Home / Journals / IASC / Vol.36, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    Memory-Type Control Charts Through the Lens of Cost Parameters

    Sakthiseswari Ganasan1, You Huay Woon2,*, Zainol Mustafa1, Dadasaheb G. Godase3
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1-10, 2023, DOI:10.32604/iasc.2023.032062
    Abstract A memory-type control chart utilizes previous information for chart construction. An example of a memory-type chart is an exponentially-weighted moving average (EWMA) control chart. The EWMA control chart is well-known and widely employed by practitioners for monitoring small and moderate process mean shifts. Meanwhile, the EWMA median chart is robust against outliers. In light of this, the economic model of the EWMA and EWMA median control charts are commonly considered. This study aims to investigate the effect of cost parameters on the out-of-control average run length in implementing EWMA and EWMA median control charts. The… More >

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    ARTICLE

    Early Detection of Autism in Children Using Transfer Learning

    Taher M. Ghazal1,2, Sundus Munir3,4, Sagheer Abbas3, Atifa Athar5, Hamza Alrababah1, Muhammad Adnan Khan6,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 11-22, 2023, DOI:10.32604/iasc.2023.030125
    Abstract Autism spectrum disorder (ASD) is a challenging and complex neuro-development syndrome that affects the child’s language, speech, social skills, communication skills, and logical thinking ability. The early detection of ASD is essential for delivering effective, timely interventions. Various facial features such as a lack of eye contact, showing uncommon hand or body movements, babbling or talking in an unusual tone, and not using common gestures could be used to detect and classify ASD at an early stage. Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based More >

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    ARTICLE

    Hybrid Multi-Object Optimization Method for Tapping Center Machines

    Ping-Yueh Chang1, Fu-I Chou1, Po-Yuan Yang2,*, Shao-Hsien Chen3
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 23-38, 2023, DOI:10.32604/iasc.2023.031609
    Abstract This paper proposes a hybrid multi-object optimization method integrating a uniform design, an adaptive network-based fuzzy inference system (ANFIS), and a multi-objective particle swarm optimizer (MOPSO) to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control (CNC) machines. First, rigid tapping parameters and uniform (including 41-level and 19-level) layouts were adopted to collect representative data for modeling. Next, ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data. In tapping center machines, the synchronization errors and cycle times are important… More >

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    ARTICLE

    Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

    S. Kalavathi1,*, R. Nedunchelian2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 39-56, 2023, DOI:10.32604/iasc.2023.031522
    Abstract Network analysis is a promising field in the area of network applications as different types of traffic grow enormously and exponentially. Reliable route prediction is a challenging task in the Large Scale Networks (LSN). Various non-self-learning and self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP. In the current era, dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.… More >

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    ARTICLE

    A Novel Method for Heat Exchange Evaluation in EV

    Mohammad Saraireh*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 57-70, 2023, DOI:10.32604/iasc.2023.032050
    Abstract With the growing global energy and environmental problems, electric vehicles that are both environmentally friendly and cost effective have seen rapid growth. An electrified vehicle’s effective thermal management must include all of the vehicle’s systems. However, optimizing the thermal behavior of each component is insufficient. A lithium-ion battery’s operating temperature has a significant impact on its performance. When working at low temperatures, the internal resistance of lithium-ion batteries increases, the available energy and power of the system decreases, and lithium precipitation caused by low-temperature charging may cause safety issues; high-temperature operation and temperature inconsistency between… More >

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    ARTICLE

    ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

    Byeongmin Choi1, YongHyun Lee1, Yeunwoong Kyung2, Eunchan Kim3,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783
    Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, More >

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    ARTICLE

    Robust Frequency Estimation Under Additive Symmetric α-Stable Gaussian Mixture Noise

    Peng Wang1, Yulu Tian2, Bolong Men1,*, Hailong Song1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 83-95, 2023, DOI:10.32604/iasc.2023.027602
    Abstract Here the estimating problem of a single sinusoidal signal in the additive symmetric α-stable Gaussian (ASαSG) noise is investigated. The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric α-stable distributed variable. As the probability density function (PDF) of the ASαSG is complicated, traditional estimators cannot provide optimum estimates. Based on the Metropolis-Hastings (M-H) sampling scheme, a robust frequency estimator is proposed for ASαSG noise. Moreover, to accelerate the convergence rate of the developed algorithm, a new criterion of reconstructing the proposal covariance is derived, whose main idea is More >

