Home / Journals / IASC / Vol.36, No.3, 2023
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  • Open AccessOpen Access

    ARTICLE

    Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

    Mesfer Al Duhayyim*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585 - 15 March 2023
    Abstract Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim… More >

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    ARTICLE

    Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

    B. Sophia1,*, D. Chitra2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2491-2504, 2023, DOI:10.32604/iasc.2023.029799 - 15 March 2023
    Abstract Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid… More >

  • Open AccessOpen Access

    ARTICLE

    Accurate Phase Detection for ZigBee Using Artificial Neural Network

    Ali Alqahtani1, Abdulaziz A. Alsulami2,*, Saeed Alahmari3, Mesfer Alrizq4
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2505-2518, 2023, DOI:10.32604/iasc.2023.033243 - 15 March 2023
    Abstract The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise… More >

  • Open AccessOpen Access

    ARTICLE

    Guided Dropout: Improving Deep Networks Without Increased Computation

    Yifeng Liu1, Yangyang Li1,*, Zhongxiong Xu1, Xiaohan Liu1, Haiyong Xie2, Huacheng Zeng3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2519-2528, 2023, DOI:10.32604/iasc.2023.033286 - 15 March 2023
    Abstract Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one of the crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during training. It effectively improves the performance of deep networks but ignores the importance of the differences between neurons. To optimize this issue, this paper presents a new dropout method called guided dropout, which selects the neurons to switch off according to the differences between the convolution kernel and preserves the informative neurons. It uses an unsupervised clustering algorithm… More >

  • Open AccessOpen Access

    ARTICLE

    Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

    Badriyya B. Al-onazi1, Mohamed K. Nour2, Hassan Alshamrani3, Mesfer Al Duhayyim4,*, Heba Mohsen5, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6, Abu Sarwar Zamani6
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623 - 15 March 2023
    Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification… More >

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    ARTICLE

    Artificial Intelligence-Based Image Reconstruction for Computed Tomography: A Survey

    Quan Yan1, Yunfan Ye1, Jing Xia1, Zhiping Cai1,*, Zhilin Wang2, Qiang Ni3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2545-2558, 2023, DOI:10.32604/iasc.2023.029857 - 15 March 2023
    Abstract Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction More >

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    ARTICLE

    Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

    D. Vidyabharathi1,*, V. Mohanraj2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2559-2573, 2023, DOI:10.32604/iasc.2023.032255 - 15 March 2023
    Abstract For training the present Neural Network (NN) models, the standard technique is to utilize decaying Learning Rates (LR). While the majority of these techniques commence with a large LR, they will decay multiple times over time. Decaying has been proved to enhance generalization as well as optimization. Other parameters, such as the network’s size, the number of hidden layers, dropouts to avoid overfitting, batch size, and so on, are solely based on heuristics. This work has proposed Adaptive Teaching Learning Based (ATLB) Heuristic to identify the optimal hyperparameters for diverse networks. Here we consider three More >

  • Open AccessOpen Access

    ARTICLE

    Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

    V. Prakash*, S. Gopalakrishnan
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122 - 15 March 2023
    Abstract Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software… More >

  • Open AccessOpen Access

    ARTICLE

    Energy-Efficient Clustering Using Optimization with Locust Game Theory

    P. Kavitha Rani1, Hee-Kwon Chae2, Yunyoung Nam2,*, Mohamed Abouhawwash3,4
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2591-2605, 2023, DOI:10.32604/iasc.2023.033697 - 15 March 2023
    Abstract Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the… More >

  • Open AccessOpen Access

    ARTICLE

    Convolutional Neural Network-Based Classification of Multiple Retinal Diseases Using Fundus Images

    Aqsa Aslam, Saima Farhan*, Momina Abdul Khaliq, Fatima Anjum, Ayesha Afzaal, Faria Kanwal
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2607-2622, 2023, DOI:10.32604/iasc.2023.034041 - 15 March 2023
    Abstract Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique. The increase in retinal diseases is alarming as it may lead to permanent blindness if left untreated. Automation of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also. Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted feature selection or binary classification. This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research,… More >

  • Open AccessOpen Access

    ARTICLE

    Analysis of Power Quality for Distribution Networks Using Active Compensator

    K. Naresh Kumar1,*, S. Srinath2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2623-2638, 2023, DOI:10.32604/iasc.2023.031713 - 15 March 2023
    Abstract This paper concentrates on compensating the power quality issues which have been increased in day-to-day life due to the enormous usage of loads with power electronic control. One such solution is compensating devices like Pension Protection Fund (PPF), Active power filter (APF), hybrid power filter (HPF), etc., which are used to overcome Power Quality (PQ) issues. The proposed method used here is an active compensator called unified power quality conditioner (UPQC) which is a combination of shunt and series type active filter connected via a common DC link. The primary objective is to investigate the… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

