Home / Journals / IASC / Vol.35, No.2, 2023
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    ARTICLE

    Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

    M. Vinoth Kumar1,*, M. Prakash2, M. Naresh Kumar3, H. Abdul Shabeer4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1277-1293, 2023, DOI:10.32604/iasc.2023.025591
    Abstract The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the… More >

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    ARTICLE

    An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

    Mubashir Javaid1, Muazzam Maqsood2, Farhan Aadil2, Jibran Safdar1, Yongsung Kim3,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1295-1310, 2023, DOI:10.32604/iasc.2023.028262
    Abstract Currently, worldwide industries and communities are concerned with building, expanding, and exploring the assets and resources found in the oceans and seas. More precisely, to analyze a stock, archaeology, and surveillance, several cameras are installed underseas to collect videos. However, on the other hand, these large size videos require a lot of time and memory for their processing to extract relevant information. Hence, to automate this manual procedure of video assessment, an accurate and efficient automated system is a greater necessity. From this perspective, we intend to present a complete framework solution for the task… More >

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    ARTICLE

    An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions

    N. Rajeswari1,*, S. Venkatanarayanan2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1311-1322, 2023, DOI:10.32604/iasc.2023.028552
    Abstract Due to the enormous utilization of solar energy, the photovoltaic (PV) system is used. The PV system is functioned based on a maximum power point (MPP). Due to the climatic change, the Partial shading conditions have occurred under non-uniform irradiance conditions. In the PV system, the global maximum power point (GMPP) is complex to track in the P-V curve due to the Partial shading. Therefore, several tracking processes are performed using various methods like perturb and observe (P & O), hill climbing (HC), incremental conductance (INC), Fuzzy Logic, Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO)… More >

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    ARTICLE

    Fin Field Effect Transistor with Active 4-Bit Arithmetic Operations in 22 nm Technology

    S. Senthilmurugan1,*, K. Gunaseelan2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1323-1336, 2023, DOI:10.32604/iasc.2023.027650
    Abstract A design of a high-speed multi-core processor with compact size is a trending approach in the Integrated Circuits (ICs) fabrication industries. Because whenever device size comes down into narrow, designers facing many power density issues should be reduced by scaling threshold voltage and supply voltage. Initially, Complementary Metal Oxide Semiconductor (CMOS) technology supports power saving up to 32 nm gate length, but further scaling causes short severe channel effects such as threshold voltage swing, mobility degradation, and more leakage power (less than 32) at gate length. Hence, it directly affects the arithmetic logic unit (ALU),… More >

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    ARTICLE

    A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

    W. Gracy Theresa1,*, A. Gayathri2, P. Rama3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1337-1351, 2023, DOI:10.32604/iasc.2023.028425
    Abstract Mobile computing is the most powerful application for network communication and connectivity, given recent breakthroughs in the field of wireless networks or Mobile Ad-hoc networks (MANETs). There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate precision. For most applications, a framework must ensure that the retrieved data reflects the transmitted data. Before driving to other nodes, if the frame between the two nodes is deformed in the data-link layer, it must be repaired. Most link-layer protocols immediately disregard the frame… More >

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    ARTICLE

    DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

    S. Raghavendra1, Vasudev Parvati2, R. Manjula3, Ashok Kumar Nanda4, Ruby Singh5, D. Lakshmi6, S. Velmurugan7,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1353-1368, 2023, DOI:10.32604/iasc.2023.027977
    Abstract In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is… More >

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    ARTICLE

    A Process Oriented Integration Model for Smart Health Services

    Farzana Kausar Gondal1,*, Syed Khuram Shahzad2, Muhammad Arfan Jaffar3, Muhammad Waseem Iqbal4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1369-1386, 2023, DOI:10.32604/iasc.2023.028407
    Abstract Cities are facing challenges of high rise in population number and consequently need to be equipped with latest smart services to provide luxuries of life to its residents. Smart integrated solutions are also a need to deal with the social and environmental challenges, caused by increasing urbanization. Currently, the development of smart services’ integrated network, within a city, is facing the barriers including; less efficient collection and sharing of data, along with inadequate collaboration of software and hardware. Aiming to resolve these issues, this paper recommended a solution for a synchronous functionality in the smart… More >

