Home / Journals / IASC / Vol.34, No.2, 2022
  • Open Access

    ARTICLE

    Deep Contextual Learning for Event-Based Potential User Recommendation in Online Social Networks

    T. Manojpraphakar*, A. Soundarrajan
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 699-713, 2022, DOI:10.32604/iasc.2022.025090
    Abstract Event recommendation allows people to identify various recent upcoming social events. Based on the Profile or User recommendation people will identify the group of users to subscribe the event and to participate, despite it faces cold-start issues intrinsically. The existing models exploit multiple contextual factors to mitigate the cold-start issues in essential applications on profile recommendations to the event. However, those existing solution does not incorporate the correlation and covariance measures among various contextual factors. Moreover, recommending similar profiles to various groups of the events also has not been well analyzed in the existing literature. The proposed prototype model Correlation… More >

  • Open Access

    ARTICLE

    Smartphone Sensors Based Physical Life-Routine for Health Education

    Tamara al Shloul1, Usman Azmat2, Suliman A. Alsuhibany3, Yazeed Yasin Ghadi4, Ahmad Jalal2, Jeongmin Park5,*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 715-732, 2022, DOI:10.32604/iasc.2022.025421
    Abstract The physical and the mental health of a human being largely depends upon his physical life-routine (PLR) and today’s much advanced technological methods make it possible to recognize and keep track of an individual’s PLR. With the successful and accurate recognition of PLR, a sublime service of health education can be made copious. In this regard, smartphones can play a vital role as they are ubiquitous and have utilitarian sensors embedded in them. In this paper, we propose a framework that extracts the features from the smartphone sensors data and then uses the sequential feature selection to select the most… More >

  • Open Access

    ARTICLE

    Practical Machine Learning Techniques for COVID-19 Detection Using Chest X-Ray Images

    Yurananatul Mangalmurti, Naruemon Wattanapongsakorn*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 733-752, 2022, DOI:10.32604/iasc.2022.025073
    Abstract This paper presents effective techniques for automatic detection/classification of COVID-19 and other lung diseases using machine learning, including deep learning with convolutional neural networks (CNN) and classical machine learning techniques. We had access to a large number of chest X-ray images to use as input data. The data contains various categories including COVID-19, Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition, chest X-ray images with many findings (abnormalities and diseases) from the National Institutes of Health (NIH) was also considered. Our deep learning approach used a CNN architecture with VGG16 and VGG19 models which were pre-trained with ImageNet. We… More >

  • Open Access

    ARTICLE

    Errorless Underwater Channel Selection Scheme Using Forward Error Rectification and Modulation

    A. Herald1,*, C. Vennila2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 753-768, 2022, DOI:10.32604/iasc.2022.025362
    Abstract Acoustic and optical communication are the best options for data transmission in underwater communication. This paper presents the simulation model of an underwater wireless optical communication channel using the Errorless Channel Selection Using Forward Error Rectification and Modulation Progression (ECFM). The suitable modulation methods are used to encode and transfer the packets properly, the data is encoded in differential phase shift key mode at the phase of the light wave carrier. In addition, to send and receive data, an error rectification method is developed in the transport layer, which improves network speed. In addition, we create connections by observing the… More >

  • Open Access

    ARTICLE

    Another View of Weakly Open Sets Via DNA Recombination

    Samirah Alzahrani1,*, A.I. El-Maghrabi2, M.S. Badr3
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 769-783, 2022, DOI:10.32604/iasc.2022.024682
    Abstract The generalized structure of deoxyribonucleic acid (DNA) is based on the rules of topological spaces. DNA recombination is one of the most important processes within DNA, as it is essential in the pharmaceutical industry as well as in gene therapy. In this paper, we are discussing the relationship between rough sets, nano topological spaces (N), nano Z open (N) sets, and DNA recombination. We also created a new recombination mapping using the properties of the DNA recombination process. Further, by using the process of cutting and sticking of a sequence of genes, new topological structures are constructed and some of… More >

  • Open Access

    ARTICLE

    Adaptive Neuro-Fuzzy Based Load Frequency Control in Presence of Energy Storage Devices

