International Journal of Intelligent Engineering Informatics (8 papers in press)
ELECTRONIC SERVICE QUALITY MEASUREMENT (eSQM); DEVELOPMENT OF A SURVEY INSTRUMENT TO MEASURE THE QUALITY OF E-SERVICE
by Hamed Taherdoost
Abstract: Service quality is increasingly recognized as an important aspect of online business activities and is considered as a key determinant for successful electronic service (e-service) as well. Even though, e-service quality has increasingly attracted the attention of researchers, the existing research in this area identified quality dimensions of e-service without deeper attention for development of a new measurement instrument. The absence of a valid and reliable instrument to measure e-service quality in online environment has, however, bedeviled the endeavors of both scholars and practitioners. Therefore, there is a clear need to address the gaps by a theoretical survey tool which integrates all aspects of e-service quality to be deployed in information systems research. In order to respond to the need, the purpose of this article is to examine the theoretical foundation of e-service quality and to develop a survey instrument to measure the quality of e-service. Given the exploratory nature of this research, all characteristics of e-service quality have been extracted from the previous studies. Then, the exploratory factor analysis was used to cluster the factors effectively, thereby, further analysis including content, discriminate, and convergent validity of the proposed survey instrument were tested. The contribution of this research relates to the fact that the proposed theoretical survey instrument integrates in a holistic way various relevant factors affecting e-service quality into a single template and develop E-service Quality Measurement (eSQM) tool.
Keywords: E-service Quality; Service Quality; Survey Instrument; Questionnaire; Exploratory Factor Analysis; E-service Quality Measurement (eSQM).
Optimal self-tuning decoupled sliding mode control for a class of nonlinear systems
by Mohammad Javad Mahmoodabadi, Seyed Mehdi Mortazavi Yazdi, Mohsen Talebipour
Abstract: Decoupled Sliding Mode Control (DSMC) is well-known as a simple way to achieve asymptotic stability of the nonlinear under-actuated systems. This method has many advanced features such as good performance and robustness against parameter variations. However, because of changing the system states, the controller with the constant parameters would not be optimum in any state of the system, and designing DSMC requires an adaptation for the controller parameters. Hence, in this paper, a Robust Self-tuning Decoupled Sliding Mode Controller (RSDSMC) is presented and optimized with five conflicting objective functions and twelve design variables using a multi-objective genetic algorithm. The proposed controller is applied to a highly nonlinear inverted pendulum and cart system via the computer simulation. The final results depict the appropriate performance of this new controller and demonstrate its superiority in comparison with those reported in literature.
Keywords: Robust Self-tuning Controller; Decoupled Sliding Mode Controller; Genetic Algorithm; Inverted Pendulum and Cart System.
Performance Analysis of SEP, I-SEP, PSO and WCA based Clustering Protocols in WSN
by Ankit Gambhir, Ashish Payal, Rajeev Arya
Abstract: Wireless sensor networks (WSNs) are significantly resource restrained by their delimited power supply. Hierarchical routing based on clustering is an effectual methodology; however, preferring an energy-sensible cluster by optimally selection of cluster head (CH) is also very perplexing. In the present paper, performance analysis of the hierarchical routing protocols based on clustering such as stable election protocol (SEP), improved stable election protocol (I-SEP), conventional LEACH (Low-energy adaptive clustering hierarchy) and optimized versions of LEACH based on nature motivated optimization algorithm such as particle swarm optimization (PSO) (derived from swarm intelligence) and water cycle algorithm (WCA) (derived from observing how rivers and streams flow to the sea) are presented. Numerous parameters have been well-thought-out as the base of the study to present useful results in comparison of clustering protocols in WSN that lead to noteworthy findings in the form of results. The study is examined over a variety of performance metrics such as alive nodes per round, packet to base station (BS) per round and dead nodes per round. Based on the thorough consideration and observations, a qualitative conclusion is also drawn at the end.
Keywords: improved stable election protocol; LEACH; particle swarm optimization; stable election protocol; water cycle algorithm; wireless sensor network.
A survey on Database Intrusion Detection: Approaches, Challenges and Application
by Rajni Jindal, Indu Singh
Abstract: Databases store vital information of an organization and are therefore integral for its efficient working. This necessitates the establishment of Database Intrusion Detection Systems (DIDSs) which can detect and prevent unauthorized user access to the critical information stored in database.A lot of work has been done in the field of DIDSs which has grown at a very rapid pace. A large number of publications emerging every year to further improve upon the existing state of the art solutions. This paper investigates research on major approaches proposed in the field of database intrusion detection and analyzes the drawbacks of the proposed methods in order to drive future research towards more efficient and effective DIDSs. A systematic survey is conducted in order to classify various approaches for detecting intrusion in databases. The work identifies open research questions and challenges, by methodically comparing existing strategies to combat malicious transactions in a database system, and also provides an insight to the applications of DIDSs.
