Home / Journals / IASC / Vol.34, No.1, 2022
  • Open Access

    ARTICLE

    Classification of Multi-Frame Human Motion Using CNN-based Skeleton Extraction

    Hyun Yoo1, Kyungyong Chung2,*
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 1-13, 2022, DOI:10.32604/iasc.2022.024890
    Abstract Human pose estimation has been a major concern in the field of computer vision. The existing method for recognizing human motion based on two-dimensional (2D) images showed a low recognition rate owing to motion depth, interference between objects, and overlapping problems. A convolutional neural network (CNN) based algorithm recently showed improved results in the field of human skeleton detection. In this study, we have combined human skeleton detection and deep neural network (DNN) to classify the motion of the human body. We used the visual geometry group network (VGGNet) CNN for human skeleton detection, and the generated skeleton coordinates were… More >

  • Open Access

    ARTICLE

    Discrete Firefly Algorithm for Optimizing Topology Generation and Core Mapping of Network-on-Chip

    S. Parvathi*, S. Umamaheswari
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 15-32, 2022, DOI:10.32604/iasc.2022.025290
    Abstract Network-on-chip (NoC) proves to be the best alternative to replace the traditional bus-based interconnection in Multi-Processor System on a Chip (MPSoCs). Irregular NoC topologies are highly recommended and utilised in various applications as they are application specific. Optimized mapping of the cores in a NoC plays a major role in its performance. Firefly algorithm is a bio-inspired meta-heuristic approach. Discretized firefly algorithm is used in our proposed work. In this work, two optimization algorithms are proposed: Topology Generation using Discrete Firefly Algorithm (TGDFA) and Core Mapping using Discrete Firefly Algorithm (CMDFA) for multimedia benchmark applications, Video Object Plane Decoder (VOPD),… More >

  • Open Access

    ARTICLE

    Optimized CUK Converter Based 1Φ Grid Tied Photovoltaic System

    S. K. Janarthanan*, C. Kathirvel
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 33-50, 2022, DOI:10.32604/iasc.2022.023165
    Abstract Renewable energy-based power generation, particularly photovoltaic (PV)-connected grid systems, has gained popularity in recent years due to its widespread adoption for residential and commercial customers of all sizes, from kilowatt (KW) to megawatt (MW). The purpose of this work is to demonstrate how an efficient CUK-integrated boost converter with continuous current flow may be used to maximise the output of solar arrays. The constant voltage at the converter output is maintained with increased dynamic performance using a Proportional Integral (PI) controller based on a hybrid optimization technique GWO-PSO (Grey Wolf Optimization-Particle Swarm Optimization). This hybrid solution permits accurate and speedy… More >

  • Open Access

    ARTICLE

    Core-based Approach to Measure Pairwise Layer Similarity in Multiplex Network

    Debasis Mohapatra1, Sourav Kumar Bhoi1, Kalyan Kumar Jena1, Chittaranjan Mallick2, Kshira Sagar Sahoo3, N. Z. Jhanjhi4,*, Mehedi Masud5
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 51-64, 2022, DOI:10.32604/iasc.2022.024561
    Abstract Most of the recent works on network science are focused on investigating various interactions among a set of entities present in a system that can be represented by multiplex network. Each type of relationship is treated as a layer of multiplex network. Some of the recent works on multiplex networks are focused on deriving layer similarity from node similarity where node similarity is evaluated using neighborhood similarity measures like cosine similarity and Jaccard similarity. But this type of analysis lacks in finding the set of nodes having the same influence in both the network. The discovery of influence similarity between… More >

  • Open Access

    ARTICLE

    Speech Quality Enhancement Using Phoneme with Cepstrum Variation Features

    K. C. Rajeswari1,*, R. S. Mohana2, S. Manikandan3, S. Beski Prabaharan4
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 65-86, 2022, DOI:10.32604/iasc.2022.022681
    Abstract In recent years, Text-to-Speech (TTS) synthesis is taking a new dimension. People prefer voice embedded toys, online buyers are interested in interactive chat application in the form of text-to-speech facility, screen readers for visually challenged people, and many more applications use TTS module. TTSis a system that is capable of converting the arbitrary text input into natural sounding speech. It’s success lies in producing more human like speech sounding more natural. The most importanttechnical aspect of TTS is feature extraction process. Both text and speech features are needed but it is not that easy to select meaningful and useful features… More >