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    ARTICLE

    Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

    T. Thanya1,*, S. Wilfred Franklin2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 97-112, 2023, DOI:10.32604/iasc.2023.029675
    Abstract At an early point, the diagnosis of pancreatic cancer is mediocre, since the radiologist is skill deficient. Serious threats have been posed due to the above reasons, hence became mandatory for the need of skilled technicians. However, it also became a time-consuming process. Hence the need for automated diagnosis became mandatory. In order to identify the tumor accurately, this research proposes a novel Convolution Neural Network (CNN) based superior image classification technique. The proposed deep learning classification strategy has a precision of 97.7%, allowing for more effective usage of the automatically executed feature extraction technique More >

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    ARTICLE

    Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

    C. Nandagopal1,*, P. Siva Kumar2, R. Rajalakshmi3, S. Anandamurugan4
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 113-126, 2023, DOI:10.32604/iasc.2023.031103
    Abstract Vehicle Ad hoc Networks (VANETs) have high mobility and a randomized connection structure, resulting in extremely dynamic behavior. Several challenges, such as frequent connection failures, sustainability, multi-hop data transfer, and data loss, affect the effectiveness of Transmission Control Protocols (TCP) on such wireless ad hoc networks. To avoid the problem, in this paper, mobility-aware zone-based routing in VANET is proposed. To achieve this concept, in this paper hybrid optimization algorithm is presented. The hybrid algorithm is a combination of Ant colony optimization (ACO) and artificial bee colony optimization (ABC). The proposed hybrid algorithm is designed for… More >

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    ARTICLE

    Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

    Abdullah A. Asiri1, Ahmad Shaf2,*, Tariq Ali2, Muhammad Aamir2, Ali Usman2, Muhammad Irfan3, Hassan A. Alshamrani1, Khlood M. Mehdar4, Osama M. Alshehri5, Samar M. Alqhtani6
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 127-143, 2023, DOI:10.32604/iasc.2023.032391
    Abstract The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique… More >

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    ARTICLE

    Resilient Service Authentication for Smart City Application Using IoT

    Gokulakannan Elamparithi*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 145-152, 2023, DOI:10.32604/iasc.2023.032036
    Abstract Internet of Things (IoT) support for smart city systems improves service scales by ignoring various user congestion. People are looking for different security features for reliable and robust applications. Here, the Permanent Denial of Service (PDoS) problem arises from improper user identification. This article introduces the Service-Reliant Application Authentication (SRAA) to prevent PDoS attacks in a smart area of the city. In this authentication method, the security of the application is ensured through the distribution of guarded access. The supervised access distribution uses user interface features and sync with the user device. Abnormality in linking More >

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    ARTICLE

    Transformer Internal and Inrush Current Fault Detection Using Machine Learning

    R. Vidhya1,*, P. Vanaja Ranjan2, N. R. Shanker3
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 153-168, 2023, DOI:10.32604/iasc.2023.031942
    Abstract Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush More >

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    ARTICLE

    Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

    Muhammad Irfan1, Khalil Ullah2, Fazal Muhammad3,*, Salman Khan3, Faisal Althobiani4, Muhammad Usman5, Mohammed Alshareef4, Shadi Alghaffari4, Saifur Rahman1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337
    Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features… More >

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    ARTICLE

    Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

    S. Samsudeen Shaffi1,*, I. Muthulakshmi2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 183-192, 2023, DOI:10.32604/iasc.2023.031494
    Abstract As data grows in size, search engines face new challenges in extracting more relevant content for users’ searches. As a result, a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements. Unfortunately, most existing indexes and ranking algorithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations, making it impossible to deliver exceptionally accurate results. As a result, this study investigates and analyses how search engines work, as well as the elements that contribute to higher… More >

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    ARTICLE

    Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

    Youngjun Kim1, Taekook Kim2, Hyungoo Choi1, Jinwoo Park1, Yeunwoong Kyung3,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 193-204, 2023, DOI:10.32604/iasc.2023.032784
    Abstract Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there More >