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    ARTICLE

    Buck Converter Current Measurement Using Differential Amplifier

    P. Rajeswari*, V. Manikandan
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1387-1402, 2023, DOI:10.32604/iasc.2023.025866
    Abstract The accuracy of the measured current is a preeminent parameter for Current Control based Power Converter applications to ensure genuine operation of the designed converter. The current measurement accuracy can be affected by several parameters which includes the type of technology used, components used for the selected technology, aging, usage, operating and environmental conditions. The effect of gain resistors and their manufacturing tolerances on differential amplifier-based buck converter current measurement is investigated in this work. The analysis mainly focused on the output voltage variation and its accuracy with respect to the change in gain resistance… More >

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    ARTICLE

    Real Time Control System for Metro Railways Using PLC & SCADA

    Ishu Tomar*, Indu Sreedevi, Neeta Pandey
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1403-1421, 2023, DOI:10.32604/iasc.2023.028163
    Abstract This paper proposes to adopt SCADA and PLC technology for the improvement of the performance of real time signaling & train control systems in metro railways. The main concern of this paper is to minimize the failure in automated metro railways system operator and integrate the information coming from Operational Control Centre (OCC), traction SCADA system, traction power control, and power supply system. This work presents a simulated prototype of an automated metro train system operator that uses PLC and SCADA for the real time monitoring and control of the metro railway systems. Here, SCADA… More >

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    ARTICLE

    Predicting Violence-Induced Stress in an Arabic Social Media Forum

    Abeer Abdulaziz AlArfaj1, Nada Ali Hakami2,*, Hanan Ahmed Hosni Mahmoud1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1423-1439, 2023, DOI:10.32604/iasc.2023.028067
    Abstract Social Media such as Facebook plays a substantial role in virtual communities by sharing ideas and ideologies among different populations over time. Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes, towards different issues such as violence against women and children. In this paper, we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media. We searched for Arabic posts of many countries through Facebook application programming interface (API). We discovered that the stress state of a battered woman is usually related to her… More >

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    ARTICLE

    Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

    J. Leon Bosco Raj1,*, M. Marsaline Beno2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1441-1454, 2023, DOI:10.32604/iasc.2023.026211
    Abstract This paper develops a parallel hybrid electric vehicle (PHEV) proportional integral controller with driving cycle. To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles (HEVs) combine an electric motor (EM), a battery and an internal combustion engine (ICE). The electric motor assists the engine when accelerating, driving longer highways or climbing hills. This enables the use of a smaller, more efficient engine. It also makes use of the concept of regenerative braking to maximize energy efficiency. In a Hybrid Electric Vehicle (HEV), energy dissipated while braking is utilized to charge the battery. More >

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    ARTICLE

    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703
    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of… More >

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    ARTICLE

    Integration of Wind and PV Systems Using Genetic-Assisted Artificial Neural Network

    E. Jessy Mol*, M. Mary Linda
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1471-1489, 2023, DOI:10.32604/iasc.2023.024027
    Abstract The prominence of Renewable Energy Sources (RES) in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination. A grid-tied DFIG (Doubly Fed Induction Generator) based WECS (Wind Energy Conversion System) is introduced in this work, in which a Landsman converter is implemented to improvise the output voltage of PV without any fluctuations. A novel GA (Genetic Algorithm) assisted ANN (Artificial Neural Network) is employed for tracking the Maximum power from PV. Among the rotor and grid side controllers, the former is implemented by More >

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    ARTICLE

    Optimized Neural Network-Based Micro Strip Patch Antenna Design for Radar Application

    A. Yogeshwaran1,*, K. Umadevi2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1491-1503, 2023, DOI:10.32604/iasc.2023.026424
    Abstract Microstrip antennas are low-profile antennas that are utilized in wireless communication systems. In recent years, communication engineers have been increasingly interested in it. Because of downsizing, novelty, and cost reduction, the number of wireless standards has expanded in recent years. Wideband technologies have evolved in addition to analog and digital services. Radars necessitate antenna subsystems that are low-profile and lightweight. Microstrip antennas have these qualities and are suited for radars as an alternative to the bulky and heavyweight reflector/slotted waveguide array antennas. A perforated corner single-line fed microstrip antenna is designed here. When compared to… More >