  • Open Access

    ARTICLE

    AI Powered Asthma Prediction Towards Treatment Formulation: An Android App Approach

    Saydul Akbar Murad1, Apurba Adhikary2, Abu Jafar Md Muzahid1, Md. Murad Hossain Sarker3, Md. Ashikur Rahman Khan2, Md. Bipul Hossain2, Anupam Kumar Bairagi4,*, Mehedi Masud5, Md. Kowsher6
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 87-103, 2022, DOI:10.32604/iasc.2022.024777
    Abstract Asthma is a disease which attacks the lungs and that affects people of all ages. Asthma prediction is crucial since many individuals already have asthma and increasing asthma patients is continuous. Machine learning (ML) has been demonstrated to help individuals make judgments and predictions based on vast amounts of data. Because Android applications are widely available, it will be highly beneficial to individuals if they can receive therapy through a simple app. In this study, the machine learning approach is utilized to determine whether or not a person is affected by asthma. Besides, an android application is being created to… More >

  • Open Access

    ARTICLE

    Gender-specific Facial Age Group Classification Using Deep Learning

    Valliappan Raman1, Khaled ELKarazle2,*, Patrick Then2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 105-118, 2022, DOI:10.32604/iasc.2022.025608
    Abstract Facial age is one of the prominent features needed to make decisions, such as accessing certain areas or resources, targeted advertising, or more straightforward decisions such as addressing one another. In machine learning, facial age estimation is a typical facial analysis subtask in which a model learns the different facial ageing features from several facial images. Despite several studies confirming a relationship between age and gender, very few studies explored the idea of introducing a gender-based system that consists of two separate models, each trained on a specific gender group. This study attempts to bridge this gap by introducing an… More >

  • Open Access

    ARTICLE

    Deep Neural Network Based Vehicle Detection and Classification of Aerial Images

    Sandeep Kumar1, Arpit Jain2,*, Shilpa Rani3, Hammam Alshazly4, Sahar Ahmed Idris5, Sami Bourouis6
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 119-131, 2022, DOI:10.32604/iasc.2022.024812
    Abstract The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of… More >

  • Open Access

    ARTICLE

    Multi-Agent with Multi Objective-Based Optimized Resource Allocation on Inter-Cloud

    J. Arravinth*, D. Manjula
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 133-147, 2022, DOI:10.32604/iasc.2022.025292
    Abstract Cloud computing is the ability to provide new technologies and standard cloud services. One of the essential features of cloud computing is the provision of “unlimited” computer resources to users on demand. However, single cloud resources are generally limited and may not be able to cope with the sudden rise in user needs. Therefore, the inter-cloud concept is introduced to support resource sharing between clouds. In this system, each cloud can tap the resources of other clouds when there are not enough resources to meet the demands of the consumer. In cloud computing, allocating the available resources of service nodes… More >

  • Open Access

    ARTICLE

    Negative Emotions Sensitive Humanoid Robot with Attention-Enhanced Facial Expression Recognition Network

    Rongrong Ni1, Xiaofeng Liu1,*, Yizhou Chen1, Xu Zhou1, Huili Cai1, Loo Chu Kiong2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 149-164, 2022, DOI:10.32604/iasc.2022.026813
    Abstract Lonely older adults and persons restricted in movements are apt to cause negative emotions, which is harmful to their mental health. A humanoid robot with audiovisual interactions is presented, which can correspondingly output positive facial expressions to relieve human's negative facial expressions. The negative emotions are identified through an attention-enhanced facial expression recognition (FER) network. The network is firstly trained on MMEW macro-and micro-expression databases to discover expression-related features. Then, macro-expression recognition tasks are performed by fine-tuning the trained models on several benchmarking FER databases, including CK+ and Oulu-CASIA. A transformer network is introduced to process the sequential features engineered… More >

