Home / Journals / IASC / Vol.33, No.3, 2022
  • Open Access

    ARTICLE

    MLP-PSO Framework with Dynamic Network Tuning for Traffic Flow Forecasting

    V. Rajalakshmi1,*, S. Ganesh Vaidyanathan2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310
    Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters of MLP dynamically using PSO… More >

  • Open Access

    ARTICLE

    Arrhythmia Detection and Classification by Using Modified Recurrent Neural Network

    Ajina Mohamed Ameer*, M. Victor Jose
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924
    Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is… More >

  • Open Access

    ARTICLE

    Interleaved Boost Integrated Flyback Converter for Power Factor Correction in Brushless DC Motor Drive

    S. Benisha1,*, J. Anitha Roseline2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1363-1378, 2022, DOI:10.32604/iasc.2022.023012
    Abstract The scope of this research is to manage the speed of Permanent Magnet Brushless DC Motor Drive (PMBLDCMD) for various less capacity applications. In the circuit, a 1ϕ AC power is given to Diode Rectifier and the converted DC supply is given to condenser, which leads to abnormal pulsating current. Because of this pulsating current, the power quality disturbances arise at the supply point. Hence, the PMBLDCMD requires Power Factor Correction (PFC) converter for many household and profitable applications. The rotors of PMBLDCMD are driven by 3ϕ voltage source inverter (VSI), which performs electronic commutation. The range of PFC converter… More >

  • Open Access

    ARTICLE

    Research on Cross-domain Representation Learning Based on Multi-network Space Fusion

    Ye Yang1, Dongjie Zhu2,*, Xiaofang Li3, Haiwen Du4, Yundong Sun4, Zhixin Huo2, Mingrui Wu2, Ning Cao1, Russell Higgs5
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1379-1391, 2022, DOI:10.32604/iasc.2022.025181
    Abstract In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation learning research for cross-domain scenarios.… More >

  • Open Access

    ARTICLE

    Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

    J. Faritha Banu1,*, S. B. Rajeshwari2, Jagadish S. Kallimani2, S. Vasanthi3, Ahmed Mateen Buttar4, M. Sangeetha5, Sanjay Bhargava6
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176
    Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as… More >

  • Open Access

    ARTICLE

    A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

    Iqra Abid1, Sultan Almakdi2, Hameedur Rahman3, Ahmed Almulihi4, Ali Alqahtani2, Khairan Rajab2,5, Abdulmajeed Alqhatani2,*, Asadullah Shaikh2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1407-1421, 2022, DOI:10.32604/iasc.2022.023753
    Abstract Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment involves sensitivity in the surgical… More >

  • Open Access

    ARTICLE

    Ant-based Energy Efficient Routing Algorithm for Mobile Ad hoc Networks

    P. E. Irin Dorathy1,*, M. Chandrasekaran2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1423-1438, 2022, DOI:10.32604/iasc.2022.024815
    Abstract In this paper, an Ant Colony Optimization (ACO) based Energy Efficient Shortest Path Routing (AESR) algorithm is developed for Mobile Ad hoc Network (MANET). The Mobile Ad hoc Network consists of a group of mobile nodes that can communicate with each other without any predefined infrastructure. The routing process is critical for this type of network due to its dynamic topology, limited resources and wireless channel. The technique incorporated in this paper for optimizing the routing in a Mobile ad hoc network is Ant Colony Optimization. The ACO algorithm is used to solve network problems related to routing, security, etc.… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction-error Label Map

    Yu Ren1, Jiaohua Qin1,*, Yun Tan1, Neal N. Xiong2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1439-1453, 2022, DOI:10.32604/iasc.2022.025485
    Abstract In the field of reversible data hiding in encrypted images (RDH-EI), predict an image effectively and embed a message into the image with lower distortion are two crucial aspects. However, due to the linear regression prediction being sensitive to outliers, it is a challenge to improve the accuracy of predictions. To address this problem, this paper proposes an RDH-EI scheme based on adaptive prediction-error label map. In the prediction stage, an adaptive threshold estimation algorithm based on local complexity is proposed. Then, the pixels selection method based on gradient of image is designed to train the parameters of the prediction… More >

  • Open Access

    ARTICLE

    Improving the Efficiency of HEV Electronic Applications Using CAN-BUS Communication

