Home / Journals / IASC / Vol.36, No.2, 2023
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    ARTICLE

    Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

    S. Soundararajan1,*, B. R. Tapas Bapu2, C. Kotteeswaran1, S. Venkatasubramanian3, P. J. Sathish Kumar4, Ahmed Mudassar Ali2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1227-1240, 2023, DOI:10.32604/iasc.2023.025752
    Abstract Recently, Wireless Sensor Network (WSN) becomes most potential technologies for providing improved services to several data gathering and tracking applications. Because of the wireless medium, multi-hop communication, absence of physical protectivity, and accumulated traffic, WSN is highly vulnerable to security concerns. Therefore, this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis. It is challenging to determine if packet loss is caused by a collision in the medium access path, poor channel quality, or a selective forwarding More >

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    ARTICLE

    Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

    C. Anjali*, Julia Punitha Malar Dhas, J. Amar Pratap Singh
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1241-1256, 2023, DOI:10.32604/iasc.2023.029678
    Abstract The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid… More >

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    ARTICLE

    Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

    G. Bhargavi*, J. Arunnehru
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335
    Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and… More >

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    ARTICLE

    Selective Mapping Scheme for Universal Filtered Multicarrier

    Akku Madhusudhan*, Sudhir Kumar Sharma
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1273-1282, 2023, DOI:10.32604/iasc.2023.030765
    Abstract The next step in mobile communication technology, known as 5G, is set to go live in a number of countries in the near future. New wireless applications have high data rates and mobility requirements, which have posed a challenge to mobile communication technology researchers and designers. 5G systems could benefit from the Universal Filtered Multicarrier (UFMC). UFMC is an alternate waveform to orthogonal frequency-division multiplexing (OFDM), in filtering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference (ICI) between neighbouring users is reduced via the sub-band More >

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    ARTICLE

    Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

    Somasundaram Devaraj*, Senthil Kumar Arunachalam
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200
    Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal… More >

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    ARTICLE

    Deep Learning for Depression Detection Using Twitter Data

    Doaa Sami Khafaga1, Maheshwari Auvdaiappan2, K. Deepa3, Mohamed Abouhawwash4,5, Faten Khalid Karim1,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1301-1313, 2023, DOI:10.32604/iasc.2023.033360
    Abstract Today social media became a communication line among people to share their happiness, sadness, and anger with their end-users. It is necessary to know people’s emotions are very important to identify depressed people from their messages. Early depression detection helps to save people’s lives and other dangerous mental diseases. There are many intelligent algorithms for predicting depression with high accuracy, but they lack the definition of such cases. Several machine learning methods help to identify depressed people. But the accuracy of existing methods was not satisfactory. To overcome this issue, the deep learning method is… More >

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    ARTICLE

    Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

    V. Nandagopal1,*, T. S. Balaji Damodhar2, P. Vijayapriya3, A. Thamilmaran3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1315-1329, 2023, DOI:10.32604/iasc.2023.032039
    Abstract

    The development of non-linear loads at consumers has significantly impacted power supply systems. Since, the poor power quality has been found in the three-phase distribution system due to unbalanced loads, harmonic current, undesired voltage regulation, and extreme reactive power demand. To overcome this issue, Distributed STATicCOMpensator (DSTATCOM) is implemented. DSTATCOM is a shunt-connected Voltage Source Converter (VSC) that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor. DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation. A rectified resistive and

    More >

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    ARTICLE

    Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

    Aravind Athimoolam1,*, Karthik Balasubramanian2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1331-1347, 2023, DOI:10.32604/iasc.2023.033465
    Abstract This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In More >

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    ARTICLE

    Novel Multimodal Biometric Feature Extraction for Precise Human Identification

    J. Vasavi1, M. S. Abirami2,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1349-1363, 2023, DOI:10.32604/iasc.2023.032604
    Abstract In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model… More >

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    ARTICLE

    Heartbeat and Respiration Rate Prediction Using Combined Photoplethysmography and Ballisto Cardiography

    Valarmathi Ramasamy1,*, Dhandapani Samiappan2, R. Ramesh3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1365-1380, 2023, DOI:10.32604/iasc.2023.032155
    Abstract Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation… More >

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    ARTICLE

    Cryptographic Algorithm for Enhancing Data Security in Wireless IoT Sensor Networks