  • Open Access

    ARTICLE

    False Alarm Reduction in ICU Using Ensemble Classifier Approach

    V. Ravindra Krishna Chandar1,*, M. Thangamani2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 165-181, 2022, DOI:10.32604/iasc.2022.022339
    Abstract

    During patient monitoring, false alert in the Intensive Care Unit (ICU) becomes a major problem. In the category of alarms, pseudo alarms are regarded as having no clinical or therapeutic significance, and thus they result in fatigue alarms. Artifacts are misrepresentations of tissue structures produced by imaging techniques. These Artifacts can invalidate the Arterial Blood Pressure (ABP) signal. Therefore, it is very important to develop algorithms that can detect artifacts. However, ABP has algorithmic shortcomings and limitations of design. This study is aimed at developing a real-time enhancement of independent component analysis (EICA) and time-domain detection of QRS that can… More >

  • Open Access

    ARTICLE

    Deep Sentiment Learning for Measuring Similarity Recommendations in Twitter Data

    S. Manikandan1,*, P. Dhanalakshmi2, K. C. Rajeswari3, A. Delphin Carolina Rani4
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.023469
    Abstract The similarity recommendation of twitter data is evaluated by using sentiment analysis method. In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data. Convolutional neural network is applied for analyzing multimedia contents which is received from various sources. Recurrent neural network is used for handling the natural language data. The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method. In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc. The content is selected based on repetitive comments… More >

  • Open Access

    ARTICLE

    Detection of Attackers in Cognitive Radio Network Using Optimized Neural Networks

    V. P. Ajay1,*, M. Nesasudha2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 193-204, 2022, DOI:10.32604/iasc.2022.024839
    Abstract Cognitive radio network (CRN) is a growing technology targeting more resourcefully exploiting the available spectrum for opportunistic network usage. By the concept of cognitive radio, the wastage of available spectrum reduced about 30% worldwide. The key operation of CRN is spectrum sensing. The sensing results about the spectrum are directly proportional to the performance of the network. In CRN, the final result about the available spectrum is decided by combing the local sensing results. The presence or participation of attackers in the network leads to false decisions and the performance of the network will be degraded. In this work, an… More >

  • Open Access

    ARTICLE

    Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

    K. Aruna1,*, G. Pradeep2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 205-219, 2022, DOI:10.32604/iasc.2022.024317
    Abstract Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing… More >

  • Open Access

    ARTICLE

    Integrity Assurance Method of Multi-Keyword Query for Encrypted Outsourced Data

    Ling Wang1, Shan Ji2, Zhaokang Wang3, Xiaowan Wang4,*, Ghulam Mohiuddin5, Yongjun Ren1
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 221-234, 2022, DOI:10.32604/iasc.2022.027536
    Abstract As the data scale exceeds the petabyte level, more and more users outsource their local databases to third-party service providers to save local storage costs and avoid cumbersome local data management. However, because third-party service providers are not fully trusted, they may leak outsourced data and bring security risks to the personal privacy of data users. The service provider may also return incorrect or incomplete query results to the data user. Therefore, in this paper, we propose a Lightweight and Verifiable Multi-keyword Query Scheme for the integrity verification of multi-keyword query of outsourced data. This scheme supports multi-keyword query integrity… More >

  • Open Access

    ARTICLE

    A Stacked Ensemble-Based Classifier for Breast Invasive Ductal Carcinoma Detection on Histopathology Images

    Ali G. Alkhathami*
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 235-247, 2022, DOI:10.32604/iasc.2022.024952
    Abstract Breast cancer is one of the main causes of death in women. When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer. Invasive ductal carcinoma (IDC) is the early stage of breast cancer. The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer. This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset. The dataset consists of 90 thousand histopathological images containing two categories: Invasive Ductal Carcinoma positive… More >

