Home / Journals / IASC / Vol.33, No.2, 2022
  • Open Access

    ARTICLE

    A Resource-Efficient Convolutional Neural Network Accelerator Using Fine-Grained Logarithmic Quantization

    Hadee Madadum*, Yasar Becerikli
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 681-695, 2022, DOI:10.32604/iasc.2022.023831
    Abstract Convolutional Neural Network (ConNN) implementations on Field Programmable Gate Array (FPGA) are being studied since the computational capabilities of FPGA have been improved recently. Model compression is required to enable ConNN deployment on resource-constrained FPGA devices. Logarithmic quantization is one of the efficient compression methods that can compress a model to very low bit-width without significant deterioration in performance. It is also hardware-friendly by using bitwise operations for multiplication. However, the logarithmic suffers from low resolution at high inputs due to exponential properties. Therefore, we propose a modified logarithmic quantization method with a fine resolution to compress a neural network… More >

  • Open Access

    ARTICLE

    An Enhanced Re-Ranking Model for Person Re-Identification

    Jayavarthini Chockalingam*, Malathy Chidambaranathan
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 697-710, 2022, DOI:10.32604/iasc.2022.024142
    Abstract Presently, Person Re-IDentification (PRe-ID) acts as a vital part of real time video surveillance to ensure the rising need for public safety. Resolving the PRe-ID problem includes the process of matching observations of persons among distinct camera views. Earlier models consider PRe-ID as a unique object retrieval issue and determine the retrieval results mainly based on the unidirectional matching among the probe and gallery images. But the accurate matching might not be present in the top-k ranking results owing to the appearance modifications caused by the difference in illumination, pose, viewpoint, and occlusion. For addressing these issues, a new Hyper-parameter… More >

  • Open Access

    ARTICLE

    OA-PU Algorithm-to Enhance WSN Life Time with Cluster Head Selection

    D. Nageswari1,*, R. Maheswar2, P. Jayarajan1
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 711-727, 2022, DOI:10.32604/iasc.2022.022270
    Abstract Clustering is the most popular strategy for increasing the lifetime of a wireless sensor network, which immediately leads to a stronger routing process. This process requires the processing of sensor nodes into clusters and assigning relevant cluster heads to each cluster. This paper aims to implement a new hybrid algorithm called Over taker Assisted Political Update (OA-PU) for selecting an efficient cluster head. This cluster head is selected based upon four factors, namely energy, distance, cluster radius, and time. The hybrid algorithm is a combination of the Fusion Rider Optimization Algorithm (F-ROA) as well as a nature-inspired Political Optimization Algorithm… More >

  • Open Access

    ARTICLE

    Efficient Heuristic for Optimal MILP-LoRa Adaptive Resource Allocation for Aquaculture

    M. Iniyan Arasu1,*, S. Subha Rani1, G. Raswin Geoffery2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 729-742, 2022, DOI:10.32604/iasc.2022.021973
    Abstract LoRa is well-known for its extensive communication range, inexpensive efficiency, and reduced or less power consumption in end devices. End-device energy consumption in LoRa networks is ludicrous because some end-devices use massive dissemination variables to reach the remote doorway. Furthermore, the batteries in these end devices deplete very quickly, reducing network life significantly. To address this issue, an optimal mixed-integer linear programming long-range technique (OMILP-LoRa) was used in this study. The primary goal of this research is to enable adaptive resource allocation using the unique OMILP-LoRa protocol. The ACCURATE heuristic and the OMILP model for LoRaWAN resource allocation are presented… More >

  • Open Access

    ARTICLE

    Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

    Kotchaporn Karoon, Yupaporn Areepong*, Saowanit Sukparungsee
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 743-759, 2022, DOI:10.32604/iasc.2022.023322
    (This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)
    Abstract Extended Exponentially Weighted Moving Average (Extended EWMA or EEWMA) control chart is one of the control charts which can quickly detect a small shift. The average run length (ARL) measures the performance of control chart. Due to the derivation of the explicit formulas for ARL on the EEWMA control chart for the autoregressive AR(p) process has not previously been reported. The aim of the article is to derive explicit formulas of ARL using a Fredholm integral equation of the second kind on EEWMA control chart for Autoregressive process, as AR(2) and AR(3) processes with exponential white noise. The accuracy of… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