    A. Bhavani, V. Nithya*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1381-1393, 2023, DOI:10.32604/iasc.2023.029397
    Abstract Wireless IoT Sensor Network can handle audio, video, text, etc., through the interconnection of ubiquitous devices. The entertainment and application-centric network relies on its autonomous nodes for handling large streams of multimedia data. Security breaches and threats due to insider attacks reduce the data handling and distribution capacity of the nodes. For addressing the insider attacks problem, Session-Critical Distributed Authentication Method (SCDAM) is proposed. The proposed method relies on short-lived concealed authentication based on an improved elliptic curve cryptography (ECC) algorithm. In this authentication, the session time and the interrupts are accounted for, providing end-to-end More >

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    ARTICLE

    Power-Sharing Enhancement Using Harmonized Membership Fuzzy Logic Droop Control Based Micro-Grid

    W. J. Praiselin, J. Belwin Edward*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1395-1415, 2023, DOI:10.32604/iasc.2023.028970
    Abstract The contribution of Renewable Energy Resources (RER) in the process of power generation is significantly high in the recent days since it paves the way for overcoming the issues like serious energy crisis and natural contamination. This paper deals with the renewable energy based micro-grid as it is regarded as the apt solution for integrating the RER with the electrical frameworks. As the fixed droop coefficients in conventional droop control approaches have caused various limitations like low power-sharing and sudden drops of grid voltage in the Direct Current (DC) side, the Harmonized Membership Fuzzy Logic… More >

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    ARTICLE

    Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

    E. Bharath1,*, T. Rajagopalan2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102
    Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made… More >

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    ARTICLE

    Clustered Wireless Sensor Network in Precision Agriculture via Graph Theory

    L. R. Bindu1,*, P. Titus2, D. Dhanya3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1435-1449, 2023, DOI:10.32604/iasc.2023.030591
    Abstract Food security and sustainable development is making a mandatory move in the entire human race. The attainment of this goal requires man to strive for a highly advanced state in the field of agriculture so that he can produce crops with a minimum amount of water and fertilizer. Even though our agricultural methodologies have undergone a series of metamorphoses in the process of a present smart-agricultural system, a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability. Towards such a futuristic method of cultivation, this… More >

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    ARTICLE

    Classification of Nonlinear Confusion Component Using Hybrid Multi-Criteria Decision Making

    Nabilah Abughazalah1, Iqra Ishaque2, Majid Khan2,*, Ammar S. Alanazi3, Iqtadar Hussain4,5
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1451-1463, 2023, DOI:10.32604/iasc.2023.031855
    Abstract In today’s digital world, the most inevitable challenge is the protection of digital information. Due to the weak confidentiality preserving techniques, the existing world is facing several digital information breaches. To make our digital data indecipherable to the unauthorized person, a technique for finding a cryptographically strong Substitution box (S-box) have presented. An S-box with sound cryptographic assets such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criteria (BIC), bit independence criteria of nonlinearity (BIC-NL), Bit independence criteria of Strict avalanche criteria (BIC-SAC), and Input/output XOR is considered as the robust S-box. The Decision-Making… More >

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    ARTICLE

    Improved Network Validity Using Various Soft Computing Techniques

    M. Yuvaraju*, R. Elakkiyavendan
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1465-1477, 2023, DOI:10.32604/iasc.2023.032417
    Abstract Nowadays, when a life span of sensor nodes are threatened by the shortage of energy available for communication, sink mobility is an excellent technique for increasing its lifespan. When communicating via a WSN, the use of nodes as a transmission method eliminates the need for a physical medium. Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources. Unless the route is revised and changed to reflect the location of the mobile sink location, it will be inefficient for delivering data effectively. In the clustering strategy, nodes are… More >

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    ARTICLE

    Numerical Analysis for the Effect of Irresponsible Immigrants on HIV/AIDS Dynamics

    Muhammad Tariq Ali1, Dumitru Baleanu2,3,4, Muhammad Rafiq5, Jan Awrejcewicz6, Nauman Ahmed7, Ali Raza8,*, Muhammad Sajid Iqbal9, Muhammad Ozair Ahmad7
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1479-1496, 2023, DOI:10.32604/iasc.2023.033157
    Abstract The human immunodeficiency viruses are two species of Lentivirus that infect humans. Over time, they cause acquired immunodeficiency syndrome, a condition in which progressive immune system failure allows life-threatening opportunistic infections and cancers to thrive. Human immunodeficiency virus infection came from a type of chimpanzee in Central Africa. Studies show that immunodeficiency viruses may have jumped from chimpanzees to humans as far back as the late 1800s. Over decades, human immunodeficiency viruses slowly spread across Africa and later into other parts of the world. The Susceptible-Infected-Recovered (SIR) models are significant in studying disease dynamics. In… More >