  • Open Access

    ARTICLE

    Extreme Learning Bat Algorithm in Brain Tumor Classification

    G. R. Sreekanth1, Adel Fahad Alrasheedi2, K. Venkatachalam3, Mohamed Abouhawwash4,5,*, S. S. Askar2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 249-265, 2022, DOI:10.32604/iasc.2022.024538
    Abstract Brain tumor is considered as an unusual cell that presents and grows in the brain. Similarly, it may lead to cancerous or non-cancerous. So, to improve the survival rate of the patient and to give the best treatment at the earliest, it’s very necessary for early prediction of tumor. Accurate classification of tumor in the brain is important for improving the diagnosis. In accordance with that, various research programs are invited for the better treatment of the patients. Machine Learning (ML) algorithms are applied to help the health associates for the classification of brain tumor and present their diagnosis. This… More >

  • Open Access

    ARTICLE

    Novel Optimized Framework for Video Processing in IoRT Driven Hospitals

    Mani Deepak Choudhry1,*, B. Aruna Devi2, M. Sundarrajan3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 267-278, 2022, DOI:10.32604/iasc.2022.024024
    Abstract Internet of Remote things (IoRT) has gained recent attention and is considered as one most prominent research topics being carried out by numerous researchers worldwide. IoRT is being used in various applications and this paper mainly concentrates on the healthcare industry wherein it could be used effectively for patient monitoring. IoRT plays a crucial role in monitoring the patients in any healthcare center remotely by allowing simultaneous video transmissions possible from the emergency areas like Intensive Care Unit (ICU). Considering general scenarios, the video transmissions are done by the main use of Gaussian distribution. With the help of the proposed… More >

  • Open Access

    ARTICLE

    Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

    Mahdi Souzanchi-K1, Mohammad-R Akbarzadeh-T1,*, Nadia Naghavi1, Ali Sharifnezhad2, Vahab Khoshdel3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 279-294, 2022, DOI:10.32604/iasc.2022.023717
    Abstract Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to… More >

  • Open Access

    ARTICLE

    Generating Synthetic Trajectory Data Using GRU

    Xinyao Liu1, Baojiang Cui1,*, Lantao Xing2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 295-305, 2022, DOI:10.32604/iasc.2022.020032
    Abstract With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the development of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of generation. The… More >

  • Open Access

    ARTICLE

    Bat-Inspired Optimization for Intrusion Detection Using an Ensemble Forecasting Method

    R. Anand Babu1,*, S. Kannan2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 307-323, 2022, DOI:10.32604/iasc.2022.024098
    Abstract An Intrusion detection system (IDS) is extensively used to identify cyber-attacks preferably in real-time and to achieve integrity, confidentiality, and availability of sensitive information. In this work, we develop a novel IDS using machine learning techniques to increase the performance of the attack detection process. In order to cope with high dimensional feature-rich traffic in large networks, we introduce a Bat-Inspired Optimization and Correlation-based Feature Selection (BIOCFS) algorithm and an ensemble classification approach. The BIOCFS is introduced to estimate the correlation of the identified features and to choose the ideal subset for training and testing phases. The Ensemble Classifier (EC)… More >

  • Open Access

    ARTICLE

    Data Offloading in the Internet of Vehicles Using a Hybrid Optimization Technique

    A. Backia Abinaya1,*, G. Karthikeyan2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 325-338, 2022, DOI:10.32604/iasc.2022.020896
    Abstract The Internet of Vehicles (IoV) is utilized for collecting enormous real time information driven traffics and alert drivers depending on situations. In recent times, all smart vehicles are developed with IoT devices. These devices communicate with a radio access unit (RAU) at road side. Moreover, a 5G system is equipped with a base station and connection interfaces that use optic fiber for their effective communication. For a fast mode of communication, the IoV must offload its data to the nearest edge nodes. The main problem with the IoV is that it generates enormous data which is offloaded randomly during the… More >

  • Open Access

    ARTICLE

    Novel L2CL-LCL Topology for Wireless Power Transmission PMSM Powered Electrical Vehicle