    Mrim M. Alnfiai1,2, Nabil Almalki1,3, Fahd N. Al-Wesabi4,*, Mesfer Alduhayyem5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2639-2652, 2023, DOI:10.32604/iasc.2023.034069 - 15 March 2023
    Abstract Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

    Madhuri Agrawal*, Shikha Agrawal
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493 - 15 March 2023
    Abstract

    Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the

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  • Open AccessOpen Access

    ARTICLE

    Automatic Team Assignment and Jersey Number Recognition in Football Videos

    Ragd Alhejaily1, Rahaf Alhejaily1, Mai Almdahrsh1, Shareefah Alessa1, Saleh Albelwi1,2,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2669-2684, 2023, DOI:10.32604/iasc.2023.033062 - 15 March 2023
    Abstract Football is one of the most-watched sports, but analyzing players’ performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player’s performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to… More >

  • Open AccessOpen Access

    ARTICLE

    A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

    Mercy Sharon Devadas1, N. Bhalaji1,*, Xiao-Zhi Gao2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2685-2702, 2023, DOI:10.32604/iasc.2023.031636 - 15 March 2023
    Abstract In Mobile ad hoc Networks (MANETs), the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss. In this paper, a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling (MFPA-FSPS) Mechanism is proposed for sustaining Quality of Service (QoS) in the network. This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’ priority for guaranteeing optimal network performance. It further used the modified Firefly More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Deep Learning Representation for Industrial Control System Data

    Bowen Zhang1,2,3, Yanbo Shi4, Jianming Zhao1,2,3,*, Tianyu Wang1,2,3, Kaidi Wang5
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2703-2717, 2023, DOI:10.32604/iasc.2023.033762 - 15 March 2023
    Abstract Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the… More >

  • Open AccessOpen Access

    ARTICLE

    Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

    R. Gnanakumari1,*, P. Vijayalakshmi2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2719-2730, 2023, DOI:10.32604/iasc.2023.034145 - 15 March 2023
    Abstract In social data analytics, Virtual Community (VC) detection is a primary challenge in discovering user relationships and enhancing social recommendations. VC formation is used for personal interaction between communities. But the usual methods didn’t find the Suspicious Behaviour (SB) needed to make a VC. The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking (GJSBS-RDNNCR) Model addresses these issues. The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks (SN). In the GJSBS-RDNNCR model, the SN is given as an input at the input layer. After that, the User’s Behaviors… More >

  • Open AccessOpen Access

    ARTICLE

    Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

    Yuan-Nong Ye1,2,3,*, Ding-Fa Liang2, Abraham Alemayehu Labena4, Zhu Zeng2,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2731-2741, 2023, DOI:10.32604/iasc.2023.026761 - 15 March 2023
    Abstract Essential ncRNA is a type of ncRNA which is indispensable for the survival of organisms. Although essential ncRNAs cannot encode proteins, they are as important as essential coding genes in biology. They have got wide variety of applications such as antimicrobial target discovery, minimal genome construction and evolution analysis. At present, the number of species required for the determination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming, laborious and costly. In addition, traditional experimental methods are limited by the organisms as less than 1%… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Time Feedforward Connections Recurrent Neural Networks

    Jin Wang1,2, Yongsong Zou1, Se-Jung Lim3,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869 - 15 March 2023
    Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

    Adel Alshamrani1,*, Abdullah Alshahrani2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835 - 15 March 2023
    Abstract The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which More >

  • Open AccessOpen Access

    ARTICLE

    A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

    Hosam Alhakami1,*, Abdullah Baz2, Mohammad Al-shareef3, Rajeev Kumar4, Alka Agrawal5, Raees Ahmad Khan5
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2773-2786, 2023, DOI:10.32604/iasc.2023.021560 - 15 March 2023
    Abstract Recent transformation of Saudi Arabian healthcare sector into a revenue producing one has signaled several advancements in healthcare in the country. Transforming healthcare management into Smart hospital systems is one of them. Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems. Given the perspective of Saudi Vision-2030, many practitioners are trying to achieve a cost-effective hospital management system by using smart ideas. In this row, the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare… More >

