604/iasc.2021.017989
    Abstract The liver is considered an essential organ in the human body. Liver disorders have risen globally at an unprecedented pace due to unhealthy lifestyles and excessive alcohol consumption. Chronic liver disease is one of the principal causes of death affecting large portions of the global population. An accumulation of liver-damaging factors deteriorates this condition. Obesity, an undiagnosed hepatitis infection, alcohol abuse, coughing or vomiting blood, kidney or hepatic failure, jaundice, liver encephalopathy, and many more disorders are responsible for it. Thus, immediate intervention is needed to diagnose the ailment before it is too late. Therefore, this work aims to evaluate… More >

  • Open Access

    ARTICLE

    Predicting Heart Disease Based on Influential Features with Machine Learning

    Animesh Kumar Dubey*, Kavita Choudhary, Richa Sharma
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 929-943, 2021, DOI:10.32604/iasc.2021.018382
    Abstract Heart disease is a major health concern worldwide. The chances of recovery are bright if it is detected at an early stage. The present report discusses a comparative approach to the classification of heart disease data using machine learning (ML) algorithms and linear regression and classification methods, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), SVM with grid search (SVMG), k-nearest neighbor (KNN), and naive Bayes (NB). The ANOVA F-test feature selection (AFS) method was used to select influential features. For experimentation, two standard benchmark datasets of heart diseases, Cleveland and Statlog, were obtained… More >

  • Open Access

    ARTICLE

    Fault-Tolerant Communication Induced Checkpointing and Recovery Protocol Using IoT

    Neha Malhotra1,2,*, Manju Bala3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 945-960, 2021, DOI:10.32604/iasc.2021.019082
    (This article belongs to this Special Issue: Intelligence 4.0: Concepts and Advances in Computational Intelligence)
    Abstract In mobile computing systems, nodes in the network take checkpoints to survive failures. Certain characteristics of mobile computing systems such as mobility, low bandwidth, disconnection, low power consumption, and limited memory make these systems more prone to failures. In this paper, a novel minimum process communication-induced checkpointing algorithm that makes full use of the computation ability and implementation of effective stable storage in a mobile computing system is proposed. The said approach initiates by taking spontaneous checkpoints by each node in phase 1 using a logistic function that is specifically used to estimate the time interval between two checkpoints and… More >

  • Open Access

    ARTICLE

    Decision Making Based on Fuzzy Soft Sets and Its Application in COVID-19

    S. A. Al blowi1, M. El Sayed2, M. A. El Safty3,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 961-972, 2021, DOI:10.32604/iasc.2021.018242
    (This article belongs to this Special Issue: Soft Computing Technologies for COVID 19 Assessment, Analysis and Control)
    Abstract Real-world applications are now dealing with a huge amount of data, especially in the area of high-dimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union - Intersection decision making method. Using these new principles, the decision-making strategy… More >

  • Open Access

    ARTICLE

    Face Image Compression and Reconstruction Based on Improved PCA

    Yu Xue1,2,*, Chen Chen1, ChiShe Wang2, Linguo Li3, Romany F. Mansour4
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 973-982, 2021, DOI:10.32604/iasc.2021.017607
    Abstract Face recognition technology has many usages in the real-world applications, and it has generated extensive interest in recent years. However, the amount of data in a digital image is growing explosively, taking up a lot of storage and transmission resources. There is a lot of redundancy in an image data representation. Thus, image compression has become a hot topic. The principal component analysis (PCA) can effectively remove the correlation of an image and condense the image information into a characteristic image with several main components. At the same time, it can restore different data images according to their principal components… More >

  • Open Access

    ARTICLE

    Cryptanalysis of an Online/Offline Certificateless Signature Scheme for Internet of Health Things

    Saddam Hussain1, Syed Sajid Ullah2,*, Mohammad Shorfuzzaman3, Mueen Uddin4, Mohammed Kaosar5
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 983-993, 2021, DOI:10.32604/iasc.2021.019486
    (This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
    Abstract Recently, Khan et al. [An online-offline certificateless signature scheme for internet of health things,” Journal of Healthcare Engineering, vol. 2020] presented a new certificateless offline/online signature scheme for Internet of Health Things (IoHT) to fulfill the authenticity requirements of the resource-constrained environment of (IoHT) devices. The authors claimed that the newly proposed scheme is formally secured against Type-I adversary under the Random Oracle Model (ROM). Unfortunately, their scheme is insecure against adaptive chosen message attacks. It is demonstrated that an adversary can forge a valid signature on a message by replacing the public key. Furthermore, we performed a comparative analysis… More >

  • Open Access

    ARTICLE

    Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications

    I.-Hung Wang1, Mahardi2, Kuang-Chyi Lee2,*, Shinn-Liang Chang1
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 995-1005, 2021, DOI:10.32604/iasc.2021.019020
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    Abstract The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general image datasets from ImageNet. In… More >