    S. Manikandan1,*, B. Nagaraj2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 761-775, 2022, DOI:10.32604/iasc.2022.023398
    Abstract Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective… More >

  • Open Access

    ARTICLE

    Distribution Network Reconfiguration Using Hybrid Optimization Technique

    S. Arun Kumar*, S. Padma, S. Madhubalan
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 777-789, 2022, DOI:10.32604/iasc.2022.023702
    Abstract Energy management carried in a power system by configuration process is a difficult activity. So, reconfiguration has been introduced to solve this problem. Numerous optimization topologies have been utilized to solve this problem so far. However, they exhibit some drawbacks such as convergence, etc. Hence to overcome this issue, this work formulated a new hybrid optimization topology Genetic Algorithm Enabled Particle Swarm Optimization (PSOGA) to solve the energy configuration problem with low power loss in the Distribution System (DS). The proposed topology’s effectiveness was evaluated on the IEEE 33 bus Distribution System, and the results were compared to methods reported… More >

  • Open Access

    ARTICLE

    Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions

    Yousef Jaradat*, Mohammad Masoud, Ismael Jannoud, Dema Zeidan
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 791-808, 2022, DOI:10.32604/iasc.2022.024218
    Abstract Optimal node clustering in wireless sensor networks (WSNs) is a major issue in reducing energy consumption and extending network node life time and reliability measures. Many techniques for optimizing the node clustering process in WSN have been proposed in the literature. The metaheuristic algorithms are a subset of these techniques. Genetic algorithm (GA) is an evolutionary metaheuristic technique utilized to improve the network reliability and extending the network life time by optimizing the clustering process in the network. The GA dynamic clustering (GA-DC) algorithm is proposed in this paper to extend the network reliability and node life time of three… More >

  • Open Access

    ARTICLE

    Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

    Tauqeer Safdar Malik1,*, Kaleem Razzaq Malik1, Muhammad Sanaullah2, Mohd Hilmi Hasan3, Norshakirah Aziz3
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 809-824, 2022, DOI:10.32604/iasc.2022.021128
    (This article belongs to this Special Issue: Artificial Intelligence Techniques for Joint Sensing and Localization in Future Wireless Networks)
    Abstract Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU requires to reduce the end-to-end… More >

  • Open Access

    ARTICLE

    Feature Selection Based on IoT Aware QDA Node Authentication in 5G Networks

    M. P. Haripriya*, P. Venkadesh
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 825-836, 2022, DOI:10.32604/iasc.2022.022940
    Abstract The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an effective manner. However, security of… More >

  • Open Access

    ARTICLE

    Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions

    Saleh Albahli1,2,*, Ghulam Nabi Ahmad Hassan Yar3
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427
    Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it… More >

  • Open Access

    ARTICLE

    Error Calibration Model of Air Pressure Sensor Based on DF-RBF

    Pengyu Liu1,2,3,*, Wenjing Zhang1,2,3, Tao Wang1,2,3, Xiaowei Jia4, Ying Ma5, Kebin Jia1,2,3, Yanming Wang1,2,3
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 855-864, 2022, DOI:10.32604/iasc.2022.022380
    Abstract The development of upper-air meteorological detection is contingent upon the improvement of detection instruments. Air pressure sensors play a key role in high altitude meteorological measurement, but they can be frequently affected by temperature fluctutations, resulting in less accurate measurement data. The need to address this limitation has served as the core problem for meteorological detection and drawn great attention from the community. In this paper, we propose a calibration model for the DF-RBF air pressure sensor. The proposed method decomposes the detection process and corrects the measurements by fitting the residuals to true pressure values. In particular, we first… More >

  • Open Access

    ARTICLE

    Cost Efficient Scheduling Using Smart Contract Cognizant Ethereum for IoMT

    G. Ravikumar1, K. Venkatachalam2, Mehedi Masud3, Mohamed Abouhawwash4,5,*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 865-877, 2022, DOI:10.32604/iasc.2022.024278
    Abstract Recently internet of medical things (IoMT) act as a smart doctor using sensor wearable’s device in human body. This smart doctor device senses necessary medical data from human and transfer via network immediately to physician. It is important to transfer sensitive data very securely. Blockchain becomes trending technology to provide high security to both end users in the network. Traditionally security structure is relying on cryptographic techniques which is very expensive and takes more time in securely transmitting data. To overcome this issue, this paper builds a cost effective, blockchain with IoMT using fog-cloud computing. The aim of research is… More >