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    ARTICLE

    Harmonics Extraction Scheme for Power Quality Improvement Using Chbmli-Dstatcom Module

    R. Hemalatha1,*, M. Ramasamy2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1505-1525, 2023, DOI:10.32604/iasc.2023.026301
    Abstract In recent day’s power distribution system is distress from acute power quality issues. In this work, for compensating Power Quality (PQ) disturbances a seven level cascaded H-bridge inverter is implemented in distribution static compensator which protects power quality problems in currents. Distribution Static Compensator (DSTATCOM) aid to enhances power factor and removes total harmonic distortion which is drawn from non-linear load. The D–Q reference theory based hysteresis current controller is employed to generate reference current for compensation of harmonics and reactive power, additionally Probabilistic Neural Network (PNN) classifier is used which easily separates exact harmonics. More >

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    ARTICLE

    Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

    P. Vigneshwaran1,*, N. Prasath1, M. Islabudeen2, A. Arun1, A. K. Sampath2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1527-1543, 2023, DOI:10.32604/iasc.2023.028444
    Abstract Traffic signs are basic security workplaces making the rounds, which expects a huge part in coordinating busy time gridlock direct, ensuring the prosperity of the road and dealing with the smooth segment of vehicles and individuals by walking, etc. As a segment of the clever transportation structure, the acknowledgment of traffic signs is basic for the driving assistance system, traffic sign upkeep, self-administering driving, and various spaces. There are different assessments turns out achieved for traffic sign acknowledgment in the world. However, most of the works are only for explicit arrangements of traffic signs, for… More >

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    ARTICLE

    An Efficient SDFRM Security System for Blockchain Based Internet of Things

    Vivekraj Mannayee1,*, Thirumalai Ramanathan2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1545-1563, 2023, DOI:10.32604/iasc.2023.027675
    Abstract Blockchain has recently sparked interest in both the technological and business firms. The Internet of Things's (IoT) core principle emerged due to the connectivity of several new technologies, including wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing (CC), Cyber-Physical System (CSP), Information and Communication Technologies (ICT) and Enterprise Model, and other technological innovations into the fourth industrial revolution referred to as Industry 4.0. Data management is defined as the process of accumulation in order to make better… More >

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    ARTICLE

    Randomized MILP framework for Securing Virtual Machines from Malware Attacks

    R. Mangalagowri1,*, Revathi Venkataraman2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1565-1580, 2023, DOI:10.32604/iasc.2023.026360
    Abstract Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources. Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers. Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs. The existing system has issues with execution time and false-positive rates. Hence, the overall system performance is degraded considerably. The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target… More >

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    ARTICLE

    Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

    J. Shanmugapriya1,*, K. Baskaran2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1581-1594, 2023, DOI:10.32604/iasc.2023.024514
    Abstract Smart Grids (SG) is a power system development concept that has received significant attention nationally. SG signifies real-time data for specific communication requirements. The best capabilities for monitoring and controlling the grid are essential to system stability. One of the most critical needs for smart-grid execution is fast, precise, and economically synchronized measurements, which are made feasible by Phasor Measurement Units (PMU). PMUs can provide synchronized measurements and measure voltages as well as current phasors dynamically. PMUs utilize GPS time-stamping at Coordinated Universal Time (UTC) to capture electric phasors with great accuracy and precision. This… More >

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    ARTICLE

    An Optimal Cluster Head and Gateway Node Selection with Fault Tolerance

    P. Rahul*, B. Kaarthick
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1595-1609, 2023, DOI:10.32604/iasc.2023.025762
    Abstract In Mobile Ad Hoc Networks (MANET), Quality of Service (QoS) is an important factor that must be analysed for the showing the better performance. The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection (NQCAFFFOCHGS) has the best network performance because it uses the Improved Weighted Clustering Algorithm (IWCA) to cluster the network and the FFO algorithm, which uses fuzzy-based network metrics to select the best CH and entryway. However, the major drawback of the fuzzy system was to appropriately select the membership functions. Also, the network metrics related to… More >

