Home / Journals / IASC / Vol.35, No.3, 2023
Special lssues
  • Open AccessOpen Access

    ARTICLE

    Improved Electro Search Algorithm with Intelligent Controller Control System: ESPID Algorithm

    Inayet Hakki Cizmeci1,*, Adem Alpaslan Altun2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2555-2572, 2023, DOI:10.32604/iasc.2023.028851
    Abstract Studies have established that hybrid models outperform single models. The particle swarm algorithm (PSO)-based PID (proportional-integral-derivative) controller control system is used in this study to determine the parameters that directly impact the speed and performance of the Electro Search (ESO) algorithm to obtain the global optimum point. ESPID algorithm was created by integrating this system with the ESO algorithm. The improved ESPID algorithm has been applied to 7 multi-modal benchmark test functions. The acquired results were compared to those derived using the ESO, PSO, Atom Search Optimization (ASO), and Vector Space Model (VSM) algorithms. As More >

  • Open AccessOpen Access

    ARTICLE

    User Interface-Based Repeated Sequence Detection Method for Authentication

    Shin Jin Kang1, Soo Kyun Kim2,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2573-2588, 2023, DOI:10.32604/iasc.2023.029893
    Abstract In this paper, we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security. The proposed method identifies personalized repeated user interface (UI) sequences by analyzing mouse and keyboard data. To this end, an Apriori algorithm based on the keystroke-level model (KLM) of the human–computer interface domain was used. The proposed system can detect repeated UI sequences based on KLM for authentication in the software. The effectiveness of the proposed method is verified through access testing using commercial applications that require intensive UI interactions. The results show using our More >

  • Open AccessOpen Access

    ARTICLE

    Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

    Tariq T. Alshammari1, Mohd Tahir Ismail1, Nawaf N. Hamadneh3,*, S. Al Wadi2, Jamil J. Jaber2, Nawa Alshammari3, Mohammad H. Saleh2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2589-2601, 2023, DOI:10.32604/iasc.2023.024001
    Abstract In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

    Yining Qi1,2,*, Yubo Luo3, Yongfeng Huang1,2, Xing Li1,2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2603-2618, 2023, DOI:10.32604/iasc.2023.030191
    Abstract Cloud storage has been widely used to team work or cooperation development. Data owners set up groups, generating and uploading their data to cloud storage, while other users in the groups download and make use of it, which is called group data sharing. As all kinds of cloud service, data group sharing also suffers from hardware/software failures and human errors. Provable Data Possession (PDP) schemes are proposed to check the integrity of data stored in cloud without downloading. However, there are still some unmet needs lying in auditing group shared data. Researchers propose four issues… More >

  • Open AccessOpen Access

    ARTICLE

    Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

    A. Naresh Kumar1,*, G. Geetha2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2619-2637, 2023, DOI:10.32604/iasc.2023.028349
    Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools. This stipulation make the dispensation period overriding, difficult and tiresome to calculate. This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance. For adaptively determining these weights, this paper applies various More >

  • Open AccessOpen Access

    ARTICLE

    Artificial Intelligence Model for Software Reusability Prediction System

    R. Subha1,*, Anandakumar Haldorai1, Arulmurugan Ramu2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2639-2654, 2023, DOI:10.32604/iasc.2023.028153
    Abstract The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Anomaly Monitoring in Public Surveillance Areas

    Mohammed Alarfaj1, Mahwish Pervaiz2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Suliman A. Alsuhibany5, Ahmad Jalal6, Jeongmin Park7,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2655-2671, 2023, DOI:10.32604/iasc.2023.027205
    Abstract With the dramatic increase in video surveillance applications and public safety measures, the need for an accurate and effective system for abnormal/suspicious activity classification also increases. Although it has multiple applications, the problem is very challenging. In this paper, a novel approach for detecting normal/abnormal activity has been proposed. We used the Gaussian Mixture Model (GMM) and Kalman filter to detect and track the objects, respectively. After that, we performed shadow removal to segment an object and its shadow. After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and… More >