    Pankaj Jood*, Sanjeev Kumar Aggarwal, Vikram Chopra
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 785-804, 2022, DOI:10.32604/iasc.2022.025217
    Abstract Energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. In this paper, the modeling and simulation of a two-area power system is done to evaluate and compare the impact of three different energy storage applications on load frequency control performance. Capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study. On the basis of peak overshoot and settling time, the performance of these energy storage devices is compared. The power system consists of thermal, wind, and solar resources. All nonlinearities are incorporated in the system… More >

  • Open Access

    ARTICLE

    Early DDoS Detection and Prevention with Traced-Back Blocking in SDN Environment

    Sriramulu Bojjagani1, D. R. Denslin Brabin2,*, K. Saravanan2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 805-819, 2022, DOI:10.32604/iasc.2022.023771
    Abstract The flow of information is a valuable asset for every company and its consumers, and Distributed Denial-of-Service (DDoS) assaults pose a substantial danger to this flow. If we do not secure security, hackers may steal information flowing across a network, posing a danger to a business and society. As a result, the most effective ways are necessary to deal with the dangers. A DDoS attack is a well-known network infrastructure assault that prevents servers from servicing genuine customers. It is necessary to identify and block a DDoS assault before it reaches the server in order to avoid being refused services.… More >

  • Open Access

    ARTICLE

    A Novel Controller for Microgrid Interactive Hybrid Renewable Power Sources

    P. Kavitha*, P. Subha Karuvelam
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 821-836, 2022, DOI:10.32604/iasc.2022.023035
    Abstract In this paper, a self-sufficient electric power generation is proposed by using hybrid renewable sources like solar and wind turbines to favor a smart and green environment. This distributed generation unit is connected to the grid through an 3Φ inverter. The power drawn from the hybrid unit is stored in the batteries to transfer power during the non-availability of power sources. This standalone power conversion and storage system are developed by using power electronic converters and controllers to ensure balanced power flow operation. A PI (Proportional Integral) controller is utilized for generating the PWM (Pulse Width Modulation) pulses for the… More >

  • Open Access

    ARTICLE

    Deep Reinforcement Extreme Learning Machines for Secured Routing in Internet of Things (IoT) Applications

    K. Lavanya1,*, K. Vimala Devi2, B. R. Tapas Bapu3
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 837-848, 2022, DOI:10.32604/iasc.2022.023055
    Abstract Multipath TCP (SMPTCP) has gained more attention as a valuable approach for IoT systems. SMPTCP is introduced as an evolution of Transmission Control Protocol (TCP) to pass packets simultaneously across several routes to completely exploit virtual networks on multi-homed consoles and other network services. The current multipath networking algorithms and simulation software strategies are confronted with sub-flow irregularity issues due to network heterogeneity, and routing configuration issues can be fixed adequately. To overcome the issues, this paper proposes a novel deep reinforcement-based extreme learning machines (DRLELM) approach to examine the complexities between routes, pathways, sub-flows, and SMPTCP connections in different… More >

  • Open Access

    ARTICLE

    Mango Leaf Stress Identification Using Deep Neural Network

    Vinay Gautam1,*, Jyoti Rani2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 849-864, 2022, DOI:10.32604/iasc.2022.025113
    Abstract Mango is a widely growing and consumable fruit crop. The quantity and quality of production are most important to satisfy the needs of the huge population. Numerous research has been conducted to increase the yield of the crop. But a good number of crop harvests were destroyed due to various factors and leaf stress is one of them. The various types of stresses include biotic and abiotic that impact the mangoes productivity. But here the focus is on biotic stress factors such as fungus and bacteria. The effect of the stress can be reduced in the preliminary stage by taking… More >

