Journals / IASC / Vol.31, No.1

  • ARTICLE

    Adversarial Training for Multi Domain Dialog System

    Sudan Prasad Uprety, Seung Ryul Jeong*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 1-11, 2022, DOI:10.32604/iasc.2022.018757
    Abstract Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent… More >

  • ARTICLE

    Cyber-Attack Detection and Mitigation Using SVM for 5G Network

    Sulaiman Yousef Alshunaifi, Shailendra Mishra*, Mohammed Alshehri
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 13-28, 2022, DOI:10.32604/iasc.2022.019121
    Abstract 5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G(LTE) and 5G technology. The findings in this research were obtained by using primary and secondary data. Secondary data… More >

  • ARTICLE

    H-infinity Controller Based Disturbance Rejection in Continuous Stirred-Tank Reactor

    Sikander Hans1, Smarajit Ghosh2,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 29-41, 2022, DOI:10.32604/iasc.2022.019525
    (This article belongs to this Special Issue: Intelligence 4.0: Concepts and Advances in Computational Intelligence)
    Abstract This paper offers an H-infinity (H∞) controller-based disturbance rejection along with the utilization of the water wave optimization (WWO) algorithm. H∞ controller is used to synthesize the guaranteed performance of certain applications as well as it provides maximum gain at any situation. The proposed work focuses on the conflicts of continuous stirred-tank reactor (CSTR) such as variation in temperature and product concentration. The elimination of these issues is performed with the help of the WWO algorithm along with the controller operation. In general, the algorithmic framework of WWO algorithm is simple, and easy to implement with a small-size population and… More >

  • ARTICLE

    An Efficient HAPS Cross-Layer Design to Mitigate COVID-19 Consequences

    Sameer Alsharif*, Rashid A. Saeed, Yasser Albagory
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 43-59, 2022, DOI:10.32604/iasc.2022.019493
    (This article belongs to this Special Issue: Recent Advances in Intelligent Systems and Communication)
    Abstract This paper proposes a new cross-layer communication system for the provision of Internet services and applications to mitigate the negative impacts of COVID-19, due to which the massive online demands are affecting the current communication systems’ infrastructures and capabilities. The system requirements and model are investigated where it utilizes high-altitude platform (HAP) for fast and efficient connectivity provision to bridge the communication infrastructure gap in the current pandemic. The HAP is linked to the main server or gateway station located on ground and can provide communication narrow beams towards isolated areas which suffer from poor terrestrial radio coverage or lack… More >

  • ARTICLE

    Multi-Level Hesitant Fuzzy Based Model for Usable-Security Assessment

    Mohd Nadeem1, Jehad F. Al-Amri2, Ahmad F. Subahi3, Adil Hussain Seh1, Suhel Ahmad Khan4, Alka Agrawal1, Raees Ahmad Khan1,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 61-82, 2022, DOI:10.32604/iasc.2022.019624
    Abstract Present day healthcare sector is frequently victimized by the intruders. Healthcare data industry has borne the brunt of the highest number of data breach episodes in the last few years. The key reason for this is attributed to the sensitivity of healthcare data and the high costs entailed in trading the data over the dark web. Hence, usable-security evaluation of healthcare information systems is the need of hour so as to identify the vulnerabilities and provide preventive measures as a shield against the breaches. Usable-security assessment will help the software designers and developers to prioritize usable-security attributes according to the… More >

  • ARTICLE

    Cloud-Based Knowledge Management Framework for Decision Making in Higher Education Institutions

    Muhammad Younas1,2,*, Ahmad Shukri Mohd Noor2, Muhammad Arshad1
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 83-99, 2022, DOI:10.32604/iasc.2022.018332
    (This article belongs to this Special Issue: Digital Technologies in Business, Education and Social Transformation)
    Abstract The education sector is now necessitated to align itself with the latest innovative technological trends in the industry to be competitive while transforming database management practices. The Higher Education Institutions (HEIs) are currently using cloud-based applications at varied implementation levels while others are following the shifting trends. On-demand availability of consolidated information is a critical success factor to perform decision-making activities; as a result, quite a bit of research work has been performed in this realm. The study is aimed to propose an efficient framework that is capable of extracting the desired information from an educational setup intended to be… More >

