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  • Open Access

    ARTICLE

    An E-Assessment Methodology Based on Artificial Intelligence Techniques to Determine Students’ Language Quality and Programming Assignments’ Plagiarism

    Farhan Ullah1,4,*, Abdullah Bajahzar2, Hamza Aldabbas3, Muhammad Farhan4, Hamad Naeem1, S. Sabahat H. Bukhari4,5, Kaleem Razzaq Malik6

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 169-180, 2020, DOI:10.31209/2019.100000138

    Abstract This research aims to an electronic assessment (e-assessment) of students’ replies in response to the standard answer of teacher’s question to automate the assessment by WordNet semantic similarity. For this purpose, a new methodology for Semantic Similarity through WordNet Semantic Similarity Techniques (SS-WSST) has been proposed to calculate semantic similarity among teacher’ query and student’s reply. In the pilot study-1 42 words’ pairs extracted from 8 students’ replies, which marked by semantic similarity measures and compared with manually assigned teacher’s marks. The teacher is provided with 4 bins of the mark while our designed methodology provided an exact measure of… More >

  • Open Access

    ARTICLE

    Noise Cancellation Based on Voice Activity Detection Using Spectral Variation for Speech Recognition in Smart Home Devices

    Jeong-Sik Park1, Seok-Hoon Kim2,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 149-159, 2020, DOI:10.31209/2019.100000136

    Abstract Variety types of smart home devices have a main function of a human-machine interaction by speech recognition. Speech recognition system may be vulnerable to rapidly changing noises in home environments. This study proposes an efficient noise cancellation approach to eliminate the noises directly on the devices in real time. Firstly, we propose an advanced voice activity detection (VAD) technique to efficiently detect speech and non-speech regions on the basis of spectral property of speech signals. The VAD is then employed to enhance the conventional spectral subtraction method by steadily estimating noise signals in non-speech regions. On several experiments, our approach… More >

  • Open Access

    ARTICLE

    Word Embedding Based Knowledge Representation with Extracting Relationship Between Scientific Terminologies

    Mucheol Kim*, Junho Kim, Mincheol Shin

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 141-147, 2020, DOI:10.31209/2019.100000135

    Abstract With the trends of big data era, many people want to acquire the reliable and refined information from web environments. However, it is difficult to find appropriate information because the volume and complexity of web information is increasing rapidly. So many researchers are focused on text mining and personalized recommendation for extracting users’ interests. The proposed approach extracted semantic relationship between scientific terminologies with word embedding approach. We aggregated science data in BT for supporting users’ wellness. In our experiments, query expansion is performed with relationship between scientific terminologies with user’s intention. More >

  • Open Access

    ARTICLE

    Distinction Between Real Faces and Photos by Analysis of Face Data

    Byong Kwon Lee1, Yang Sun Lee2,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 133-139, 2020, DOI:10.31209/2019.100000134

    Abstract Biometric user authentication using the face has been applied mainly to access control systems. However, access is allowed even when a photo is presented instead of an actual face. This can facilitate illegal access including attending as a substitute or substitute authentication. An alternative approach has been implemented to solve this problem. The approach determines between a real face and a photo of a face using a UV sensor but this requires substantial cost and installation process because additional hardware (the UV sensor) is necessary. This paper proposes a three-step approach to identify between a real image and a photo.… More >

  • Open Access

    ARTICLE

    A Method for Planning the Routes of Harvesting Equipment using Unmanned Aerial Vehicles

    Vitaliy Mezhuyev1,*, Yurii Gunchenko2, Sergey Shvorov3, Dmitry Chyrchenko3

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 121-132, 2020, DOI:10.31209/2019.100000133

    Abstract The widespread distribution of precision farming systems necessitates improvements in the methods for the control of unmanned harvesting equipment (UHE). While unmanned aerial vehicles (UAVs) provide an effective solution to this problem, there are many challenges in the implementation of technology. This paper considers the problem of identifying optimal routes of UHE movement as a multicriteria evaluation problem, which can be solved by a nonlinear scheme of compromises. The proposed method uses machine learning algorithms and statistical processing of the spectral characteristics obtained from UAV digital images. Developed method minimizes the resources needed for a harvesting campaign and reduces the… More >

