Papers by Sitanshu S Sahu
Advances in Intelligent Systems and Computing
Encapsulation of information using mathematical barrier for forbidding malicious access is a trad... more Encapsulation of information using mathematical barrier for forbidding malicious access is a traditional approach from past to modern era of information technology. Recent advancement in security field is not restricted to the traditional symmetric and asymmetric cryptography; rather, immense security algorithms were proposed in the recent past, from which biometric-based security, steganography, visual cryptography, etc. gained prominent focus within research communities. In this paper, we have proposed a robust cryptographic scheme to original message. First, each message byte, the ASCII characters ranging from Space (ASCII-32) to Tilde (ASCII-126), is represented as object using flat texture in a binary image which is decorated as n by n geometrical-shaped object in images of size N × N. Create a chaotic arrangement pattern by using the prime number encrypted by Advanced Encryption Standard (AES). The sub-images are shuffled and united as rows and columns to form a host covert or cipher image which looks like a grid-structured image where each sub-grid represents the coded information. The performance of the proposed method has been analyzed with empirical examples.
Data hiding from external malicious access is an important and timely issue. Cryptography is the ... more Data hiding from external malicious access is an important and timely issue. Cryptography is the backbone of information or processed data security. The existing cryptography techniques provide good security; however, its computational complexity is also very high. Hence, there is a need of an efficient as well simple cryptography approach. In this context, the paper proposes a novel technique for cryptography in the form of binary textures. The binary textures provide a form of security corresponding to the original message. The binary textures are generated, reshuffled, and arranged in an image form to make it robust from malicious access. The reliability of the proposed approach has been illustrated with some empirical case studies. The overall cryptography process in a digital image makes it a simple, low-cost, and effective methodology for the secure communication.
Data hiding from external malicious access is an important and timely issue. Cryptography is the ... more Data hiding from external malicious access is an important and timely issue. Cryptography is the backbone of information or processed data security. The existing cryptography techniques provide good security; however, its computational complexity is also very high. Hence, there is a need of an efficient as well simple cryptography approach. In this context, the paper proposes a novel technique for cryptography in the form of binary textures. The binary textures provide a form of security corresponding to the original message. The binary textures are generated, reshuffled, and arranged in an image form to make it robust from malicious access. The reliability of the proposed approach has been illustrated with some empirical case studies. The overall cryptography process in a digital image makes it a simple, low-cost, and effective methodology for the secure communication.
2009 International Conference on Signal Processing Systems, 2009
A noisy time series, with both signal and noise varying in frequency and in time, presents specia... more A noisy time series, with both signal and noise varying in frequency and in time, presents special challenges for improving the signal to noise ratio. A modified S-transform time-frequency representation is used to filter a synthetic time series in a two step filtering process. The filter method appears robust within a wide range of background noise levels.
AoB Plants, 2020
The subcellular localization of proteins is very important for characterizing its function in a c... more The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is based on integrated machine learning approaches to efficiently predict the subcellular localizations in plant proteomes. The proposed approach predicts with high accuracy 11 single localizations and three dual locations of plant cell. Several hybrid features based on composition and physicochemical properties of a protein such as amino acid composition, pseudo amino acid composition, auto-correlation descriptors, quasi-sequence-order descriptors and hybrid features are used to repr...
2017 Third International Conference on Biosignals, Images and Instrumentation (ICBSII)
Parkinson's disease (PD) is a widespread chronic neurological disease prevalent in old age. S... more Parkinson's disease (PD) is a widespread chronic neurological disease prevalent in old age. Speech is found to be an effective marker for the identification of Parkinson's disease. In the following paper, we have proposed using factor analysis to select meaningful and dominant features from the speech signals, which are relevant for prediction of Parkinson's disease. We infer that along with the jitter variants, shimmer variants and noise to harmonic ratio, pitch period entropy (PPE), the recurrence period density entropy (RPDE), and spread parameters are important in identifying PD. For classification, Support Vector Machine (SVM) is used. The proposed model discriminates Parkinson afflicted individuals from healthy ones with an average accuracy, sensitivity and specificity of about 90%. Further, from the study, it is inferred that sustained phonations carry sufficient information for predicting Parkinson's disease.
2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016
2009 World Congress on Nature Biologically Inspired Computing, Dec 1, 2009
The problem of portfolio optimization is a well-known standard problem in financial world. It has... more The problem of portfolio optimization is a well-known standard problem in financial world. It has received a lot of attention among many researchers. Choosing an optimal weighting of assets is a critical issue for which the decision maker takes several aspects into consideration. In this paper we consider a multi-objective portfolio assets selection problem where the total profit of is maximized while total risk to be minimized simultaneously. Three well-known multiobjective evolutionary algorithms i.e. Pareto Envelope-based Selection Algorithm(PESA), Strength Pareto Evolutionary Algorithm 2(SPEA2), Nondominated Sorting Genetic Algorithm II(NSGA II) for solving the bi-objective portfolio optimization problem has been applied. Performance comparison carried out in this paper by performing different numerical experiments. These experiments are performed using real-world data. The results show that NSGA-II outperforms other two for the considered test cases.
