International Journal of Advanced Computer Science and Applications, 2017
Community detection in network is of vital importance to find cohesive subgroups. Node attributes... more Community detection in network is of vital importance to find cohesive subgroups. Node attributes can improve the accuracy of community detection when combined with link information in a graph. Community detection using node attributes has not been investigated in detail. To explore the aforementioned idea, we have adopted an approach by modifying the Louvain algorithm. We have proposed Louvain-AND-Attribute (LAA) and Louvain-OR-Attribute (LOA) methods to analyze the effect of using node attributes with modularity. We compared this approach with existing community detection approaches using different datasets. We found the performance of both algorithms better than Newman's Eigenvector method in achieving modularity and relatively good results of gain in modularity in LAA than LOA. We used density, internal and external edge density for the evaluation of quality of detected communities. LOA provided highly dense partitions in the network as compared to Louvain and Eigenvector algorithms and close values to Clauset. Moreover, LOA achieved few numbers of edges between communities.
Nowadays, cloud-based storage systems play a vital role in IoT data storage, processing, and shar... more Nowadays, cloud-based storage systems play a vital role in IoT data storage, processing, and sharing. Despite its contribution, the current cloud-based architecture may cause severe data leakage or jeopardize user privacy. Meanwhile, the cloud-based architecture heavily relies on a trusted third-party auditor (TPA) and runs in a centralized control manner. However, the TPA may not be a completely trustworthy entity, and a single point of failure might cause the centralized system to collapse. Fortunately, with the advent of blockchain technology, the decentralized storage model has gained popularity. A decentralized storage system successfully eradicates the rule of TPA, solves the problem of a single point of failure, and has many advantages over a centralized control architecture, such as low storage prices and high throughput. This study offers a blockchain-based decentralized distributed storage and sharing scheme that provides end-to-end encryption and fine-grained access control. In our proposed IoTChain model, finegrained permission is based on attribute-based access control (A-BAC) policy by employing the Ethereum blockchain as an auditable access control layer. Smart contracts are tailored for the IoTChain model, which combines the Ethereum blockchain and the interplanetary file system (IPFS). We used an advanced encryption standard (AES) for encryption and the elliptic curve Diffie-Hellman key exchange protocol for secret key sharing between data owners and users. Also, the proof-of-work (PoW) consensus mechanism is replaced with a proof-of-authority (PoA) to minimize system transaction cost and boost system throughput. Additionally, our solution has been tested on the Ethereum official test network Rinkeby, and the results demonstrate that our approach is realistic and economical on the IoT data. INDEX TERMS Access control, data encryption, data storage via blockchain, Ethereum blockchain, Internet of Things (IoT), IPFS, smart contract.
Today's rapidly developing communication technologies and dynamic collaborative business models m... more Today's rapidly developing communication technologies and dynamic collaborative business models made the security of data and resources more crucial than ever especially in multi-domain environments like Cloud and Cyber-Physical Systems (CPS). It enforced the research community to develop enhanced access control techniques and models for resources across multi-domain distributed environments so that the security requirements of all participating organizations can be fulfilled through considering dynamicity of changing environments and versatility of access control policies. The popularity of Role-Based Access Control (RBAC) model is irrefutable because of low administrative overhead and largescale implementation in business organizations. However, it does not incorporate the dynamically changing policies and lacks semantically meaningful business roles which could have a diverse impact upon access decisions in multi-domain business environments. This paper describes our proposed novel access control framework that uses semantic business roles and intelligent agents through implementation of our Intelligent RBAC (I-RBAC) model. It encompasses occupational entitlements as roles for multiple domains. We use the dataset of original occupational roles provided by Standard Occupational Classification (SOC), USA. The novelty of the paper lies in developing a core I-RBAC ontology using real-world semantic business roles and intelligent agent technologies together for achieving required level of access control in highly dynamic multi-domain environment. The intelligent agents use WordNet and bidirectional LSTM deep neural network for automated population of organizational ontology from unstructured text policies. This dynamically learned organizational ontology is further matched with our core I-RBAC ontology in order to extract unified semantic business roles. The proposed I-RBAC model is mathematically described and the overall I-RBAC framework and its implementation architecture is explained. At the end, the I-RBAC model is validated through the implementation results that show a linear runtime trend of the model in presence of a large number of permission assignments and multiple queries.
Bloggers play a role in individual decision making of users in online social networking platforms... more Bloggers play a role in individual decision making of users in online social networking platforms. Their capability of addressing a wide audience gives them influence over their audience, which companies seek to exploit. Identification of influential bloggers can be seen as a machine learning (ML) task and different ML techniques can help in classifying the professional blogger. In this paper, we propose a predictive and adaptive model named as Influential Blogger based Case-Based Reasoning (IB-CBR) model for the recognition of unseen influential bloggers. It incorporates self-prediction and self-adaptation (self-management) capabilities which are the essence of an automated system. The integration of Random Forest is found contributing to the efficiency of the IB-CBR model as compared to Nearest-Neighbor, and Artificial Neural Network. The performance of the proposed IB-CBR model is evaluated against other ML techniques by using standard performance measures on a standard blogger's dataset. It is observed that our proposed model exhibits 88-95% Accuracy and 94-97% True Positive Rate in the prediction and adaptation of professional bloggers, respectively, in three iterations of the proposed model. What's more, the IB-CBR model achieved 91-96% (increasing) F-measure, 91-98% (increasing) ROC AUC, and 36-11% (decreasing) False Positive Rate due to adaptivity. The IB-CBR model performed well when it is compared with other ML techniques using different standard datasets. INDEX TERMS Blogging, blogger classification, case based reasoning (CBR), machine learning.
