International Journal of Applied Pattern Recognition (5 papers in press)

Regular Issues

• Nature Inspired Hybrid Algorithm for Binding Shared Key with User Trait  
  by Suresh Padmanabhan, Radhika K R 
  Abstract: Increased digital transactions accentuate need for secure communication over open channels. Confidentiality and safe distribution of shared key is a mandatory requirement in symmetric key based systems. Binding user biometric to cryptographic key ensures security, non-transferrability, safe storage and secure distribution of key over a network. The work proposes a novel nature inspired optimization technique for binding secret key with user traits extracted from iris biometrics. Iris features are selected for key binding as the physical trait has lesser intra-user variation. The work encrypts and binds a key to optimal features extracted from iris. Validation of key binding is demonstrated by ensuring that successful decryption happens by authorized user alone. Nature inspired swarm and population algorithms are used to extract optimal feature vectors from user trait. A hybrid combination of swarm optimisation and hunting algorithms is used for optimal feature extraction and training of neural network that assists in identifying the user authorized to extract key. Chicken swarm optimization and deer hunting optimization algorithms have been used for the first time with iris traits to achieve optimal key binding. Experiments for different shared key lengths have been carried out with IIT Delhi and Multimedia University iris datasets. Proposed work has achieved better results over reviewed state-of-art optimization algorithms. Accuracy of proposed model is 7% better than whale optimisation algorithm and 4% better than grey wolf optimization. The improved performance of model over compared state-of-art algorithms is due to refinement of hunting based algorithm with optimized fitness values obtained from swarm based technique and the use of machine learning to enable optimal feature extraction.
  Keywords: Symmetric Key; Iris Biometric; Nature Inspired Algorithm; Neural Network.
  
  
• A hybrid Gene Selection Model for Molecular Breast Cancer Classification using Deep Neural Network  
  by Monika Lamba, Geetika Munjal, Yogita Gigras 
  Abstract: Microarray-based gene expression outlining portrays a dominant part in a healthier understanding of breast cancer. From the large quantum of data, a powerful technique is required to understand and extract the required information. The molecular subtype extraction is one of such important information regarding breast cancer, which is very crucial in definingrnits treatment strategy. This manuscript has formulated a Deep Neural Network-based Model for molecular classification of breast cancer. The proposed model exploits pre-processing steps along with the hybrid approach of filter and wrapper-based feature selection to extract relevantrngenes. The extracted genes are evaluated using various machine learning approaches where it is observed that selected features are successful in solving this multiclass problem. Using the proposed hybrid model, we have achieved the highest accuracy with six microarray datasets. The model outperforms magnificently in standings of sensitivity, f-measure, specificity, MCC, and recall. Hence, Deep Neural Network is identified as the best efficient classifiers concluding brilliant performance with all the selected micro-array gene expression datasets for a range of selected genes.
  Keywords: breast cancer; deep neural network; molecular subtype; feature selection; CFS; BFS; SMOTE.
  
  
• Exploring the Mel scale features using supervised learning classifiers for emotion classification  
  by Kalpana Rangra, Monit Kapoor 
  Abstract: Human emotions are inherently ambiguous and impure but emotions are important while considering the human uttered speech. The role of human speech is intensified by the aspect of the emotion it conveys. There are several characteristics of speech that differentiate it among different utterances. Various prosodic features like pitch, timbre, loudness, and vocal tone categorize speech into several emotions and other domains. Sample speech is changed when it is subjected to various emotional environments. Researches support various experimental analysis for phonetics and prosodic parameters that quantify the quality of speech. Identification of different emotional states of an actor (speaker) can also be done on the basis of the Mel scale. MFCC is one such variant to study the emotional aspects of the utterances by the speaker. The paper implements a model to identify several emotional states from MFCC for two datasets. The work classifies emotions for two datasets on the basis of MFCC features and gives a comparison of both. This work implements a classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate on different machine learning algorithms.
  Keywords: Speech Recognition; Emotion recognition; MFCC; Machine Learning ,supervised learning; decision trees.
  
  
• A comprehensive survey on reduction of semantic gap in content based image retrieval  
  by Jayant Jagtap, Nilesh Bhosale 
  Abstract: In the last few decades, content based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorized as : 1) Image annotation techniques to define the high-level concepts in image 2) Relevance feedback techniques to integrate users perception 3)Machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content based image retrieval. The paper concludes with promising future research directions.
  Keywords: Content based image retrieval; Deep learning; Image annotation; Image retrieval; Information retrieval; Relevance feedback; Semantic gap; Survey.
  
  
• How Machine Learning is Transforming Insurance Sector: Case of Fraud Detection in Morocco  
  by Nabila HAMDOUN 
  Abstract: Artificial Intelligence and Machine Learning can play a crucial role in Fraud Detection, especially in Insurance Sector by providing an effective way to identify fraudulent activity, reducing costs and increasing profitability for the company. This paper illustrates the business value of applying Machine Learning algorithms for Predicting Fraudulent behavior in Auto Insurance Claims. In addition the paper offers a comparison between two algorithms known for their high performance: Random Forest and XG-Boosting Machine with a statistical model: Linear Discriminant Analysis (LDA), and find that XG-Boosting Machine performs better on Moroccan customer data than others. This study aims to encourage insurance companies to take advantage of recent advances in artificial intelligence and machine learning to solve business challenges especially in the fraud detection process.
  Keywords: Machine Learning; Random Forest; XG-Boosting Machine; Insurance; Fraud Detection.