  • ARTICLE

    Development of A Low-Cost Exoskeleton for Rehabilitation and Mobility

    Muhatasim Intisar1, Mohammad Monirujjaman Khan1,*, Mehedi Masud2, Mohammad Shorfuzzaman2
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 101-115, 2022, DOI:10.32604/iasc.2022.019083
    Abstract Paralysis is detrimental to people in catastrophic ways: losing income opportunities, becoming a burden to their friends and family, further physical deterioration, and the combination of these occurrences can often lead to depression. In third world countries, suffering from paralysis can be extra deleterious, where a large proportion of the population is engaged in some form of physical labor. The number of people exposed to paralysis risk factors is also increasing, with more and more people having hypertension, smoking, and other abnormalities. Besides, low workplace safety precautions may lead to an increased risk of spinal cord injury in developing nations.… More >

  • ARTICLE

    Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study

    Sajid Habib Gill1, Mirza Abdur Razzaq1, Muneer Ahmad2, Fahad M. Almansour3, Ikram Ul Haq4, NZ Jhanjhi5,*, Malik Zaib Alam6, Mehedi Masud7
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 117-128, 2022, DOI:10.32604/iasc.2022.016597
    Abstract The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost-effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages… More >

  • ARTICLE

    Bayesian Approximation Techniques for the Generalized Inverted Exponential Distribution

    Rana A. Bakoban, Maha A. Aldahlan*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 129-142, 2022, DOI:10.32604/iasc.2022.018041
    Abstract In this article, Bayesian techniques are adopted to estimate the shape parameter of the generalized inverted exponential distribution (GIED) in the case of complete samples. Normal approximation, Lindley’s approximation, and Tierney and Kadane’s approximation are used for deriving Bayesian estimators. Different informative priors are considered, such as Jeffrey’s prior, Quasi prior, modified Jeffrey’s prior, and the extension of Jeffrey’s prior. Non-informative priors are also used, including Gamma prior, Pareto prior, and inverse Levy prior. The Bayesian estimators are derived under the quadratic loss function. Monte Carlo simulations are carried out to make a comparison among estimators based on the mean… More >

  • ARTICLE

    Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

    M. Bhoopathi1,*, P. Palanivel2
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 143-159, 2022, DOI:10.32604/iasc.2022.017705
    Abstract At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is utilized in restructured electricity markets.… More >

  • ARTICLE

    Plant Disease Classification Using Deep Bilinear CNN

    D. Srinivasa Rao1, Ramesh Babu Ch2, V. Sravan Kiran1, N. Rajasekhar3,*, Kalyanapu Srinivas4, P. Shilhora Akshay1, G. Sai Mohan1, B. Lalith Bharadwaj1
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 161-176, 2022, DOI:10.32604/iasc.2022.017706
    Abstract

    Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease identification and classification is proposed.… More >

  • ARTICLE

    Visual Protection Using RC5 Selective Encryption in Telemedicine

    Osama S. Faragallah1,*, Ahmed I. Sallam2, Hala S. El-sayed3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 177-190, 2022, DOI:10.32604/iasc.2022.019348
    Abstract The dissemination of the COVID-19 viruses now extends the usage of video consultations to share perspectives and virtual medical consultations, save expenses and health procedures, track the success of care proposals with detail, consistency, and ease from moment to time. The research aims to study the security of video consultations. We will also present the advantages and disadvantages of video consultations and the complications of their implementation. This paper mainly proposes a practical, high-efficiency video encoding technique for the new video encoding technique (HEVC) used in video consultations. The technology offered uses the RC5 block encryption algorithm for encrypting the… More >

  • ARTICLE

    Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

    Adnan Ashraf1, Abdulwahab Ali Almazroi2, Waqas Haider Bangyal3,*, Mohammed A. Alqarni4
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 191-206, 2022, DOI:10.32604/iasc.2022.015810
    Abstract Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the… More >