  • Open Access

    ARTICLE

    A Novel Knowledge-Based Battery Drain Reducer for Smart Meters

    Isma Farah Siddiqui1, Scott Uk-Jin Lee2,*, Asad Abbas3

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 107-119, 2020, DOI:10.31209/2019.100000132

    Abstract The issue of battery drainage in the gigantic smart meters network such as semantic-aware IoT-enabled smart meter has become a serious concern in the smart grid framework. The grid core migrates existing tabular datasets i.e., Relational data to semantic-aware tuples in its Resource Description Framework (RDF) format, for effective integration among multiple components to work aligned with IoT. For this purpose, WWW Consortium (W3C) recommends two specifications as mapping languages. However, both specifications use entire RDB schema to generate data transformation mapping patterns and results large quantity of unnecessary transformation. As a result, smart meters use huge computing resources, maximum… More >

  • Open Access

    ARTICLE

    Implementation of Local Area VR Environment using Mobile HMD and Multiple Kinects

    Soo-Kyun Kim1, Chang-Hee Lee2, Sun-Jeong Kim2, Chang-Geun Song2, Jung Lee2,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 99-105, 2020, DOI:10.31209/2019.100000131

    Abstract Recently, the development of HMDs such as Oculus Rift, HTC Vive, and PSVR has led to an increase in the interest of people in virtual reality (VR), and many related studies have been published. This leads to an additional cost increase in configuring the VR system. Also, space problems are caused. When the treadmill is installed, additional space is required, which may adversely affect the popularization of VR. In this paper, we propose a local area VR environment that solves cost and space problems using human tracking using several Kinect and solves the hygiene problems using smartphone-based mobile HMD. More >

  • Open Access

    ARTICLE

    Finding Temporal Influential Users in Social Media Using Association Rule Learning

    Babar Shazad1, Hikmat Ullah khan2, Zahoor-ur-Rehman1, Muhammad Farooq2, Ahsan Mahmood1, Irfan Mehmood3,*, Seungmin Rho3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 87-98, 2020, DOI:10.31209/2019.100000130

    Abstract The social media has become an integral part of our daily life. The social web users interact and thus influence each other influence in many aspects. Blogging is one of the most important features of the social web. The bloggers share their views, opinions and ideas in the form of blog posts. The influential bloggers are the leading bloggers who influence the other bloggers in their online communities. The relevant literature presents several studies related to identification of top influential bloggers in last decade. The research domain of finding the top influential bloggers mainly focuses on feature centric models. This… More >

  • Open Access

    EDITORIAL

    Advanced ICT and IoT Technologies for the Fourth Industrial Revolution

    Soo Kyun Kim*, Mario Köppen, Ali Kashif Bashir, Yuho Jin

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 83-85, 2020, DOI:10.31209/2019.100000129

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Model Predictive Control for Nonlinear Energy Management of a Power Split Hybrid Electric Vehicle

    Dehua Shi1,4, Shaohua Wang1,2,*, Yingfeng Cai1, Long Chen1, ChaoChun Yuan1, ChunFang Yin3

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 27-39, 2020, DOI:10.31209/2018.100000062

    Abstract Model predictive control (MPC), owing to the capability of dealing with nonlinear and constrained problems, is quite promising for optimization. Different MPC strategies are investigated to optimize HEV nonlinear energy management for better fuel economy. Based on Bellman’s principle, dynamic programming is firstly used in the limited horizon to obtain optimal solutions. By considering MPC as a nonlinear programming problem, sequential quadratic programming (SQP) is used to obtain the descent directions of control variables and the current control input is further derived. To reduce computation and meet the requirements of real-time control, the nonlinear model of the system is approximated… More >

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