  • Open AccessOpen Access

    ARTICLE

    Pre Screening of Cervical Cancer Through Gradient Boosting Ensemble Learning Method

    S. Priya1,*, N. K. Karthikeyan1, D. Palanikkumar2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2673-2685, 2023, DOI:10.32604/iasc.2023.028599
    Abstract In recent years, cervical cancer is one of the most common diseases which occur in any woman regardless of any age. This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage. For women at a certain age, it is better to have a proper screening for cervical cancer. In most underdeveloped nations, it is very difficult to have frequent scanning for cervical cancer. Data Mining and machine learning methodologies help widely in finding the important causes for cervical cancer. The proposed work describes a multi-class More >

  • Open AccessOpen Access

    ARTICLE

    Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

    Tolga Memika*, Tulay Korkusuz Polat
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2687-2702, 2023, DOI:10.32604/iasc.2023.026955
    Abstract The existing systems sustained with the investments made require more automation and digital transformation with the continuous advancement of technology. The aviation industry is a sector that is open to more automation and digital transformation, mainly because of the intense competition and the analysis of a large variety of data. The long duration of operations in current airline processes and some process flows cause customer dissatisfaction and cost increase. In this study, the boarding process, which is one of the operational processes of airline transportation and is open to improvement, was discussed. The classical boarding… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Approach for Improving the PQ in SPIM

    P. Jenitha Deepa*, H. Habeebullah Sait
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2703-2715, 2023, DOI:10.32604/iasc.2023.030496
    Abstract Single Phase Induction Motor (SPIM) is widely used in industries at starting stage to provide high starting torque. The objective of the work is to develop a drive for Single Phase Induction Motor that does not use a start or run capacitor. In this work, the researchers present the details about Maximum Power Point Tracking using series-compensated Buck Boost Converter, resonant Direct Current (DC) to Alternate Current (AC) inverter and matrix converter-based drive. The proposed method provides a variable starting torque feature that can be adjusted depending upon machine load to ensure Power Quality (PQ). More >

  • Open AccessOpen Access

    ARTICLE

    Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

    P. Samuel Pakianathan*, R. V. Maheswari
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2717-2736, 2023, DOI:10.32604/iasc.2023.029950
    Abstract This research investigates the dielectric performance of Natural Ester (NE) using the Partial Differential Equation (PDE) tool and analyzes dielectric performance using fuzzy logic. NE nowadays is found to replace Mineral Oil (MO) due to its extensive dielectric properties. Here, the heat-tolerant Natural Esters Olive oil (NE1), Sunflower oil (NE2), and Ricebran oil (NE3) are subjected to High Voltage AC (HVAC) under different electrodes configurations. The breakdown voltage and leakage current of NE1, NE2, and NE3 under Point-Point (P-P), Sphere-Sphere (S-S), Plane-Plane (PL-PL), and Rod-Rod (R-R) are measured, and survival probability is presented. The electric… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Expressive Attribute-Based Encryption with Keyword Search over Prime-Order Groups

    Qing Miao1, Lan Guo1, Yang Lu1,*, Zhongqi Wang2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2737-2754, 2023, DOI:10.32604/iasc.2023.028693
    Abstract Attribute-based encryption with keyword search (ABEKS) is a novel cryptographic paradigm that can be used to implement fine-grained access control and retrieve ciphertexts without disclosing the sensitive information. It is a perfect combination of attribute-based encryption (ABE) and public key encryption with keyword search (PEKS). Nevertheless, most of the existing ABEKS schemes have limited search capabilities and only support single or simple conjunctive keyword search. Due to the weak search capability and inaccurate search results, it is difficult to apply these schemes to practical applications. In this paper, an efficient expressive ABEKS (EABEKS) scheme supporting More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