  • ARTICLE

    Determination of COVID-19 Patients Using Machine Learning Algorithms

    Marium Malik1, Muhammad Waseem Iqbal1,*, Syed Khuram Shahzad2, Muhammad Tahir Mushtaq2, Muhammad Raza Naqvi3,4, Maira Kamran1, Babar Ayub Khan4, Muhammad Usman Tahir4
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 207-222, 2022, DOI:10.32604/iasc.2022.018753
    Abstract Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential… More >

  • ARTICLE

    Efficacy of Unconventional Penetration Testing Practices

    Bandar Abdulrhman Bin Arfaj1, Shailendra Mishra2,*, Mohammed Alshehri1
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 223-239, 2022, DOI:10.32604/iasc.2022.019485
    Abstract The financial and confidential cost of cyberattack has presented a significant loss to the organization and government where the privacy of worthless information has become vulnerable to cyber threat. In terms of efforts implemented to avoid this risk, the cyberattack continues to evolve, making the cybersecurity systems weekend. This has necessitated the importance of comprehensive penetration testing, assessment techniques, and tools to analyze and present the currently available unconventional penetration techniques and tactics to test and examine their key features and role in supporting cybersecurity and measure their effectiveness. The importance of cyberspace and its value make it an eminent… More >

  • ARTICLE

    An Enhanced Deep Learning Model for Automatic Face Mask Detection

    Qazi Mudassar Ilyas1, Muneer Ahmad2,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 241-254, 2022, DOI:10.32604/iasc.2022.018042
    Abstract The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPs) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an enhanced lightweight deep learning model.… More >

  • ARTICLE

    Dynamic Hyperparameter Allocation under Time Constraints for Automated Machine Learning

    Jeongcheol Lee, Sunil Ahn*, Hyunseob Kim, Jongsuk Ruth Lee
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 255-277, 2022, DOI:10.32604/iasc.2022.018558
    Abstract Automated hyperparameter optimization (HPO) is a crucial and time-consuming part in the automatic generation of efficient machine learning models. Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations. These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time. That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline. Although these time and resource constraints… More >

  • ARTICLE

    Enrichment of Crop Yield Prophecy Using Machine Learning Algorithms

    R. Kingsy Grace*, K. Induja, M. Lincy
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947
    Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in the choice of crop to… More >

  • ARTICLE

    Implementation of Artificial Intelligence Based Analyzer Using Multi-Agent System Approach

    Norah S. Farooqi1, Mohamed O. Khozium2,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 297-309, 2022, DOI:10.32604/iasc.2022.019060
    Abstract Using Business Intelligence (BI) applications is a critical factor for modern enterprises’ success. BI is one of the key components that persistently required for the modern high-tech companies and industries were used to handle huge amounts of data in every minute of the operations. The existing literature suggested that the lack of dynamic decision making, accuracy, and the degree of flexibility are the key limitations for handling the operational data. Many industries and companies adopted the software-based solution; however, the intelligence is there due to the dependence of the operational engagement for each of the sectors. Therefore, artificial intelligence business… More >

  • ARTICLE

    Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

    A. Jayamathi1, T. Jayasankar2,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 311-324, 2022, DOI:10.32604/iasc.2022.019473
    Abstract Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, and which is considered as the central part of many wired and wireless standards. Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) achieves maximum spectral efficiency and data rates for wireless mobile communication systems. Though it offers better quality of services, high peak-to-average power ratio (PAPR) is the major issue that needs to be resolved in the MIMO-OFDM system. Earlier studies have addressed the high PAPR of OFDM system using clipping, coding, selected mapping, tone injection, peak windowing, etc. Recently, deep learning (DL) models have exhibited… More >

  • ARTICLE

    Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

    Noha E. El-Attar1,*, Bossy M. Moustafa2, Wael A. Awad3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 325-338, 2022, DOI:10.32604/iasc.2022.019970
    Abstract DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken. In this research, the authors… More >

  • ARTICLE

    A Novel Classification Method with Cubic Spline Interpolation

    Husam Ali Abdulmohsin1,*, Hala Bahjat Abdul Wahab2, Abdul Mohssen Jaber Abdul Hossen3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 339-355, 2022, DOI:10.32604/iasc.2022.018045
    (This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
    Abstract Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the… More >

  • ARTICLE

    Energy Saving Control Approach for Trajectory Tracking of Autonomous Mobile Robots