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    ARTICLE

    Popularity Prediction of Social Media Post Using Tensor Factorization

    Navdeep Bohra1,2, Vishal Bhatnagar3, Amit Choudhary4, Savita Ahlawat2, Dinesh Sheoran2, Ashish Kumari2,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 205-221, 2023, DOI:10.32604/iasc.2023.030708
    Abstract The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about… More >

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    ARTICLE

    Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

    S. Sridevi*, Jeevaa Katiravan
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 223-233, 2023, DOI:10.32604/iasc.2023.031928
    Abstract Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth… More >

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    ARTICLE

    Performance Analysis of a Chunk-Based Speech Emotion Recognition Model Using RNN

    Hyun-Sam Shin1, Jun-Ki Hong2,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 235-248, 2023, DOI:10.32604/iasc.2023.033082
    Abstract Recently, artificial-intelligence-based automatic customer response system has been widely used instead of customer service representatives. Therefore, it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly. Therefore, we analyzed the performance of the emotion recognition (ER) accuracy as a function of the simulation time using the proposed chunk-based speech ER (CSER) model. The proposed CSER model divides voice signals into 3-s long chunks to efficiently recognize characteristically inherent emotions in the customer’s voice. We evaluated the performance of the ER of voice signal chunks More >

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    ARTICLE

    Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

    Elumalaivasan Poongavanam1,*, Padmanathan Kasinathan2, Kulothungan Kanagasabai3
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 249-265, 2023, DOI:10.32604/iasc.2023.030101
    Abstract The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy policy. This energy demand will have to be fulfilled by an adequate mix of existing energy sources, considering the constraints imposed by future economic and social changes in the direction of a more sustainable world. Forecasting energy demand, on the other hand, is a tricky task because it is influenced by numerous micro-variables. As a result, an macro model with only a few factors that may be predicted globally, rather than a detailed analysis for… More >

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    ARTICLE

    Region Centric GL Feature Approximation Based Secure Routing for Improved QoS in MANET

    S. Soundararajan1, R. Prabha2, M. Baskar3,*, T. J. Nagalakshmi4
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 267-280, 2023, DOI:10.32604/iasc.2023.032239
    Abstract Secure routing in Mobile Adhoc Network (Manet) is the key issue now a day in providing secure access to different network services. As mobile devices are used in accessing different services, performing secure routing becomes a challenging task. Towards this, different approaches exist which find the trusted route based on their previous transmission details and behavior of different nodes. Also, the methods focused on trust measurement based on tiny information obtained from local nodes or with global information which are incomplete. However, the adversary nodes are more capable and participate in each transmission not just… More >

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    ARTICLE

    A New Modified EWMA Control Chart for Monitoring Processes Involving Autocorrelated Data

    Korakoch Silpakob1, Yupaporn Areepong1,*, Saowanit Sukparungsee1, Rapin Sunthornwat2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 281-298, 2023, DOI:10.32604/iasc.2023.032487
    Abstract Control charts are one of the tools in statistical process control widely used for monitoring, measuring, controlling, improving the quality, and detecting problems in processes in various fields. The average run length (ARL) can be used to determine the efficacy of a control chart. In this study, we develop a new modified exponentially weighted moving average (EWMA) control chart and derive explicit formulas for both one and the two-sided ARLs for a p-order autoregressive (AR(p)) process with exponential white noise on the new modified EWMA control chart. The accuracy of the explicit formulas was compared… More >

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    ARTICLE

    Machine Learning-Based Models for Magnetic Resonance Imaging (MRI)-Based Brain Tumor Classification

    Abdullah A. Asiri1, Bilal Khan2, Fazal Muhammad3,*, Shams ur Rahman4, Hassan A. Alshamrani1, Khalaf A. Alshamrani1, Muhammad Irfan5, Fawaz F. Alqhtani1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 299-312, 2023, DOI:10.32604/iasc.2023.032426
    Abstract In the medical profession, recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans. Automatic (computerized) illness detection in medical imaging has found you the emergent region in several medical diagnostic applications. Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio. The brain tumor is one of the most common causes of death. Researchers have already proposed… More >

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    ARTICLE

    Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

    P. S. S. Gopi*, M. Karthikeyan
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 313-326, 2023, DOI:10.32604/iasc.2023.029756
    Abstract Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. More >

