Home / Journals / IASC / Vol.35, No.2, 2023
  • Open Access

    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 level of disease severity, the… More >

  • Open Access

    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 of video summarization and object… More >

  • Open Access

    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) and Flying Squirrel Search… More >

  • Open Access

    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), which suffers a significant power… More >

  • Open Access

    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 and enable the high-layer protocols… More >

  • Open Access

    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 the improper use of pharmaceuticals… More >

  • Open Access

    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 services’ integration process through modeling… More >

  • Open Access

    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 tolerances. The gain resistors with… More >

  • Open Access

    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 is used for the visualization… More >

  • Open Access

    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 friend’s stress states on Facebook.… More >

  • Open Access

    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. The proportional integral controller was… More >

  • Open Access

    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 Tweets, it is difficult to… More >

  • Open Access

    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 combining the stator flux with… More >

  • Open Access

    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 the basic square microstrip antenna,… More >

  • Open Access

    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. In the meantime fuzzy logic… More >

  • Open Access

    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 example, beyond what many would… More >

  • Open Access

    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 business decisions, and process, secure… More >

  • Open Access

    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 server beyond a certain point.… More >

  • Open Access

    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 research tends to Deep Learning… More >

  • Open Access

    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 the path or link connectivity… More >

  • Open Access

    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, SA (Simulated Annealing), MBBO (Modified… More >

  • Open Access

    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 choosing the most appropriate quality… More >

  • Open Access

    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 (WPD) technique is employed for… More >

  • Open Access

    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 mentioned problems, this research work… More >

  • Open Access

    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 motivation, this paper presents a… More >

  • Open Access

    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 the ORB-SLAM2 framework algorithm. In… More >

  • Open Access

    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 the weighted factor of the… More >

  • Open Access

    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 to examine the X-radiation (X-rays)… More >

  • Open Access

    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 exceptional outcomes. This study proposes… More >

  • Open Access

    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. From the output of BF,… More >

  • Open Access

    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 intend to solve the contention… More >

  • Open Access

    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 used a field-programmable gate array… More >

  • Open Access

    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 the words for retrieving correct… More >

  • Open Access

    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 novel ensemble classification strategy dubbed… More >

  • Open Access

    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 Apache Spark Parallel Model, this… More >

  • Open Access

    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 the training and testing data… More >

  • Open Access

    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 CEED problem is converted to… More >

  • Open Access

    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 Grey Wolf Ant Colony Optimisation… More >

  • Open Access

    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 cybersecurity of blockchain in healthcare… More >

  • Open Access

    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 for the fusion of the… More >

  • Open Access

    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 Fault Tolerant Aware Scheduling Scheme… More >

  • Open Access

    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. Thus, this research proposes an… More >

  • Open Access

    ARTICLE

    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 is based on the Gaussian… More >

  • Open Access

    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., quality of life) after… More >

  • Open Access

    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 based on convolutional recurrent neural… More >

  • Open Access

    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 face images collect from different… More >

  • Open Access

    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 Ring-based Fully Homomorphic Encryption Scheme… More >

  • Open Access

    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 patient records and access rights… More >

  • Open Access

    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. Many parameters and indexes exist… More >

  • Open Access

    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) models, it is possible to… More >

  • Open Access

    ARTICLE

    Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

    S. Prabu, K. Joseph Abraham Sundar*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105
    Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain… More >

  • Open Access

    ARTICLE

    Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

    Yu Shih, Chien-Chih Kuo, Ching-Hung Lee*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2087-2099, 2023, DOI:10.32604/iasc.2023.027659
    Abstract The recent trends in Industry 4.0 and Internet of Things have encouraged many factory managers to improve inspection processes to achieve automation and high detection rates. However, the corresponding cost results of sample tests are still used for quality control. A low-cost automated optical inspection system that can be integrated with production lines to fully inspect products without adjustments is introduced herein. The corresponding mechanism design enables each product to maintain a fixed position and orientation during inspection to accelerate the inspection process. The proposed system combines image recognition and deep learning to measure the dimensions of the thread and… More >

