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  • Open Access

    ARTICLE

    Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

    T. Edwin Ponraj1,*, J. Charles2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1189-1203, 2023, DOI:10.32604/iasc.2023.033383

    Abstract The mutation is a critical element in determining the proteins’ stability, becoming a core element in portraying the effects of a drug in the pharmaceutical industry. Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential mutations, computational approaches that can reliably anticipate the consequences of amino acid mutations are critical. This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure. Initially, the context in a collection of words is determined using a knowledge graph for feature selection purposes.… More >

  • Open Access

    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 by applying four RNN techniques—long… More >

  • Open Access

    ARTICLE

    E-MOGWO Algorithm for Computation Offloading in Fog Computing

    Jyoti Yadav*, Suman

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1063-1078, 2023, DOI:10.32604/iasc.2023.032883

    Abstract Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Model for Real Time Hand Gestures Recognition

    S. Gnanapriya1,*, K. Rahimunnisa2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1105-1119, 2023, DOI:10.32604/iasc.2023.032832

    Abstract The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks. The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc. The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition. The first stage is segmenting the regions of the hand and the second stage is gesture identification. The modified UNET segmentation model is trained using… More >

  • Open Access

    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 are few mobile stations (MSs)… More >

  • Open Access

    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, schema graph expansion to recent… More >

  • Open Access

    ARTICLE

    Adaptive Nonlinear Sliding Mode Control for DC Power Distribution in Commercial Buildings

    R. Muthamil Arasi1,*, S. Padma2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 997-1012, 2023, DOI:10.32604/iasc.2023.032645

    Abstract The developing populace and industrialization power demand prompted the requirement for power generation from elective sources. The desire for this pursuit is solid due to the ever-present common assets of petroleum derivatives and their predominant ecological issues. It is generally acknowledged that sustainable power sources are one of the best answers for the energy emergency. Among these, Photovoltaic (PV) sources have many benefits to bestow a very promising future. If integrated into the existing power distribution infrastructure, the solar source will be more successful, requiring efficient Direct Current (DC)-Alternating Current (AC) conversion. This paper mainly aims to improve controllers’ performance… More >

  • Open Access

    ARTICLE

    Person-Dependent Handwriting Verification for Special Education Using Deep Learning

    Umut Zeki1,*, Tolgay Karanfiller2, Kamil Yurtkan1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1121-1135, 2023, DOI:10.32604/iasc.2023.032554

    Abstract Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent. However, in crowded classrooms, it is difficult for a teacher to deal with each student individually. This problem can be overcome by using supportive education applications. However, the majority of such applications are not designed for special education and therefore they are not efficient as expected. Special education students differ from their peers in terms of their development, characteristics, and educational qualifications. The handwriting skills of individuals with special needs are lower than their peers. This makes the task of Handwriting Recognition (HWR)… More >

  • Open Access

    ARTICLE

    Multisensor Information Fusion for Condition Based Environment Monitoring

    A. Reyana1,*, P. Vijayalakshmi2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1013-1025, 2023, DOI:10.32604/iasc.2023.032538

    Abstract Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. Thus to predict the occurrence… More >

  • Open Access

    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 gradient problems, to overcome these… More >

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