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    ARTICLE

    Hybrid Optimization Based PID Controller Design for Unstable System

    Saranya Rajeshwaran1,*, C. Agees Kumar2, Kanthaswamy Ganapathy3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1611-1625, 2023, DOI:10.32604/iasc.2023.029299
    Abstract PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as,… More >

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    ARTICLE

    Using a Software-Defined Air Interface Algorithm to Improve Service Quality

    Madiraju Sirisha1,*, P. Abdul Khayum2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1627-1641, 2023, DOI:10.32604/iasc.2023.025980
    Abstract In the digital era, the Narrowband Internet of Things (Nb-IoT) influences the massive Machine-Type-Communication (mMTC) features to establish secure routing among the 5G/6G mobile networks. It supports global coverage to the low-cost IoT devices distributed in terrestrial networks. Its key traffic characteristics include robust uplink, moderate data rate/device, extremely high energy efficiency, prolonging device lifetime, and Quality of Service (QoS). This paper proposes a Deep Reinforcement Learning (DRL) combined software-defined air interface algorithm applied on the switching system, satisfying the user requirement and enabling them with the network resources to extend quality of service by More >

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    ARTICLE

    Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

    Ashit Kumar Dutta1,*, Yasser Albagory2, Majed Alsanea3, Hamdan I. Almohammed4, Abdul Rahaman Wahab Sait5
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1643-1655, 2023, DOI:10.32604/iasc.2023.027865
    Abstract Eye state classification acts as a vital part of the biomedical sector, for instance, smart home device control, drowsy driving recognition, and so on. The modifications in the cognitive levels can be reflected via transforming the electroencephalogram (EEG) signals. The deep learning (DL) models automated extract the features and often showcased improved outcomes over the conventional classification model in the recognition processes. This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classification (EDLCOA-ESC). The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step. Besides, wavelet packet decomposition More >

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    ARTICLE

    Ensemble Based Learning with Accurate Motion Contrast Detection

    M. Indirani*, S. Shankar
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1657-1674, 2023, DOI:10.32604/iasc.2023.026148
    Abstract Recent developments in computer vision applications have enabled detection of significant visual objects in video streams. Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization (SPSOM) and Incremental Deep Convolution Neural Networks (IDCNN) for detecting multiple objects. However, the study considered optical flows resulting in assessing motion contrasts. Existing methods have issue with accuracy and error rates in motion contrast detection. Hence, the overall object detection performance is reduced significantly. Thus, consideration of object motions in videos efficiently is a critical issue to be solved. To overcome the above… More >

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    ARTICLE

    Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

    G. Kavipriya*, D. Manjula
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1675-1689, 2023, DOI:10.32604/iasc.2023.027670
    Abstract Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this… More >

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    ARTICLE

    Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

    Tingting Yang1,*, Shuwen Jia1, Ying Yu1, Zhiyong Sui2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1691-1704, 2023, DOI:10.32604/iasc.2023.027466
    Abstract Augmented Reality (AR) tries to seamlessly integrate virtual content into the real world of the user. Ideally, the virtual content would behave exactly like real objects. This necessitates a correct and precise estimation of the user’s viewpoint (or that of a camera) with regard to the virtual content’s coordinate system. Therefore, the real-time establishment of 3-dimension (3D) maps in real scenes is particularly important for augmented reality technology. So in this paper, we integrate Simultaneous Localization and Mapping (SLAM) technology into augmented reality. Our research is to implement an augmented reality system without markers using… More >

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    ARTICLE

    AEECA for Reliable Communication to Enhance the Network Life Time for WSN

    Ganesh Jayaraman1,*, V R Sarma Dhulipala2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1705-1719, 2023, DOI:10.32604/iasc.2023.025253
    Abstract Nowadays, wireless sensor networks play a vital role in our day to day life. Wireless communication is preferred for many sensing applications due its convenience, flexibility and effectiveness. The sensors to sense the environmental factor are versatile and send sensed data to central station wirelessly. The cluster based protocols are provided an optimal solution for enhancing the lifetime of the sensor networks. In this paper, modified K-means ++ algorithm is used to form the cluster and cluster head in an efficient way and the Advanced Energy-Efficient Cluster head selection Algorithm (AEECA) is used to calculate More >