    A. George Ansfer*, M. Marsaline Beno
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1455-1472, 2022, DOI:10.32604/iasc.2022.023058
    Abstract A new energy management technique with aid of Controller Area Network (CAN) bus for hybrid electric vehicle (HEV) is proposed in this research. HEV is a type of hybrid vehicle that combines with an electric propulsion system a conventional internal combustion engine system. The electric powertrain operation is intended to achieve greater fuel economy than a conventional vehicle. To accurately distribute power from one of the battery sources, the Energy Management System determines the reference speed for the electric motor drive and the internal combustion engine. The Proportional and Integral (PI) controller is used to maximize the gain of the… More >

  • Open Access

    ARTICLE

    Electricity Theft Detection and Localization in Smart Grids for Industry 4.0

    Worakamol Wisetsri1, Shamimul Qamar2, Gaurav Verma3,*, Deval Verma4, Varun Kumar Kakar5, Thanyanant Chansongpol6, Chanyanan Somtawinpongsai6, Chai Ching Tan7
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1473-1483, 2022, DOI:10.32604/iasc.2022.024610
    Abstract Industry 4.0 is considered as the fourth revolution in industrial sector that represents the digitization of production process in a smarter way. Industry 4.0 refers to the intelligent networking of machines, their processes, and infrastructure, as well as the use of information and computer technology to transform industry. The technologies like industrial internet of things (IIoT), big data analytics, cloud computing, augmented reality and cyber security are the main pillars of industry 4.0. Industry 4.0, in particular, is strongly reliant on the IIoT that refers to the application of internet of things (IoT) in industrial sector like smart grids (SG).… More >

  • Open Access

    ARTICLE

    Moving Object Detection and Tracking Algorithm Using Hybrid Decomposition Parallel Processing

    M. Gomathy Nayagam1,*, K. Ramar2, K. Venkatesh3, S. P. Raja4
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1485-1499, 2022, DOI:10.32604/iasc.2022.023953
    Abstract Moving object detection, classification and tracking are more crucial and challenging task in most of the computer vision and machine vision applications such as robot navigation, human behavior analysis, traffic flow analysis and etc. However, most of object detection and tracking algorithms are not suitable for real time processing and causes slower processing speed due to the processing and analyzing of high resolution video from high-end multiple cameras. It requires more computation and storage. To address the aforementioned problem, this paper proposes a way of parallel processing of temporal frame differencing algorithm for object detection and contour tracking using the… More >

  • Open Access

    ARTICLE

    Multilayer Functional Connectome Fingerprints: Individual Identification via Multimodal Convolutional Neural Network

    Yuhao Chen1, Jiajun Liu1, Yaxi Peng1, Ziyi Liu2, Zhipeng Yang1,*
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1501-1516, 2022, DOI:10.32604/iasc.2022.026346
    Abstract As a neural fingerprint, functional connectivity networks (FCNs) have been used to identify subjects from group. However, a number of studies have only paid attention to cerebral cortex when constructing the brain FCN. Other areas of the brain also play important roles in brain activities. It is widely accepted that the human brain is composed of many highly complex functional networks of cortex. Moreover, recent studies have confirmed correlations between signals of cortex and white matter (WM) bundles. Therefore, it is difficult to reflect the functional characteristics of the brain through a single-layer FCN. In this paper, a multilayer FCN… More >

  • Open Access

    ARTICLE

    Classification of Liver Tumors from Computed Tomography Using NRSVM

    S. Priyadarsini1,*, Carlos Andrés Tavera Romero2, M. Mrunalini3, Ganga Rama Koteswara Rao4, Sudhakar Sengan5
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1517-1530, 2022, DOI:10.32604/iasc.2022.024786
    Abstract A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region… More >

  • Open Access

    ARTICLE

    Total Cross Tied-Inverted Triangle View Configuration for PV System Power Enhancement

    P. Rajesh1,*, K. S. Saji2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1531-1545, 2022, DOI:10.32604/iasc.2022.023331
    Abstract Electricity can be generated from a photovoltaic cell depending on the amount of solar radiation received from the solar system. But due to some factors such as partial shade conditions, as the thickness of the shade increases, the peak power output from the solar photovoltaic system decreases. Photovoltaic cells can be connected in parallel and in series to generate the required voltage and power. Peak power can be obtained even under shade conditions using the appropriate configuration of solar cells. A novel configuration as Total Cross Tied-Inverted Triangle View (TCT-ITV) is developed in the research by augmenting the Total Cross… More >