    Yung-Hsiang Chen1, Yung-Yue Chen2, Shi-Jer Lou3, Chiou-Jye Huang4,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 357-372, 2022, DOI:10.32604/iasc.2022.018663
    Abstract This research presents an adaptive energy-saving H2 closed-form control approach to solve the nonlinear trajectory tracking problem of autonomous mobile robots (AMRs). The main contributions of this proposed design are as follows: closed-form approach, simple structure of the control law, easy implementation, and energy savings through trajectory tracking design of the controlled AMRs. It is difficult to mathematically obtained this adaptive H2 closed-form solution of AMRs. Therefore, through a series of mathematical analyses of the trajectory tracking error dynamics of the controlled AMRs, the trajectory tracking problem of AMRs can be transformed directly into a solvable problem, and an adaptive… More >

  • ARTICLE

    Sine Power Lindley Distribution with Applications

    Abdullah M. Almarashi*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 373-386, 2022, DOI:10.32604/iasc.2022.018043
    Abstract Sine power Lindley distribution (SPLi), a new distribution with two parameters that extends the Lindley model, is introduced and studied in this paper. The SPLi distribution is more flexible than the power Lindley distribution, and we show that in the application part. The statistical properties of the proposed distribution are calculated, including the quantile function, moments, moment generating function, upper incomplete moment, and lower incomplete moment. Meanwhile, some numerical values of the mean, variance, skewness, and kurtosis of the SPLi distribution are obtained. Besides, the SPLi distribution is evaluated by different measures of entropy such as Rényi entropy, Havrda and… More >

  • ARTICLE

    CMMI Compliant Workflow Models to Track and Control Changes

    Islam Ali1, Syed Muhammad Ali1, Waqar Mehmood2, Wasif Nisar1, Muhammad Qaiser Saleem3, Majzoob K. Omer3, Mahmood Niazi4, Muhammad Shafiq5, Jin-Ghoo Choi5,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 387-405, 2022, DOI:10.32604/iasc.2022.015955
    (This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
    Abstract The Capability Maturity Model Integration (CMMI) is a renowned Software Process Improvement (SPI) framework. Research studies have revealed that CMMI adoption needs a lot of resources in terms of training, funds, and professional workers. However, the software SMEs (SSMEs) have few resources and cannot adopt CMMI. One of the challenges of adopting CMMI is that CMMI tells “What to do?” as requirements to be met, and leaves “How to do?” to the implementers. The software industry especially SSMEs faces difficulties in successfully implementing various process areas (PAs) particularly Configuration Management Process Area (CM-PA). SG-2 (Track and control changes) is one… More >

  • ARTICLE

    Dynamic Feature Subset Selection for Occluded Face Recognition

    Najlaa Hindi Alsaedi*, Emad Sami Jaha
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 407-427, 2022, DOI:10.32604/iasc.2022.019538
    Abstract Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded… More >

  • ARTICLE

    A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

    K. Gayathri Devi*, R. S. Mishra, A. K. Madan
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 429-448, 2022, DOI:10.32604/iasc.2022.019330
    Abstract An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires more computational effort to optimize the objectives with contradictory measures. This paper aims to address the FJSP problem with combined and contradictory objectives, like minimization of make-span, maximum workload, and total workload. This paper proposes ‘Hybrid Adaptive Firefly Algorithm’ (HAdFA), a new enhanced version of the classic Firefly Algorithm (FA) embedded with adaptive parameters to optimize the multi objectives concurrently. The proposed algorithm has adopted two adaptive strategies, i.e., an adaptive randomization parameter (α) and an effective heterogeneous update rule for fireflies. The adaptations… More >

  • ARTICLE

    Model Predictive Control of H7 Transformerless Inverter Powered by PV

    Ibrahim Atawi1, Sherif Zaid1,2,3,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 449-469, 2022, DOI:10.32604/iasc.2022.019959
    (This article belongs to this Special Issue: Artificial Techniques: Application, Challenges, Performance Improvement of Smart Grid and Renewable Energy Systems)
    Abstract Transformerless inverters have become an important integration of the modern photovoltaic (PV) grid-tied systems. Unfortunately, it has a general safety problem regarding the earth leakage current that must be less than the recommended standards. Lately, the H7 transformerless inverter, which is a three-phase inverter with an additional switch on the DC side, is introduced to mitigate the earth leakage current. Different modulation techniques and controllers are proposed to optimize its performance. This paper proposed the application of model predictive control (MPC) to grid-connected H7 transformerless inverter supplied by the PV power system. In modeling the system, the grid inductance has… More >