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    ARTICLE

    A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

    Ali Alqahtani1, Shumaila Akram2, Muhammad Ramzan2,3,*, Fouzia Nawaz2, Hikmat Ullah Khan4, Essa Alhashlan5, Samar M. Alqhtani1, Areeba Waris6, Zain Ali7
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1721-1736, 2023, DOI:10.32604/iasc.2023.025597
    Abstract Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is… More >

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    ARTICLE

    WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

    C. Edwin Singh1,*, S. Maria Celestin Vigila2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1737-1751, 2023, DOI:10.32604/iasc.2023.028022
    Abstract Mobile ad-hoc networks (MANET) are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communications. MANETs are more vulnerable to security threats. Changes in nodes, bandwidth limits, and centralized control and management are some of the characteristics. IDS (Intrusion Detection System) are the aid for detection, determination, and identification of illegal system activity such as use, copying, modification, and destruction of data. To address the identified issues, academics have begun to concentrate on building IDS-based machine learning algorithms. Deep learning is a type of machine learning that can produce… More >

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    ARTICLE

    Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

    G. Karthiha*, Dr. S. Allwin
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1753-1765, 2023, DOI:10.32604/iasc.2023.022631
    Abstract In spite of the advancement in computerized imaging, many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis. In this way, the research in the zone of image denoising is very dynamic. Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise suppression is a basic and productive methodology. In this paper, Bilateral Filter (BF) based Modular Neural Networks (MNN) has been utilized for speckle noise suppression in the ultrasound image. Initial step the BF filter is used to filter the input image.… More >

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    ARTICLE

    Adaptive Sub-Threshold Voltage Level Control for Voltage Deviate-Domino Circuits

    C. Arun Prasath1,*, C. Gowri Shankar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1767-1781, 2023, DOI:10.32604/iasc.2023.028758
    Abstract Leakage power and propagation delay are two significant issues found in sub-micron technology-based Complementary Metal-Oxide-Semiconductor (CMOS)-based Very Large-Scale Integration (VLSI) circuit designs. Positive Channel Metal Oxide Semiconductor (PMOS) has been replaced by Negative Channel Metal Oxide Semiconductor (NMOS) in recent years, with low dimension-switching changes in order to shape the mirror of voltage comparator. NMOS is used to reduce stacking leakage as well as total exchange. Domino Logic Circuit is a powerful and versatile digital programmer that gained popularity in recent years. In this study regarding Adaptive Sub Threshold Voltage Level Control Problem, the researchers… More >

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    ARTICLE

    Implementation of FPGA Based MPPT Techniques for Grid-Connected PV System

    Thamatapu Eswara Rao*, S. Elango
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1783-1798, 2023, DOI:10.32604/iasc.2023.028835
    Abstract Global energy demand is growing rapidly owing to industrial growth and urbanization. Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources (e.g., fossil fuels).Solar photovoltaic (PV), as a source of electricity, has grown in popularity over the last few decades because of their clean, noise-free, low-maintenance, and abundant availability of solar energy. There are two types of maximum power point tracking (MPPT) techniques: classical and evolutionary algorithm-based techniques. Precise and less complex perturb and observe (P&O) and incremental conductance (INC) approaches are extensively employed among classical techniques. This study More >

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    ARTICLE

    SF-CNN: Deep Text Classification and Retrieval for Text Documents

    R. Sarasu1,*, K. K. Thyagharajan2, N. R. Shanker3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1799-1813, 2023, DOI:10.32604/iasc.2023.027429
    Abstract Researchers and scientists need rapid access to text documents such as research papers, source code and dissertations. Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords. An efficient classification algorithm for retrieving documents based on keyword words is required. The traditional algorithm performs less because it never considers words’ polysemy and the relationship between bag-of-words in keywords. To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching More >