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    ARTICLE

    Deep Learning-Based Swot Analysis in Construction and Demolition Waste Management

    R. Rema*, N. Nalanth
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1497-1506, 2023, DOI:10.32604/iasc.2023.032540
    Abstract Researchers worldwide have employed a varied array of sources to calculate the successful management of Construction and Demolition (C&DW). Limited research has been undertaken in the domain of Construction and Demolition Waste Management (C&DWM) and consequently leaving a large gap in the availability of effective management techniques. Due to the limited time available for building removal and materials collection, preparing for building materials reuse at the end of life is frequently a challenging task. In this research work Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) is proposed to predict the number of waste materials that are More >

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    ARTICLE

    Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

    Madona B. Sahaai*, G. R. Jothilakshmi, E. Praveen, V. Hemath Kumar
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1507-1521, 2023, DOI:10.32604/iasc.2023.032256
    Abstract Magnetic Resonance Imaging (MRI) is one of the important resources for identifying abnormalities in the human brain. This work proposes an effective Multi-Class Classification (MCC) system using Binary Robust Invariant Scalable Keypoints (BRISK) as texture descriptors for effective classification. At first, the potential Region Of Interests (ROIs) are detected using features from the accelerated segment test algorithm. Then, non-maxima suppression is employed in scale space based on the information in the ROIs. The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour (kNN), Support Vector Machine (SVM) and Random Forest More >

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    ARTICLE

    Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle

    J. Aran Glenn1,*, Srinivasan Alavandar2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1523-1545, 2023, DOI:10.32604/iasc.2023.033545
    Abstract Nowadays, researchers are becoming increasingly concerned about developing a highly efficient emission free transportation and energy generation system for addressing the pressing issue of environmental crisis in the form of pollution and climate change. The introduction of Electric Vehicles (EVs) solves the challenge of emission-free transportation while the necessity for decarbonized energy production is fulfilled by the installation and expansion of solar-powered Photovoltaic (PV) systems. Hence, this paper focuses on designing an effective PV based EV charging system that aids in stepping towards the achievement of a pollution free future. For overcoming the inherent intermittency… More >

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    ARTICLE

    Cooperative Channel and Optimized Route Selection in Adhoc Network

    D. Manohari1,*, M. S. Kavitha2, K. Periyakaruppan3, B. Chellapraba4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1547-1560, 2023, DOI:10.32604/iasc.2023.030540
    Abstract Over the last decade, mobile Adhoc networks have expanded dramatically in popularity, and their impact on the communication sector on a variety of levels is enormous. Its uses have expanded in lockstep with its growth. Due to its instability in usage and the fact that numerous nodes communicate data concurrently, adequate channel and forwarder selection is essential. In this proposed design for a Cognitive Radio Cognitive Network (CRCN), we gain the confidence of each forwarding node by contacting one-hop and second level nodes, obtaining reports from them, and selecting the forwarder appropriately with the use… More >

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    ARTICLE

    A Multi-Modal Deep Learning Approach for Emotion Recognition

    H. M. Shahzad1,3, Sohail Masood Bhatti1,3,*, Arfan Jaffar1,3, Muhammad Rashid2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525
    Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal… More >

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    ARTICLE

    An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

    Reem Alkanhel1, Kalaiselvi Chinnathambi2, C. Thilagavathi3, Mohamed Abouhawwash4,5, Mona A. Al duailij6, Manal Abdullah Alohali7, Doaa Sami Khafaga6,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1571-1583, 2023, DOI:10.32604/iasc.2023.033430
    Abstract Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are… More >

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    ARTICLE

    Parallel Iterative FEM Solver with Initial Guess for Frequency Domain Electromagnetic Analysis

    Woochan Lee1, Woobin Park1, Jaeyoung Park2, Young-Joon Kim3, Moonseong Kim4,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1585-1602, 2023, DOI:10.32604/iasc.2023.033112
    Abstract The finite element method is a key player in computational electromagnetics for designing RF (Radio Frequency) components such as waveguides. The frequency-domain analysis is fundamental to identify the characteristics of the components. For the conventional frequency-domain electromagnetic analysis using FEM (Finite Element Method), the system matrix is complex-numbered as well as indefinite. The iterative solvers can be faster than the direct solver when the solver convergence is guaranteed and done in a few steps. However, such complex-numbered and indefinite systems are hard to exploit the merit of the iterative solver. It is also hard to… More >