  • Open Access

    ARTICLE

    Control and Automation of Hybrid Renewable Energy Harvesting System

    R. S. Jothilakshmi*, S. Chitra Selvi
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 865-876, 2022, DOI:10.32604/iasc.2022.025643
    Abstract The hybrid renewable energy harvesting and grid integration system has been proposed and validated in this work. The proposed system uses a Wind Energy Conversion System (WECS) and a Solar Photo Voltaic Energy Conversion System (SPVECS). The WECS uses Permanent Magnet Synchronous Generator (PMSG) driven by a wind turbine. The variable frequency and variable voltage output of PMSG is rectified by Diode Bridge Rectifier (DBR) and stepped up by Super Lift Luo Converter (SLLC 1), and finally, the harvested wind energy is delivered to the Direct Current (DC) link of a Distributed Static Synchronous Compensator (DSTATCOM). Further, SPV energy is… More >

  • Open Access

    ARTICLE

    Adaptive Resource Allocation Neural Network-Based Mammogram Image Segmentation and Classification

    P. Indra, G. Kavithaa*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 877-893, 2022, DOI:10.32604/iasc.2022.025982
    Abstract Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN). At first, breast cancer… More >

  • Open Access

    ARTICLE

    PMSG Based Wind Energy Conversion System Using Intelligent MPPT with HGRSC Converter

    S. Kirubadevi*, S. Sutha
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 895-910, 2022, DOI:10.32604/iasc.2022.025395
    Abstract Wind power conversion systems play a significant position in grid-coupled renewable source networks. In this paper, a permanent magnet based synchronous alternator type wind energy scheme is considered for analysis. The enhanced performance of wind power conversion could be reached by improving maximum power point tracking (MPPT) and by modernising the control circuit of the power electronic circuit. The main task is to enrich its performance level by proposing fuzzy gain scheduling (FGS) based optimal torque management for maximum power point tracking. In addition to the improved MPPT, this article analyses different topologies of direct current–direct current (DC–DC) converters such… More >

  • Open Access

    ARTICLE

    Hybrid Optimized Learning for Lung Cancer Classification

    R. Vidhya1,*, T. T. Mirnalinee2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060
    Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks to improve the classification maps… More >

  • Open Access

    ARTICLE

    Dynamic Selection of Optional Feature for Object Detection

    Jun Wang1, Tingjuan Zhang2,*, Yong Cheng3, Prof Mingshun Jiang4
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 927-940, 2022, DOI:10.32604/iasc.2022.026847
    Abstract To obtain the most intuitive pedestrian target detection results and avoid the impact of motion pose uncertainty on real-time detection, a pedestrian target detection system based on a convolutional neural network was designed. Dynamic Selection of Optional Feature (DSOF) module and a center branch were proposed in this paper, and the target was detected by an anchor-free method. Although almost all the most advanced target detectors use pre-defined anchor boxes to run through the possible positions, scales, and aspect ratios of search targets, their effectualness, and generalization ability are also limited by the anchor boxes. Most anchor-based detectors use heuristically… More >

  • Open Access

    ARTICLE

    Object Detection Learning for Intelligent Self Automated Vehicles

    Ahtsham Alam1, Syed Ahmed Abdullah1, Israr Akhter1, Suliman A. Alsuhibany2,*, Yazeed Yasin Ghadi3, Tamara al Shloul4, Ahmad Jalal1
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 941-955, 2022, DOI:10.32604/iasc.2022.024840
    Abstract Robotics is a part of today's communication that makes human life simpler in the day-to-day aspect. Therefore, we are supporting this cause by making a smart city project that is based on Artificial Intelligence, image processing, and some touch of hardware such as robotics. In particular, we advocate a self automation device (i.e., autonomous car) that performs actions and takes choices on its very own intelligence with the assist of sensors. Sensors are key additives for developing and upgrading all forms of self-sustaining cars considering they could offer the information required to understand the encircling surroundings and consequently resource the… More >

  • Open Access

    ARTICLE

    Dark and Bright Channel Priors for Haze Removal in Day and Night Images

    U. Hari, A. Ruhan Bevi*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 957-967, 2022, DOI:10.32604/iasc.2022.023605
    Abstract Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the… More >

  • Open Access

    ARTICLE

    Secured Medical Data Transfer Using Reverse Data Hiding System Through Steganography

