Open Access
ARTICLE
Mesfer Al Duhayyim*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585
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 is to detect and distinguish… More >
Open Access
ARTICLE
B. Sophia1,*, D. Chitra2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2491-2504, 2023, DOI:10.32604/iasc.2023.029799
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 Network based Anomaly Detection and… More >
Open Access
ARTICLE
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
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 in the BPSK signal is… More >
Open Access
ARTICLE
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
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 to cluster similar neurons in… More >
Open Access
ARTICLE
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
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 on Arabic Twitter (OMSDAE-GIAT) model.… More >
Open Access
ARTICLE
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
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 speed. In particular, conventional reconstruction… More >
Open Access
ARTICLE
D. Vidyabharathi1,*, V. Mohanraj2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2559-2573, 2023, DOI:10.32604/iasc.2023.032255
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 architectures Recurrent Neural Networks (RNN),… More >
Open Access
ARTICLE
V. Prakash*, S. Gopalakrishnan
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122
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 test case optimization. Hospital Information… More >
Open Access
ARTICLE
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
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 optimum routing, while the LS-II… More >
Open Access
ARTICLE
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
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, the data has been collected… More >
Open Access
ARTICLE
K. Naresh Kumar1,*, S. Srinath2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2623-2638, 2023, DOI:10.32604/iasc.2023.031713
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 behavior of the compensators in… More >
Open Access
ARTICLE
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
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 and readers can guess them… More >
Open Access
ARTICLE
Madhuri Agrawal*, Shikha Agrawal
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493
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 video frame in both… More >
Open Access
ARTICLE
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
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 their teams, identifying individual players,… More >
Open Access
ARTICLE
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
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 Optimization Algorithm to optimize the… More >
Open Access
ARTICLE
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
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 problem of high-dimensional and heterogeneous… More >
Open Access
ARTICLE
R. Gnanakumari1,*, P. Vijayalakshmi2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2719-2730, 2023, DOI:10.32604/iasc.2023.034145
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 (UB) are extracted in the… More >
Open Access
ARTICLE
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
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% of bacteria can be cultured… More >
Open Access
ARTICLE
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
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 branch was introduced for the… More >
Open Access
ARTICLE
Adel Alshamrani1,*, Abdullah Alshahrani2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835
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 defenders visit a system multiple… More >
Open Access
ARTICLE
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
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 data and medical practices.… More >
Open Access
ARTICLE
Jaspreet Chawla1,*, Anil Kr Ahlawat2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2787-2802, 2023, DOI:10.32604/iasc.2023.034775
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 parameters like memory use, central… More >
Open Access
ARTICLE
C. Thiruvengadam1,*, M. Palanivelan2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2803-2818, 2023, DOI:10.32604/iasc.2023.030493
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 arithmetic units need to be… More >
Open Access
ARTICLE
J. Jalaney1,*, R. S. Ganesh2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2819-2834, 2023, DOI:10.32604/iasc.2023.034844
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 vehicle speed limit, current vehicle… More >
Open Access
ARTICLE
Mohsin Ghani, Ghulam Gilanie*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2835-2847, 2023, DOI:10.32604/iasc.2023.034857
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 diagnosed. through these modalities. So,… More >
Open Access
ARTICLE
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
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). To play out this handling… More >
Open Access
ARTICLE
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
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 proposed a data-driven probabilistic system… More >
Open Access
ARTICLE
Manar Ahmed Hamza*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719
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 way for highly intellectual and… More >
Open Access
ARTICLE
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
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 proposed OGCNN-MDC model aims to… More >
Open Access
ARTICLE
S. Vijayalakshmi*, S. Magesh Kumar
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2915-2931, 2023, DOI:10.32604/iasc.2023.034165
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 approach for classifying the vegetation… More >
Open Access
ARTICLE
Hesham Alhumyani*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2933-2945, 2023, DOI:10.32604/iasc.2023.034689
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 transformed image is scrambled using… More >
Open Access
ARTICLE
M. Hari Prabhu*, K. Sundararaju
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2947-2958, 2023, DOI:10.32604/iasc.2023.032328
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 link to the PV source,… More >
Open Access
ARTICLE
P. S. Arthy1,*, A. Kavitha2
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511
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. The proposed deep learning architecture… More >
Open Access
ARTICLE
A. Selvi*, S. Thilagamani
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2973-2987, 2023, DOI:10.32604/iasc.2022.029850
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 filter and scale-invariant
feature transform… More >
Open Access
ARTICLE
P. Prabhu1, P. Valarmathie2,*, K. Dinakaran3
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2989-3005, 2023, DOI:10.32604/iasc.2023.028659
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 concentrates on modelling an efficient… More >
Open Access
ARTICLE
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
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
plan once a patient has… More >
Open Access
ARTICLE
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
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 Air Quality HCMC dataset. Only… More >
Open Access
ARTICLE
S. Ashwini*, S. Magesh Kumar
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3043-3056, 2023, DOI:10.32604/iasc.2023.034551
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 developed. For the test purpose… More >
Open Access
ARTICLE
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
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 proposes an end-to-end multi-path attention… More >
Open Access
ARTICLE
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
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 and EL-Sharkasy techniques. Via illustrated… More >
Open Access
ARTICLE
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
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 response power-phase transform (SRP-PHAT) weighted… More >
Open Access
ARTICLE
P. Shanmuga Prabha*, S. Magesh Kumar
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3101-3119, 2023, DOI:10.32604/iasc.2023.034885
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 identify the maximum correlation. Novel… More >
Open Access
ARTICLE
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
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 a nascent stage. The unique… More >
Open Access
ARTICLE
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
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 text automatic classification into phishing… More >
Open Access
ARTICLE
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
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 cyberattacks. With this motivation, the… More >
Open Access
ARTICLE
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
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 the endogenous feedback between the… More >
Open Access
ARTICLE
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
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 fine motor quotient (PDMS-FM), WeeFIM… More >
Open Access
ARTICLE
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
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 Deep Learning (ALTS-MLODL) model. The… More >
Open Access
ARTICLE
Caiyun Liu, Peng Liu*
Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3221-3242, 2023, DOI:10.32604/iasc.2023.035114
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 time nodes, enables the dynamic… More >
Open Access
ARTICLE
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
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 of inaccurate 2D-3D matching on… More >
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