  • Open Access

    ARTICLE

    Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

    M. Vinoth Kumar1, K. Venkatachalam2, Mehedi Masud3, Mohamed Abouhawwash4,5,*
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1547-1559, 2022, DOI:10.32604/iasc.2022.023961
    Abstract Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim of this research work. Initially… More >

  • Open Access

    ARTICLE

    Improve Representation for Cross-Language Clone Detection by Pretrain Using Tree Autoencoder

    Huading Ling1, Aiping Zhang1, Changchun Yin1, Dafang Li2,*, Mengyu Chang3
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1561-1577, 2022, DOI:10.32604/iasc.2022.027349
    Abstract With the rise of deep learning in recent years, many code clone detection (CCD) methods use deep learning techniques and achieve promising results, so is cross-language CCD. However, deep learning techniques require a dataset to train the models. The dataset is typically small and has a gap between real-world clones due to the difficulty of collecting datasets for cross-language CCD. This creates a data bottleneck problem: data scale and quality issues will cause that model with a better design can still not reach its full potential. To mitigate this, we propose a tree autoencoder (TAE) architecture. It uses unsupervised learning… More >

  • Open Access

    ARTICLE

    Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

    P. R. Sheebha Rani1,*, R. Jemila Rose2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1579-1592, 2022, DOI:10.32604/iasc.2022.023587
    Abstract Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward… More >

  • Open Access

    ARTICLE

    Blockchain Enabled Optimal Lightweight Cryptography Based Image Encryption Technique for IIoT

    R. Bhaskaran1, R. Karuppathal1, M. Karthick2, J. Vijayalakshmi3, Seifedine Kadry4, Yunyoung Nam5,*
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1593-1606, 2022, DOI:10.32604/iasc.2022.024902
    Abstract Industrial Internet of Things (IIoT) and Industry 4.0/5.0 offer several interconnections between machinery, equipment, processes, and personnel in diverse application areas namely logistics, supply chain, manufacturing, transportation, and healthcare. The conventional security-based solutions in IIoT environment get degraded due to the third parties. Therefore, the recent blockchain technology (BCT) can be employed to resolve trust issues and eliminate the need for third parties. Therefore, this paper presents a novel blockchain enabled secure optimal lightweight cryptography based image encryption (BC-LWCIE) technique for industry 4.0 environment. In addition, the BC-LWCIE technique involves the design of an optimal LWC based hash function with… More >

  • Open Access

    ARTICLE

    A New Color Model for Fire Pixels Detection in PJF Color Space

    Amal Ben Hamida1,*, Chokri Ben Amar2, Yasser Albagory2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1607-1621, 2022, DOI:10.32604/iasc.2022.024939
    Abstract Since the number of fires in the world is rising rapidly, automatic fire detection is getting more and more interest in computer vision community. Instead of the usual inefficient sensors, captured videos by video surveillance cameras can be analyzed to quickly detect fires and prevent damages. This paper presents an early fire-alarm raising method based on image processing. The developed work is able to discriminate fire and non-fire pixels. Fire pixels are identified thanks to a rule-based color model built in the PJF color space. PJF is a newly designed color space that enables to better reflect the structure of… More >

  • Open Access

    ARTICLE

    CI-Block: A Blockchain System for Information Management of Collaborative Innovation

    Ruhao Ma1,*, Fansheng Meng1, Haiwen Du2,3
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1623-1637, 2022, DOI:10.32604/iasc.2022.026748
    Abstract Blockchain technology ensures the security of cross-organizational data sharing in the process of collaborative innovation. It drives the development of collaborative innovation in discrete manufacturing to intelligent innovation. However, collaborative innovation is a multi-role, networked, and open resource-sharing process. Therefore, it is easy to form information barriers and increase the risk of cooperation between organizations. In this paper, we firstly analyze the blockchain-based information management models in the traditional discrete manufacturing collaborative innovation process. Then, we found that in the process of industry-university-research (IUR) collaborative innovation, consensus servers maintain too many connections due to the high latency between them, which… More >

  • Open Access

    ARTICLE

    Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

    R. Saravana Ram1, K. Venkatachalam2, Mehedi Masud3, Mohamed Abouhawwash4,5,*
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962
    Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT device in the network. With… More >