    S. Aiswarya*, R. Gomathi
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 969-982, 2022, DOI:10.32604/iasc.2022.025475
    Abstract Reversible Data Hiding (RDH) is the process of transferring secret data hidden inside cover media to the recipient so the recipient can securely retrieve both the secret data and cover media. The RDH approach is applied in this study in the field of telemedicine, and medical-secret data is conveyed privately via medical cover video. Morse code-based data encryption technique tends to encrypt the medical-secret data by compression using the Arithmetic coding technique. Discrete Shearlet transform (DST) compresses the selected frame from the medical cover video and the compressed secret data is embedded into the compressed frame using logical operations. On… More >

  • Open Access

    ARTICLE

    Crack Detection in Composite Materials Using McrowDNN

    R. Saveeth1,*, S. Uma Maheswari2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 983-1000, 2022, DOI:10.32604/iasc.2022.023455
    Abstract In the aerospace industry, composite materials are becoming more common. The presence of a crack in an aircraft makes it weaker and more dangerous, and it can lead to complete fracture and catastrophic failure. To predict the position and depth of a crack, various methods have been developed. For aircraft repair, crack diagnosis is extremely important. Even then, due to uncertainties arising from sources such as environmental conditions, packing, and intrinsic material property changes, accurate diagnosis in real engineering applications remains a challenge. Deep learning (DL) approaches have demonstrated powerful recognition potential in a variety of fields in recent years.… More >

  • Open Access

    ARTICLE

    Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

    Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710
    Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

  • Open Access

    ARTICLE

    Aggregated PSO for Secure Data Transmission in WSN Using Fog Server

    M. Manicka Raja1,*, S. Manoj Kumar2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1017-1032, 2022, DOI:10.32604/iasc.2022.025665
    Abstract Privacy of data in Internet of Things (IoT) over fog networks is the biggest challenge in security of Wireless communication networks. In Wireless Sensor Network (WSN), current research on fog computing with IoT is gaining popularity among IoT devices over network. Moreover, the data aggregation will reduce the energy consumption in WSN. Due to the open and hostile nature of WSN, secure data aggregation is the major issue. The existing data aggregation methods in IoT and its associated approaches are lack of limited aggregation functions, heavyweight, issues related to the performance overhead. Besides, the overload on fog node will result… More >

  • Open Access

    ARTICLE

    Field Programmable Gate Arrays (FPGA) Based Computational Complexity Analysis of Multicarrier Waveforms

    C. Ajitha1,*, T. Jaya2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1033-1048, 2022, DOI:10.32604/iasc.2022.021984
    Abstract Multicarrier waveforms with enhanced spectral efficiency, low latency, and high throughput are required for 5G wireless networks. The Orthogonal Frequency Division Multiplexing (OFDM) method is well-known in research, but due to its limited spectral efficiency, various alternative waveforms are being considered for 5G systems. In the recent communication world, NOMA (non-orthogonal multiple access) plays a significant part due to its wider transmission of data with less bandwidth allocation. Even if a high data rate can be attained, the transmission problem will arise due to the spread of multiple paths. In order to reduce complexity and area utilization, a novel PL-based… More >

  • Open Access

    ARTICLE

    Optimal and Energy Effective Power Allocation Using Multi-Scale Resource GOA-DC-EM in DAS

    J. Rajalakshmi*, S. Siva Ranjani
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1049-1063, 2022, DOI:10.32604/iasc.2022.025127
    Abstract Recently many algorithms for allocation of power approaches have been suggested to increase the Energy Efficiency (EE) and Spectral Efficiency (EE) in the Distributed Antenna System (DAS). In addition, the method of conservation developed for the allocation of power is challenging for the enhancement because of their high complication during estimation. With the intention of increasing the EE and SE, the optimization of allocation of power is done on the basis of capacity of the antenna. The main goal is for the optimization of the power allocation to improve the spectral and energy efficiency with the increased capacity of the… More >

  • Open Access

    ARTICLE

    An Experimental Approach to Diagnose Covid-19 Using Optimized CNN

    Anjani Kumar Singha1, Nitish Pathak2,*, Neelam Sharma3, Abhishek Gandhar4, Shabana Urooj5, Swaleha Zubair6, Jabeen Sultana7, Guthikonda Nagalaxmi8
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1065-1080, 2022, DOI:10.32604/iasc.2022.024172
    Abstract The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess… More >