    T. Manikandan1, S. Karthikeyan2,*, J. Jai Jaganath Babu3, G. Babu4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2755-2771, 2023, DOI:10.32604/iasc.2023.030497
    Abstract Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at… More >

  • Open AccessOpen Access

    ARTICLE

    Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

    Amal H. Alharbi1, S. Karthick2, K. Venkatachalam3, Mohamed Abouhawwash4,5, Doaa Sami Khafaga1,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763
    Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Approach to Design Distribution Preserving Framework for Big Data

    Mini Prince1,*, P. M. Joe Prathap2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2789-2803, 2023, DOI:10.32604/iasc.2023.029533
    Abstract

    In several fields like financial dealing, industry, business, medicine, et cetera, Big Data (BD) has been utilized extensively, which is nothing but a collection of a huge amount of data. However, it is highly complicated along with time-consuming to process a massive amount of data. Thus, to design the Distribution Preserving Framework for BD, a novel methodology has been proposed utilizing Manhattan Distance (MD)-centered Partition Around Medoid (MD–PAM) along with Conjugate Gradient Artificial Neural Network (CG-ANN), which undergoes various steps to reduce the complications of BD. Firstly, the data are processed in the pre-processing phase by

    More >

  • Open AccessOpen Access

    ARTICLE

    LSTM Based Spectrum Prediction for Real-Time Spectrum Access for IoT Applications

    R. Nandakumar1, Vijayakumar Ponnusamy2,*, Aman Kumar Mishra2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2805-2819, 2023, DOI:10.32604/iasc.2023.028645
    Abstract In the Internet of Things (IoT) scenario, many devices will communicate in the presence of the cellular network; the chances of availability of spectrum will be very scary given the presence of large numbers of mobile users and large amounts of applications. Spectrum prediction is very encouraging for high traffic next-generation wireless networks, where devices/machines which are part of the Cognitive Radio Network (CRN) can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sensing radio spectrum. Long short-term memory (LSTM) is employed to simultaneously predict the Radio Spectrum More >

  • Open AccessOpen Access

    ARTICLE

    Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

    Riaz Muhammad1, Ahmed Ali1, M. Abid Anwar1, Toufique Ahmed Soomro2,*, Omar AlShorman3, Adel Alshahrani4, Mahmoud Masadeh5, Ghulam Md Ashraf6,7, Naif H. Ali8, Muhammad Irfan9, Athanasios Alexiou10
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2821-2835, 2023, DOI:10.32604/iasc.2023.026279
    Abstract Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10–20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG… More >

  • Open AccessOpen Access

    ARTICLE

    Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

    P. P. Fathimathul Rajeena1,*, R. Sivakumar2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2837-2855, 2023, DOI:10.32604/iasc.2023.030144
    Abstract An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in… More >

  • Open AccessOpen Access

    ARTICLE

    Routing with Cooperative Nodes Using Improved Learning Approaches

    R. Raja1,*, N. Satheesh2, J. Britto Dennis3, C. Raghavendra4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2857-2874, 2023, DOI:10.32604/iasc.2023.026153
    Abstract In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput,… More >

  • Open AccessOpen Access

    ARTICLE

    Smart-Grid Monitoring using IoT with Modified Lagranges Key Based Data Transmission

    C. K. Morarji1,*, N. Sathish Kumar2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2875-2892, 2023, DOI:10.32604/iasc.2023.025776
    Abstract One of the recent advancements in the electrical power systems is the smart-grid technology. For the effective functioning of the smart grid, the process like monitoring and controlling have to be given importance. In this paper, the Wireless Sensor Network (WSN) is utilized for tracking the power in smart grid applications. The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data. The data is transmitted quickly by using the Probabilistic Neural Network (PNN), which aids in identifying the shortest path of the nodes. More >

  • Open AccessOpen Access

    ARTICLE

    Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

    C. Santhosh Kumar1, K. Vishnu Kumar2,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669
    Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy More >