Breast cancer is the major cause of death among women. The best and most efficient approach for c... more Breast cancer is the major cause of death among women. The best and most efficient approach for controlling cancer progression is early detection and diagnosis. As opposed to biopsy, mammography helps in early detection of cancer and hence saves lives. Mass classification in mammograms remains a major challenge and plays a vital role in helping radiologists in accurate diagnosis. In this work, we propose a MobileNet based architecture for early breast cancer detection and further classify mass into malignant and benign. It requires less memory space and provides faster computations with 86.8% and 74.5% accurate results for DDSM and CBIS-DDSM, respectively. We have achieved better results than other deep CNN models such as AlexNet, VGG16, GoogleNet, and ResNet.
Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its appli... more Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their de...
The volume of research articles in digital repositories is increasing. This spectacular growth of... more The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.
The use of online social media is also connected with the real world. A very common example of th... more The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidate's own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as wel...
International Journal of Advanced Computer Science and Applications, 2017
Companies are investing more in analytics to obtain a competitive edge in the market and decision... more Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In this paper, a decision support system has been proposed, which can predict the churning behaviour of a customer efficiently. We have proposed a procedure to develop an analytical system using data mining as well as machine learning techniques C5, CHAID, QUEST, and ANN for the churn analysis and prediction for the telecommunication industry. Prediction performance can be significantly improved by using a large volume and several features from both Business Support Systems (BSS) and Operations Support Systems (OSS). Extensive experiments are performed; marginal increases in predictive performance can be seen by using a larger volume and multiple attributes from both Telco BSS and OSS data. From the results, it is observed that using a combination of techniques can help to figure out a better and precise churn prediction model.
International Journal of the Physical Sciences, 2012
Rapid growth in data, maximum functionality requirements and changing behavior in the database wo... more Rapid growth in data, maximum functionality requirements and changing behavior in the database workload tends the workload management to be more complex. Organizations have complex type of workloads that are very difficult to manage by humans and even in some cases this management becomes impossible. Human experts take long time to get sufficient experience so that they can manage the workload efficiently. The versatility in workload due to huge data size and user requirements leads us towards the new challenges. One of the challenges is the identification of the problematic queries and the decision about these, i.e. whether to continue their execution or stop. The other challenge is how to characterize the workload, as the tasks such as configuration, prediction and adoption are fully dependent on the workload characterization. Correct and timely characterization leads managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce a workload management strategy or framework that is fully adoptive. The paper provides a summary of the structure and achievements of the database tools that exhibit Autonomic Computing or self-* characteristics in workload management. We have categorized the
International Journal of Computer Theory and Engineering, 2012
Workload in Database Management System (DBMS) consists of huge amount of data and number of concu... more Workload in Database Management System (DBMS) consists of huge amount of data and number of concurrent users who are executing different requests that require some resources. To manage these types of activities, organizations hire different database experts. There is versatility in workload due to the huge data size and different types of requests (workload). These factors contribute to some new challenges in the workload management. These challenges are identification of the workload and decision about the problem queries, identification of resource oriented and contention queries, accurate workload classification, optimal plan selection, prediction and adoption. In DBMS, where workload management and tuning is performed through if-then approach, unforeseen behavior of the workload cannot be handled and sometime leads to unpredictable state. In this research a prediction framework has been proposed called as workload queries performance Predictor. The predictor will predict the performance metrics (workload size, elapsed time, record accessed, record used, disk I/Os, memory required, message count and bytes) for queries in a given workload. We are improving efficiency and reducing search time when projection of query feature vector is performed over performance feature vector. The predictor will take help from the optimizer and store the information in database which saves the information as history for the future.
AbstractEvolution of computer starts from human dependent to self-management in order to reduce ... more AbstractEvolution of computer starts from human dependent to self-management in order to reduce time and complexity with increase in accuracy, reliability and efficiency. The need of self-management is realized due to maximum functionality, huge complexity and data ...
International Journal of Advanced Computer Science and Applications, 2017
In today's world, information security of an organization has become a major challenge as well as... more In today's world, information security of an organization has become a major challenge as well as a critical business issue. Managing and mitigating these internal or external security related issues, organizations hire highly knowledgeable security expert persons. Insider threats in database management system (DBMS) are inherently a very hard problem to address. Employees within the organization carry out or harm organization data in a professional manner. To protect and monitor organization information from insider user in DBMS, the organization used different techniques, but these techniques are insufficient to secure their data. We offer an autonomous approach to self-protection architecture based on policy implementation in DBMS. This research proposes an autonomic model for protection that will enforce Access Control policies, Database Auditing policies, Encryption policies, user authentication policies, and database configuration setting policies in DBMS. The purpose of these policies to restrict insider user or Database Administrator (DBA) from malicious activities to protect data.
With the growing size of enterprise data, the task of managing a database is becoming more and mo... more With the growing size of enterprise data, the task of managing a database is becoming more and more complex as well as time-consuming. A database administrator spends most of his time in activities that can be made automatic. Also, scarcity of skilled database administrators ...
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