International Journal of Intelligent Engineering Informatics
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International Journal of Intelligent Engineering Informatics (5 papers in press)
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.
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.
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.
Human Face Gender Identification using Thepades Sorted N-ary Block Truncation Coding and Machine Learning Classifiers by Sudeep Thepade, Deepa Abin, R.I.K. DAS, TANUJA SARODE Abstract: Human face gender identification is increasingly gaining popularity because of exponential popularity and interest in ubiquitous and pervasive computing. The computing embedded in the environment is pervasive computing, where the environment can be made to feel the person is present there and according to the person whether male or female the environment may induce certain decisions with help of ubiquitous computing devices to make environment suited to the person. The challenge of detecting a face is male or female is very trivial due to similarity of features of faces. The paper presents use of Thepades Sorted N-ary Block Truncation Coding (TSBTC N-ary) for extraction of features for the faces and further deploys various machine learning classifiers to identify the face as male or female. Here TSnBTC is explored for six different combinations (from two-ary to seven-ary). Fourteen machine learning classifiers are explored with each of these feature extraction generation methods for face gender identification giving 96 variations of the proposed method, all these are tested using Faces94 dataset. Classification accuracy is used as performance measure. Overall Random Forest has given best performance and TSBTC-7ary outperformed other feature extraction variations. Keywords: Gender Identification; Machine learning Classifier; Thepade’s Sorted BTC N-ary; TSBTC N-ary.