Forthcoming and Online First Articles

International Journal of Intelligent Information and Database Systems

International Journal of Intelligent Information and Database Systems (IJIIDS)

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International Journal of Intelligent Information and Database Systems (7 papers in press)

Regular Issues

  • An echo-based autonomous real-time system for efficient maintenance of students attendance (E.A.R)   Order a copy of this article
    by Shritesh Jamulkar, Milind Kumar Verma, K. Jairam Naik 
    Abstract: The conventional method of taking attendance by calling names or signing is a very tedious and insecure process with loopholes, like missing records, paper consumption, etc. To overcome such difficulties, an echo-based attendance system is proposed in this paper, which provides an effective and efficient way of recording and maintaining the attendance of students on a daily basis. The attendance is recorded for an echo-receiving device (microphone) from an echo-sending device (speaker) and all the records are saved in a real-time cloud database. Data is encoded in unique frequency at the transmitter’s end and sent over sound, which is then decoded at the receiver’s end and attendance is marked in the database. The process is one-shot, where each receiver’s attendance is marked simultaneously, unlike the existing autonomous attendance systems. The proposed system uses liquid software-defined radio digital signal processing (DSR) to transmit data from the transmitter’s end and to process received data at the receiver’s end. This abolishes capturing the specimen of students (like fingerprints, facial images), required for the dataset to train the model in case of other autonomous attendance systems and hence, saves time and effort by completely eliminating human intervention and ensures high reliability as well.
    Keywords: automatic attendance; time to record attendance; data over sound; DoS; mean attendance; cloud database; reliability; re-transmission time.
    DOI: 10.1504/IJIIDS.2022.10044370
  • Drunkenness detection using a CNN with adding Gaussian noise and blur in the thermal infrared images   Order a copy of this article
    by Kha Tu Huynh, Huynh Phuong Thanh Nguyen 
    Abstract: Drunkenness is now often regarded as one of society’s most serious issues. The majority of road accidents are caused by drunk driving. This paper proposes a methodology based on evaluating a facial thermal infrared image, adding noise and filters for augmentation, and determining intoxication using machine learning algorithms. In drunkenness detection, most research focus on using RGB image of facial expressions like eye sate, head position, or functional state indicators. Sometimes it is not trusty when attempting to predict on certain people who have certain facial feature patterns that the machine learning algorithm learned to be a factor of drunkenness. The combination of using the thermal infrared image with some noise and filter then predicting by optimised convolution neural network (CNN) model approach 93% on accuracy proves the efficiency as well as the feasibility of the proposed method.
    Keywords: drunkenness detection; convolutional neural network; CNN; thermal infrared image; Gaussian noise; blur; machine learning; augmentation.
    DOI: 10.1504/IJIIDS.2022.10047468
  • Elementary discourse unit segmentation for Vietnamese texts   Order a copy of this article
    by Chinh Trong Nguyen, Dang Tuan Nguyen 
    Abstract: Elementary discourse unit (EDU) segmentation is an important problem in discourse analysis of text. In Vietnam, we do not have any tool or model official published to solve this problem yet. Therefore, we would like to propose a solution for this problem. Our approach is to apply a sequential labelling method for identifying the beginning of each EDU in a sentence. For sequential labelling method, we use a deep neural network architecture containing a BERT for generating word feature vectors as transfer learning approach and a feed forward neural network for identifying the tag of every word. For building the model, we have automatically built an EDU segmentation dataset from a Vietnamese constituent treebank NIIVTB and used this dataset to fine-tune PhoBERT pretrained model. The results show that our EDU segmentation model has span-based F1 score of 0.8, which is sufficient to be used in practical tasks.
    Keywords: EDU segmentation; sequential labelling; BERT; transfer learning.
    DOI: 10.1504/IJIIDS.2022.10046229
  • A novel recursive privacy-preserving information retrieval approach for private retrieval   Order a copy of this article
    by Radhakrishna Bhat, K.M. Mahesh KumarKumar, N.R. Sunitha 
    Abstract: The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called 'pepperPBR' using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter.
    Keywords: database system; private information retrieval; PIR; quadratic residuosity assumption; QRA; probabilistic encryption.
    DOI: 10.1504/IJIIDS.2021.10041195
  • Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations   Order a copy of this article
    by Dharmendra Pathak, Ramgopal Kashyap 
    Abstract: There is a high surge in usage of online e-learning platforms due to the current ongoing COVID-19 scenario. There are specific problems that persist in the current e-learning online models, i.e., validations and tracking of students' learning curves, validation of presented course material, content-based personalisation as per the requirements of the students, identification of learning disabilities among students, etc. Our paper proposes the deep learning model to solve the issues related to existing machine learning models of manual feature extraction and training on limited data. Also, real-time e-learning data will be collected from students wearing EEG-headband while taking online classes. It solves the issues associated with conventional machine learning models and historical data. The proposed CNN model will classify the students on different grades and help in the development of an automated framework for the tracking of a student learning curve, providing recommendations for the betterment of e-learning course materials.
    Keywords: automated framework; convolution neural network; CNN; deep learning; EEG data; e-learning; electronic learning; feature extraction; machine learning.
    DOI: 10.1504/IJIIDS.2021.10041828
  • Imbalanced big data classification model using social spider-cat swarm optimisation weighted incremental learning ensemble classifier   Order a copy of this article
    by Vikas Gajananrao Bhowate, T. Hanumantha Reddy 
    Abstract: Traditional strategies of data classification based on machine learning fail when handling highly imbalanced big data. This research proposes an incremental learning-based ensemble classifier for data imbalance classification. Initially, the big data are pre-processed, and the synthetic samples are then generated using the synthetic minority oversampling technique (SMOTE)-based data balancing strategy. The balanced big data is handled using the MapReduce architecture, which is inbuilt with the proposed incremental learning-based ensemble classifier comprising of artificial neural network (ANN), K-nearest neighbour (K-NN), support vector machine (SVM), decision tree (DT) and the naïve Bayes (NB) classifier. The class output for the proposed method is generated through the fusion parameter, which is decided using the proposed social spider-cat swarm optimisation (SSPCS) algorithm. The proposed method attained an accuracy of 94.37%, a sensitivity of 97%, and a specificity of 97%, which shows the effectiveness of the proposed strategy in imbalanced data classification.
    Keywords: ensemble classifier; data imbalance classification; MapReduce framework; incremental learning; optimisation.
    DOI: 10.1504/IJIIDS.2022.10046230
  • The comparing two approaches for the detecting and locating abnormalities problem of on coronary artery image   Order a copy of this article
    by Le Nhị Lam Thuy, Tran Vi Van, Quang Ngoc Trieu, Le Huu Uyn, Nguyen Ngoc Tuan, Tang Thi Phuong Linh, Pham The Bao 
    Abstract: Coronary artery disease (CAD) is believed to be one of the most harmful fatal diseases in the world. An experienced doctor needs a lot of time to diagnose CAD in a patient. We proposed two methods to detect abnormal positions from coronary artery imaging to improve efficiency and performance in diagnostic abnormalities. In the first method, we introduce the vessel wall browsing algorithm to locate abnormalities on the blood vessels by comparing the distances from baseline to points under consideration. This algorithm reached 71.4%. We apply a convolutional neural network (CNN) model to predict whether a coronary image is normal or abnormal in the second method. The result from the experiment using our private dataset shows that our methods have an accuracy of 67.7%.
    Keywords: abnormal coronary detection; coronary artery stenosis detection; coronary artery aneurysm detection; convolutional neural network; CNN.
    DOI: 10.1504/IJIIDS.2022.10046231