  • Open Access

    ARTICLE

    A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

    R. Rajakumar1,*, S. Sathiya Devi2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889
    Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In addition, Local Correlation Integral (LOCI)… More >

  • Open Access

    ARTICLE

    Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

    Anas Quteishat1,2,*, M. A. A. Younis2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2117-2134, 2023, DOI:10.32604/iasc.2023.029419
    Abstract Renewable energy is created by renewable natural resources such as geothermal heat, sunlight, tides, rain, and wind. Energy resources are vital for all countries in terms of their economies and politics. As a result, selecting the optimal option for any country is critical in terms of energy investments. Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming. In the present work, the authors suggest fuzzy multi-characteristic decision-making approaches for renewable energy source selection, and fuzzy set theory is a valuable methodology for dealing with uncertainty in… More >

  • Open Access

    ARTICLE

    Wireless Network Security Using Load Balanced Mobile Sink Technique

    Reem Alkanhel1, Mohamed Abouhawwash2,3, S. N. Sangeethaa4, K. Venkatachalam5, Doaa Sami Khafaga6,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2135-2149, 2023, DOI:10.32604/iasc.2023.028852
    Abstract Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering strategies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the network’s lifetime due to network inequalities and less security. These possibilities arise due to the cluster head’s limited life span. These cluster heads (CH)… More >

  • Open Access

    ARTICLE

    Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

    Wahidul Hasan Abir1, Faria Rahman Khanam1, Kazi Nabiul Alam1, Myriam Hadjouni2, Hela Elmannai3, Sami Bourouis4, Rajesh Dey5, Mohammad Monirujjaman Khan1,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653
    Abstract Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human… More >

  • Open Access

    ARTICLE

    Harnessing LSTM Classifier to Suggest Nutrition Diet for Cancer Patients

    S. Raguvaran1,*, S. Anandamurugan2, A. M. J. Md. Zubair Rahman3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2171-2187, 2023, DOI:10.32604/iasc.2023.028605
    Abstract A customized nutrition-rich diet plan is of utmost importance for cancer patients to intake healthy and nutritious foods that help them to be strong enough to maintain their body weight and body tissues. Consuming nutrition-rich diet foods will prevent them from the side effects caused before and after treatment thereby minimizing it. This work is proposed here to provide them with an effective diet assessment plan using deep learning-based automated medical diet system. Hence, an Enhanced Long-Short Term Memory (E-LSTM) has been proposed in this paper, especially for cancer patients. This proposed method will be very useful for cancer patients… More >

  • Open Access

    ARTICLE

    Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

    T. S. Balaji1,2, S. Srinivasan3,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2189-2203, 2023, DOI:10.32604/iasc.2023.028785
    Abstract The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops… More >

  • Open Access

    ARTICLE

    Hybrid Color Texture Features Classification Through ANN for Melanoma

    Saleem Mustafa1, Arfan Jaffar1, Muhammad Waseem Iqbal2,*, Asma Abubakar2, Abdullah S. Alshahrani3, Ahmed Alghamdi4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549
    Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

  • Open Access

    ARTICLE

    Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

    Dalel Ben Ismail1, Hela Elmannai2,*, Souham Meshoul2, Mohamed Saber Naceur1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2219-2236, 2023, DOI:10.32604/iasc.2023.028944
    Abstract Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies… More >

  • Open Access

    ARTICLE

    Defending Adversarial Examples by a Clipped Residual U-Net Model

    Kazim Ali1,*, Adnan N. Qureshi1, Muhammad Shahid Bhatti2, Abid Sohail2, Mohammad Hijji3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810
    Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising… More >

  • Open Access

    ARTICLE

    Realtime Object Detection Through M-ResNet in Video Surveillance System

    S. Prabu1,*, J. M. Gnanasekar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2257-2271, 2023, DOI:10.32604/iasc.2023.029877
    Abstract Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images… More >

