Home / Journals / IASC / Vol.35, No.3, 2023
  • Open 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 a consequence, it was determined… More >

  • Open 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 cognitive mouse-and-keystroke dynamics system can… More >

  • Open 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 most volatile data, and improve… More >

  • Open 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 necessary for a secure group… More >

  • Open 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 optimization algorithms such as Opposition… More >

  • Open 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 the SN software system, which… More >

  • Open 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 we implemented a novel method… More >

  • Open 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 classification approach is implemented for… More >

  • Open 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 process has been redesigned using… More >

  • Open 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). The system uses Series Compensated… More >

  • Open 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 field distribution is analyzed using… More >

  • Open 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 unbounded keyword universe over prime-order… More >

  • Open 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 the detection stage by applying… More >

  • Open 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 of the sequenced set of… More >

  • Open 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 mitigating the data repetition… More >

  • Open 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 State (RSS) for two-time slots,… More >

  • Open 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 Headset, and signals were recorded.… More >

  • Open 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 recent years. This paper intends… More >

  • Open 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, etc. The anticipated… More >

  • Open 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. While transmitting the data from… More >

  • Open 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 and delay aware computational offloading… More >

  • Open 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 evaluated with parametric metric measures… More >

  • Open 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 to group the nodes into… More >

  • Open 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 gender classification approaches have been… More >

  • Open 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 and to eliminate the faults… More >

  • Open 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 resulting in reduced rate of… More >

  • Open 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 measure it in the pumping… More >

  • Open 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 of devices and sensors connected… More >

  • Open 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. However, developing a codebook with… More >

  • Open 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 classification. To overcome the challenges,… More >

  • Open 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 and tweaking hyperparameters to improve… More >

  • Open 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 modulation (PWM) technique for pulse… More >

  • Open 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 cross-fusion features from the RGB-D… More >

  • Open 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 region. The manuscript takes the… More >

  • Open 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 information. To precisely extract accurate… More >

  • Open 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 on resonance filtering, named resonance… More >

  • Open 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 sensitive to initial conditions. In… More >

  • Open 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 of services. The topology of… More >

  • Open 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 in the WiMAX physical layer.… More >

  • Open 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 model mainly aims to cluster… More >

  • Open 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 input parameters, the stability… More >

  • Open 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 and routing, multi objective based… More >

  • Open 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 based on the user’s profile… More >

  • Open 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 can be tuned to provide… More >

  • Open 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, a novel Hypergrah based Convolutional… More >

  • Open 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 steps. The first step is… More >

  • Open 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 , and In this manuscript, we… More >

  • Open 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 that tracks the anonymous behavior… More >

  • Open 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 exist in the PDFs. The… More >

  • Open 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 to filter user data… More >

  • Open Access

    ARTICLE

    Artificial Bee Colony with Cuckoo Search for Solving Service Composition

    Fadl Dahan1,2,*, Abdulelah Alwabel3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3385-3402, 2023, DOI:10.32604/iasc.2023.030651
    Abstract In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The… More >

  • Open Access

    ARTICLE

    Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

    S. Ayshwarya Lakshmi1,*, K. Anandavelu2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3403-3413, 2023, DOI:10.32604/iasc.2023.030970
    Abstract Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection. The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process. Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties. One of the most significant requirements is to design optimal solutions to optimize their use. There is no particular technique that can answer all optimization issues. The proposed enhanced cuckoo search optimization method indicates… More >

  • Open Access

    ARTICLE

    DeepQ Based Automated Irrigation Systems Using Deep Belief WSN

    E. Gokulakannan*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3415-3427, 2023, DOI:10.32604/iasc.2023.030965
    Abstract Deep learning is the subset of artificial intelligence and it is used for effective decision making. Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop. Our system consists of Distributed wireless sensor environment to handle the moisture of the soil and temperature levels. It is automated process and useful for minimizing the usage of resources such as water level, quality of the soil, fertilizer values and controlling the whole system. The mobile app based smart control system is designed using deep belief network. This system has multiple sensors placed in agricultural field and collect the data.… More >

  • Open Access

    ARTICLE

    Harmonics Mitigation Using MMC Based UPFC and Particle Swarm Optimization

    C. Gnana Thilaka*, M. Mary Linda
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3429-3445, 2023, DOI:10.32604/iasc.2023.024028
    Abstract The application of non-linear loads in the power electronic device causes serious harmonic issues in the power system since it has the intrinsic property of retrieving harmonic current and reactive power from Alternating Current (AC) supply that leads to voltage instability. To maintain a reliable power flow in the power system, an innovative Unified Power Flow Converter (UPFC) is utilized in this proposed approach. The conventional series converter is replaced with the Modular Multilevel Converter (MMC) that improves the power handling capability and achieves higher modular level with minimized distortions. The shunt compensator assists in minimizing the voltage fluctuations and… More >