  • Open Access

    ARTICLE

    Contourlet and Gould Transforms for Hybrid Image Watermarking in RGB Color Images

    Reena Thomas1,*, M. Sucharitha2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 879-889, 2022, DOI:10.32604/iasc.2022.024070
    Abstract The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on the initial coefficients of each… More >

  • Open Access

    ARTICLE

    Bendlets and Ensemble Learning Based MRI Brain Classification System

    R. Muthaiyan1,*, M. Malleswaran2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 891-907, 2022, DOI:10.32604/iasc.2022.024635
    Abstract Brain tumours are composed of cells where the growth is unrestrained. Though the incidence rate is lower, it is a serious threatening disease to human lives. For effective treatment, an accurate and quick method to classify Magnetic Resonance Imaging (MRI) is required. To identify the meaningful patterns and to interpret images, pattern recognition algorithms are developed. In this work, an extension of Shearlet transform named Bendlets is employed to interpret MRI images and decision making is done by ensemble learning using k-Nearest Neighbor (kNN), Naive Bayesian and Support Vector Machine (SVM) classifiers. The Bendlet and Ensemble Learning (BEL) based system… More >

  • Open Access

    ARTICLE

    ANN Based Reduced Switch Multilevel Inverter in UPQC for Power Quality Improvement

    Y. Alexander Jeevanantham1,*, S. Srinath2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 909-921, 2022, DOI:10.32604/iasc.2022.022907
    Abstract A unified power quality conditioner (UPQC) plays a crucial role in the Power quality improvement of a power system. In this paper, a reduced switch multilevel inverter is with artificial neural network, soft computing technique control is proposed for UPQC. This proposed topology is employed for the mitigation of various power quality issues such as voltage sag, voltage swell, power factor, harmonics, and restoration time of voltage compensation. To show the enriched performance of the proposed topology comparative analysis is made with other two topologies of UPQC such as Conventional UPQC and UPQC using cascaded H bridge (CHB) five-level Inverter.… More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

    Fatmah Alanazi*, Kamal Jambi, Fathy Eassa, Maher Khemakhem, Abdullah Basuhail, Khalid Alsubhi
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 923-938, 2022, DOI:10.32604/iasc.2022.024668
    Abstract Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on… More >

  • Open Access

    ARTICLE

    Identification of Bio-Markers for Cancer Classification Using Ensemble Approach and Genetic Algorithm

    K. Poongodi1,*, A. Sabari2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038
    Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which uses Mahalanobis Distance (MD) as… More >

  • Open Access

    ARTICLE

    Incredible VLSI Design for MIMO System Using SEC-QPSK Detection

    L. Vasanth*, N. J. R. Muniraj
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 955-966, 2022, DOI:10.32604/iasc.2022.022979
    Abstract Multiple Input Multiple Output (MIMO) is an advanced communication technology that is often used for secure data transfer for military and other applications while transmitting data with high error and noise. To address this issue, a step-by-step hybrid Quadrature Phase Shift Keying (QPSK) modulation scheme in the MIMO system for a complex Very Large-Scale Integration (VLSI) format is recommended. When compared to Binary Phase Shift Keying (BPSK), this approach provides twice the data rate while using half the bandwidth. The complexity is lowered through multiplication and addition, as well as error and noise reduction in data transport, and MIMO detection… More >

  • Open Access

    ARTICLE

    Smart Lamp Using Google Firebase as Realtime Database

    Wen-Tsai Sung1, Ihzany Vilia Devi1, Sung-Jung Hsiao2,*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 967-982, 2022, DOI:10.32604/iasc.2022.024664
    (This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)
    Abstract Along with modernization in Indonesia, electricity users often do not realize that electrical energy is still used from electronic devices left on and unused. Much electrical energy is wasted due to unwise use. Modernization requires creative automation. This significantly minimizes the amount of human labor needed to complete the job. Energy efficiency is critical due to environmental concerns and limited research on alternative renewable energy sources. When evaluating the impact of technology on the environment, energy is an essential factor to consider. Most big cities and provinces in Indonesia still use conventional lighting systems where electricity users manually turn on… More >