2009 IEEE International Workshop on Genomic Signal Processing and Statistics, 2009
ABSTRACT Prediction of protein function from its sequence is an important and challenging task in... more ABSTRACT Prediction of protein function from its sequence is an important and challenging task in bioinformatics. The biological function of a protein primarily depends on the amino acid sequence within it. Identification of the amino acids (hot spots) that leads to the characteristic frequency signifying a particular biological function is really a tedious job in proteomic signal processing. In this paper we have proposed a new technique for identification of hot spots in proteins using an efficient time-frequency filtering approach known as the S-Transform filtering. The S-Transform is a powerful linear time-frequency representation and is especially useful for the filtering in the time-frequency domain. The potentiality of the new technique is analysed in identifying hot spots in proteins and the result obtained is compared with other existing methods. It reveals that the proposed method is superior to its counterparts and is consistent with results based on biological methodologies for identification of the hot spots. This new method also reveals some new hot spots which needs further investigation by the biological community.
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Predicting the structure of a protein from primary sequence is one of the challenging problems in... more Predicting the structure of a protein from primary sequence is one of the challenging problems in Molecular biology. In this context, protein structural class information provides a key idea of their structure and also other features related to the biological function. In this paper we present a new optimization approach based on Genetic algorithm (GA) and artificial immune system (AIS) for predicting the protein structural class. It uses the maximum component coefficient principle in association with the amino acid composition feature vector to efficiently classify the protein structures. The effectiveness is evaluated by comparing the results with that obtained from other existing methods using a standard database. Especially for all α and α + β class protein, the rate of accurate prediction by the proposed methods is much higher than their counterparts.
BMC Bioinformatics, 2014
Background: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in man... more Background: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases. Results: In this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%. Conclusion: This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/ SubClPred).
BMC Bioinformatics, 2014
Background: Every year pathogenic organisms cause billions of dollars' worth damage to crops and ... more Background: Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets. Results: In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus. Conclusion: The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.
2009 IEEE International Advance Computing Conference, 2009
The time-frequency representation (TFR) has been used as a powerful technique to identify, measur... more The time-frequency representation (TFR) has been used as a powerful technique to identify, measure and process the time varying nature of signals. In the recent past S-transform gained a lot of interest in time-frequency localization due to its superiority over all the existing identical methods. It produces the progressive resolution of the wavelet transform maintaining a direct link to the Fourier transform. The S-transform has an advantage in that it provides multi resolution analysis while retaining the absolute phase of each frequency component of the signal. But it suffers from poor energy concentration in the timefrequency domain. It gives degradation in time resolution at lower frequency and poor frequency resolution at higher frequency. In this paper we propose a modified Gaussian window which scales with the frequency in a efficient manner to provide improved energy concentration of the S-transform. The potentiality of the proposed method is analyzed using a variety of test signals. The results of the study reveal that the proposed scheme can resolve the time-frequency localization in a better way than the standard S-transform.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2000
Protein-protein interactions govern almost all biological processes and the underlying functions ... more Protein-protein interactions govern almost all biological processes and the underlying functions of proteins. The interaction sites of protein depend on the 3D structure which in turn depends on the amino acid sequence. Hence, prediction of protein function from its primary sequence is an important and challenging task in bioinformatics. Identification of the amino acids (hot spots) that leads to the characteristic frequency signifying a particular biological function is really a tedious job in proteomic signal processing. In this paper, we have proposed a new promising technique for identification of hot spots in proteins using an efficient time-frequency filtering approach known as the S-transform filtering. The S-transform is a powerful linear time-frequency representation and is especially useful for the filtering in the time-frequency domain. The potential of the new technique is analyzed in identifying hot spots in proteins and the result obtained is compared with the existing methods. The results demonstrate that the proposed method is superior to its counterparts and is consistent with results based on biological methods for identification of the hot spots. The proposed method also reveals some new hot spots which need further investigation and validation by the biological community.
Genomics, Proteomics & Bioinformatics, 2011
Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging... more Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.
... It is my great pleasure to show indebtedness to my friends like Sudhansu, Trilochan, Upendra,... more ... It is my great pleasure to show indebtedness to my friends like Sudhansu, Trilochan, Upendra, Pyari, Nithin, Vikas, Rama and Maitrayee for their help during the course of this work. ... iv Page 6. I am also grateful to NIT Rourkela for providing me adequate infrastructure ...
Computational Biology and Chemistry, 2010
During last few decades accurate determination of protein structural class using a fast and suita... more During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid composition (Chou, K.C., 2001. Proteins 43, 246-255), a new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein. Thus the sample of a protein is represented by a set of discrete components which incorporate both the sequence order and the length effect. On the basis of such a statistical framework a simple radial basis function network based classifier is introduced to predict protein structural class. A set of exhaustive simulation studies demonstrates high success rate of classification using the self-consistency and jackknife test on the benchmark datasets.
protein structural class prediction has been a challenging problem in protein science for many ye... more protein structural class prediction has been a challenging problem in protein science for many years. In this paper we present a new optimization approach using the Differential evolution (DE) for predicting the protein structural class. It uses the maximum component coefficient principle in association with the amino acid composition feature vector to efficiently classify the protein domains. The effectiveness is evaluated by comparing the results with that obtained from other existing methods using a standard database. Especially for all α and α +β class protein, the rate of accurate prediction by the proposed methods is much higher than their counterparts.
Pure and Applied Geophysics, 2012
... NITHIN V. GEORGE,1 KRISTY F. TIAMPO,2 SITANSHU S. SAHU,1 STÉ PHANE MAZZOTTI,3 LALU MANSINHA,2... more ... NITHIN V. GEORGE,1 KRISTY F. TIAMPO,2 SITANSHU S. SAHU,1 STÉ PHANE MAZZOTTI,3 LALU MANSINHA,2 and GANAPATI PANDA ... The weight of this ice depressed the lithosphere, and the resulting viscoelastic flow in the mantle caused a peripheral bulge (MITROVICA ...
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Papers by Sitanshu S Sahu