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    ARTICLE

    Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

    Saud S. Alotaibi*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1815-1829, 2023, DOI:10.32604/iasc.2023.029446
    Abstract One of the most critical objectives of precision farming is to assess the germination quality of seeds. Modern models contribute to this field primarily through the use of artificial intelligence techniques such as machine learning, which present difficulties in feature extraction and optimization, which are critical factors in predicting accuracy with few false alarms, and another significant difficulty is assessing germination quality. Additionally, the majority of these contributions make use of benchmark classification methods that are either inept or too complex to train with the supplied features. This manuscript addressed these issues by introducing a More >

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    ARTICLE

    A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

    M. Mohamed Iqbal1,*, K. Latha2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1831-1842, 2023, DOI:10.32604/iasc.2023.029036
    Abstract The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics. As a result, social media has emerged as the most effective and largest open source for obtaining public opinion. Single node computational methods are inefficient for sentiment analysis on such large datasets. Supercomputers or parallel or distributed processing are two options for dealing with such large amounts of data. Most parallel programming frameworks, such as MPI (Message Processing Interface), are difficult to use and scale in environments where supercomputers are expensive. Using the… More >

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    ARTICLE

    Analysis of Brain MRI: AI-Assisted Healthcare Framework for the Smart Cities

    Walid El-Shafai1,*, Randa Ali1, Ahmed Sedik2, Taha El-Sayed Taha1, Mohammed Abd-Elnaby3, Fathi E. Abd El-Samie1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1843-1856, 2023, DOI:10.32604/iasc.2023.019198
    Abstract The use of intelligent machines to work and react like humans is vital in emerging smart cities. Computer-aided analysis of complex and huge MRI (Magnetic Resonance Imaging) scans is very important in healthcare applications. Among AI (Artificial Intelligence) driven healthcare applications, tumor detection is one of the contemporary research fields that have become attractive to researchers. There are several modalities of imaging performed on the brain for the purpose of tumor detection. This paper offers a deep learning approach for detecting brain tumors from MR (Magnetic Resonance) images based on changes in the division of… More >

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    ARTICLE

    SMO Algorithm to Unravel CEED Problem using Wind and Solar

    A. Prabha1,*, G. Themozhi2, Rama Reddy Sathi3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1857-1872, 2023, DOI:10.32604/iasc.2023.027442
    Abstract This research proposes a more advanced way to address Combined Economic Emission Dispatch (CEED) concerns. Economic Load Dispatch (ELD) and Economic Emission Dispatch (EED) have been implemented to reduce generating unit fuel costs and emissions. When both economics and emission targets are taken into account, the dispatch of an aggregate cost-effective emission challenge emerges. This research affords a mathematical modeling-based analytical technique for solving economic, emission, and collaborative economic and emission dispatch problems with only one goal. This study takes into account both the fuel cost target and the environmental impact of emissions. This bi-intention… More >

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    ARTICLE

    Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

    S. Siamala Devi1, Chandrakala Kuruba2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1873-1887, 2023, DOI:10.32604/iasc.2023.025504
    Abstract Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm’s goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid… More >

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    ARTICLE

    Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

    D. Doreen Hephzibah Miriam1, Deepak Dahiya2, Nitin3, C. R. Rene Robin4,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1889-1906, 2023, DOI:10.32604/iasc.2023.028850
    Abstract Blockchain technology is critical in cyber security. The most recent cryptographic strategies may be hacked as efforts are made to build massive electronic circuits. Because of the ethical and legal implications of a patient’s medical data, cyber security is a critical and challenging problem in healthcare. The image secrecy is highly vulnerable to various types of attacks. As a result, designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection. To resolve this issue, this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security (LGE-HES) algorithm for the… More >

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    ARTICLE

    Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

    Kanupriya Mittal*, V. Mary Anita Rajam
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037
    Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed More >

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    ARTICLE

    Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

    R. Rengaraj alias Muralidharan1,*, K. Latha2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1923-1937, 2023, DOI:10.32604/iasc.2023.029495
    Abstract In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way. Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery. Task execution failure becomes common in the CC environment. Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures. Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics. With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based… More >