  • Open Access

    ARTICLE

    Real Time Brain Tumor Prediction Using Adaptive Neuro Fuzzy Technique

    Duraimurugan Nagendiran1,*, S. P. Chokkalingam2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982
    Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out the appropriate treatment. To enhance… More >

  • Open Access

    ARTICLE

    ISmart: Protecting Smart Contract Against Integer Bugs

    Xingyu Zeng1, Hua Zhang1,*, Chaosong Yan2, Liu Zhao1, Qiaoyan Wen1
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 997-1008, 2022, DOI:10.32604/iasc.2022.022801
    Abstract Blockchain technology is known as a decentralized, distributed ledger that records digital asset. It has been applied in numbers of aspects of society, including finance, judiciary and commerce. Ethereum is referred to as the next generation decentralized application platform. It is one of the most popular blockchain platforms that supports smart contracts. Smart contract is a set of codes that sored on blockchain and can be called and created as turing-complete programs running on the blockchain. Developers use smart contracts to build decentralized applications (Dapp) which has widely used cryptocurrency related project. As smart contracts become more popular and more… More >

  • Open Access

    ARTICLE

    Envisaging Employee Churn Using MCDM and Machine Learning

    Meenu Chaudhary1, Loveleen Gaur1, NZ Jhanjhi2,*, Mehedi Masud3, Sultan Aljahdali3
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1009-1024, 2022, DOI:10.32604/iasc.2022.023417
    Abstract Employee categorisation differentiates valuable employees as eighty per cent of profit comes from twenty per cent of employees. Also, retention of all employees is quite challenging and incur a cost. Previous studies have focused on employee churn analysis using various machine learning algorithms but have missed the categorisation of an employee based on accomplishments. This paper provides an approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques, i.e., criteria importance through inter-criteria correlation (CRITIC) to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution… More >

  • Open Access

    ARTICLE

    Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

    P. Karuppanan1,*, K. Dhanalakshmi2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1025-1043, 2022, DOI:10.32604/iasc.2022.023712
    Abstract Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset… More >

  • Open Access

    ARTICLE

    Differential Evolution Algorithm with Hierarchical Fair Competition Model

    Amit Ramesh Khaparde1,*, Fawaz Alassery2, Arvind Kumar3, Youseef Alotaibi4, Osamah Ibrahim Khalaf5, Sofia Pillai6, Saleh Alghamdi7
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270
    Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, the two variants of HFC-DE… More >

  • Open Access

    ARTICLE

    Interactive Human Interface for ERP Component Extraction from Gifted Children

    Kawther Benharrath1, Amine Ben Slama2,*, Balkine Khadoumi1, Mounir Sayadi1, Hervé Rix3, Olivier Meste3, Sophie Guetat5, Jérôme Lebrun3, Marie-Noële Magnie-Mauro4,5
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1063-1080, 2022, DOI:10.32604/iasc.2022.023446
    Abstract In the last century, scientists started to give importance to gifted children (GC) and to understand their behavior. Since then, research has pursued the various categories of these children and their early diagnosis in order to find the best control of their skills. Therefore, most researchers focus on recent advances in electroencephalogram (EEG) and cognitive events. The event-related brain potentials (ERPs) technique is generally used in the cognitive neuroscience process. However, it is still a challenge to extract these potentials from a few trials of electroencephalogram (EEG) data. The N400 ERP component is an important part of the studies of… More >

  • Open Access

    ARTICLE

    A Novel Medical Image Encryption Using Rössler System

    K. Sundara Krishnan1,*, Syed Suhaila1, S. P. Raja2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1081-1101, 2022, DOI:10.32604/iasc.2022.024023
    Abstract The technological advances made possible by the Internet, coupled with the unforeseen critical circumstances set in motion by the Covid-19 pandemic, have greatly increased the generation and transmission of medical images every day. Medical image transmission over an unsecured public network threatens the privacy of sensitive patient information. We have, in this paper, designed a new secure color medical image encryption algorithm based on binary plane decomposition, DNA (deoxyribonucleic acid) computing, and the chaotic Rössler dynamical system. At first, a bit-by-bit swap is performed on twenty four binary planes of the input image and encoded using DNA encoding rules. Thereafter,… More >

