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  • 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 - 29 September 2022

    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 >

  • 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 - 29 September 2022

    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 >

  • 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 - 29 September 2022

    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 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 - 29 September 2022

    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… 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 - 29 September 2022

    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. 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 - 29 September 2022

    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 Access

    ARTICLE

    Deep Transfer Learning Approach for Robust Hand Detection

    Stevica Cvetkovic1,*, Nemanja Savic1, Ivan Ciric2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 967-979, 2023, DOI:10.32604/iasc.2023.032526 - 29 September 2022

    Abstract Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter. To achieve highly accurate results as well as provide real-time execution, we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector. Our method, denoted as YOLOHANDS, is built on top of the You Only Look Once (YOLO) deep learning architecture, which is modified to adapt to the single class hand detection task. The model transfer is performed by modifying the higher convolutional layers including the last fully connected More >

  • Open Access

    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 - 29 September 2022

    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 >

  • Open Access

    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 - 29 September 2022

    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 >

  • Open Access

    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 - 29 September 2022

    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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