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    ARTICLE

    A Proposed Architecture for Local-Host and AWS with Multiagent System

    Jaspreet Chawla1,*, Anil Kr Ahlawat2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2787-2802, 2023, DOI:10.32604/iasc.2023.034775 - 15 March 2023
    Abstract Cloud computing has become one of the leading technologies in the world today. The benefits of cloud computing affect end users directly. There are several cloud computing frameworks, and each has ways of monitoring and providing resources. Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability. From the user’s perspective, cloud computing makes it easy to upload multiagents and operate on different web services. In this paper, the authors used a restful web service and an agent system to discuss, deployments, and analysis of load performance… More >

  • Open AccessOpen Access

    ARTICLE

    Reconfigurable Logic Design of CORDIC Based FFT Architecture for 5G Communications

    C. Thiruvengadam1,*, M. Palanivelan2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2803-2818, 2023, DOI:10.32604/iasc.2023.030493 - 15 March 2023
    Abstract There are numerous goals in next-generation cellular networks (5G), which is expected to be available soon. They want to increase data rates, reduce end-to-end latencies, and improve end-user service quality. Modern networks need to change because there has been a significant rise in the number of base stations required to meet these needs and put the operators’ low-cost constraints to the test. Because it can withstand interference from other wireless networks, and Adaptive Complex Multicarrier Modulation (ACMM) system is being looked at as a possible choice for the 5th Generation (5G) of wireless networks. Many… More >

  • Open AccessOpen Access

    ARTICLE

    Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

    J. Jalaney1,*, R. S. Ganesh2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2819-2834, 2023, DOI:10.32604/iasc.2023.034844 - 15 March 2023
    Abstract Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average… More >

  • Open AccessOpen Access

    ARTICLE

    The IOMT-Based Risk-Free Approach to Lung Disorders Detection from Exhaled Breath Examination

    Mohsin Ghani, Ghulam Gilanie*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2835-2847, 2023, DOI:10.32604/iasc.2023.034857 - 15 March 2023
    Abstract The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body. Lung disorders, including Coronavirus (Covid-19), are among the world’s deadliest and most life-threatening diseases. Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity. Even though X-rays or Computed Tomography (CT) scans are the imaging techniques to analyze lung-related disorders, medical practitioners still find it challenging to analyze and identify lung cancer from scanned images. unless COVID-19 reaches the lungs, it is unable to be… More >

  • Open AccessOpen Access

    ARTICLE

    Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

    Umashankar Ganesan1,*, A. Vimala Juliet2, R. Amala Jenith Joshi3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.033759 - 15 March 2023
    Abstract Brain signal analysis plays a significant role in attaining data related to motor activities. The parietal region of the brain plays a vital role in muscular movements. This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements; perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm. This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease (PD).… More >

  • Open AccessOpen Access

    ARTICLE

    Data-Driven Probabilistic System for Batsman Performance Prediction in a Cricket Match

    Fawad Nasim1,2,*, Muhammad Adnan Yousaf1, Sohail Masood1,2, Arfan Jaffar1,2, Muhammad Rashid3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2865-2877, 2023, DOI:10.32604/iasc.2023.034258 - 15 March 2023
    Abstract Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success. A good batsman not only scores run but also provides stability to the team’s innings. The most important factor in selecting a batsman is their ability to score runs. It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record. This hypothesis is based on the fact that a player’s batting average is generally considered to be a good indicator of their future performance. We… More >

  • Open AccessOpen Access

    ARTICLE

    Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

    Manar Ahmed Hamza*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719 - 15 March 2023
    Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the… More >

  • Open AccessOpen Access

    ARTICLE

    Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

    Faisal S. Alsubaei1, Haya Mesfer Alshahrani2, Khaled Tarmissi3, Abdelwahed Motwakel4,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2897-2914, 2023, DOI:10.32604/iasc.2023.034907 - 15 March 2023
    Abstract Cybersecurity has become the most significant research area in the domain of the Internet of Things (IoT) owing to the ever-increasing number of cyberattacks. The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process. Furthermore, Android malware is increasing on a daily basis. So, precise malware detection analytical techniques need a large number of hardware resources that are significantly resource-limited for mobile devices. In this research article, an optimal Graph Convolutional Neural Network-based Malware Detection and classification (OGCNN-MDC) model is introduced for an IoT-cloud environment. The… More >

  • Open AccessOpen Access

    ARTICLE

    Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

    S. Vijayalakshmi*, S. Magesh Kumar
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2915-2931, 2023, DOI:10.32604/iasc.2023.034165 - 15 March 2023
    Abstract Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based… More >