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    ARTICLE

    An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

    I. Adumbabu1,*, K. Selvakumar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1939-1956, 2023, DOI:10.32604/iasc.2023.029489
    Abstract A wireless sensor network (WSN) is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions. In WSN, the location of the base station is critical. Although base stations are fixed, they may move in response to data received from sensor nodes under specific conditions. Clustering is a highly efficient approach of minimising energy use. The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper. It has been established that integrating mobile sinks into wireless sensor networks extends their longevity.… More >

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    Fault Diagnosis in Robot Manipulators Using SVM and KNN

    D. Maincer1,*, Y. Benmahamed2, M. Mansour1, Mosleh Alharthi3, Sherif S. M. Ghonein3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1957-1969, 2023, DOI:10.32604/iasc.2023.029210
    Abstract In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper… More >

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    ARTICLE

    An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

    Lina M. K. Al-Ebbini*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1971-1985, 2023, DOI:10.32604/iasc.2023.030100
    Abstract A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e.,… More >

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    ARTICLE

    Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

    S. Girirajan, A. Pandian*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090
    Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal… More >

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    ARTICLE

    Assessing Conscientiousness and Identify Leadership Quality Using Temporal Sequence Images

    T. S. Kanchana*, B. Smitha Evelin Zoraida
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2003-2013, 2023, DOI:10.32604/iasc.2023.029412
    Abstract Human Facial expressions exhibits the inner personality. Evaluating the inner personality is performed through questionnaires during recruitment process. However, the evaluation through questionnaires performs less due to anxiety, and stress during interview and prediction of leadership quality becomes a challenging problem. To the above problem, Temporal sequence based SENet architecture (TSSA) is proposed for accurate evaluation of personality trait for employing the correct person for leadership position. Moreover, SENet is integration with modern architectures for performance evaluation. In Proposed TSSA, face book facial images of a particular person for a period of one month and… More >

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    ARTICLE

    Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog Computing

    V. Balaji, P. Selvaraj*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2015-2027, 2023, DOI:10.32604/iasc.2023.029260
    Abstract Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge servers. It facilitates the benefits of the network edge with the maximized probability of offering interaction with the cloud. However, the fog computing characteristics are susceptible to counteract the challenges of security. The issues present with the Physical Layer Security (PLS) aspect in fog computing which included authentication, integrity, and confidentiality has been considered as a reason for the potential issues leading to the security breaches. In this work, the Octonion Algebra-inspired Non- Commutative… More >

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    ARTICLE

    Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

    K. Saranya*, K. Premalatha
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2029-2042, 2023, DOI:10.32604/iasc.2023.027949
    Abstract Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy. In this background, several authentication and accessibility issues emerge with an intention to protect the sensitive details of the patients over getting published in open domain. To solve this problem, Multi Attribute Case based Privacy Preservation (MACPP) technique is proposed in this study to enhance the security of privacy-preserving data. Private information can be any attribute information which is categorized as sensitive logs in a patient’s records. The semantic relation between transactional More >

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    ARTICLE

    Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

    Neetika Bhandari1,*, Payal Pahwa2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2043-2055, 2023, DOI:10.32604/iasc.2023.028368
    Abstract Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems. This data can be very important and useful if it is harnessed carefully and correctly. Useful information can be extracted from this massive data using the Data Mining process. The information extracted can be used to make vital decisions in various industries. Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group. Clustering methods are of various types. More >

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    ARTICLE

    A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

    Abdul Rahaman Wahab Sait1,*, Mohamad Khairi Ishak2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2057-2070, 2023, DOI:10.32604/iasc.2023.029602
    Abstract In healthcare sector, image classification is one of the crucial problems that impact the quality output from image processing domain. The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases. Magnetic Resonance Imaging (MRI) is one of the effective non-invasive strategies that generate a huge and distinct number of tissue contrasts in every imaging modality. This technique is commonly utilized by healthcare professionals for Brain Tumor (BT) diagnosis. With recent advancements in Machine Learning (ML) and Deep Learning (DL)… More >

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