  • Open Access

    ARTICLE

    Control Charts for the Shape Parameter of Skewed Distribution

    Azam Zaka1, Riffat Jabeen2,*, Kanwal Iqbal Khan3
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1007-1018, 2021, DOI:10.32604/iasc.2021.016491
    Abstract The weighted distributions are useful when the sampling is done using an unequal probability of the sampling units. The Weighted Power function distribution (WPFD) has applications in the fields of reliability engineering, management sciences and survival analysis. WPFD is more beneficial in Statistical process control (SPC). SPC is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help to monitor process behaviour, discover problems in internal systems, and find solutions for production issues. To identify and remove the variation in different reliability processes and also to monitor the reliability of… More >

  • Open Access

    ARTICLE

    Compression of Grayscale Images in DRPE-based Encrypted Domain

    Osama S. Faragallah1,*, Ensherah A. Naeem2, Hala S. Elsayed3, Fathi E. Abd El-Samie4
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1019-1031, 2021, DOI:10.32604/iasc.2021.019185
    Abstract Compressing the encrypted images is considered an important issue in many applications such as cloud computing. From this perspective, this paper introduces an efficient approach for compression processing of images in the encrypted domain. The images are optically encrypted using the Double Random Phase Encoding (DRPE). The Joint Photographic Experts Group (JPEG) and the Set Partitioning in Hierarchical Trees (SPIHT) compression schemes have been used to compress the encrypted images. The process starts by converting the original image into an optical signal by an optical emitter like an optical source and encrypting it with DRPE. The DRPE applies two-phase modulations… More >

  • Open Access

    ARTICLE

    Lowest-Opportunities User First-Based Subcarrier Allocation Algorithm for Downlink NOMA Systems

    Mohammed Abd-Elnaby*, Sameer Alsharif, Hesham Alhumyani, Fahad Alraddady
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1033-1048, 2021, DOI:10.32604/iasc.2021.019341
    (This article belongs to this Special Issue: Recent Advances in Intelligent Systems and Communication)
    Abstract Non-orthogonal multiple access (NOMA) is one of the promising 5G technologies to improve spectral efficiency massive connectivity and cell-edge throughput. The performance of NOMA systems mainly depends on the efficiency of the subcarrier allocation algorithm. This paper aims to jointly optimize spectral efficiency (SE), outage probability, and fairness among users with respect to the subcarrier allocation for downlink NOMA systems. We propose a low-complexity greedy-based subcarrier allocation algorithm based on the lowest-opportunities user’s first precept. This precept is based on computing the number of opportunities for each user to select a subcarrier with good channel gain by counting the number… More >

  • Open Access

    ARTICLE

    The Data Acquisition and Control System Based on IoT-CAN Bus

    He Gong1,2,3,4, Ji Li1, RuiWen Ni1, Pei Xiao1, Hang Ouyang1, Ye Mu1,*, Thobela Louis Tyasi5
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1049-1062, 2021, DOI:10.32604/iasc.2021.019730
    Abstract Presently, the adoption of Internet of things(IOT)-related technologies in the Smart Farming domain is rapidly emerging. The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms, which we will need pressingly in the future. This paper designs a heterogeneous data acquisition and control system for differentiated agricultural information monitoring terminal. Based on the IoT-CAN bus architecture, the system can… More >

  • Open Access

    ARTICLE

    Hardware Acceleration of Image and Video Processing on Xilinx Zynq Platform

    Praveenkumar Babu, Eswaran Parthasarathy*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1063-1071, 2021, DOI:10.32604/iasc.2021.018903
    Abstract Advancements in image and video processing are growing over the years for industrial robots, autonomous vehicles, indexing databases, surveillance, medical imaging and computer-human interaction applications. One of the major challenges in real-time image and video processing is the execution of complex functions and high computational tasks. In this paper, the hardware acceleration of different filter algorithms for both image and video processing is implemented on Xilinx Zynq®-7000 System on-Chip (SoC) device. It consists of Dual-core Cortex-A9 processors which provide computing ability to perform I/O and processing functions and software libraries using Vivado® High-Level Synthesis (HLS). In the proposed work, Sobel-Feldman… More >

  • Open Access

    ARTICLE

    On Parametric Fuzzy Linear Programming Formulated by a Fractal

    Rafid A. Al-Saeedi1, Rabha W. Ibrahim2, Rafida M. Elobaid3,*
    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1073-1084, 2021, DOI:10.32604/iasc.2021.018011
    (This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
    Abstract Fractal strategy is an important tool in manufacturing proposals, including computer design, conserving, power supplies and decorations. In this work, a parametric programming, analysis is proposed to mitigate an optimization problem. By employing a fractal difference equation of the spread functions (local fractional calculus operator) in linear programming, we aim to analyze the restraints and the objective function. This work proposes a new technique of fractal fuzzy linear programming (FFLP) model based on the symmetric triangular fuzzy number. The parameter fuzzy number is selected from the fractal power of the difference equation. Note that this number indicates the fractal parameter,… More >

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