  • Open AccessOpen Access

    ARTICLE

    Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

    Hesham Alhumyani*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2933-2945, 2023, DOI:10.32604/iasc.2023.034689 - 15 March 2023
    Abstract This paper introduces an efficient image cryptography system. The proposed image cryptography system is based on employing the two-dimensional (2D) chaotic henon map (CHM) in the Discrete Fourier Transform (DFT). The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures. In the proposed DFT-based CHM image cryptography, the confusion is employed using the CHM while the diffusion is realized using the DFT. So, the proposed DFT-based CHM image cryptography achieves both confusion and diffusion characteristics. The encryption procedure starts by applying the DFT on the image then the DFT More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Level Inverter Linear Predictive Phase Composition Strategy for UPQC

    M. Hari Prabhu*, K. Sundararaju
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2947-2958, 2023, DOI:10.32604/iasc.2023.032328 - 15 March 2023
    Abstract The power system is facing numerous issues when the distributed generation is added to the existing system. The existing power system has not been planned with flawless power quality control. These restrictions in the power transmission generation system are compensated by the use of devices such as the Static Synchronous Compensator (STATCOM), the Unified Power Quality Conditioner (UPQC) series/shunt compensators, etc. In this work, UPQC’s plan with the joint activity of photovoltaic (PV) exhibits is proposed. The proposed system is made out of series and shunt regulators and PV. A boost converter connects the DC… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

    P. S. Arthy1,*, A. Kavitha2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511 - 15 March 2023
    Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge.… More >

  • Open AccessOpen Access

    ARTICLE

    Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

    A. Selvi*, S. Thilagamani
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2973-2987, 2023, DOI:10.32604/iasc.2022.029850 - 15 March 2023
    Abstract Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman More >

  • Open AccessOpen Access

    ARTICLE

    A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

    P. Prabhu1, P. Valarmathie2,*, K. Dinakaran3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2989-3005, 2023, DOI:10.32604/iasc.2023.028659 - 15 March 2023
    Abstract Supportive learning plays a substantial role in providing a quality education system. The evaluation of students’ performance impacts their deeper insight into the subject knowledge. Specifically, it is essential to maintain the baseline foundation for building a broader understanding of their careers. This research concentrates on establishing the students’ knowledge relationship even in reduced samples. Here, Synthetic Minority Oversampling TEchnique (SMOTE) technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy. When the initial processing is not done substantially, it leads to misleading prediction accuracy. This research… More >

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    Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

    Emad Abd Al Rahman1, Nur Intan Raihana Ruhaiyem1,*, Majed Bouchahma2, Kamarul Imran Musa3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3007-3028, 2023, DOI:10.32604/iasc.2023.032580 - 15 March 2023
    Abstract This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment… More >

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    ARTICLE

    A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

    Minh Thanh Vo1, Anh H. Vo2, Huong Bui3, Tuong Le4,5,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3029-3041, 2023, DOI:10.32604/iasc.2023.034636 - 15 March 2023
    Abstract Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone’s health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in… More >

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    ARTICLE

    Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

    S. Ashwini*, S. Magesh Kumar
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3043-3056, 2023, DOI:10.32604/iasc.2023.034551 - 15 March 2023
    Abstract Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is… More >

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    ARTICLE

    Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

    Xiaorui Zhang1,2,3,4,*, Yingying Wang1, Wei Sun4,5, Qi Cui6, Xindong Wei7
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3057-3071, 2023, DOI:10.32604/iasc.2023.033578 - 15 March 2023
    Abstract Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study… More >

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    ARTICLE

    Approximations by Ideal Minimal Structure with Chemical Application

    Rodyna A. Hosny1, Radwan Abu-Gdairi2, Mostafa K. El-Bably3,*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3073-3085, 2023, DOI:10.32604/iasc.2023.034234 - 15 March 2023
    Abstract The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space via ideal . The novel approach (indicated by ) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak’s More >

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    ARTICLE

    Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

    Xiaoyan Zhao1,*, Lin Zhou2, Yue Xie1, Ying Tong1, Jingang Shi3
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3087-3100, 2023, DOI:10.32604/iasc.2023.030180 - 15 March 2023
    Abstract Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered… More >

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    ARTICLE

    A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

    P. Shanmuga Prabha*, S. Magesh Kumar
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3101-3119, 2023, DOI:10.32604/iasc.2023.034885 - 15 March 2023
    Abstract Today, securing devices connected to the internet is challenging as security threats are generated through various sources. The protection of cyber-physical systems from external attacks is a primary task. The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters. The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis (MEDA) through Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) for the extraction of unique parameters. The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network (R2CNN) and Gradient Boost Regression (GBR) to More >