  • Open Access

    ARTICLE

    Sensor Location Optimisation Design Based on IoT and Geostatistics in Greenhouse

    Yang Liu1,3, Xiaoyu Liu2,3, Xiu Dai1, Guanglian Xun1, Ni Ren1,*, Rui Kang1,4, Xiaojuan Mao1
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1653-1663, 2022, DOI:10.32604/iasc.2022.017049
    Abstract Environmental parameters such as air temperature (T) and air relative humidity (RH) should be intensively monitored in a greenhouse in real time. In most cases, one set of sensors is installed in the centre of a greenhouse. However, as the microclimate of a greenhouse is always heterogeneous, the sensor installation location is crucial for practical cultivation. In this study, the T and RH monitoring performance of different sensors were compared. Two types of real-time environmental sensors (Air Temperature and Humidity sensor and Activity Monitoring sensor, referred as ATH and AM) were selected and calibrated by reliable non-real-time sensors (Honest Observer… More >

  • Open Access

    ARTICLE

    Design of Logically Obfuscated Memory and Arithmetic Logic Unit for Improved Hardware Security

    M. Usharani1,*, B. Sakthivel2, K. Jayaram3, R. Renugadevi4
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1665-1675, 2022, DOI:10.32604/iasc.2022.023284
    Abstract In any kind of digital system, the processor and memories are used to play a vital role in today’s trend. The processors and memories are done many critical tasks in the system. Whereas the processor used to do several functions and memories used to store and retrieve the data. But these processors and memories are more vulnerable to various hardware attacks. By using several new devices may lead to many security issues which the attackers can leverage to introduce a new hardware attack. Various hardware security (HS) studies have been presented to prevent hardware from a security issue. Some of… More >

  • Open Access

    ARTICLE

    Indoor Scene Splicing Based on Genetic Algorithm and ORB

    Tao Zhang1,*, Yi Cao2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1677-1685, 2022, DOI:10.32604/iasc.2022.027082
    Abstract The images generated by the image stitching algorithm have false shadow and poor real-time performance, and are difficult to maintain visual consistency. For this reason, a panoramic image stitching algorithm based on genetic algorithm is proposed. First, the oriented fast and rotated brief (ORB) algorithm is used to quickly perform detection and description of feature, then the initial feature point pairs are extracted according to the Euclidean distance for feature point rough matching, the parallelism of genetic algorithm is used to optimize the feature point matching performance. Finally, the PROSAC algorithm is used to remove mismatched point pairs and get… More >

  • Open Access

    ARTICLE

    Modified Optimization for Efficient Cluster-based Routing Protocol in Wireless Sensor Network

    Marwah Mohammad Almasri1,*, Abrar Mohammed Alajlan2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1687-1710, 2022, DOI:10.32604/iasc.2022.023240
    Abstract Wireless Sensor Networks (WSN) comprise numerous sensor nodes for monitoring specific areas. Great deals of efforts have been achieved to obtain effective routing approaches using clustering methods. Clustering is considered an effective way to provide a better route for transmitting the data, but cluster head selection and route generation is considered as a complicated task. To manage such complex issues and to enhance network lifetime and energy consumption, an energy-effective cluster-based routing approach is proposed. As the major intention of this paper is to select an optimal cluster head, this paper proposes a modified golden eagle optimization (M-GEO) algorithm to… More >

  • Open Access

    ARTICLE

    Energy Enhancement of Permanent Magnet Synchronous Generators Using Particle Swarm Optimization

    S. Marisargunam1,*, L. Kalaivani2, R. V. Maheswari2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1711-1727, 2022, DOI:10.32604/iasc.2022.023643
    Abstract Wind Energy Conversion Systems (WECS) are extensively used for connecting directly to grid sources. Permanent magnet synchronous generator (PMSG) based WECS is coupled to both grid and machine through converters. PMSG usually associated rectifiers with converters and voltage source converters at machine side. In this work, PMSG associated rectifiers with converters are considered for analysis of grid stability. The proposed work used Particle Swarm Optimization (PSO) based optimization methods for extraction of maximum power within boundary condition in WECS operation using PMSG. This high-tech optimization has MPPT controller for pitch angle controller (PAC) combines with PSO optimized controllers for converter… More >

  • Open Access

    ARTICLE

    Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

    Vineetha Mathai*, P. Indumathi
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234
    Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish… More >