    Jenson Jose1,*, Jose P. Therattil2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 339-355, 2022, DOI:10.32604/iasc.2022.023863
    Abstract The Wireless Power Transmission (WPT) technology is a significant source of operation in the field of power transmission with tremendous potential in a wide range of applications. This paper proposes a novel strategy for L2CL-LCL topology, which comprises two capacitors and one inductor in the essential and one capacitor and one inductor in the auxiliary. Using MATLAB simulation, this paper compares the traditional DSLCL system and the proposed L2CL-LCL. The various parameters of this system are simulated. In the current system, input and output power are set to 200.1 and 182.4 W. The common framework’s start to finish efficiency can… More >

  • Open Access

    ARTICLE

    Energy Efficient Clustering and Optimized LOADng Protocol for IoT

    Divya Sharma1,*, Sanjay Jain2, Vivek Maik3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 357-370, 2022, DOI:10.32604/iasc.2022.025637
    Abstract In recent years, the use of Internet of Things (IoT) devices has increased exponentially due to the advancement of information and communication technologies. Wireless sensor networks (WSNs) are vital in the development of IoT and include low-cost smart devices for data collection. However, such smart devices hold some restrictions in terms of calculation, processing, storage, and energy resources. With such constraints, one of the primary difficulties for the WSN is to achieve the lowest possible energy consumption across the network. This article aims to develop an Energy-Efficient cluster-based Lightweight On-Demand Ad hoc Distance Vector Routing Protocol–Next Generation (LOADng) routing protocol… More >

  • Open Access

    ARTICLE

    End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

    Yintong Wang1,2,*, Yingjie Yang2, Haiyan Chen3, Hao Zheng1, Heyou Chang1
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 371-388, 2022, DOI:10.32604/iasc.2022.027146
    Abstract Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual attention gate block is more… More >

  • Open Access

    ARTICLE

    Bayesian Feed Forward Neural Network-Based Efficient Anomaly Detection from Surveillance Videos

    M. Murugesan*, S. Thilagamani
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641
    Abstract Automatic anomaly activity detection is difficult in video surveillance applications due to variations in size, type, shape, and objects’ location. The traditional anomaly detection and classification methods may affect the overall segmentation accuracy. It requires the working groups to judge their constant attention if the captured activities are anomalous or suspicious. Therefore, this defect creates the need to automate this process with high accuracy. In addition to being extraordinary or questionable, the display does not contain the necessary recording frame and activity standard to help the quick judgment of the parts’ specialized action. Therefore, to reduce the wastage of time… More >

  • Open Access

    ARTICLE

    Background Subtraction in Surveillance Systems Using Local Spectral Histograms and Linear Regression

    S. Hariharan1,*, R. Venkatesan2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 407-422, 2022, DOI:10.32604/iasc.2022.025309
    Abstract Background subtraction is a fundamental and crucial task for computer vision-based automatic video analysis due to various challenging situations that occur in real-world scenarios. This paper presents a novel background subtraction method by estimating the background model using linear regression and local spectral histogram which captures combined spectral and texture features. Different linear filters are applied on the image window centered at each pixel location and the features are captured via these filter responses. Each feature has been approximated by a linear combination of two representative features, each of which corresponds to either a background or a foreground pixel. These… More >

  • Open Access

    ARTICLE

    Modeling of Chaotic Political Optimizer for Crop Yield Prediction

    Gurram Sunitha1,*, M. N. Pushpalatha2, A. Parkavi3, Prasanthi Boyapati4, Ranjan Walia5, Rachna Kohar6, Kashif Qureshi7
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 423-437, 2022, DOI:10.32604/iasc.2022.024757
    Abstract Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions to complicated data like weather… More >

  • Open Access

    ARTICLE

    Wireless Intrusion Detection Based on Optimized LSTM with Stacked Auto Encoder Network

    S. Karthic1,*, S. Manoj Kumar2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153
    Abstract In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM… More >

  • Open Access

    ARTICLE

    Influencing Factors Analysis of Rehabilitation for Patients with Spinal Cord Injury