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    ARTICLE

    Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

    Najm Alotaibi1, Badriyya B. Al-onazi2, Mohamed K. Nour3, Abdullah Mohamed4, Abdelwahed Motwakel5,*, Gouse Pasha Mohammed5, Ishfaq Yaseen5, Mohammed Rizwanullah5
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3121-3137, 2023, DOI:10.32604/iasc.2023.033915 - 15 March 2023
    Abstract Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in… More >

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    ARTICLE

    Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

    Hala J. Alshahrani1, Khaled Tarmissi2, Ayman Yafoz3, Abdullah Mohamed4, Abdelwahed Motwakel5,*, Ishfaq Yaseen5, Amgad Atta Abdelmageed5, Mohammad Mahzari6
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3139-3155, 2023, DOI:10.32604/iasc.2023.034841 - 15 March 2023
    Abstract Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body… More >

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    ARTICLE

    Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

    Abdelwahed Motwakel1,*, Fadwa Alrowais2, Khaled Tarmissi3, Radwa Marzouk4, Abdullah Mohamed5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Mohamed I. Eldesouki6
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3157-3173, 2023, DOI:10.32604/iasc.2023.034908 - 15 March 2023
    Abstract The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such… More >

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    ARTICLE

    An Endogenous Feedback and Entropy Analysis in Machine Learning Model for Stock’s Return Forecast

    Edson Vinicius Pontes Bastos1,*, Jorge Junio Moreira Antunes2, Lino Guimarães Marujo1, Peter Fernandes Wanke2, Roberto Ivo da Rocha Lima Filho1
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3175-3190, 2023, DOI:10.32604/iasc.2023.034582 - 15 March 2023
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract Stock markets exhibit Brownian movement with random, non-linear, uncertain, evolutionary, non-parametric, nebulous, chaotic characteristics and dynamism with a high degree of complexity. Developing an algorithm to predict returns for decision-making is a challenging goal. In addition, the choice of variables that will serve as input to the model represents a non-triviality, since it is possible to observe endogeneity problems between the predictor and the predicted variables. Thus, the goal is to analyze the endogenous origin of the stock return prediction model based on technical indicators. For this, we structure a feed-forward neural network. We evaluate More >

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    ARTICLE

    Precise Rehabilitation Strategies for Functional Impairment in Children with Cerebral Palsy

    Yaojin Sun1, Nan Jiang1,*, Min Zhu1, Hao Hua2
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3191-3202, 2023, DOI:10.32604/iasc.2023.035425 - 15 March 2023
    Abstract This paper explores the effect of precise rehabilitation strategies under the international classification of functioning, disability and health for children and youth (ICF-CY) on the motor function of children with cerebral palsy. Under the framework of ICF-CY, the observation team is designed and evaluated from physical functions, activities and participation, environmental factors, and develops individualized rehabilitation strategies that are tailored to individual characteristics. The control group was assessed by traditional methods and treatment plans and measures were formulated and guided. The course of treatment was 12 months. The scores of GMFM-88, Peabody Motor Development Scale-2concluding… More >

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    Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

    Hala J. Alshahrani1, Khaled Tarmissi2, Ayman Yafoz3, Abdullah Mohamed4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammad Mahzari6
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3203-3219, 2023, DOI:10.32604/iasc.2023.034848 - 15 March 2023
    Abstract The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with… More >

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    ARTICLE

    Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

    Caiyun Liu, Peng Liu*
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3221-3242, 2023, DOI:10.32604/iasc.2023.035114 - 15 March 2023
    Abstract Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various More >

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    ARTICLE

    Dual Branch PnP Based Network for Monocular 6D Pose Estimation

    Jia-Yu Liang1, Hong-Bo Zhang1,*, Qing Lei2, Ji-Xiang Du3, Tian-Liang Lin4
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3243-3256, 2023, DOI:10.32604/iasc.2023.035812 - 15 March 2023
    Abstract Monocular 6D pose estimation is a functional task in the field of computer vision and robotics. In recent years, 2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based scenes. However, for monocular 6D pose estimation, these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the perspective-n-point (PnP) algorithm. There is still a difference in the distance from the expected estimation effect. To obtain a more effective feature representation result, edge enhancement is proposed to increase the shape information of the object by analyzing the influence… More >

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