International Journal of Intelligent Information and Database Systems (12 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.
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.
Toward a framework for graph-based keyword search over relational data
by Vittoria Cozza
Abstract: Keyword-based access to structured data has attracted research and industry as a means for facilitating access to information. In recent years, the research community and big data technology vendors put a lot of efforts into developing new proof of concept systems for the task at hand. Two major limitations have been identified for such prototypes to transition into fully developed products: 1) systems are not designed to scale up; 2) the absence of a complete evaluation approach oriented towards effectiveness. This work presents a framework for supporting the development and the evaluation of graph-based keyword search systems. Furthermore, the implementation of a core module of this framework is detailed and shared open-source with the community.
Keywords: relational data; database search; structured data; graph search; keyword search; database applications; knowledge management applications.
A novel recursive privacy-preserving information retrieval approach for private retrieval
by Radhakrishna Bhat, K.M. Mahesh Kumar, 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.
Visual relationship extraction in images and a semantic interpretation with ontologies
by Adel Zga, Brahim Nini
Abstract: Nowadays, three challenges should be considered in order to build a strong model that used to extract and semantically interpret the relationship between objects in images. Namely: long-tail problem, large intra-class divergence, and the semantic dependency or semantic gap. In order to overcome those challenges, we propose three main contributions: 1) an ontological semantic model to filter false negatives/positives using a statistical ranking module; 2) we propose a combination of semantic ontological module and visual relationship module that both takes as input the results of the statistical ranking module and produces as output classification of < human - predicate - object >; 3) we propose a semantic model for
the visual relationship module that ranks each prediction of relation classes by
transferring the spatial relationship onto a high dimension spatial feature. We
use HCVRD that highlights two important practical problems, the long-tail distribution issue, and the zero-shot problem. The experimental results on the HCVRD dataset demonstrate the superior performance of the proposed approach.
Keywords: deep learning; semantic gap; ontologies; human-object interaction; large intra-class divergence.
Taylor-feedback deer hunting optimisation algorithm for intrusion detection in cloud using deep maxout network
by Sobin Soniya S, Maria Celestin Vigila S
Abstract: Today, cloud computing is a fast emergent computational model and has become popular among users in IT world. It is the distributed computing paradigm that is continually exposed to various threats and attacks of diverse origins. On the other hand, such difficult and distributed model becomes an attractive target for intruders. Identifying the intrusions poses a great challenge for the users and providers of cloud services. Intrusion detection is one of the techniques to protect the cloud operations from severe attacks. Hence, an effective approach is designed using the proposed Taylor-feedback deer hunting optimisation-based deep maxout network (Taylor-FDHO-based deep maxout network) to detect the malicious behaviours in cloud infrastructure. However, the proposed method, named Taylor-FDHO is derived by the integration of Taylor series with feedback artificial tree (FAT) and deer hunting optimisation algorithm (DHOA), respectively. Based on the binary classification step, the process of intrusion detection is accomplished using deep maxout network. However, the proposed approach achieved the maximal accuracy, higher sensitivity, and maximum specificity of 0.9567, 0.9598, and 0.9589based on the training data.
Keywords: cloud computing; intrusion detection; deep maxout network; least square SVM; Taylor series.
Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations
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.
Special Issue on: WIMS 2020 Web Intelligence
MEI2JSON: a pre-processing music scores converter
by Charbel El Achkar, Talar Atechian
Abstract: Converting music score content from symbolic formats to simplified data formats is found useful for artificial intelligence purposes. The conversion can be applied using XSL stylesheets and ontologies to ensure the preserving of the data quality throughout the transformation. In this paper, we proposed a new converter capable of transforming music scores encoded in MEI to JSON format for pre-processing purposes, and future usage into artificial intelligence techniques. The proposed converter uses an eastern music score ontology capable of structuring standard music scores content in addition to elements and attributes specific to eastern music. Thus, the converter shares the same support for eastern music scores. We illustrate the conversion process by assessing the performance analysis, the data quality, and the storage of the proposed converter in comparison with a combined approach composed of two state-of-the-art converters.
Keywords: MEI; MEI2JSON; music scores converter; MusicPatternOWL; eastern music.