  • Open Access

    ARTICLE

    Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

    U. Muthaiah1,*, S. Chitra2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3447-3458, 2023, DOI:10.32604/iasc.2023.028869
    Abstract Image processing, agricultural production, and field monitoring are essential studies in the research field. Plant diseases have an impact on agricultural production and quality. Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops. Manually identifying the agricultural pests is usually evident in plants; also, it takes more time and is an expensive technique. A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations. An atmosphere generates vast amounts of data as it is monitored closely; the evaluation of this big data would increase the production… More >

  • Open Access

    ARTICLE

    Improved Interleaved Single-Ended Primary Inductor-Converter for Single-Phase Grid-Connected System

    T. J. Thomas Thangam*, K. Muthu Vel
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3459-3478, 2023, DOI:10.32604/iasc.2023.025521
    Abstract The generation of electricity based on renewable energy sources, particularly Photovoltaic (PV) system has been greatly increased and it is simply instigated for both domestic and commercial uses. The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power. An improved interleaved Single-ended Primary Inductor-Converter (SEPIC) converter is employed in proposed work to extricate most of power from renewable source. This proposed converter minimizes ripples, reduces electromagnetic interference due to filter elements and the continuous input current improves the power output of PV panel. A… More >

  • Open Access

    ARTICLE

    Design of Fuzzy Logic Control Framework for QoS Routing in MANET

    M. Vargheese1,*, S. Vanithamani2, D. Stalin David3, Ganga Rama Koteswara Rao4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3479-3499, 2023, DOI:10.32604/iasc.2023.030948
    Abstract Wireless networks with no infrastructure arise as a result of multiple wireless devices working together. The Mobile Ad hoc Network (MANET) is a system for connecting independently located Mobile Nodes (MNs) via wireless links. A MANET is self-configuring in telecommunications, while MN produces non-infrastructure networks that are entirely decentralized. Both the MAC and routing layers of MANETs take into account issues related to Quality of Service (QoS). When culling a line of optical discernment communication, MANET can be an effective and cost-saving route cull option. To maintain QoS, however, more or fewer challenges must be overcome. This paper proposes a… More >

  • Open Access

    ARTICLE

    A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

    M. Daniel Nareshkumar1,*, B. Jaison2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3501-3515, 2023, DOI:10.32604/iasc.2023.027848
    Abstract With advancements in computing powers and the overall quality of images captured on everyday cameras, a much wider range of possibilities has opened in various scenarios. This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier. More than ever before, there is a plethora of info about sign language usage in the real world. Sign languages, and by extension the datasets available, are of two forms, isolated sign language and continuous sign language. The main difference between the two types is that in isolated sign… More >

  • Open Access

    ARTICLE

    Eye Strain Detection During Online Learning

    Le Quang Thao1,2,*, Duong Duc Cuong2, Vu Manh Hung3, Le Thanh Vinh3, Doan Trong Nghia4, Dinh Ha Hai3, Nguyen Nhan Nhi3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3517-3530, 2023, DOI:10.32604/iasc.2023.031026
    Abstract The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student’s ophthalmic health because of their young age and a relative lack… More >

  • Open Access

    ARTICLE

    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    Vani Haridasan*, Kavitha Muthukumaran, K. Hariharanath
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628
    Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm… More >

  • Open Access

    ARTICLE

    Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

    C. P. Thamil Selvi1,*, P. Muneeshwari2, K. Selvasheela3, D. Prasanna4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3545-3555, 2023, DOI:10.32604/iasc.2023.031097
    Abstract The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network. The sentiment classification accuracy is evaluated using various selection methods, especially those that deal with algorithm selection. In this work, every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative. In order to do so, the work focus on Query Expansion Ranking (QER) algorithm that takes user text as input and process for sentiment analysis and finally produces the results as informative or non-informative. The challenge is to convert non-informative into informative using… More >

  • Open Access

    ARTICLE

    Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

    Harish Gunigari1,*, S. Chitra2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3557-3571, 2023, DOI:10.32604/iasc.2023.029155
    Abstract Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network’s lifespan. In the proposed system, a… More >