  • Open Access

    ARTICLE

    Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

    Chunhua Pan1,*, Yi Cao2, Jinglong Ren3
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1081-1091, 2022, DOI:10.32604/iasc.2022.028763
    Abstract Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary… More >

  • Open Access

    ARTICLE

    A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

    S. Velmurugan1,*, P. Ezhumalai2, E. A. Mary Anita3
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1093-1108, 2022, DOI:10.32604/iasc.2022.026308
    Abstract Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy… More >

  • Open Access

    ARTICLE

    Person Re-Identification Using LBPH and K-Reciprocal Encoding

    V. Manimaran*, K. G. Srinivasagan
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1109-1121, 2022, DOI:10.32604/iasc.2022.023145
    Abstract Individual re-identification proof (Re-ID) targets recovering an individual of interest across different non-covering cameras. With the recent development of technological algorithm and expanding request of intelligence video observation, it has acquired fundamentally expanded interest in the computer vision. Person re-identification is characterized as the issue of perceiving an individual caught in different occasions and additionally areas more than a few nonoverlapping camera sees, thinking about a huge arrangement of up-and-comers. This issue influences essentially the administration of disseminated, multiview observation frameworks, in which subjects should be followed across better places, either deduced or on-the-fly when they travel through various areas.… More >

  • Open Access

    ARTICLE

    A Novel Deep Learning Framework for Pulmonary Embolism Detection for Covid-19 Management

    S. Jeevitha1,*, K. Valarmathi2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1123-1139, 2022, DOI:10.32604/iasc.2022.024746
    Abstract Pulmonary Embolism is a blood clot in the lung which restricts the blood flow and reduces blood oxygen level resulting in mortality if it is untreated. Further, pulmonary embolism is evidenced prominently in the segmental and sub-segmental regions of the computed tomography angiography images in COVID-19 patients. Pulmonary embolism detection from these images is a significant research problem in the challenging COVID-19 pandemic in the venture of early disease detection, treatment, and prognosis. Inspired by several investigations based on deep learning in this context, a two-stage framework has been proposed for pulmonary embolism detection which is realized as a segmentation… More >

  • Open Access

    ARTICLE

    Hybrid Renewable Energy System Using Cuckoo Firefly Optimization

    M. E. Shajini Sheeba1,*, P. Jagatheeswari2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1141-1156, 2022, DOI:10.32604/iasc.2022.024549
    Abstract With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization… More >

  • Open Access

    ARTICLE

    Multi-Classification and Distributed Reinforcement Learning-Based Inspection Swarm Offloading Strategy

    Yuping Deng1, Tao Wu1, Xi Chen2,*, Amir Homayoon Ashrafzadeh3
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1157-1174, 2022, DOI:10.32604/iasc.2022.022606
    Abstract In meteorological and electric power Internet of Things scenarios, in order to extend the service life of relevant facilities and reduce the cost of emergency repair, the intelligent inspection swarm is introduced to cooperate with monitoring tasks, which collect and process the current scene data through a variety of sensors and cameras, and complete tasks such as emergency handling and fault inspection. Due to the limitation of computing resources and battery life of patrol inspection equipment, it will cause problems such as slow response in emergency and long time for fault location. Mobile Edge Computing is a promising technology, which… More >

  • Open Access

    ARTICLE

    Bayesian Convolution for Stochastic Epidemic Model

    Mukhsar1,*, Ansari Saleh Ahmar2, M. A. El Safty3, Hamed El-Khawaga4,5, M. El Sayed6
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1175-1186, 2022, DOI:10.32604/iasc.2022.025214
    Abstract Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model… More >