    Min Rao1, Yufeng Li1,*, Hongye Liu2, Isabel Wang3, Yongjun Ren4
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 455-466, 2022, DOI:10.32604/iasc.2022.027694
    Abstract The aim of spinal cord injury rehabilitation is to increase the independent ability of patients, so that patients can return to society and live a creative life. Regaining independence living is the primary goal of rehabilitation. Rehabilitation of patients with spinal cord injury is a long and lengthy process, which needs comprehensive dimensions support from medical workers, family members, and social support. At present, medical institutions and researchers mainly focus on the level of physical recovery and the treatment of complications in patients with spinal cord injury and pay less attention to social factors during the rehabilitation treatment of patients.… More >

  • Open Access

    ARTICLE

    Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing

    K. Naveen Durai*, R. Subha, Anandakumar Haldorai
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 467-483, 2022, DOI:10.32604/iasc.2022.026020
    Abstract In cloud computing, the processes of load balancing and task scheduling are major concerns as they are the primary mechanisms responsible for executing tasks by allocating and utilizing the resources of Virtual Machines (VMs) in a more optimal way. This problem of balancing loads and scheduling tasks in the cloud computing scenario can be categorized as an NP-hard problem. This problem of load balancing needs to be efficiently allocated tasks to VMs and sustain the trade-off among the complete set of VMs. It also needs to maintain equilibrium among VMs with the objective of maximizing throughput with a minimized time… More >

  • Open Access

    ARTICLE

    Self-Balancing Vehicle Based on Adaptive Neuro-Fuzzy Inference System

    M. L. Ramamoorthy1, S. Selvaperumal2,*, G. Prabhakar3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 485-497, 2022, DOI:10.32604/iasc.2022.025824
    Abstract The scope of this research is to design and fuse the sensors used in the self-balancing vehicle through Adaptive Neuro-Fuzzy Inference systems (ANFIS) algorithm to optimize the output. The self-balancing vehicle is a wheeled inverted pendulum, which is extremely complex, nonlinear and unstable. Homogeneous and Heterogeneous sensors are involved in this sensor fusion research to identify the best feasible value among them. The data fusion algorithm present inside the controller of the self-balancing vehicle makes the inputs of the homogeneous sensors and heterogeneous sensors separately for ameliorate surrounding perception. Simulation is performed by modeling the sensors in Simulink. The outcomes… More >

  • Open Access

    ARTICLE

    A Secure E-commerce Environment Using Multi-agent System

    Farah Tawfiq Abdul Hussien*, Abdul Monem S. Rahma, Hala Bahjat Abdul Wahab
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 499-514, 2022, DOI:10.32604/iasc.2022.025091
    Abstract Providing security for the customers in the e-commerce system is an essential issue. Providing security for each single online customer at the same time is considered a time consuming process. For a huge websites such task may cause several problems including response delay, losing the customer orders and system deadlock or crash, in which reduce system performance. This paper aims to provide a new prototype structure of multi agent system that solve the problem of providing security and avoid the problems that may reduce system performance. This is done by creating a software agent which is settled into the customer… More >

  • Open Access

    ARTICLE

    Motor Torque Measurement Using Dual-Function Radar Polarized Signals of Flux

    B. Chinthamani1,*, N. S. Bhuvaneswari2, R. Senthil Kumar3, N. R. Shanker4
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 515-530, 2022, DOI:10.32604/iasc.2022.025410
    Abstract Motor Torque (MT) measurement plays a vital role for evaluating the performance of squirrel cage induction motor during operating conditions. Accurate and continuous measurements of MT provide information regarding driving load capacity, performance degradation of motor, reduces downtime and increases the efficiency. Traditional inline torque sensors-based measurement becomes inaccurate during abrupt change in load during starting condition of motor due to torque spikes. Mounting of torque sensor on motor is a major problem during torque measurement. Improper mounting of sensor acquires signals from other inefficient driveline components such as gearbox, couplings, and bearing. In this paper, we propose a non-contact… More >