  • Open Access

    ARTICLE

    Recent Advances in Fatigue Detection Algorithm Based on EEG

    Fei Wang1,2, Yinxing Wan1, Man Li1,2, Haiyun Huang1,2, Li Li1, Xueying Hou1, Jiahui Pan1,2, Zhenfu Wen3, Jingcong Li1,2,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3573-3586, 2023, DOI:10.32604/iasc.2023.029698
    Abstract Fatigue is a state commonly caused by overworked, which seriously affects daily work and life. How to detect mental fatigue has always been a hot spot for researchers to explore. Electroencephalogram (EEG) is considered one of the most accurate and objective indicators. This article investigated the development of classification algorithms applied in EEG-based fatigue detection in recent years. According to the different source of the data, we can divide these classification algorithms into two categories, intra-subject (within the same subject) and cross-subject (across different subjects). In most studies, traditional machine learning algorithms with artificial feature extraction methods were commonly used… More >

  • Open Access

    ARTICLE

    A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

    Monika Khandelwal1, Ranjeet Kumar Rout1, Saiyed Umer2, Kshira Sagar Sahoo3, NZ Jhanjhi4,*, Mohammad Shorfuzzaman5, Mehedi Masud5
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3587-3598, 2023, DOI:10.32604/iasc.2023.029785
    Abstract Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using… More >

  • Open Access

    ARTICLE

    An Optimized Technique for RNA Prediction Based on Neural Network

    Ahmad Ali AlZubi*, Jazem Mutared Alanazi
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3599-3611, 2023, DOI:10.32604/iasc.2023.027913
    Abstract Pathway reconstruction, which remains a primary goal for many investigations, requires accurate inference of gene interactions and causality. Non-coding RNA (ncRNA) is studied because it has a significant regulatory role in many plant and animal life activities, but interacting micro-RNA (miRNA) and long non-coding RNA (lncRNA) are more important. Their interactions not only aid in the in-depth research of genes’ biological roles, but also bring new ideas for illness detection and therapy, as well as plant genetic breeding. Biological investigations and classical machine learning methods are now used to predict miRNA-lncRNA interactions. Because biological identification is expensive and time-consuming, machine… More >

  • Open Access

    ARTICLE

    Robust Deep Transfer Learning Based Object Detection and Tracking Approach

    C. Narmadha1, T. Kavitha2, R. Poonguzhali2, V. Hamsadhwani3, Ranjan walia4, Monia5, B. Jegajothi6,*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3613-3626, 2023, DOI:10.32604/iasc.2023.029323
    Abstract At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use of Faster RCNN as an… More >

  • Open Access

    ARTICLE

    Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference

    Wenjia Xiang1, Xiaoyu Li1,*, Zirui He1, Chenjing Su1, Wangchi Cheng2, Chao Lu3, Shan Yang4
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3627-3639, 2023, DOI:10.32604/iasc.2023.030904
    Abstract Estimating the intention of space objects plays an important role in aircraft design, aviation safety, military and other fields, and is an important reference basis for air situation analysis and command decision-making. This paper studies an intention estimation method based on fuzzy theory, combining probability to calculate the intention between two objects. This method takes a space object as the origin of coordinates, observes the target’s distance, speed, relative heading angle, altitude difference, steering trend and etc., then introduces the specific calculation methods of these parameters. Through calculation, values are input into the fuzzy inference model, and finally the action… More >

  • Open Access

    ARTICLE

    Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

    Meherab Mamun Ratul, Kazi Ayesha Rahman, Javeria Fazal, Naimur Rahman Abanto, Riasat Khan*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3641-3658, 2023, DOI:10.32604/iasc.2023.030638
    Abstract The coronavirus (COVID-19) is a lethal virus causing a rapidly infectious disease throughout the globe. Spreading awareness, taking preventive measures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay. Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly. To fight the spread of this virus, technologically developed systems have become… More >

  • Open Access

    ARTICLE

    Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

    Mohamed Ezz1, Ayman Mohamed Mostafa1,*, Ayman Elshenawy2,3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3659-3675, 2023, DOI:10.32604/iasc.2023.031561
    Abstract Face authentication is an important biometric authentication method commonly used in security applications. It is vulnerable to different types of attacks that use authorized users’ facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating security applications. This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique. The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions. The proposed model is applied to… More >

  • Open Access

    ARTICLE

    Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

    S. Maheswari, R. Vijayabhasker*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3677-3692, 2023, DOI:10.32604/iasc.2023.031422
    Abstract Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust… More >

  • Open Access

    ARTICLE

    An Optimal Algorithm for Secure Transactions in Bitcoin Based on Blockchain

    Jazem Mutared Alanazi, Ahmad Ali AlZubi*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3693-3712, 2023, DOI:10.32604/iasc.2023.030287
    Abstract Technological advancement has made a significant contribution to the change of the economy and the advancement of humanity. Because it is changing how economic transactions are carried out, the blockchain is one of the technical developments that has a lot of promise for this progress. The public record of the Bitcoin blockchain provides dispersed users with evidence of transaction ownership by publishing all transaction data from block reward transactions to unspent transaction outputs. Attacks on the public ledger, on the other hand, are a result of the fact that all transaction information are exposed. De-anonymization attacks allow users to link… More >