  • Open Access

    ARTICLE

    Optimized Control of Single Phase Reboost Luo Converter Fed Grid-Connected PV System

    S. Baskaran1,*, Raghuraman Sivalingam2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1103-1119, 2022, DOI:10.32604/iasc.2022.023093
    Abstract An epic topology of PV system for 1Ф grid tied framework with high gain altered Re Boost Luo converter is developed. In this epic topology, the PV (Photovoltaic) system is connected with single phase network via a Re Boost Luo converter and Voltage Source Inverter. The voltage variance issues of PV framework are overwhelmed by extricating most extreme power from the array. The Whale Optimization is utilized to extract the vast majority of the power from photovoltaic array. Similar to the existing topology, the proposed scheme is designed using reference casings of direct and quadrature hub components. The Re Boost… More >

  • Open Access

    ARTICLE

    Transformer Less Grid Integrated Single Phase PV Inverter Using Prognosticative Control

    Chandla Ellis1,*, C. Chellamuthu1, J. Jayaseelan2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1121-1138, 2022, DOI:10.32604/iasc.2022.023079
    Abstract Nature's remarkable and merciful gift to the planet Earth is sunlight which may be highly lucrative if harvested and harnessed properly. Photovoltaic (PV) panels are used to convert the solar energy to electrical energy which are currently used to feed AC loads/grid. In this paper, modelling, performance and power flow studies of grid connected single phase inverter fed from PV array under steady state as well as transient conditions are considered. This paper focuses on the study and development of analytical model of micro-grid integrated single phase five level cascaded H-bridge inverter (MGISPFLCHBPVI) powered by a PV panel. A simple… More >

  • Open Access

    ARTICLE

    Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

    S. Jacophine Susmi*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1139-1152, 2022, DOI:10.32604/iasc.2022.023865
    Abstract The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections… More >

  • Open Access

    ARTICLE

    Research on Thunderstorm Identification Based on Discrete Wavelet Transform

    Xiaopeng Li1, Ziyuan Xu3,4, Jin Han1,*, Xingming Sun1,2, Yi Cao5
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1153-1166, 2022, DOI:10.32604/iasc.2022.023261
    Abstract Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a… More >

  • Open Access

    ARTICLE

    Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

    A. Saranya1,*, R. Anandan2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1167-1182, 2022, DOI:10.32604/iasc.2022.023445
    Abstract Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to only seven expressions, detection of… More >

  • Open Access

    ARTICLE

    An Effective Blockchain Based Secure Searchable Encryption System

    Aitizaz Ali1, Mehedi Masud2, Ateeq ur Rehman3, Can Chen1, Mehmood4, Mohammad A. AlZain5, Jehad Ali6,*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1183-1195, 2022, DOI:10.32604/iasc.2022.023930
    Abstract Security of Patient health records (PHR) is the most important aspect of cryptography over the Internet due to its value and importance preferably in the medical Internet of Things (IoT). Search keywords access mechanism is one of the common approaches which is used to access PHR from database, but it is susceptible to various security vulnerabilities. Although Blockchain-enabled healthcare systems provide security, but it may lead to some loopholes in the existing schemes. However, these methods primarily focused on data storage, and blockchain is used as a database. In this paper, Blockchain as a distributed database is proposed with homomorphic… More >

  • Open Access

    ARTICLE

    Classification of Echocardiographic Standard Views Using a Hybrid Attention-based Approach

    Zi Ye1, Yogan Jaya Kumar2, Goh Ong Sing2, Fengyan Song3, Xianda Ni4,*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1197-1215, 2022, DOI:10.32604/iasc.2022.023555
    Abstract The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardiograms at the video level is complicated, and previous observations concluded that the most significant challenge lies in distinguishing among the various adjacent views. To this end, we propose an ECHO-Attention architecture consisting of two parts. We first design an ECHO-ACTION block, which efficiently encodes Spatio-temporal features, channel-wise features, and motion features. Then, we can insert this block into existing ResNet architectures, combined with a self-attention module to ensure its task-related focus, to form an effective ECHO-Attention network. The experimental results are confirmed… More >