  • Open Access

    ARTICLE

    Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

    A. Deepak Kumar1,2,*, T. Sasipraba1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996
    Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only one augmentation technique, which produces… More >

  • Open Access

    ARTICLE

    An Optimized Method for Information System Transactions Based on Blockchain

    Jazem Mutared Alanazi, Ahmad Ali AlZubi*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2289-2308, 2023, DOI:10.32604/iasc.2023.029181
    Abstract Accounting Information System (AIS), which is the foundation of any enterprise resource planning (ERP) system, is often built as centralized system. The technologies that allow the Internet-of-Value, which is built on five aspects that are network, algorithms, distributed ledger, transfers, and assets, are based on blockchain. Cryptography and consensus protocols boost the blockchain platform implementation, acting as a deterrent to cyber-attacks and hacks. Blockchain platforms foster innovation among supply chain participants, resulting in ecosystem development. Traditional business processes have been severely disrupted by blockchains since apps and transactions that previously required centralized structures or trusted third-parties to authenticate them may… More >

  • Open Access

    ARTICLE

    Human Fatty Liver Monitoring Using Nano Sensor and IoMT

    Srilekha Muthukaruppankaruppiah1,*, Shanker Rajendiran Nagalingam2, Priya Murugasen3, Rajesh Nandaamarnath4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2309-2323, 2023, DOI:10.32604/iasc.2023.029598
    Abstract Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrhosis. The various imaging techniques for measuring fatty liver are Magnetic Resonance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials… More >

  • Open Access

    ARTICLE

    THD Reduction for Permanent Magnet Synchronous Motor Using Simulated Annealing

    R. Senthil Rama1, C. R. Edwin Selva Rex2, N. Herald Anantha Rufus3,*, J. Annrose4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2325-2336, 2023, DOI:10.32604/iasc.2023.028930
    Abstract Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion (THD) technique. Reduced THD achieves lower peak current, higher efficiency and longer equipment life span. Simulated annealing (SA) is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine (PMSM). The parameters of direct torque controllers (DTC) for the drive are automatically adjusted by the optimization algorithm. Advantages of the PI-Fuzzy-SA algorithm are retained when used together. It also improves the rate of system convergence. Speed response improvement and harmonic… More >

  • Open Access

    ARTICLE

    Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

    S. M. Udhaya Sankar1,*, Mary Subaja Christo2, P. S. Uma Priyadarsini3
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2337-2351, 2023, DOI:10.32604/iasc.2023.030278
    Abstract Energy conservation has become a significant consideration in wireless sensor networks (WSN). In the sensor network, the sensor nodes have internal batteries, and as a result, they expire after a certain period. As a result, expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efficient way remains a challenge. Also, the clustering strategy employs to enhance or extend the life cycle of WSNs. We identify the supervisory head node (SH) or cluster head (CH) in every grouping considered the feasible strategy for power-saving route discovery in the clustering model, which diminishes the communication… More >

  • Open Access

    ARTICLE

    Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

    B. Chellapraba1,*, D. Manohari2, K. Periyakaruppan3, M. S. Kavitha4
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2353-2366, 2023, DOI:10.32604/iasc.2023.029923
    Abstract Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed… More >

  • Open Access

    ARTICLE

    Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

    S. Sathish Kumar1,*, G. Ravi2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2367-2382, 2023, DOI:10.32604/iasc.2023.030054
    Abstract Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible… More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning for IoT Based COVID 19 Health Care Pollution Monitor

    Nithya Rekha Sivakumar*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2383-2398, 2023, DOI:10.32604/iasc.2023.028574
    Abstract Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud… More >

  • Open Access

    ARTICLE

    Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

    G. Rajasekaran1,*, J. Raja Sekar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2399-2412, 2023, DOI:10.32604/iasc.2023.029119
    Abstract Abnormal behavior detection is challenging and one of the growing research areas in computer vision. The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events. In this work, Pyramidal Lucas Kanade algorithm is optimized using EMEHOs to achieve the objective. First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs. Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior. University of Minnesota (UMN) dataset is used to evaluate the proposed system. The experimental result shows… More >