  • Open AccessOpen Access

    ARTICLE

    Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

    S. J. K. Jagadeesh Kumar1, P. Parthasarathi2, Mehedi Masud3, Jehad F. Al-Amri4, Mohamed Abouhawwash5,6,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2909-2924, 2023, DOI:10.32604/iasc.2023.030335
    Abstract The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is More >

  • Open AccessOpen Access

    ARTICLE

    Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

    H. Manikandan1,*, D. Narasimhan2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2925-2939, 2023, DOI:10.32604/iasc.2023.029754
    Abstract Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed More >

  • Open AccessOpen Access

    ARTICLE

    Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

    Raqinah Alrabiah, Muhammad Hussain*, Hatim A. AboAlSamh
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2941-2962, 2023, DOI:10.32604/iasc.2023.030036
    Abstract The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing… More >

  • Open AccessOpen Access

    ARTICLE

    Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

    Anwar R Shaheen*, Sundar Santhosh Kumar
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2963-2978, 2023, DOI:10.32604/iasc.2023.025780
    Abstract Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service. One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling. The main advantage of this scheduling is to maximize the performance and minimize the time loss. Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time. However, it had disadvantages in terms of low throughput and high response time. Hence, this study aimed to schedule the task efficiently… More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

    P. Ganesan1,*, S. Arockia Edwin Xavier2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2979-2996, 2023, DOI:10.32604/iasc.2023.029264
    Abstract Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus… More >

  • Open AccessOpen Access

    ARTICLE

    SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

    Nabanita Dutta1, Palanisamy Kaliannan1,*, Paramasivam Shanmugam2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2997-3020, 2023, DOI:10.32604/iasc.2023.028704
    Abstract Vibration failure in the pumping system is a significant issue for industries that rely on the pump as a critical device which requires regular maintenance. To save energy and money, a new automated system must be developed that can detect anomalies at an early stage. This paper presents a case study of a machine learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive (VFD). Since the intensity of the vibrational effect depends on which axis has the most significant effect, a three-axis accelerometer is used to… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Quarantine Environment Privacy through IoT Gadgets Using Blockchain

    Nitish Pathak1, Shams Tabrez Siddiqui2, Anjani Kumar Singha3, Heba G Mohamed4, Shabana Urooj4,*, Abhinandan R Patil5
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3021-3036, 2023, DOI:10.32604/iasc.2023.029053
    Abstract The coronavirus, formerly known as COVID-19, has caused massive global disasters. As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations. Furthermore, governments around the world have used cutting-edge technologies to track citizens’ activity. Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound. In this study, we suggest a blockchain-based methodology for safeguarding data in the billions More >

  • Open AccessOpen Access

    ARTICLE

    Evaluation of Codebook Design Using SCMA Scheme Based on An and Dn Lattices

    G. Rajamanickam1,*, G. Ravi2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3037-3048, 2023, DOI:10.32604/iasc.2023.029996
    Abstract The sparse code multiple access (SCMA) scheme is a Non-Orthogonal Multiple Access (NOMA) type of scheme that is used to handle the uplink component of mobile communication in the current generation. A need of the 5G mobile network is the ability to handle more users. To accommodate this, the SCMA allows each user to deploy a variety of sub-carrier broadcasts, and several consumers may contribute to the same frequency using superposition coding. The SCMA approach, together with codebook design for each user, is used to improve channel efficiency through better management of the available spectrum.… More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

    G. Anurekha*, P. Geetha
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3049-3064, 2023, DOI:10.32604/iasc.2023.029127
    Abstract Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and… More >

  • Open AccessOpen Access

    ARTICLE

    Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

    P. Anantha Prabha1,*, G. Suchitra2, R. Saravanan3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3065-3079, 2023, DOI:10.32604/iasc.2023.030017
    Abstract Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist. Manual observation and identification take time and are always contingent on the involvement of experts. A system is proposed to alleviate this challenge that uses transfer learning techniques to classify the cephalopods automatically. In the proposed method, only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition. First, the efficiency of the chosen models is determined by evaluating their performance and comparing the findings. Second, the models are fine-tuned by adding dense layers… More >