  • Open Access

    ARTICLE

    Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images

    P. L. Joseph Raj, K. Kalimuthu*, Sabitha Gauni, C. T. Manimegalai
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1187-1196, 2022, DOI:10.32604/iasc.2022.026052
    Abstract Speckle Reduction Anisotropic Diffusion filter which is used to despeckle ultrasound images, perform well at homogeneous region than in heterogeneous region resulting in loss of information available at the edges. Extended SRAD filter does the same, preserving better the edges in addition, compared to the existing SRAD filter. The proposed Extended SRAD filter includes the intensity of four more neighboring pixels in addition with other four that is meant for SRAD filter operation. So, a total of eight pixels are involved in determining the intensity of a single pixel. This improves despeckling performance by maintaining the information accessible at an… More >

  • Open Access

    ARTICLE

    Energy Efficient Mobile Harvesting Scheme for Clustered SDWSN with Beamforming Technique

    Subaselvi Sundarraj*, Gunaseelan Konganathan
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1197-1213, 2022, DOI:10.32604/iasc.2022.025026
    Abstract Software Defined Wireless Sensor Networks (SDWSN) provides a centralized scheduling algorithm to decrease energy consumption compared to WSN. The sensor nodes have a finite battery capacity in the SDWSN that reduces the lifetime of the nodes. To harvest energy for energy depleted nodes without interfering with the eventful data transfer in the clustered SDWSN, an energy efficient mobile harvesting scheme with the Multiple Input Single Output (MISO) beamforming technique is proposed. The mobile harvesting scheme transfer the energy to the energy starving node and the beamforming algorithm which transmits the energy in the desired direction increases the lifetime of the… More >

  • Open Access

    ARTICLE

    IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques

    R. Gopi1,*, S. Veena2, S. Balasubramanian3, D. Ramya4, P. Ilanchezhian5, A. Harshavardhan6, Zatin Gupta7
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792
    Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM)… More >

  • Open Access

    ARTICLE

    Design Features of Grocery Product Recognition Using Deep Learning

    E. Gothai1,*, Surbhi Bhatia2, Aliaa M. Alabdali3, Dilip Kumar Sharma4, Bhavana Raj Kondamudi5, Pankaj Dadheech6
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1231-1246, 2022, DOI:10.32604/iasc.2022.026264
    Abstract At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a preconfigured item planning that was… More >

  • Open Access

    ARTICLE

    A Deep Learning Framework for COVID-19 Diagnosis from Computed Tomography

    Nabila Mansouri1,2,*, Khalid Sultan3, Aakash Ahmad4, Ibrahim Alseadoon4, Adal Alkhalil4
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1247-1264, 2022, DOI:10.32604/iasc.2022.025046
    Abstract The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that is applied to Computed Tomography… More >

  • Open Access

    ARTICLE

    Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks

    T. Tamilvizhi1, R. Surendran2,*, Carlos Andres Tavera Romero3, M. Sadish Sendil4
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1265-1279, 2022, DOI:10.32604/iasc.2022.026331
    Abstract VANETs are a subclass of mobile ad hoc networks (MANETs) that enable efficient data transmission between vehicles and other vehicles, road side units (RSUs), and infrastructure. The purpose of VANET is to enhance security, road traffic management, and traveler services. Due to the nature of real-time issues such as reliability and privacy, messages transmitted via the VANET must be secret and confidential. As a result, this study provides a method for privacy-preserving reliable data transmission in a cluster-based VANET employing Fog Computing (PPRDA-FC). The PPRDA-FC technique suggested here seeks to ensure reliable message transmission by utilising FC and an optimal… More >

  • Open Access

    ARTICLE

    Evolutionary Algorithm Based Adaptive Load Balancing (EA-ALB) in Cloud Computing Framework

    J. Noorul Ameen1,*, S. Jabeen Begum2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1281-1294, 2022, DOI:10.32604/iasc.2022.025137
    Abstract In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work. Initially, process… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Features PSO-ReliefF Based Classification of Brain Tumor

    Alaa Khalid Alduraibi*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601
    Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, the particle swarm optimization algorithm… More >

  • Open Access

    ARTICLE

    Automatic Annotation Performance of TextBlob and VADER on Covid Vaccination Dataset

