International Journal of Intelligent Information and Database Systems
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International Journal of Intelligent Information and Database Systems (7 papers in press)
Cat Deep System for Intrusion Detection in Software Defined Networking by Yogita Hande, Akkalashmi Muddana Abstract: The development of Software-Defined networks (SDN) has made the application of the network control more convenient and easy to develop and manage. It has provided a better capability of adapting the condition to the changing demands of applications and the network conditions. The maintenance of the network becomes very easier with the improved security due to the presence of the SDN. This paper proposes an intrusion detection system in SDN with the newly developed Cat Deep System (CDS). The training is done using the Deep Convolutional Neural Network (DCNN) with the integration of the Stochastic Gradient Descent (SGD) with the cat swarm optimization. The proposed system consists of three important components, such as sniffer, detector, and the sensor. All the packets are inspected with the sniffer in order to extract the features and the extracted features are used to detect the abnormality using DCNN and the features are then used to check the boundary to find the presence of attack in the system. The experimentation is done using the KDD Cup99 database. The accuracy, precision, recall, and F_1 measure of the Cat Deep system is 0.8954, 0.8928, 0.8668, and 0.7770 respectively that implies the effectiveness of the proposed system. rnrn Keywords: Software-defined Networks; Cat Deep System; cat swarm optimization; duration; and sniffer.
Real-time long short-term glance-based fire detection using CNN-LSTM neural network by Huan Van Nguyen, Xuan Thang Pham, Cuong Nguyen Le Abstract: Vision-based fire detection is widely studied recently to reduce the damage of fire disaster thanks to the advantages of software-based methods comparing to traditional hardware-based fire detection using sensors. This paper presents a novel method for fire detection using the convolutional neural networks on image sequences of videos to extract both the spatial and temporal information for fire classification. The system includes a CNN network to extract the image features, and short-term and long-term stages at the end for classification. Experiments carried out on the common public datasets show promising results in terms of performance in comparison to the previous works. Keywords: fire detection; long short-term memory; LSTM; temporal CNN. DOI: 10.1504/IJIIDS.2021.10036113
Performance evaluation of reformulated query for information retrieval using real estate ontology by Namrata Rastogi, Parul Verma, Pankaj Kumar Abstract: The data over the internet is growing at an unprecedented rate daily. Hence efficient information retrieval has always been at stake. The scenario becomes more tedious in an e-government sector like real estate where fetching of legal documentation used for buying and selling of land property is unknown to novice users. The uncommon legal terminology also dilutes the keyword-based retrieval system. The proposed system thus insists on creating and further utilising ontology in this semantic web era. The performance of information retrieval is measured by calculating various parameters like mean average precision, precision@k, and normalised discounted cumulative gain for general user query first and then comparing it with the reformulated query after applying the real estate ontology. The experimental results are further statistically checked to depict an improvement in all the parameters thereby indicating that an initial user query, after ontological reformulation improves the efficiency of the information retrieval process. Keywords: e-government; information retrieval; legal ontology; mean average precision; mAP; performance evaluation; precision; query reformulation; real estate; semantic web. DOI: 10.1504/IJIIDS.2021.10036483
A novel index retrieval and query optimisation method for private information retrieval in location-based service application by K.M.Mahesh Kumar, Radhakrishna Bhat, N.R. Sunitha Abstract: Location-based service is a popular information and communications technology. Security, trust and privacy are the major concerns preventing the wide deployment of LBS. In this paper, we address privacy issues by employing computational private information retrieval schemes and highlight a few optimisation methods. We propose a novel index retrieval technique which helps the user to identify their grid id and know the index value for the point-of-interest (POI) type of his interest and an adaptive computation method (flip-optimisation) to reduce multiplication cost for PIR query used to retrieve the POI item at the specified index. Adaptive computation method proposed in this paper is generic and can be applied to any application which uses PIR protocol to access data privately. Our work empirically evaluated the proposed method by implementing the PIR prototype and found it suitable for a practical purpose. Keywords: index retrieval; location-based service; LBS; location privacy; private information retrieval; PIR; quadratic residuosity assumption; QRA; query optimisation. DOI: 10.1504/IJIIDS.2021.10036534
The extended Kullback-Leibler divergence measure in the unknown probability density function cases and applications by Hoa Le, Hoang Van Truong, Pham The Bao Abstract: The Kullback-Leibler divergence measure is used to evaluate the similarity between two probability distributions. In theory, the probability density functions are known before applying the formula. However, estimating this information of real data is challenging. For that reason, the Kullback-Leibler divergence needs to be modified for similarity measures in these cases. In this paper, we proposed and evaluated an extended Kullback-Leibler divergence similarity measure by two experiments. The first experiment is based on two datasets that have unknown probability density functions), while the second one is conducted on one dataset with an unknown probability density function and the other with a known probability density function. Besides, the proposed method is applied to the simulated data and the plagiarism detection cases. Keywords: Kullback-Leibler divergence; relative entropy; mixture models; similarity measure; extended Kullback-Leibler divergence. DOI: 10.1504/IJIIDS.2021.10037753
Application of fuzzy logic on CT-scan images of COVID-19 patients by Fariha Noor, Md Rashad Tanjim, Muhammad Jawadur Rahim, Md Naimul Islam Suvon, Faria Karim Porna, Shabbir Ahmed, Md. Abdullah Al Kaioum, Rashedur M. Rahman Abstract: Image processing is crucial in any image analysis to determine the problem. If it is a medical area, a suitable image processing method becomes even more imperative to get as accurate results as possible. Due to the widespread outbreak of coronavirus disease 2019 (COVID-19), an infectious respiratory disease, it has become quite urgent that a reliable method for identification of the disease is sought. In this paper, we have segmented images with two different techniques, fuzzy c-means, and k-means clustering. Our images include CT-scan data and X-rays of both two categories. The first being the COVID-19 infected patients. The other being a collection of normal persons, viral pneumonia infected persons. Among the two clustering techniques, the k-means performed better. Later, we trained our CNN model with the segmented images and raw images. Interestingly, the segmented images of CT-scan, as well as X-rays, are performing well in CNN classification rather than raw images. After applying fuzzy edge detection, the segmentation was improved. The f1-score for our model is 91% and the support is 89%.
Keywords: medical image processing; fuzzy c-means clustering; image segmentation; convolutional neural network. DOI: 10.1504/IJIIDS.2021.10039512
DeepO: An ontology-based deep learning system for disease prediction by Thi Phuong Trang Nguyen, Ngan Luu-Thuy Nguyen, Trong Hai Duong Abstract: Recently, there is a lot of research about building supporting diagnosis and treatment in medical care using deep learning. In this paper, we propose a method for building a deep learning model base ontology structure for disease prediction (DeepO). The Ontology structure will be automatically built based on the improvements of formal concepts analysis and pattern structure. We use deep restricted Boltzmann machine (DRBM) for disease prediction task in DeepO. By using OCA in DeepO, our model learn more knowledge than RBM model. In experiments, we build a digestive disease diagnosis model with Vietnamese patient dataset. Keywords: disease prediction; deep learning; restricted Boltzmann machine; Formal concept analysis; Ontology. DOI: 10.1504/IJIIDS.2021.10039599