  • ARTICLE

    Cloud-IoT Integration: Cloud Service Framework for M2M Communication

    Saadia Malik1, Nadia Tabassum2, Muhammad Saleem3, Tahir Alyas4, Muhammad Hamid5,*, Umer Farooq4
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 471-480, 2022, DOI:10.32604/iasc.2022.019837
    Abstract With the ongoing revolution in the Internet of Things (IoT) and cloud computing has made the potential of every stack holder that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction with devices as very helpful and serviceable in their daily life. However, an improperly configured network system is a soft target to security threats, therefore there is a dire need for a security embedded framework for IoT and cloud communication models is the latest research area. In this paper, different IoT and cloud computing frameworks are discussed in detail and describes… More >

  • ARTICLE

    Hysteresis Compensation of Dynamic Systems Using Neural Networks

    Jun Oh Jang*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 481-494, 2022, DOI:10.32604/iasc.2022.019848
    Abstract A neural networks(NN) hysteresis compensator is proposed for dynamic systems. The NN compensator uses the back-stepping scheme for inverting the hysteresis nonlinearity in the feed-forward path. This scheme provides a general step for using NN to determine the dynamic pre-inversion of the reversible dynamic system. A tuning algorithm is proposed for the NN hysteresis compensator which yields a stable closed-loop system. Nonlinear stability proofs are provided to reveal that the tracking error is small. By increasing the gain we can reduce the stability radius to some extent. PI control without hysteresis compensation requires much higher gains to achieve similar performance.… More >

  • ARTICLE

    Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction

    Chandrasekar Thaventhiran, K. R. Sekar*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 495-506, 2022, DOI:10.32604/iasc.2022.019546
    Abstract Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given… More >

  • ARTICLE

    Leaf Blights Detection and Classification in Large Scale Applications

    Abdul Muiz Fayyaz1, Kawther A. Al-Dhlan2, Saeed Ur Rehman1, Mudassar Raza1, Waqar Mehmood3, Muhammad Shafiq4, Jin-Ghoo Choi4,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 507-522, 2022, DOI:10.32604/iasc.2022.016392
    (This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
    Abstract Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of… More >

  • ARTICLE

    A Parametric Study of Arabic Text-Based CAPTCHA Difficulty for Humans

    Suliman A. Alsuhibany*, Hessah Abdulaziz Alhodathi
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 523-537, 2022, DOI:10.32604/iasc.2022.019913
    Abstract The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed scheme, we ran four filter… More >

  • ARTICLE

    Energy Demand Forecasting Using Fused Machine Learning Approaches

    Taher M. Ghazal1,2, Sajida Noreen3, Raed A. Said4, Muhammad Adnan Khan5,*, Shahan Yamin Siddiqui3,6, Sagheer Abbas3, Shabib Aftab3, Munir Ahmad3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658
    Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to… More >

  • ARTICLE

    Semantic Human Face Analysis for Multi-level Age Estimation

    Rawan Sulaiman Howyan1,2,*, Emad Sami Jaha1
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 555-580, 2022, DOI:10.32604/iasc.2022.019533
    Abstract Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by… More >

  • ARTICLE

    Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum

    Raham Hashim Yosuf1, Rania A. Mokhtar2, Rashid A. Saeed2,*, Hesham Alhumyani2, S. Abdel-Khalek3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 581-590, 2022, DOI:10.32604/iasc.2022.019928
    Abstract The grid computing is one of the strong initiatives and technologies that has been introduced in the last decade for improve the resources utilization, optimization and provide very high throughput computation for wide range of applications. To attain these goals an effective scheduling for grid systems is a vital issue to realize the intended performance. The processes scheduling could be executed in various methods and protocols that have been extensively address in the literature. This works utilized shortest process first (SPF) protocol which gives the shortest jobs the highest priorities. For longer jobs, it should have lower priorities and wait… More >