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    ARTICLE

    Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

    Mahendrakumar Subramaniam1,*, V. Vedanarayanan2, Azath Mubarakali3, S. Sathiya Priya4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1603-1612, 2023, DOI:10.32604/iasc.2023.032396
    Abstract Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning… More >

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    ARTICLE

    Hopping-Aware Cluster Header Capability for Sensor Relocation in Mobile IoT Networks

    Moonseong Kim1, Jaeyoung Park2, Young-Joon Kim3, Woochan Lee4,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1613-1625, 2023, DOI:10.32604/iasc.2023.033081
    Abstract Mobile sensor nodes such as hopping sensors are of critical importance in data collection. However, the occurrence of sensing holes is unavoidable due to the energy limitation of the nodes. Thus, it is evident that the relocation of mobile sensors is the most desirable method to recover the sensing holes. The previous research conducted by the authors so far demonstrated the most realistic hopping sensor relocation scheme, which is suitable for the distributed environment. In previous studies, the cluster header plays an essential role in detecting the sensing hole and requesting the neighboring cluster to… More >

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    ARTICLE

    Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

    R. Mahalakshmi1,*, V. Prasanna Srinivasan2, S. Aghalya3, D. Muthukumaran4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1627-1637, 2023, DOI:10.32604/iasc.2023.032709
    Abstract A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This… More >

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    ARTICLE

    Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

    M. S. Vivekanandan1, T. Jesudas2,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1639-1652, 2023, DOI:10.32604/iasc.2023.032301
    Abstract In an urban city, the daily challenges of managing cleanliness are the primary aspect of routine life, which requires a large number of resources, the manual process of labour, and budget. Street cleaning techniques include street sweepers going away to different metropolitan areas, manually verifying if the street required cleaning taking action. This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation. For the large volume of the process, the deep learning-based methods can be better to achieve a high level of classification, object detection, and… More >

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    ARTICLE

    Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

    Mrim M. Alnfiai*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1653-1669, 2023, DOI:10.32604/iasc.2023.033577
    Abstract Sign language is mainly utilized in communication with people who have hearing disabilities. Sign language is used to communicate with people having developmental impairments who have some or no interaction skills. The interaction via Sign language becomes a fruitful means of communication for hearing and speech impaired persons. A Hand gesture recognition system finds helpful for deaf and dumb people by making use of human computer interface (HCI) and convolutional neural networks (CNN) for identifying the static indications of Indian Sign Language (ISL). This study introduces a shark smell optimization with deep learning based automated… More >

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    ARTICLE

    Fast Segmentation Method of Sonar Images for Jacket Installation Environment

    Hande Mao1,2, Hongzhe Yan1, Lei Lin1, Wentao Dong1,3, Yuhang Li1, Yuliang Liu2,4,*, Jing Xue5
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1671-1686, 2023, DOI:10.32604/iasc.2023.028819
    Abstract It has remained a hard nut for years to segment sonar images of jacket installation environment, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds. To realize this goal, we follow the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, the image is divided into More >

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    ARTICLE

    Sustainable Learning of Computer Programming Languages Using Mind Mapping

    Shahla Gul1, Muhammad Asif1, Zubair Nawaz2, Muhammad Haris Aziz3, Shahzada Khurram4, Muhammad Qaiser Saleem5, Elturabi Osman Ahmed Habib5, Muhammad Shafiq6,*, Osama E. Sheta7
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1687-1697, 2023, DOI:10.32604/iasc.2023.032494
    Abstract In the current era of information technology, students need to learn modern programming languages efficiently. The art of teaching/learning programming requires many logical and conceptual skills. So it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efficiently. Mind mapping is a useful visual tool for establishing ideas and connecting them to solve problems. This research proposed an effective way to teach programming languages through visual tools. This experimental study uses a mind mapping tool to teach two programming environments: Text-based Programming and Blocks-based Programming. We performed the experiments with one More >

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    ARTICLE

    Temporal Preferences-Based Utility Control for Smart Homes

    Salman Naseer1, Raheela Saleem2, Muhammad Mudasar Ghafoor3, Shahzada Khurram4, Shafiq Ahmad5, Abdelaty Edrees Sayed5, Muhammad Shafiq6,*, Jin-Ghoo Choi6
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1699-1714, 2023, DOI:10.32604/iasc.2023.034032
    Abstract The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy… More >