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    ARTICLE

    Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

    Nandhi Kesavan*, Latha
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 327-337, 2023, DOI:10.32604/iasc.2023.030910
    Abstract Climate change is the most serious causes and has a direct impact on biodiversity. According to the world’s biodiversity conservation organization, reptile species are most affected since their biological and ecological qualities are directly linked to climate. Due to a lack of time frame in existing works, conservation adoption affects the performance of existing works. The proposed research presents a knowledge-driven Decision Support System (DSS) including the assisted translocation to adapt to future climate change to conserving from its extinction. The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying More >

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    ARTICLE

    A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

    S. Saranya*, G. Usha
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 339-352, 2023, DOI:10.32604/iasc.2023.031987
    Abstract Laterally with the birth of the Internet, the fast growth of mobile strategies has democratised content production owing to the widespread usage of social media, resulting in a detonation of short informal writings. Twitter is microblogging short text and social networking services, with posted millions of quick messages. Twitter analysis addresses the topic of interpreting users’ tweets in terms of ideas, interests, and views in a range of settings and fields. This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or… More >

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    ARTICLE

    Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

    R. Anandha Murugan1,*, B. Sathyabama2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 353-368, 2023, DOI:10.32604/iasc.2023.032355
    Abstract Improved picture quality is critical to the effectiveness of object recognition and tracking. The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions, such as mist, fog, dust etc. The pictures then shift in intensity, colour, polarity and consistency. A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient environments. In recent years, target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of… More >

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    ARTICLE

    Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

    Abdalla Alameen*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 369-383, 2023, DOI:10.32604/iasc.2023.031039
    Abstract A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail. It is possible to create and study 3D models of anatomical structures to improve treatment outcomes, develop more effective medical devices, or arrive at a more accurate diagnosis. This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction. The classification process was conducted with the aid of a convolutional neural network (CNN) with dual graphs. Evaluation of the performance of the fused… More >

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    ARTICLE

    Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

    Ahmed M. Elmogy1,2,*, Wael M. Elawady2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.032349
    Abstract One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty. Many researchers have been working on developing such type of controllers. One of the most efficient techniques employed to develop such controllers is sliding mode control (SMC). However, the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications. In this paper, the drawbacks of low order traditional sliding mode control (FOTSMC) are resolved by presenting a… More >

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    ARTICLE

    IC Pattern Based Power Factor Maximization Model for Improved Power Stabilization

    N. Hariharan1,*, Y. Sukhi2, N. Kalaiarasi1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 401-414, 2023, DOI:10.32604/iasc.2023.030768
    Abstract The voltage fluctuation in electric circuits has been identified as key issue in different electric systems. As the usage of electricity growing in rapid way, there exist higher fluctuations in power flow. To maintain the flow or stability of power in any electric circuit, there are many circuit models are discussed in literature. However, they suffer to maintain the output voltage and not capable of maintaining power stability. To improve the performance in power stabilization, an efficient IC pattern based power factor maximization model (ICPFMM) in this article. The model is focused on improving the… More >

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    ARTICLE

    Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

    Subramanian Kanageswari1, D. Gladis2, Irshad Hussain3,*, Sultan S. Alshamrani4, Abdullah Alshehri5
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 415-428, 2023, DOI:10.32604/iasc.2023.032053
    Abstract One of the leading cancers for both genders worldwide is lung cancer. The occurrence of lung cancer has fully augmented since the early 19th century. In this manuscript, we have discussed various data mining techniques that have been employed for cancer diagnosis. Exposure to air pollution has been related to various adverse health effects. This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer. We have introduced data mining in lung cancer to air pollution, and our approach… More >

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    ARTICLE

    An Integrated Multilayered Framework for IoT Security Intrusion Decisions

    Hassen Sallay*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 429-444, 2023, DOI:10.32604/iasc.2023.030791
    Abstract Security breaches can seriously harm the Internet of Things (IoT) and Industrial IoT (IIoT) environments. The damage can exceed financial and material losses to threaten human lives. Overcoming these security risks is challenging given IoT ubiquity, complexity, and restricted resources. Security intrusion management is a cornerstone in fortifying the defensive security process. This paper presents an integrated multilayered framework facilitating the orchestration of the security intrusion management process and developing security decision support systems. The proposed framework incorporates four layers with four dedicated processing phases. This paper focuses mainly on the analytical layer. We present… More >