  • Open Access

    ARTICLE

    Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis

    Singamaneni Kranthi Kumar1,*, Alhassan Alolo Abdul-Rasheed Akeji2, Tiruvedula Mithun3, M. Ambika4, L. Jabasheela5, Ranjan Walia6, U. Sakthi7
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1217-1227, 2022, DOI:10.32604/iasc.2022.024311
    Abstract In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

    İhsan Uluocak1,*, Hakan Yavuz2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1229-1243, 2022, DOI:10.32604/iasc.2022.023835
    Abstract This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial… More >

  • Open Access

    ARTICLE

    Two-Stage Fuzzy MCDM for Green Supplier Selection in Steel Industry

    Chia-Nan Wang, Thi-Ly Nguyen*, Thanh-Tuan Dang
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1245-1260, 2022, DOI:10.32604/iasc.2022.024548
    Abstract Steel is one of the most powerful industries globally, and the associated products have a tremendous impact on nurturing a sustainable society. Considering environmental concerns within this industry's supply chain is highly successful in saving both energy and natural resources and lowering greenhouse gas emissions. In light of this, sustainable suppliers are considered input partners who play a specific role in the chain of business operations of every enterprise, maintaining them to achieve higher levels of customer satisfaction and thus gain more market share. Supplier selection can be characterized as a multi-criteria decision-making (MCDM) problem under a vague and uncertain… More >

  • Open Access

    ARTICLE

    A Novel Approach for Deciphering Big Data Value Using Dark Data

    Surbhi Bhatia*, Mohammed Alojail
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1261-1271, 2022, DOI:10.32604/iasc.2022.023501
    Abstract The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in ‘dark data’, data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized… More >

  • Open Access

    ARTICLE

    Improved Homomorphic Encryption with Optimal Key Generation Technique for VANETs

    G. Tamilarasi1,*, K. Rajiv Gandhi2, V. Palanisamy1
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1273-1288, 2022, DOI:10.32604/iasc.2022.024687
    Abstract In recent years, vehicle ad hoc networks (VANETs) have garnered considerable interest in the field of intelligent transportation systems (ITS) due to the added safety and preventive measures for drivers and passengers. Regardless of the benefits provided by VANET, it confronts various challenges, most notably in terms of user/message security and privacy. Due to the decentralised nature of VANET and its changeable topologies, it is difficult to detect rogue or malfunctioning nodes or users. Using an improved grasshopper optimization algorithm (IGOA-PHE) technique in VANETs, this research develops a new privacy-preserving partly homomorphic encryption with optimal key generation. The suggested IGOA-PHE… More >

  • Open Access

    ARTICLE

    Cancelable Multi-biometric Template Generation Based on Dual-Tree Complex Wavelet Transform

    Ahmed M. Ayoup1,*, Ashraf A. M. Khalaf1, Fahad Alraddady2, Fathi E. Abd El-Samie3, Walid El-Shafai3,5, Salwa M. Serag Eldin2,4
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1289-1304, 2022, DOI:10.32604/iasc.2022.024381
    (This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)
    Abstract In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix CM × N to provide an… More >

  • Open Access

    ARTICLE

    Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

    R. Kavitha1,*, N. Viswanathan2
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1305-1320, 2022, DOI:10.32604/iasc.2022.022816
    Abstract Bone marrow cancer is considered to be the most complex and dangerous disease which results due to an uncontrolled growth of white blood cells called leukocytes. Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) are considered to be important categories of bone cancers, which induces a larger number of cancer cells in the bone marrow, results in preventing the production of healthy blood cells. The advent of Artificial Intelligence, especially machine and deep learning, has expanded humanity’s capacity to analyze and detect these increasingly complex diseases. But, accurate detection of cancer cells and reducing the probability of false alarm rates… More >

  • Open Access

    ARTICLE

    A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

    Sharifa Khalid Alduraibi*
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1321-1334, 2022, DOI:10.32604/iasc.2022.025021
    (This article belongs to this Special Issue: Intelligent Systems for Smart and Sustainable Healthcare)
    Abstract This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase the classification accuracy at the… More >

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