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    ARTICLE

    Identifying Influential Communities Using IID for a Multilayer Networks

    C. Suganthini*, R. Baskaran
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1715-1731, 2023, DOI:10.32604/iasc.2023.034019
    Abstract In online social networks (OSN), they generate several specific user activities daily, corresponding to the billions of data points shared. However, although users exhibit significant interest in social media, they are uninterested in the content, discussions, or opinions available on certain sites. Therefore, this study aims to identify influential communities and understand user behavior across networks in the information diffusion process. Social media platforms, such as Facebook and Twitter, extract data to analyze the information diffusion process, based on which they cascade information among the individuals in the network. Therefore, this study proposes an influential… More >

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    ARTICLE

    An Intelligent Deep Neural Sentiment Classification Network

    Umamaheswari Ramalingam1,*, Senthil Kumar Murugesan2, Karthikeyan Lakshmanan2, Chidhambararajan Balasubramaniyan3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108
    Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS).… More >

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    ARTICLE

    EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

    S. Kavi Priya*, K. Pon Karthika
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160
    Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and… More >

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    ARTICLE

    Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

    M. P. Thiruvenkatasuresh1,*, Surbhi Bhatia2, Shakila Basheer3, Pankaj Dadheech4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1767-1782, 2023, DOI:10.32604/iasc.2023.033711
    Abstract The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard… More >

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    ARTICLE

    ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

    Jiangyuan Yao1, Jiawen Wang1, Shuhua Weng1, Minrui Wang1, Deshun Li1,*, Yahui Li2, Xingcan Cao3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1783-1796, 2023, DOI:10.32604/iasc.2023.034265
    Abstract With the increasing number of switches in Software-Defined Networking (SDN), there are more and more faults rising in the data plane. However, due to the existence of link redundancy and multi-path forwarding mechanisms, these problems cannot be detected in time. The current faulty path detection mechanisms have problems such as the large scale of detection and low efficiency, which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN. Concerning this issue, we propose an efficient network path fault testing model ProbD based on probability detection. This model achieves a high… More >

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    ARTICLE

    Women Entrepreneurship Index Prediction Model with Automated Statistical Analysis

    V. Saikumari*, V. Sunitha
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1797-1810, 2023, DOI:10.32604/iasc.2023.034038
    Abstract Recently, gender equality and women’s entrepreneurship have gained considerable attention in global economic development. Prior to the design of any policy interventions to increase women’s entrepreneurship, it is significant to comprehend the factors motivating women to become entrepreneurs. The non-understanding of the factors can result in the endurance of low living standards and the design of expensive and ineffectual policies. But female involvement in entrepreneurship becomes higher in developing economies compared to developed economies. Women Entrepreneurship Index (WEI) plays a vital role in determining the factors that enable the flourishment of high potential female entrepreneurs… More >

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    ARTICLE

    Adaptive Route Sink Relocation Using Cluster Head Chain Cycling Model in WSN

    M. Sudha1,*, P. Shanmugapriya2, Rami Q. Malik3, Ahmed Alkhayyat4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1811-1826, 2023, DOI:10.32604/iasc.2023.032265
    Abstract Wireless Sensor Networks (WSN) have revolutionized the processes involved in industrial communication. However, the most important challenge faced by WSN sensors is the presence of limited energy. Multiple research investigations have been conducted so far on how to prolong the energy in WSN. This phenomenon is a result of inability of the network to have battery powered-sensor terminal. Energy-efficient routing on packet flow is a parallel phenomenon to delay nature, whereas the primary energy gets wasted as a result of WSN holes. Energy holes are present in the vicinity of sink and it is an… More >

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    ARTICLE

    Trust and QoS-Driven Query Service Provisioning Using Optimization

    K. Narmatha1,*, K. Karthikeyan2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1827-1844, 2023, DOI:10.32604/iasc.2023.028473
    Abstract The growing advancements with the Internet of Things (IoT) devices handle an enormous amount of data collected from various applications like healthcare, vehicle-based communication, and smart city. This research analyses cloud-based privacy preservation over the smart city based on query computation. However, there is a lack of resources to handle the incoming data and maintain them with higher privacy and security. Therefore, a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment. A querying service model is proposed to handle the incoming data collected from various… More >