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    ARTICLE

    Multiobjective Economic/Environmental Dispatch Using Harris Hawks Optimization Algorithm

    T. Mahalekshmi1,*, P. Maruthupandi2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 445-460, 2023, DOI:10.32604/iasc.2023.028718
    Abstract The eminence of Economic Dispatch (ED) in power systems is significantly high as it involves in scheduling the available power from various power plants with less cost by compensating equality and inequality constrictions. The emission of toxic gases from power plants leads to environmental imbalance and so it is highly mandatory to rectify this issues for obtaining optimal performance in the power systems. In this present study, the Economic and Emission Dispatch (EED) problems are resolved as multi objective Economic Dispatch problems by using Harris Hawk’s Optimization (HHO), which is capable enough to resolve the… More >

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    ARTICLE

    Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

    S. Sadhasivam1, P. Valarmathie2, K. Dinakaran3,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 461-479, 2023, DOI:10.32604/iasc.2023.028650
    Abstract With the vast advancements in Information Technology, the emergence of Online Social Networking (OSN) has also hit its peak and captured the attention of the young generation people. The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user. However, the attackers also target this height of OSN utilization, explicitly creating the clones of the user’s account. Various clone detection mechanisms are designed based on social-network activities. For instance, monitoring the occurrence of clone edges is done to restrict the generation of clone activities.… More >

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    ARTICLE

    Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

    R. Jayaraj1,*, S. Lokesh2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 481-497, 2023, DOI:10.32604/iasc.2023.030495
    Abstract Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.… More >

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    ARTICLE

    Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

    N. M. Balamurugan1,*, K. Maithili2, T. K. S. Rathish Babu3, M. Adimoolam4
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 499-514, 2023, DOI:10.32604/iasc.2023.031661
    Abstract Diabetic Eye Disease (DED) is a fundamental cause of blindness in human beings in the medical world. Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy (DR). The Machine Learning (ML) and the Deep Learning (DL) algorithms are the predominant techniques to project and explore the images of DR. Even though some solutions were adapted to challenge the cause of DR disease, still there should be an efficient and accurate DR prediction to be adapted to refine its performance. In this work, a hybrid technique was proposed for classification… More >

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    ARTICLE

    A Boosted Tree-Based Predictive Model for Business Analytics

    Mohammad Al-Omari1, Fadi Qutaishat1, Majdi Rawashdeh1, Samah H. Alajmani2, Mehedi Masud3,*
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374
    Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested More >

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    ARTICLE

    An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

    Manjur Kolhar*, Mohammed Misfer
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 529-542, 2023, DOI:10.32604/iasc.2023.030098
    Abstract Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively. Further improvement, however, is required when both of these technologies are combined to reap maximum benefits. A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused. The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process. Treatment of… More >

  • Open AccessOpen Access

    ARTICLE

    A Construction of Object Detection Model for Acute Myeloid Leukemia

    K. Venkatesh1,*, S. Pasupathy1, S. P. Raja2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701
    Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the More >

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    ARTICLE

    Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

    Farid Nahli11, Alexander Paramonov1, Naglaa F. Soliman2, Hussah Nasser AlEisa3,*, Reem Alkanhel2, Ammar Muthanna1, Abdelhamied A. Ateya4
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 561-572, 2023, DOI:10.32604/iasc.2023.031299
    Abstract Next-generation networks, including the Internet of Things (IoT), fifth-generation cellular systems (5G), and sixth-generation cellular systems (6G), suffer from the dramatic increase of the number of deployed devices. This puts high constraints and challenges on the design of such networks. Structural changing of the network is one of such challenges that affect the network performance, including the required quality of service (QoS). The fractal dimension (FD) is considered one of the main indicators used to represent the structure of the communication network. To this end, this work analyzes the FD of the network and its… More >

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    ARTICLE

    Estimation of Higher Heating Value for MSW Using DSVM and BSOA

    Jithina Jose*, T. Sasipraba
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 573-588, 2023, DOI:10.32604/iasc.2023.030479
    Abstract In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen,… More >