  • ARTICLE

    Robust Speed Regulation of Induction Motor Subjected to Unknown Load Torque

    H. Abdelfattah1, A. A. Abouelsoud2, Fahd A. Banakhr3, Mohamed I. Mosaad3,*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 591-605, 2022, DOI:10.32604/iasc.2022.018765
    (This article belongs to this Special Issue: Recent Trends in Artificial Intelligence for Automated Complex Industrial Systems)
    Abstract Induction motors are still the most used in industrial applications due to the simplicity of installation and low maintenance cost, especially for the squirrel cage type. The significant development in power electronics in terms of high-speed technologies in power electronic switches, their availability in high ratings, and the considerable decrease in the cost of the power electronics components supports this increase in uses. However, changing the induction motor's speed with loading, load torque measurement devices, and speed sensors limit this increase in using such motors. This paper proposes a state feedback controller-based backstepping technique for robust speed regulators of induction… More >

  • ARTICLE

    Design and Analysis of a Novel Antenna for THz Wireless Communication

    Omar A. Saraereh1,*, Luae Al-Tarawneh2, Ashraf Ali1, Amani M. Al Hadidi3
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 607-619, 2022, DOI:10.32604/iasc.2022.020216
    (This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)
    Abstract The frequency range of the terahertz (THz) band is usually defined as 0.3~3.0 THz, and some scholars have also extended it to 0.1~10 THz. THz technology has the characteristics of low photon radiation energy and rich spectrum information, and the THz band contains the vibration and rotation resonance frequencies of many material macromolecules, which can realize fingerprint detection. Therefore, THz technology has great academic value and a wide range of applications in basic research and applied science. Application prospects, such as THz spectroscopy technology provides a new means for studying the interaction between electromagnetic waves and matter, and its application… More >

  • ARTICLE

    Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images

    G. Reshma1,*, Chiai Al-Atroshi2, Vinay Kumar Nassa3, B.T. Geetha4, Gurram Sunitha5, Mohammad Gouse Galety6, S. Neelakandan7
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117
    (This article belongs to this Special Issue: Intelligence 4.0: Concepts and Advances in Computational Intelligence)
    Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker),… More >

  • ARTICLE

    Mixed Moving Average-Cumulative Sum Control Chart for Monitoring Parameter Change

    Nongnuch Saengsura, Saowanit Sukparungsee*, Yupaporn Areepong
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 635-647, 2022, DOI:10.32604/iasc.2022.019997
    Abstract In this research, we propose the new mixed control chart called the mixed Moving Average-Cumulative Sum (MA-CUSUM) control chart used for monitoring parameter changes in asymmetrical and symmetrical processes. Its efficiency was compared with that of the Shewhart, Cumulative Sum (CUSUM), Moving Average (MA), mixed Cumulative Sum-Moving Average (CUSUM-MA) and mixed Moving Average-Cumulative Sum (MA-CUSUM) control charts by using their average run lengths (ARLs), the standard deviation of the run length (SDRL), and median run length (MRL) via the Monte Carlo simulation (MC). The simulation results show that the MA-CUSUM control chart was more efficient than the other control charts… More >

  • ARTICLE

    Wireless Underground Sensor Networks Channel Using Energy Efficient Clustered Communication

    R. Kanthavel1,*, R. Dhaya2
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 649-659, 2022, DOI:10.32604/iasc.2022.019779
    Abstract Wireless Underground Sensor Networks (WUSNs) refer to a group of nodes working underneath the earth plane that has been predicted to render concurrent observation capability in the hostile subversive and underwater surroundings. The accurate monitoring in places like underground earth, water, lubricates so on called non-conventional media need high accuracy of tiny sized sensors with antennas at a similar size. Therefore, an investigation is needed to study the opportunities and drawbacks of utilizing WUSNs without compromising the effectiveness of real-time monitoring procedures. With this, the major confrontation is to institute a trustworthy underground communication regardless of the complex environment that… More >

  • ARTICLE

    A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

    Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour*
    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 661-675, 2022, DOI:10.32604/iasc.2022.020132
    Abstract Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and… More >

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