    Badriya Murdhi Alenzi, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar*, Mohammed AlKhathami, Abdullah AlTameem
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1311-1331, 2022, DOI:10.32604/iasc.2022.025861
    Abstract With the recent boom in the corpus size of sentiment analysis tasks, automatic annotation is poised to be a necessary alternative to manual annotation for generating ground truth dataset labels. This article aims to investigate and validate the performance of two widely used lexicon-based automatic annotation approaches, TextBlob and Valence Aware Dictionary and Sentiment Reasoner (VADER), by comparing them with manual annotation. The dataset of 5402 Arabic tweets was annotated manually, containing 3124 positive tweets, 1463 negative tweets, and 815 neutral tweets. The tweets were translated into English so that TextBlob and VADER could be used for their annotation. TextBlob… More >

  • Open Access

    ARTICLE

    Generating Intelligent Remedial Materials with Genetic Algorithms and Concept Maps

    Che-Chern Lin*, Chien-Chun Pan
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1333-1349, 2022, DOI:10.32604/iasc.2022.025387
    Abstract This study proposes an intelligent remedial learning framework to improve students’ learning effectiveness. Basically, this framework combines a genetic algorithm with a concept map in order to select a set of remedial learning units according to students’ weaknesses of learning concepts. In the proposed algorithm, a concept map serves to represent the knowledge structure of learning concepts, and a genetic algorithm performs an iteratively evolutionary procedure in order to establish remedial learning materials based on students’ understanding of these learning concepts. This study also conducted simulations in order to validate the proposed framework using artificially generated data sets, and problematic… More >

  • Open Access

    ARTICLE

    Class Imbalance Handling with Deep Learning Enabled IoT Healthcare Diagnosis Model

    T. Ragupathi1,*, M. Govindarajan1, T. Priyaradhikadevi2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1351-1366, 2022, DOI:10.32604/iasc.2022.025756
    Abstract The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH for medical data classification in… More >

  • Open Access

    ARTICLE

    Performance Analysis of PTS PAPR Reduction Method for NOMA Waveform

    Himanshu Sharma1, Nidhi Gour1, Sumit Chakravarty2, Fahad Alraddady3, Mehedi Masud4, Rajneesh Pareek1, Arun Kumar5,*
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1367-1375, 2022, DOI:10.32604/iasc.2022.025655
    Abstract Cellular systems utilize single and multicarrier waveforms for high-speed data transmission. The Fifth-Generation (5G) system proposes several techniques based on multicarrier waveforms. However, the Peak to Average Power Ratio (PAPR) is one of the significant concerns in advanced waveforms as it degrades the framework's efficiency. Non Orthogonal Multiple Access (NOMA) can provide massive connectivity, which is the crucial requirement of the Internet of Things (IoT). The 3rd generation tested NOMA applications in downlink and uplink transmission. However, NOMA uplink transmission in the power domain has performance degradation and is not considered a possible technique in 3rd generation power projects (3GPP).… More >

  • Open Access

    ARTICLE

    Evolutionary Algorithm Based Z-Source DC-DC Boost Converter for Charging EV Battery

    P. Anitha1, K. Karthik Kumar2,*, M. Ravindran2, A. Saravanaselvan2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1377-1397, 2022, DOI:10.32604/iasc.2022.025396
    Abstract In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around… More >

  • Open Access

    ARTICLE

    Optimum Tuning of Photovoltaic System Via Hybrid Maximum Power Point Tracking Technique

    M. Nisha1,*, M. Germin Nisha2
    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1399-1413, 2022, DOI:10.32604/iasc.2022.024482
    Abstract A new methodology is used in this paper, for the optimal tuning of Photovoltaic (PV) by integrating the hybrid Maximum Power Point Tracking (MPPT) algorithms is proposed. The suggested hybrid MPPT algorithms can raise the performance of PV systems under partial shade conditions. It attempts to address the primary research issues in partial shading conditions in PV systems caused by clouds, trees, dirt, and dust. The proposed system computes MPPT utilizing an innovative adaptive model-based approach. In order to manage the input voltage at the Maximum PowerPoint, the MPPT algorithm changes the duty cycle of the switch in the DC-DC… More >

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