Keywords: Database Security; Database Intrusion Detection; Insider Attack; Role Based Access Control; Data Dependency Mining;.
Attention-Based Word-Level Contextual Feature Extraction and Cross-Modality Fusion for Sentiment Analysis and Emotion Classification
by Mahesh Huddar, Sanjeev Sannakki, Vijay Rajpurohit
Abstract: Multimodal affective computing has become a popular research area, due to the availability of a large amount of multimodal content. Feature alignment between the modalities and multimodal fusion are the most important issues in multimodal affective computing. To address these issues, first the proposed model extracts the features at word-level and forced alignment is used to understand the time-dependent interaction among the modalities. The contextual information among the words of an utterance is extracted using bidirectional LSTM. Further bidirectional LSTM is used to extract the contextual information between the nearby utterances before fusion. Weighted pooling based attention model is used to select the important features within the modalities and importance of each modality before fusion. Initially, two-two modalities are fused and then all modalities using cross-modality fusion technique. The performance of the proposed model was tested on two standard published datasets such as IEMOCAP and CMU-MOSI for sentiment analysis and emotion classification respectively. By incorporating the word-level features, feature alignment, and cross-modality fusion, the proposed architecture outperforms the baselines in terms of classification accuracy.
Keywords: Affective Computing; Attention Model; Contextual Fusion; Cross-Modality Fusion; Feature Alignment.
A Knowledge-based Diagnosis Algorithm for Broken Rotor Bar Fault Classification Using FFT, Principle Component Analysis and Support Vector Machines
by Hayri Arabaci, Mohamed Ali Mohamed
Abstract: Despite their ruggedness and reliability, induction motors experience faults due to stresses and manufacturing errors. Early detection of these faults is important in preventing further damages and minimizing down-time. In this study, a machine learning algorithm is proposed for detection and classification of broken rotor bar faults according to their severity. Removal of high frequency components then amplification was performed on the measured single-phase current. Features were then extracted using FFT and Principal Component Analysis (PCA). Support Vector machines (SVM) was used for classification. 2 classification schemes were analysed; one classifying in 1 step and another in 2 steps. Experiments were performed to evaluate the algorithms by analysing their recognition rates. 6 different SVM kernels were studied. Recognition rates as high as 97.9% were achieved. False negative rate as low as 0% was also realized. Furthermore, it was found out that using more principle components does not yield significant improvements.
Keywords: squirrel cage induction motor; IM; support vector machines; SVM; principal component analysis; PCA; BRB; fault diagnosis; machine learning.
Solving the E-Commerce Logistics Problem using Anti-Predatory NIA
by ROHIT KUMAR SACHAN, Tarun Kumar, Dharmender Singh Kushwaha
Abstract: E-commerce is expanding their roots in every business. Fast, efficient, reliable, timely delivery of goods and optimal transportation cost are the major challenges in e-commerce. To overcome these challenges, e-commerce companies are using a well-planned arrangement of warehouses and distribution centers (logistics network). This logistics network also reduces the operational cost and capital investment of an e-commerce company. This study proposes a novel solution to deal with e-commerce logistics problem using anti-predatory NIA. The proposed approach is useful for identifying cities where warehouses and distribution centers can be established; and allocating the distribution centers to warehouse in order to reduce total cost of goods transportation. The proposed approach is also useful for predicting the number of warehouses to be established for optimal logistics network. The experimental evaluation reveals that the proposed method achieves 2.30% lower gap value and 20% more consistent optimal results as compared to the genetic algorithm.
Keywords: Anti-predatory NIA; Distribution center; E-Commerce; Genetic algorithm; Hub location problem; Logistics; Meta-heuristic; Spoke; Transportation cost; Warehouse.
Special Issue on: ICCSDET-2018 Advances and Applications in Intelligent Control Systems
A privacy preservation model for big data in Map-reduced framework based on k-anonymization and Swarm-based algorithms
by Suman Madan, Puneet Goswami
Abstract: In recent years, two mainstream technologies that has become center of IT world are big data and cloud computing. Both these fields are generally used together but fundamentally they are different. The big data deals with huge scales of data however the cloud computing is majorily about the infrastructre. Together these fields are giving beneficial outcomes in enterprises varying from government sector to social sites, from academic to medical sectors etc. Thus, it becomes very important to safeguard the datasets so that the end users of data may not access the information delivered by the users of cloud. This paper is presenting a hybrid k-anonymization model for map-reduce framework which guarantees the preservation of privacy in cloud data based on combination of swarm-based algorithms. In proposed model, the focus is on deriving a fitness function which will give high value of privacy and low information loss. The simulation and comparison with other algorithms shows that the proposed model is yielding better privacy and utility.
Keywords: Big data publishing; privacy preservaton; cloud computing; k-anonymization; privacy; swarm-based algorithm.