  • Open Access

    ARTICLE

    Security Data Sharing of Shipbuilding Information Based on Blockchain

    Jun Zhu1,*, Chaosong Yan2, Yinglong Ouyang3, Yao Chen4, Xiaowan Wang5
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1747-1756, 2022, DOI:10.32604/iasc.2022.026934
    Abstract The shipbuilding industry has problems such as long transaction cycles, many participating suppliers, and data sensitivity. A time-dimensional shipbuilding information security sharing scheme based on an alliance chain is proposed to solve this problem. The blockchain can better deliver value and protect user privacy, the blockchain can directly complete the instant transfer of value through smart contracts and tokens on the blockchain. We divide the program into three parts: data preprocessing, data storage, and data sharing. For data sensitivity, data confidentiality and reliability are handled separately. Aiming at the problem of user privacy leakage in data storage, a privacy-protecting blockchain… More >

  • Open Access

    ARTICLE

    Hybrid Sensorless Speed Control Technique for BLDC Motor Using ANFIS Automation

    S. S. Selva Pradeep*, M. Marsaline Beno
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1757-1770, 2022, DOI:10.32604/iasc.2022.023470
    Abstract The Brushless Direct Current (BLDC) motors have shown to be a cost-effective alternative to traditional motors. The smooth and efficient operation of the BLDC motor is dependent on speed regulation. This research proposes a sensorless intelligent speed control technique for BLDC using an Adaptive Network-based Fuzzy Inference Systems (ANFIS) based Artificial Bee Colony (ABC) algorithm. The motor’s back EMF is measured, and ANFIS is used to generate Hall signals. The ABC is then utilized to provide the pulses needed for the three-phase inverter, avoiding the requirement of logic gate circuits. The input DC voltage to the inverter is controlled by… More >

  • Open Access

    ARTICLE

    Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center

    B. Gomathi1, B. Saravana Balaji2, V. Krishna Kumar3, Mohamed Abouhawwash4,5,*, Sultan Aljahdali6, Mehedi Masud6, Nina Kuchuk7
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1771-1785, 2022, DOI:10.32604/iasc.2022.024052
    Abstract Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in Cloud Environment. In this paper,… More >

  • Open Access

    ARTICLE

    Tuning Rules for Fractional Order PID Controller Using Data Analytics

    P. R. Varshini*, S. Baskar, M. Varatharajan, S. Sadhana
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1787-1799, 2022, DOI:10.32604/iasc.2022.024192
    Abstract

    Flexibility and robust performance have made the FOPID (Fractional Order PID) controllers a better choice than PID (Proportional, Integral, Derivative) controllers. But the number of tuning parameters decreases the usage of FOPID controllers. Using synthetic data in available FOPID tuners leads to abnormal controller performances limiting their applicability. Hence, a new tuning methodology involving real-time data and overcomes the drawbacks of mathematical modeling is the need of the hour. This paper proposes a novel FOPID controller tuning methodology using machine learning algorithms. Feed Forward Back Propagation Neural Network (FFBPNN), Multi Least Squares Support Vector Regression (MLSSVR) chosen to design Machine… More >

  • Open Access

    ARTICLE

    Energy-saving-oriented Berth Scheduling Model at Bulk Terminal

    Xiaona Hu1,2, Baiqing Zhou1,*, Jinyue Xia3, Yao Chen4, Gang Hu5
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1801-1813, 2022, DOI:10.32604/iasc.2022.027034
    Abstract With the global warming to the survival and development of mankind, more and more attention is paid to low-carbon, green and energy-saving production. As one of the main modes of international transportation, the wharf has been facing a serious problem of its high carbon-emission. In order to balance the relationship between port energy consumption and efficiency, it is necessary to study the berth allocation, loading and unloading of bulk terminal from the perspective of energy saving with the proposal of energy saving and emission reduction in China. Both energy saving and efficiency can be achieved at the bulk terminal in… More >

  • Open Access

    ARTICLE

    Stream Cipher Based on Game Theory and DNA Coding

    Khaled Suwais*
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1815-1834, 2022, DOI:10.32604/iasc.2022.025076
    Abstract Securing communication over public communication channels is one of the challenging issues in the field of cryptography and information security. A stream cipher is presented as an approach for securing exchanged data between different parties through encryption. The core of stream cipher relies on its keystream generator, that is responsible for generating random and secure keystream of encrypting streaming data. Thus, the security of the keystream is measured by its randomness and its resistance to statistical and cryptanalytic attacks. As there is always a trade-off between the security and performance while designing new cryptographic primitives, we introduce a game theory-based… More >

  • Open Access

    ARTICLE

    Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

    P. Saravanakumar1, T. V. P. Sundararajan2, Rajesh Kumar Dhanaraj3, Kashif Nisar4,*, Fida Hussain Memon5,6, Ag. Asri Bin Ag. Ibrahim4
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1835-1847, 2022, DOI:10.32604/iasc.2022.018953
    Abstract Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes (key generation, signcryption and unsigncryption)… More >