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    ARTICLE

    Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

    A. S. Harish*, C. Malathy
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 589-600, 2023, DOI:10.32604/iasc.2023.032030
    Abstract Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The… More >

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    ARTICLE

    An Ontology-Based Question Answering System for University Admissions Advising

    Thi Thanh Sang Nguyen*, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 601-616, 2023, DOI:10.32604/iasc.2023.032080
    Abstract Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University–VNU HCMC is taken into account… More >

  • Open AccessOpen Access

    ARTICLE

    Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

    G. Pandiyan*, E. Sasikala
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 617-632, 2023, DOI:10.32604/iasc.2023.029337
    Abstract Mobile Edge Computing (MEC) assists clouds to handle enormous tasks from mobile devices in close proximity. The edge servers are not allocated efficiently according to the dynamic nature of the network. It leads to processing delay, and the tasks are dropped due to time limitations. The researchers find it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes. The challenge relies on the offloading decision on selection of edge nodes for offloading in a centralized manner. This study focuses on minimizing task-processing time while simultaneously increasing… More >

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    ARTICLE

    Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

    Hussain Alaaedi*, Masoud Sabaei
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 633-646, 2023, DOI:10.32604/iasc.2023.032462
    Abstract Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which… More >

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    ARTICLE

    Generating of Test Data by Harmony Search Against Genetic Algorithms

    Ahmed S. Ghiduk1,2,*, Abdullah Alharbi1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 647-665, 2023, DOI:10.32604/iasc.2023.031865
    Abstract Many search-based algorithms have been successfully applied in several software engineering activities. Genetic algorithms (GAs) are the most used in the scientific domains by scholars to solve software testing problems. They imitate the theory of natural selection and evolution. The harmony search algorithm (HSA) is one of the most recent search algorithms in the last years. It imitates the behavior of a musician to find the best harmony. Scholars have estimated the similarities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains. The test data generation process represents a… More >

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    ARTICLE

    Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

    R. Ganesan1,*, G. Sankaranarayanan1, M. Pradeep Kumar2, V. K. Bupesh Raja1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 667-681, 2023, DOI:10.32604/iasc.2023.031932
    Abstract This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to… More >

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    ARTICLE

    Improved Clamped Diode Based Z-Source Network for Three Phase Induction Motor

    D. Bensiker Raja Singh1,*, R. Suja Mani Malar2
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 683-702, 2023, DOI:10.32604/iasc.2023.028492
    Abstract The 3Φ induction motor is a broadly used electric machine in industrial applications, which plays a vital role in industries because of having plenty of beneficial impacts like low cost and easiness but the problems like decrease in motor speed due to load, high consumption of current and high ripple occurrence of ripples have reduced its preferences. The ultimate objective of this study is to control change in motor speed due to load variations. An improved Trans Z Source Inverter (ΓZSI) with a clamping diode is employed to maintain constant input voltage, reduce ripples and… More >

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    ARTICLE

    Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

    G. Geetha1,2,*, K. Mohana Prasad1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 703-718, 2023, DOI:10.32604/iasc.2023.032530
    Abstract Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing… More >

  • Open AccessOpen Access

    ARTICLE

    Proposed Privacy Preservation Technique for Color Medical Images

    Walid El-Shafai1,2, Hayam A. Abd El-Hameed3, Noha A. El-Hag4, Ashraf A. M. Khalaf3, Naglaa F. Soliman5, Hussah Nasser AlEisa6,*, Fathi E. Abd El-Samie1
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 719-732, 2023, DOI:10.32604/iasc.2023.031079
    Abstract Nowadays, the security of images or information is very important. This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security. First, the secret medical image is encrypted using Advanced Encryption Standard (AES) algorithm. Then, the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit (LSB) watermarking algorithm. After that, the encrypted secret medical image with the secret report is concealed in a cover medical image, using Kekre’s Median Codebook Generation (KMCG) algorithm. Afterwards, the stego-image obtained is split into 16 parts.… More >

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    ARTICLE

    Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

    Shakunthala Masi*, Helenprabha Kuttiappan
    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 733-744, 2023, DOI:10.32604/iasc.2023.025919
    Abstract In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is… More >

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