Supporting user-centred ontology visualisation: predictive analytics using eye gaze to enhance human-ontology interaction
by Bo Fu, Ben Steichen
Abstract: Visualisation is an important aspect to support human-ontology interaction, as visual cues amplify cognition and offload cognitive efforts to the human perceptual system. While significant research efforts have focused on visualisation layouts, adapting to the individual user has been largely overlooked in typical ontology visualisation systems. This provides an opportunity to potentially seek more personalised support in ontology visualisation. As such, this paper utilises a tumbling window analytical technique and demonstrates accurate predictions of a user's likelihood to succeed in a given task based on this persons latest gaze data during an interactive session. We show several trial scenarios where statistically significant accuracies are achieved for two commonly used ontology visualisations in the presence of mixed user backgrounds and task domains. In addition, depending on the gaze features that emphasise a users search or processing activities, or cognitive workload, trial results show earlier predictions as well as higher accuracies can be achieved in some cases. Furthermore, an investigation of influential gaze features reveals a combination of gaze traits is often associated with higher user success. These findings motivate and highlight potentially ample opportunities to adapt to the individual user throughout various interactive stages in the realisation of adaptive ontology visualisation.
Keywords: semantic web; semantic data interaction; predictive analytics; adaptive ontology visualisation; eye tracking; applied machine learning.
Spot extraction and analysis using an automatic detection method of tourist spots using SNS
by Munenori Takahashi, Masaki Endo, Shigeyoshi Ohno, Masaharu Hirota, Hiroshi Ishikawa
Abstract: Tourism information collection using the social network services (SNSs) has become popular in recent years. Geotagged tweets are useful as a social sensor for estimating and acquiring local tourist information in real time because the information can reflect real-world situations. Earlier studies of methods of estimating cherry blossom viewing times have typically relied on the assumption that one knows a tourist destination: it is impossible to estimate cherry blossom tourist spots that a system user does not know. In its early stages, it can use tweets to find spots already featured in magazines and on the internet. As described herein, spots were detected automatically using a geotagged tweet by visualisation with a heat map and by setting conditions. The proposed method achieved it in about 80% of cases. We also used geotagged Tweets to assess observations of cherry blossom 'front lines' of viewing.
Keywords: social network service; SNS; mining; sightseeing; spot detection.
Towards combined semantic and lexical scores based on a new representation of textual data to extract experimental data from scientific publications
by Martin Lentschat, Patrice Buche, Juliette Dibie-Barthelemy, Mathieu Roche
Abstract: This article presents an ontological and terminological resource guided process for targeted extraction of scientific experimental data. Our method relies on the scientific publication representation (SciPuRe) describing the extracted data through ontological, lexical and structural (using segments in the scientific documents) features. Relevance scores based on these features are computed to rank the results and filter out the numerous false positives. Linear and sequential combinations of these scores are presented and evaluated. Experiments were carried out on a corpus of 50 English language scientific papers in the food packaging field. They revealed that article segment are an effective criterion for filtering out a majority of the quantitative entity false positives using lexical scores. Moreover the best symbolic entity extraction results were obtained with a sequential combinations of semantic and lexical scores. These results enable the ranking of entities by relevance and the filtering of false positive results.
Keywords: data extraction; data relevance; data representation; ontological and terminological resource; OTR; information retrieval; web scientific documents.
Estimating deflation representing people spreading in stream data and estimating a specific position
by Takuma Toyoshima, Masaki Endo, Takuo Kikuchi, Shigeyoshi Ohno, Hiroshi Ishikawa
Abstract: With the expanded use of social media such as Twitter in recent years, it has become easy to add various information such as location data using mobile devices. Using those data, one can observe the real world without using physical sensors. Therefore, social media have high operational value as social sensors. As described herein, we aim to support decision-making for people who intend to visit a specific place at which an event or some trouble recently occurred. After proposing a method of real-time extraction of data reflecting a burst state showing peoples concentration, their inactivity, and continuous flow and dispersion, we confirm the methods effectiveness. We will also try to estimate the location information of tweets for the purpose of further improving the estimation accuracy. Since few tweets have accurate location information, we use the content text of the tweet to find the tweet posted at the event occurrence location by machine learning. We will study changes in the accuracy of the proposed method due to the increase in the data to be analysed.
Keywords: deflation; burst; Twitter; real-time extraction; social sensor.