  • Open AccessOpen Access

    ARTICLE

    Genetic Algorithm Based 7-Level Step-Up Inverter with Reduced Harmonics and Switching Devices

    T. Anand Kumar1,*, M. Kaliamoorthy1, I. Gerald Christopher Raj2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3081-3097, 2023, DOI:10.32604/iasc.2023.028769
    Abstract This paper presents a unique voltage-raising topology for a single-phase seven-level inverter with triple output voltage gain using single input source and two switched capacitors. The output voltage has been boosted up to three times the value of input voltage by configuring the switched capacitors in series and parallel combinations which eliminates the use of additional step-up converters and transformers. The selective harmonic elimination (SHE) approach is used to remove the lower-order harmonics. The optimal switching angles for SHE is determined using the genetic algorithm. These switching angles are combined with a level-shifted pulse width More >

  • Open AccessOpen Access

    ARTICLE

    Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

    Junhua Xi1, Kouquan Zheng1, Yifan Zhong2, Longjiang Li3, Zhiping Cai1,*, Jinjing Chen4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3099-3111, 2023, DOI:10.32604/iasc.2023.030298
    Abstract In geometry processing, symmetry research benefits from global geometric features of complete shapes, but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Different from the existing works predicting symmetry from the complete shape, we propose a learning approach for symmetry prediction based on a single RGB-D image. Instead of directly predicting the symmetry from incomplete shapes, our method consists of two modules, i.e., the multi-modal feature fusion module and the detection-by-reconstruction module. Firstly, we build a channel-transformer network (CTN) to extract More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

    R. Radha1,*, R. Gopalakrishnan2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3113-3127, 2023, DOI:10.32604/iasc.2023.030667
    Abstract In the field of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity. Locating the defective cells precisely during the diagnosis phase helps to fight the greatest exterminator of mankind. Early detection of these defective cells requires an accurate computer-aided diagnostic system (CAD) that supports early treatment and promotes survival rates of patients. An earlier version of CAD systems relies greatly on the expertise of radiologist and it consumed more time to identify the defective… More >

  • Open AccessOpen Access

    ARTICLE

    Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

    Hanan Abdullah Mengash1, Alaaeldin M. Hafez2, Hanan A. Hosni Mahmoud3,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3129-3140, 2023, DOI:10.32604/iasc.2023.030836
    Abstract Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate More >

  • Open AccessOpen Access

    ARTICLE

    A Mixed Method for Feature Extraction Based on Resonance Filtering

    Xia Zhang1,2, Wei Lu3, Youwei Ding1,*, Yihua Song1, Jinyue Xia4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3141-3154, 2023, DOI:10.32604/iasc.2023.027219
    Abstract Machine learning tasks such as image classification need to select the features that can describe the image well. The image has individual features and common features, and they are interdependent. If only the individual features of the image are emphasized, the neural network is prone to overfitting. If only the common features of images are emphasized, neural networks will not be able to adapt to diversified learning environments. In order to better integrate individual features and common features, based on skeleton and edge individual features extraction, this paper designed a mixed feature extraction method based… More >

  • Open AccessOpen Access

    ARTICLE

    VLSI Implementation of Optimized 2D SIMM Chaotic Map for Image Encryption

    M. Sundar Prakash Balaji1,*, V. R. Vijaykumar2, Kamalraj Subramaniam3, M. Kannan4, V. Ayyem Pillai5
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3155-3168, 2023, DOI:10.32604/iasc.2023.028969
    Abstract The current research work proposed a novel optimization-based 2D-SIMM (Two-Dimensional Sine Iterative chaotic map with infinite collapse Modulation Map) model for image encryption. The proposed 2D-SIMM model is derived out of sine map and Iterative Chaotic Map with Infinite Collapse (ICMIC). In this technique, scrambling effect is achieved with the help of Chaotic Shift Transform (CST). Chaotic Shift Transform is used to change the value of pixels in the input image while the substituted value is cyclically shifted according to the chaotic sequence generated by 2D-SIMM model. These chaotic sequences, generated using 2D-SIMM model, are More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