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    ARTICLE

    Smart Nutrient Deficiency Prediction System for Groundnut Leaf

    Janani Malaisamy*, Jebakumar Rethnaraj
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1845-1862, 2023, DOI:10.32604/iasc.2023.034280
    Abstract Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield. Distributing fertiliser in optimum amounts will protect the environment’s condition and human health risks. Early identification also prevents the disease’s occurrence in groundnut crops. A convolutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitrogen nutrient deficiency through image features. Since chlorophyll and nitrogen are proportionate to one… More >

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    ARTICLE

    Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

    S. Amutha*, J. Raja Sekar
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1863-1880, 2023, DOI:10.32604/iasc.2023.028903
    Abstract In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are… More >

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    ARTICLE

    Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

    B. Divya*, M. Santhi
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1881-1898, 2023, DOI:10.32604/iasc.2023.031573
    Abstract Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring… More >

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    ARTICLE

    SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation

    T. Merlin Inbamalar1,*, Dhandapani Samiappan2, R. Ramesh3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1899-1913, 2023, DOI:10.32604/iasc.2023.031202
    Abstract Conferring to the American Association of Neurological Surgeons (AANS) survey, 85% to 99% of people are affected by spinal cord tumors. The symptoms are varied depending on the tumor’s location and size. Up-to-the-minute, back pain is one of the essential symptoms, but it does not have a specific symptom to recognize at the earlier stage. Numerous significant research studies have been conducted to improve spine tumor recognition accuracy. Nevertheless, the traditional systems are consuming high time to extract the specific region and features. Improper identification of the tumor region affects the predictive tumor rate and More >

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    ARTICLE

    Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

    Rab Nawaz Bashir1, Imran Sarwar Bajwa2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1915-1930, 2023, DOI:10.32604/iasc.2023.030844
    Abstract Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship… More >

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    ARTICLE

    Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

    Vaibhav Rupapara1, Furqan Rustam2, Abid Ishaq2, Ernesto Lee3, Imran Ashraf4,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257
    Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using… More >

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    ARTICLE

    An Optimal Method for Speech Recognition Based on Neural Network

    Mohamad Khairi Ishak1, Dag Øivind Madsen2,*, Fahad Ahmed Al-Zahrani3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1951-1961, 2023, DOI:10.32604/iasc.2023.033971
    Abstract Natural language processing technologies have become more widely available in recent years, making them more useful in everyday situations. Machine learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising breakthroughs. These methods were particularly advantageous for regional languages, as they were provided with cutting-edge language processing tools as soon as the requisite corporate information was generated. The bulk of modern people are unconcerned about the importance of reading. Reading aloud, on the other hand, is an effective… More >

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    ARTICLE

    The Impact of Hydrogen Energy Storage on the Electricity Harvesting

    Ghassan Mousa1, Ayman A. Aly2, Imran Khan3, Dag Øivind Madsen4,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1963-1978, 2023, DOI:10.32604/iasc.2023.033627
    Abstract The economics, infrastructure, transportation, and level of living of a country are all influenced by energy. The gap between energy usage and availability is a global issue. Currently, all countries rely on fossil fuels for energy generation, and these fossil fuels are not sustainable. The hydrogen proton exchange membrane fuel cell (PEMFC) power system is both clean and efficient. The fuel delivery system and the PEMFC make up the majority of the PEMFC power system. The lack of an efficient, safe, and cost-effective hydrogen storage system is still a major barrier to its widespread use.… More >

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    ARTICLE

    A Joint Optimization Algorithm for Renewable Energy System

    Imran Khan1, Firdaus Muhammad-Sukki2, Jorge Alfredo Ardila Rey3, Abdullahi Abubakar Mas’ud4, Saud Jazaa Alshammari4, Dag Øivind Madsen5,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1979-1989, 2023, DOI:10.32604/iasc.2023.034106
    Abstract Energy sustainability is a hot topic in both scientific and political circles. To date, two alternative approaches to this issue are being taken. Some people believe that increasing power consumption is necessary for countries’ economic and social progress, while others are more concerned with maintaining carbon consumption under set limitations. To establish a secure, sustainable, and economical energy system while mitigating the consequences of climate change, most governments are currently pushing renewable growth policies. Energy markets are meant to provide consumers with dependable electricity at the lowest possible cost. A profit-maximization optimal decision model is… More >

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    ARTICLE

    Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

    R. Shanthakumari1, Yun-Cheol Nam2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1991-2005, 2023, DOI:10.32604/iasc.2023.033696
    Abstract Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The… More >

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