  • Open Access

    ARTICLE

    Secure Multi-Party Quantum Summation Based on Quantum Homomorphic Encryption

    Gang Xu1,2, Fan Yun1, Xiu-Bo Chen3,*, Shiyuan Xu1, Jingzhong Wang1, Tao Shang4, Yan Chang5, Mianxiong Dong6
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 531-541, 2022, DOI:10.32604/iasc.2022.028264
    Abstract Secure multi-party computation has been playing a fundamental role in terms of classical cryptography. Quantum homomorphic encryption (QHE) could compute the encrypted data without decryption. At present, most protocols use a semi-honest third party (TP) to protect participants’ secrets. We use a quantum homomorphic encryption scheme instead of TP to protect the privacy of parties. Based on quantum homomorphic encryption, a secure multi-party quantum summation scheme is proposed in which N participants can delegate a server with strong quantum computing power to assist computation. By delegating the computation and key update processes to a server and a semi-honest key center,… More >

  • Open Access

    ARTICLE

    Analysis of Brushless DC Motor Using Enhanced Fopid Controller with ALO Algorithm

    K. Prathibanandhi1,*, R. Ramesh2, C. Yaashuwanth3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 543-557, 2022, DOI:10.32604/iasc.2022.025860
    Abstract The delivery of combined benefits of Alternating Current (AC) motor and Direct Current (DC) Motor makes the Brushless Direct Current (BLDC) motors as a unique feature in numerous industrial applications. The possibilities of running the motor at very high speed with extensive operating life span of BLDC with miniature and its compact design make it an un-ignorable option for Electrical Engineers. With many advantages, till managing as well as controlling the speed of BLDC is complicated. This work is intended to come up with an effective control of speed of the motor through Torque Ripple Minimization Route and an Enhanced-Fractional… More >

  • Open Access

    ARTICLE

    Crypto Hash Based Malware Detection in IoMT Framework

    R Punithavathi1, K Venkatachalam2, Mehedi Masud3, Mohammed A. AlZain4, Mohamed Abouhawwash5,6,*
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 559-574, 2022, DOI:10.32604/iasc.2022.024715
    Abstract The challenges in providing e-health services with the help of Internet of Medical Things (IoMT) is done by connecting to the smart medical devices. Through IoMT sensor devices/smart devices, physicians share the sensitive information of the patient. However, protecting the patient health care details from malware attack is necessary in this advanced digital scenario. Therefore, it is needed to implement cryptographic algorithm to enhance security, safety, reliability, preventing details from malware attacks and privacy of medical data. Nowadays blockchain has become a prominent technology for storing medical data securely and transmit through IoMT concept. The issues in the existing research… More >

  • Open Access

    ARTICLE

    Steering Behavior-based Multiple RUAV Obstacle Avoidance Control

    Vishnu Kumar Kaliappan1, Tuan Anh Nguyen1, Dugki Min2,*, Jae-Woo Lee1, U. Sakthi3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 575-591, 2022, DOI:10.32604/iasc.2022.024577
    Abstract In recent years, the applications of rotorcraft-based unmanned aerial vehicles (RUAV) have increased rapidly. In particular, the integration of bio-inspired techniques to enhance intelligence in coordinating multiple Rotorcraft-based Unmanned Aerial Vehicles (RUAVs) has been a focus of recent research and development. Due to the limitation in intelligence, these RUAVs are restricted in flying low altitude with high maneuverability. To make it possible, the RUAVs must have the ability to avoid both static and dynamic obstacles while operating at low altitudes. Therefore, developing a state-of-the-art intelligent control algorithm is necessary to avoid low altitude obstacles and coordinate without collision while executing… More >

  • Open Access

    ARTICLE

    Automated Crack Detection via Semantic Segmentation Approaches Using Advanced U-Net Architecture

    Honggeun Ji1,2, Jina Kim3, Syjung Hwang4, Eunil Park1,4,*
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 593-607, 2022, DOI:10.32604/iasc.2022.024405
    Abstract Cracks affect the robustness and adaptability of various infrastructures, including buildings, bridge piers, pavement, and pipelines. Therefore, the robustness and the reliability of automated crack detection are essential. In this study, we conducted image segmentation using various crack datasets by applying the advanced architecture of U-Net. First, we collected and integrated crack datasets from prior studies, including the cracks in buildings and pavements. For effective localization and detection of cracks, we used U-Net-based neural networks, ResU-Net, VGGU-Net, and EfficientU-Net. The models were evaluated by the five-fold cross-validation using several evaluation metrics including mean pixel accuracy (MPA), mean intersection over union… More >