  • Open Access

    ARTICLE

    Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

    G. Pandiaraj*, S. Muralidharan
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3713-3726, 2023, DOI:10.32604/iasc.2023.031463
    Abstract Four-wheeled, individual-driven, nonholonomic structured mobile robots are widely used in industries for automated work, inspection and exploration purposes. The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure. The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots. However, there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion. As a result, the mobile robot has limited performance, such as chattering during curved movement. In this research work, a… More >

  • Open Access

    ARTICLE

    Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

    Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2, Fahd Althukair3
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3727-3741, 2023, DOI:10.32604/iasc.2023.031442
    Abstract Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order. This process is a probability density distribution with adequate training data that can converge to the input density curve. In this paper, we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling. A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling. The solution uses data mining techniques to link datum in groups by linking their components. The learning techniques are novel presentations of Gaussian modelling that use prior knowledge of the representation hyper-parameters and… More >

  • Open Access

    ARTICLE

    Prediction of Suitable Crops Using Stacked Scaling Conjugant Neural Classifier

    P. Nithya*, A. M. Kalpana
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3743-3755, 2023, DOI:10.32604/iasc.2023.030394
    Abstract Agriculture plays a vital role in economic development. The major problem faced by the farmers are the selection of suitable crops based on environmental conditions such as weather, soil nutrients, etc. The farmers were following ancestral patterns, which could sometimes lead to the wrong selection of crops. In this research work, the feature selection method is adopted to improve the performance of the classification. The most relevant features from the dataset are obtained using a Probabilistic Feature Selection (PFS) approach, and classification is done using a Neural Fuzzy Classifier (NFC). Scaling Conjugate Gradient (SCG) optimization method is used to update… More >

  • Open Access

    ARTICLE

    Generative Adversarial Networks for Secure Data Transmission in Wireless Network

    E. Jayabalan*, R. Pugazendi
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3757-3784, 2023, DOI:10.32604/iasc.2023.031200
    Abstract In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an… More >

  • Open Access

    ARTICLE

    An IoT-Based Energy Conservation Smart Classroom System

    Talal H. Noor1,*, El-Sayed Atlam2, Abdulqader M. Almars1, Ayman Noor3, Amer S. Malki1
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3785-3799, 2023, DOI:10.32604/iasc.2023.032250
    Abstract With the increase of energy consumption worldwide in several domains such as industry, education, and transportation, several technologies played an influential role in energy conservation such as the Internet of Things (IoT). In this article, we describe the design and implementation of an IoT-based energy conservation smart classroom system that contributes to energy conservation in the education domain. The proposed system not only allows the user to access and control IoT devices (e.g., lights, projectors, and air conditions) in real-time, it also has the capability to aggregate the estimated energy consumption of an IoT device, the smart classroom, and the… More >

  • Open Access

    ARTICLE

    Dynamic Time and Location Information in Ciphertext-Policy Attribute-Based Encryption with Multi-Authorization

    P. Prathap Nayudu, Krovi Raja Sekhar*
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3801-3813, 2023, DOI:10.32604/iasc.2023.032267
    Abstract Due to the mobility of users in an organization, inclusion of dynamic attributes such as time and location becomes the major challenge in Ciphertext-Policy Attribute-Based Encryption (CP-ABE). By considering this challenge; we focus to present dynamic time and location information in CP-ABE with multi-authorization. At first, along with the set of attributes of the users, their corresponding location is also embedded. Geohash is used to encode the latitude and longitude of the user’s position. Then, decrypt time period and access time period of users are defined using the new time tree (NTT) structure. The NTT sets the encrypted duration of… More >

  • Open Access

    ARTICLE

    A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

    C. Gowdham*, S. Nithyanandam
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3815-3827, 2023, DOI:10.32604/iasc.2023.031876
    Abstract The Wireless Sensor Networks (WSN) are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the internet. A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network. Additionally, the nodes are discarding and modifying the data packets according to the requirements of the system. The assault modifies the fundamental concept of the WSNs, which is that different devices should communicate with one another. In the proposed system, there is a fuzzy idea offered for the… More >

  • Open Access

    ARTICLE

    Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

    Siva Sankari Subbiah1, Senthil Kumar Paramasivan2,*, Karmel Arockiasamy3, Saminathan Senthivel4, Muthamilselvan Thangavel2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3829-3844, 2023, DOI:10.32604/iasc.2023.030480
    Abstract Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed… More >

  • Open Access

    ARTICLE

    Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

    P. N. Ramesh1,*, S. Kannimuthu2
    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329
    Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level… More >

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