  • Open Access

    ARTICLE

    Investigation of Android Malware Using Deep Learning Approach

    V. Joseph Raymond1,2,*, R. Jeberson Retna Raj1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527
    Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits and create threat hunts on… More >

  • Open Access

    ARTICLE

    Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

    Shyla1,*, Vishal Bhatnagar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2431-2447, 2023, DOI:10.32604/iasc.2023.029126
    Abstract The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of… More >

  • Open Access

    ARTICLE

    Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

    R. Saravana Ram1, M. Vinoth Kumar2, Tareq M. Al-shami3, Mehedi Masud4, Hanan Aljuaid5, Mohamed Abouhawwash6,7,*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2449-2462, 2023, DOI:10.32604/iasc.2023.030486
    Abstract Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning-Based Adaptive Multiple Access Schemes Underwater Wireless Networks

    D. Anitha1,*, R. A. Karthika2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2463-2477, 2023, DOI:10.32604/iasc.2023.023361
    Abstract Achieving sound communication systems in Under Water Acoustic (UWA) environment remains challenging for researchers. The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts. The development of machine and deep learning algorithms has reduced the burden of achieving reliable and good communication schemes in the underwater acoustic environment. This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA), Time Division Multiple Access(TDMA), and Orthogonal Frequency Division Multiplexing(OFDM) techniques using the hybrid combination of the convolutional neural networks(CNN) and ensemble single… More >

  • Open Access

    ARTICLE

    A Deep Trash Classification Model on Raspberry Pi 4

    Thien Khai Tran1, Kha Tu Huynh2,*, Dac-Nhuong Le3, Muhammad Arif4, Hoa Minh Dinh1
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2479-2491, 2023, DOI:10.32604/iasc.2023.029078
    Abstract Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The… More >

  • Open Access

    ARTICLE

    Performance Analysis of Optimization Based FOC and DTC Methods for Three Phase Induction Motor

    V. Jesus Bobin*, M. MarsalineBeno
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2493-2511, 2023, DOI:10.32604/iasc.2023.024679
    Abstract Three-phase induction motors are becoming increasingly utilized in industrial field due to their better efficiency and simple manufacture. The speed control of an induction motor is essential in a variety of applications, but it is difficult to control. This research analyses the three-phase induction motor’s performance using field-oriented control (FOC) and direct torque control (DTC) techniques. The major aim of this work is to provide a critical evaluation of developing a simple speed controller for induction motors with improving the performance of Induction Motor (IM). For controlling a motor, different optimization approaches are accessible; in this research, a Fuzzy Logic… More >

  • Open Access

    ARTICLE

    OFDM-CFO and Resource Scheduling Algorithm Using Fuzzy Linear-CFO

    M. Prabhu1,*, B. Muthu Kumar2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2513-2525, 2023, DOI:10.32604/iasc.2023.027823
    Abstract Orthogonal Frequency-Division Multiplexing (OFDM) is the form of a digital system and a way of encoding digital data across multiple frequency components that are used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a major disadvantage of OFDM. This paper proposed a feasible and elegant fuzzy-based resource allocation technique, that overcomes the constraints of the CFO. The suggested Fuzzy linear CFO estimation (FL-CFO) not only estimates the CFO with increased precision but also allocates resources effectively, and achieves maximum utilization of dynamic resources. The suggested FL-CFO error estimation algorithm in OFDM systems employing 1-bit Quadrate errors ADC (1-bit QE)… More >

  • Open Access

    ARTICLE

    Deep Learning Prediction Model for Heart Disease for Elderly Patients

    Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud*
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168
    Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with… More >

  • Open Access

    ARTICLE

    An Enhanced Trust-Based Secure Route Protocol for Malicious Node Detection

    S. Neelavathy Pari1,*, K. Sudharson2
    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2541-2554, 2023, DOI:10.32604/iasc.2023.030284
    Abstract The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and… More >

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