  • Open Access

    ARTICLE

    Movie Recommendation Algorithm Based on Ensemble Learning

    Wei Fang1,2,*, Yu Sha1, Meihan Qi1, Victor S. Sheng3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 609-622, 2022, DOI:10.32604/iasc.2022.027067
    Abstract With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content… More >

  • Open Access

    ARTICLE

    Smart Grid Security by Embedding S-Box Advanced Encryption Standard

    Niraj Kumar1,*, Vishnu Mohan Mishra2, Adesh Kumar3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 623-638, 2022, DOI:10.32604/iasc.2022.024804
    Abstract Supervisory control and data acquisition (SCADA) systems continuously monitor the real-time processes in the smart grid. The system software, which is based on a human-machine interface (HMI), makes intelligent decisions to assist the system operator and perform normal grid management activities. The management of SCADA networks and monitoring without proper security is a major concern, as many grids and plant networks still lack necessary monitoring and detection systems, making them vulnerable to attack. SCADA networks exploit physical weaknesses as well as cyber-attacks. Researchers have developed a monitoring system based on a field-programmable gate array (FPGA) and a microcontroller that allows… More >

  • Open Access

    ARTICLE

    Computer Aided Coronary Atherosclerosis Plaque Detection and Classification

    S. Deivanayagi1,*, P. S. Periasamy2
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 639-653, 2022, DOI:10.32604/iasc.2022.025632
    Abstract Coronary artery disease (CAD) remains a major reason for increased mortality over the globe, comprising myocardial infarction and ischemic cardiomyopathy. The CAD is highly linked to coronary stenosis owing to the encumbrance of atherosclerotic plaques. Particularly, diversified atherosclerotic plaques are highly responsible for major cardiac adverse events over the calcified and non-calcified plaques. There, the recognition and classification of atherosclerotic plaques play a vital role to prevent and intervene in CAD. The process of detecting various class labels of the atherosclerotic plaques is significant to identify the disease at the earlier stages. Since several automated coronary plaque recognition models are… More >

  • Open Access

    ARTICLE

    Decentralized Link Failure Prevention Routing (DLFPR) Algorithm for Efficient Internet of Things

    D. Kothandaraman1,*, M. Manickam2, A. Balasundaram3, D. Pradeep4, A. Arulmurugan5, Arun Kumar Sivaraman6, Sita Rani7, Barnali Dey8, R. Balakrishna9
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 655-666, 2022, DOI:10.32604/iasc.2022.024134
    Abstract This work implements a Decentralized Links Failure Prevention (DLFP) routing algorithm to promote enhanced and efficient Internet of Things (IoT). The work increases the mobility as well as an opportunity for loss of IoT node meeting links due to both mobility and blockers/interferers. The proposed algorithm overcomes loss issues as well as works in dynamically allocating alternate route from other IoT nodes available in near and selecting for efficient route in the network. When the link fails, bandwidth is reduced and coverage area problems for packets sending from source to destination is managed. The proposed algorithm works with light-weight wireless… More >

  • Open Access

    ARTICLE

    CAD of BCD from Thermal Mammogram Images Using Machine Learning

    D. Banumathy1,*, Osamah Ibrahim Khalaf2, Carlos Andrés Tavera Romero3, J. Indra4, Dilip Kumar Sharma5
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 667-685, 2022, DOI:10.32604/iasc.2022.025609
    Abstract Lump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to… More >

  • Open Access

    ARTICLE

    An Intelligent Classification System for Trophozoite Stages in Malaria Species

    Siti Nurul Aqmariah Mohd Kanafiah1,*, Mohd Yusoff Mashor1, Zeehaida Mohamed2, Yap Chun Way1, Shazmin Aniza Abdul Shukor1, Yessi Jusman3
    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 687-697, 2022, DOI:10.32604/iasc.2022.024361
    Abstract Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum… More >

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