    M. Manikandan1,*, S. Sakthivel2, V. Vivekanandhan1
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3169-3185, 2023, DOI:10.32604/iasc.2023.029232
    Abstract Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability… More >

  • Open AccessOpen Access

    ARTICLE

    New Preamble Sequence for WiMAX System with Improved Synchronization Accuracy

    Suma Sekhar1,*, Sakuntala S. Pillai2, S. Santhosh Kumar3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3187-3197, 2023, DOI:10.32604/iasc.2023.028702
    Abstract Worldwide Interoperability for Microwave Access (WiMAX) trusts Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) combination for the deployment of physical layer functions and for connecting the medium access control to the wireless media. Even though Orthogonal Frequency Division Multiplexing (OFDM) facilitates reliable digital broadband transmission in the fading wireless channels, the presence of synchronization errors in the form of Carrier Frequency Offset (CFO) and Time Offset (TO) adversely affect the performance of OFDM based physical layers. The objective of this work is to improve the accuracy of the frequency and the time offset estimation More >

  • Open AccessOpen Access

    ARTICLE

    Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

    K. Arutchelvan1, R. Sathiya Priya1,*, C. Bhuvaneswari2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3199-3212, 2023, DOI:10.32604/iasc.2023.029804
    Abstract Wireless sensor network (WSN) includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications. It comprises a massive set of nodes with restricted energy and processing abilities. Energy dissipation is a major concern involved in the design of WSN. Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms. In order to design an energy aware cluster-based route planning scheme, this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing (HBAC-AVOR) protocol for WSN. The presented HBAC-AVOR… More >

  • Open AccessOpen Access

    ARTICLE

    An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

    N. Kanagaraj*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3213-3226, 2023, DOI:10.32604/iasc.2023.029901
    Abstract The design and analysis of a fractional order proportional integral derivate (FOPID) controller integrated with an adaptive neuro-fuzzy inference system (ANFIS) is proposed in this study. A first order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme. In the proposed adaptive control structure, the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors (λ and µ) of the FOPID (also known as PIλDµ) controller to achieve better control performance. When the plant experiences uncertainties like external load disturbances or sudden changes in the… More >

  • Open AccessOpen Access

    ARTICLE

    Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

    Hemant Kumar Vijayvergia1,*, Uma Shankar Modani2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3227-3239, 2023, DOI:10.32604/iasc.2023.031357
    Abstract In wireless sensor network (WSN), the gateways which are placed far away from the base station (BS) forward the collected data to the BS through the gateways which are nearer to the BS. This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load. So, to overcome this issue, loads around the gateways are to be balanced by presenting energy efficient clustering approach. Besides, to enhance the lifetime of the network, optimal routing path is to be established between the source node and BS. For energy efficient load balancing… More >

  • Open AccessOpen Access

    ARTICLE

    Content-Based Movie Recommendation System Using MBO with DBN

    S. Sridhar1,*, D. Dhanasekaran2, G. Charlyn Pushpa Latha3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3241-3257, 2023, DOI:10.32604/iasc.2023.030361
    Abstract The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS). The user explicitly or implicitly provides data in the Content-Based Recommender System. The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile. The recommendation generated via content-based filtering is provided by observing just a single user’s profile. The primary objective of this RS is to recommend a list of movies based on the user’s preferences. A content-based movie recommendation model is proposed in this research, which recommends movies… More >