  • Open Access

    ARTICLE

    Detection of Microbial Activity in Silver Nanoparticles Using Modified Convolution Network

    D. Devina Merin1,*, P. Jagatheeswari2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495
    Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the networking system and the DL… More >

  • Open Access

    ARTICLE

    Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection

    Xiaosha Qi1, Genlin Ji2,*, Jie Zhang2, Bo Sheng3
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.027182
    Abstract Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE). The proposed method improves the… More >

  • Open Access

    ARTICLE

    Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

    D. Dhinakaran1,*, P. M. Joe Prathap2
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1877-1892, 2022, DOI:10.32604/iasc.2022.024509
    Abstract These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals’ crude information. We devised two unique protocols for frequent mining itemsets in horizontally partitioned… More >

  • Open Access

    ARTICLE

    Context-Aware Service Model of a Mobile Library Based on Internet of Things

    Wei Gao1, Haixu Xi1,2,*, Gyun Yeol Park3
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1893-1906, 2022, DOI:10.32604/iasc.2022.023207
    Abstract Appropriate technology needs to be applied in libraries to provide users with more humanized, intelligent, and convenient services to improve service quality. Using theories from library science, management, and modeling, this paper examines library personalized service in the intelligent Internet of Things (IoT) environment using a literature review, comparative analysis, and UML modeling to analyze the influencing factors of mobile library users’ acceptance of personalized recommendation services. Based on the situational awareness framework, the experimental results of the effect of these personalized service recommendations show that the load factor is greater than 0.6, which indicates that the dimensions of a… More >

  • Open Access

    ARTICLE

    Design of Clustering Enabled Intrusion Detection with Blockchain Technology

    S. Vimal1, S. Nalini2,*, K. Anguraj3, T. Chelladurai4
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1907-1921, 2022, DOI:10.32604/iasc.2022.025219
    Abstract Recent advancements in hardware and networking technologies have resulted in a large growth in the number of Internet of Things (IoT) devices connected to the Internet, which is likely to continue growing in the coming years. Traditional security solutions are insufficiently suited to the IoT context due to the restrictions and diversity of the resources available to objects. Security techniques such as intrusion detection and authentication are considered to be effective. Additionally, the decentralised and distributed nature of Blockchain technology makes it an excellent solution for overcoming the security issue. This paper proposes a chaotic bird swarm algorithm (CBSA)-based clustering… More >

  • Open Access

    ARTICLE

    Computational Approximations for Real-World Application of Epidemic Model

    Shami A. M. Alsallami1, Ali Raza2,*, Mona Elmahi3, Muhammad Rafiq4, Shamas Bilal5, Nauman Ahmed6, Emad E. Mahmoud7
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1923-1939, 2022, DOI:10.32604/iasc.2022.024993
    Abstract The real-world applications and analysis have a significant role in the scientific literature. For instance, mathematical modeling, computer graphics, camera, operating system, Java, disk encryption, web, streaming, and many more are the applications of real-world problems. In this case, we consider disease modeling and its computational treatment. Computational approximations have a significant role in different sciences such as behavioral, social, physical, and biological sciences. But the well-known techniques that are widely used in the literature have many problems. These methods are not consistent with the physical nature and even violate the actual behavior of the continuous model. The structural properties… More >

  • Open Access

    ARTICLE

    Enhanced Distributed Storage System Using Lower Triangular Matrix-Luby Transform Codes

    Joe Louis Paul Ignatius*, Sasirekha Selvakumar
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1941-1959, 2022, DOI:10.32604/iasc.2022.024173
    Abstract In today’s digital environment, large volume of digital data is created daily and this data accumulates to unforeseen levels. Industries are finding it increasingly difficult to store data in an effective and trustworthy manner. Distributed storage appears to be the greatest approach for meeting current data storage demands at the moment. Furthermore, due to disc crashes or failures, efficient data recovery is becoming an issue. At present, new data storage techniques are required in order to restore data effectively even if some discs or servers are crashed. Hence, this proposed work aims to improve the storage efficiency and reliability in… More >

  • Open Access

    ARTICLE

    Image Masking and Enhancement System for Melanoma Early Stage Detection

    Fikret Yalcinkaya*, Ali Erbas
    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1961-1977, 2022, DOI:10.32604/iasc.2022.024961
    Abstract Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high… More >

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