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Sakthiseswari Ganasan¹, You Huay Woon^2,*, 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 economic model was used to… More >

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Taher M. Ghazal^1,2, Sundus Munir^3,4, Sagheer Abbas³, Atifa Athar⁵, Hamza Alrababah¹, Muhammad Adnan Khan^6,*
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 on facial features. A dataset… More >

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Ping-Yueh Chang¹, Fu-I Chou¹, Po-Yuan Yang^2,*, 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 considerations, so these two objects… More >

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S. Kalavathi^1,*, 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. A novel method named Reliable… More >

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595

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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 battery cells will cause accelerated… More >

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635

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Byeongmin Choi¹, YongHyun Lee¹, Yeunwoong Kyung², Eunchan Kim^3,*
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, schema graph expansion to recent… More >

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Peng Wang¹, Yulu Tian², Bolong Men^1,*, 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 updating the proposal variance using… More >

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T. Thanya^1,*, 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 to diagnose cancer cells. Comparative… More >

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544

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C. Nandagopal^1,*, 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 the routing process which… More >

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Abdullah A. Asiri¹, Ahmad Shaf^2,*, 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 in which two neural networks… More >

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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 user device, application, and authentication… More >

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595

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R. Vidhya^1,*, 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 or internal fault. The transformer’s… More >

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Muhammad Irfan¹, Khalil Ullah², Fazal Muhammad^3,*, 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 from sEMG channels along with… More >

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S. Samsudeen Shaffi^1,*, 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 ranks. This paper addresses the… More >

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543

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Youngjun Kim¹, Taekook Kim², Hyungoo Choi¹, Jinwoo Park¹, Yeunwoong Kyung^3,*
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 are few mobile stations (MSs)… More >

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Navdeep Bohra^1,2, Vishal Bhatnagar³, Amit Choudhary⁴, Savita Ahlawat², Dinesh Sheoran², Ashish Kumari^2,*
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 how popular the post will… More >

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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 an attempt to concentrate on… More >

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Hyun-Sam Shin¹, Jun-Ki Hong^2,*
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 by applying four RNN techniques—long… More >

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579

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Elumalaivasan Poongavanam^1,*, 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 each of these variables, is… More >

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S. Soundararajan¹, R. Prabha², M. Baskar^3,*, 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 to steal the data also… More >

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Korakoch Silpakob¹, Yupaporn Areepong^1,*, 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 to that of the well-known… More >

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Abdullah A. Asiri¹, Bilal Khan², Fazal Muhammad^3,*, 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 various models for the classification… More >

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864

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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. The proposed MMML-CRYP model mainly… More >

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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 an ecological and biological variable… More >

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453

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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 event. Although sentiment examination of… More >

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R. Anandha Murugan^1,*, 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 computer performance capabilities. However, the… More >

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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 model is carried out with… More >

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Ahmed M. Elmogy^1,2,*, 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 novel adaptive radial basis function… More >

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N. Hariharan^1,*, 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 power stability with the use… More >

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Subramanian Kanageswari¹, D. Gladis², Irshad Hussain^3,*, 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 includes preprocessing, data mining, testing… More >

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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 the architecture and models for… More >

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T. Mahalekshmi^1,*, 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 concerned issue in a wider… More >

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589

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S. Sadhasivam¹, P. Valarmathie², K. Dinakaran^3,*
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. However, this assumption is unsuitable… More >

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517

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R. Jayaraj^1,*, 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. Hence, in this paper, Adaptive… More >

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N. M. Balamurugan^1,*, 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 and prediction of DR. The… More >

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Mohammad Al-Omari¹, Fadi Qutaishat¹, Majdi Rawashdeh¹, Samah H. Alajmani², Mehedi Masud^3,*
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 on a real-world dataset of… More >

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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 heart failure may be improved… More >

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K. Venkatesh^1,*, 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 Faster R-CNN performance was significant… More >

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Farid Nahli1¹, Alexander Paramonov¹, Naglaa F. Soliman², Hussah Nasser AlEisa^3,*, 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 use for telecommunication networks investigation… More >

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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, water, hydrogen, carbon, nitrogen, sulphur… More >

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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 segmentation could be on various… More >

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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 in the proposed chatbot. A… More >

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762

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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 the success rate of service… More >

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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 is utilized in a hyperdense… More >

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546

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Ahmed S. Ghiduk^1,2,*, 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 critical task in software validation.… More >

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R. Ganesan^1,*, 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 a predetermined portion of the… More >

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D. Bensiker Raja Singh^1,*, 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 voltage overshoot. To operate induction… More >

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451

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G. Geetha^1,2,*, 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 gradient problems, to overcome these… More >

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518

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Walid El-Shafai^1,2, Hayam A. Abd El-Hameed³, Noha A. El-Hag⁴, Ashraf A. M. Khalaf³, Naglaa F. Soliman⁵, Hussah Nasser AlEisa^6,*, 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. Finally, it is sent to… More >

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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 a cerebral vascular disease causes… More >

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