  • Open AccessOpen Access

    ARTICLE

    Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

    A. K. Z Rasel Rahman1, S. M. Nabil Sakif1, Niloy Sikder1, Mehedi Masud2, Hanan Aljuaid3, Anupam Kumar Bairagi1,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3259-3277, 2023, DOI:10.32604/iasc.2023.030142
    Abstract Disasters may occur at any time and place without little to no presage in advance. With the development of surveillance and forecasting systems, it is now possible to forebode the most life-threatening and formidable disasters. However, forest fires are among the ones that are still hard to anticipate beforehand, and the technologies to detect and plot their possible courses are still in development. Unmanned Aerial Vehicle (UAV) image-based fire detection systems can be a viable solution to this problem. However, these automatic systems use advanced deep learning and image processing algorithms at their core and… More >

  • Open AccessOpen Access

    ARTICLE

    Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

    N. Venkata Subramanian1, V. S. Shankar Sriram2,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3279-3294, 2023, DOI:10.32604/iasc.2023.023700
    Abstract

    Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers’ (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue,

    More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

    S. A. Shaban*, Hosnia M. M. Ahmed, D. L. Elsheweikh
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3295-3315, 2023, DOI:10.32604/iasc.2023.030237
    Abstract With the rapid spread of the coronavirus epidemic all over the world, educational and other institutions are heading towards digitization. In the era of digitization, identifying educational e-platform users using ear and iris based multimodal biometric systems constitutes an urgent and interesting research topic to preserve enterprise security, particularly with wearing a face mask as a precaution against the new coronavirus epidemic. This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations (E-exams) during the COVID-19 pandemic. The proposed system comprises four… More >

  • Open AccessOpen Access

    ARTICLE

    Cayley Picture Fuzzy Graphs and Interconnected Networks

    Waheed Ahmad Khan1,*, Khurram Faiz1, Abdelghani Taouti2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3317-3330, 2023, DOI:10.32604/iasc.2023.024484
    Abstract Theory of the Cayley graphs is directly linked with the group theory. However, if there are uncertainties on the vertices or edges or both then fuzzy graphs have an extraordinary importance. In this perspective, numbers of generalizations of fuzzy graphs have been explored in the literature. Among the others, picture fuzzy graph (PFG) has its own importance. A picture fuzzy graph (PFG) is a pair defined on a = , where = is a picture fuzzy set on and = is a picture fuzzy set over the set such that for any edge with ,… More >

  • Open AccessOpen Access

    ARTICLE

    A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

    R. T. Pavendan1,*, K. Sankar1, K. A. Varun Kumar2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3331-3348, 2023, DOI:10.32604/iasc.2023.028029
    Abstract Attacks on the cyber space is getting exponential in recent times. Illegal penetrations and breaches are real threats to the individuals and organizations. Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats (APTs) they fails. These APTs are targeted, more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses. Hence, there is a need for an effective defense system that can achieve a complete reliance of security. To address the above-mentioned issues, this paper proposes a novel honeypot system More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Belief Network Enabled Malware Detection and Classification Model

    P. Pandi Chandran1,*, N. Hema Rajini2, M. Jeyakarthic3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3349-3364, 2023, DOI:10.32604/iasc.2023.029946
    Abstract Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Clustering of User Behaviour Profiles for Web Recommendation System

    S. Sadesh1,*, Osamah Ibrahim Khalaf2, Mohammad Shorfuzzaman3, Abdulmajeed Alsufyani3, K. Sangeetha4, Mueen Uddin5
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3365-3384, 2023, DOI:10.32604/iasc.2023.030751
    Abstract Web usage mining, content mining, and structure mining comprise the web mining process. Web-Page Recommendation (WPR) development by incorporating Data Mining Techniques (DMT) did not include end-users with improved performance in the obtained filtering results. The cluster user profile-based clustering process is delayed when it has a low precision rate. Markov Chain Monte Carlo-Dynamic Clustering (MC2-DC) is based on the User Behavior Profile (UBP) model group’s similar user behavior on a dynamic update of UBP. The Reversible-Jump Concept (RJC) reviews the history with updated UBP and moves to appropriate clusters. Hamilton’s Filtering Framework (HFF) is designed… More >

Per Page:

Share Link