Mohammad Ibrahim Khan

Performance Evaluation of Warshall Algorithm and Dynamic Programming for Markov Chain in Local Sequence Alignment

Markov Chain is very effective in prediction basically in long data set. In DNA sequencing it is always very important to find the existence of certain nucleotides based on the previous history of the data set. We imposed the Chapman Kolmogorov equation to accomplish the task of Markov Chain. Chapman Kolmogorov equation is the key to help the address the proper places of the DNA chain and this is very powerful tools in mathematics as well as in any other prediction based research. It incorporates the score of DNA sequences calculated by various techniques. Our research utilize the fundamentals of Warshall Algorithm (WA) and Dynamic Programming (DP) to measures the score of DNA segments. The outcomes of the experiment are that Warshall Algorithm is good for small DNA sequences on the other hand Dynamic Programming are good for long DNA sequences. On the top of above findings, it is very important to measure the risk factors of local sequencing during the matching of local sequence alignments whatever the length.

An improved blind watermarking method in frequency domain for image authentication

Digital watermarking has been widely used for copyright protection for multimedia data. This paper proposes a new watermarking method of image for copyright protection based on Hadamard transform. This method can embed or hide an entire image or pattern as a watermark such as a companys logo or trademark directly into original image for copyright protection. This watermarking method deals with the extraction of the watermark information in the absence of original image, hence the blind scheme is obtained. The experimental results prove that our proposed watermarking method offers better image quality and more robustness under various attacks such as JPEG compression, cropping, sharpening, filtering and so on. Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC) are computed to measure image quality and robustness. Finally, a comparative study is made against the previous technique.

An integrated algorithm for local sequence alignment

Local sequence alignment (LSA) is an essential part of DNA sequencing. LSA helps to identify the facts in biological identity, criminal investigations, disease identification, drug design and research. Large volume of biological data makes difficulties to the performance of efficient analysis and proper management of data in small space has become a serious issue. We have subdivided the data sets into various segments to reduce the data sets as well as for efficient memory use. The integration of dynamic programming (DP) and Chapman–Kolmogorov equations (CKE) makes the analysis faster. The subdivision process is named data reducing process (DRP). DRP is imposed before DP and CKE. This approach needs less space compared with other methods and the time requirement is also improved.

Chapman–Kolmogorov equations for global PPIs with Discriminant-EM

Ongoing improvements in Computational Biology research have generated massive amounts of Protein–Protein Interactions (PPIs) dataset. In this regard, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead. We illustrate a probabilistic approach among proteins nodes that are part of various networks by using Chapman–Kolmogorov (CK) formula. We have compared CK formula with semi-Markov random method, SMETANA. We significantly noticed that CK outperforms the SMETANA in all respects such as efficiency, speed, space and complexity. We have modified the SMETANA source codes available in MATLAB in the light of CK formula. Discriminant-Expectation Maximization (D-EM) accesses the parameters of a protein network datasets and determines a linear transformation to simplify the assumption of probabilistic format of data distributions and find good features dynamically. Our implementation finds that D-EM has a satisfactory performance in protein network alignment applications.

MetaG: a graph-based metagenomic gene analysis for big DNA data

Microbial interactions and relationships are significant for animals, insects and plants. Metagenomic research enables properassessments and analysis for microbial organs and communities. The analysis helps to gain detailed insights on miscopies insects. Recent machine learning techniques focused on algorithms and data mining tools to check the depth of interactions and relationships on metagenomic dataset. Accurate analysis over large genes helps to solve real-world problems for public interest. In this regard, graph-centric big gene dataset representations are very important. De Bruijn graph is one the pivotal media to demonstrate the relationships and interactions of large genes dataset or metagenomic dataset. In this research, mapping-based metagenomic graphical (MetaG) genomes representation has been demonstrated. Data cleaning is done before applying graphical illustration. Random mapping is used to assess the variations in dataset. Euler path-based De Bruijn graph is used to sketch the gene annotation, translations, signaling and coding. This research helps in computational biology to map the genomic information in graphical ways with clear conceptions. Adequate experimental comparisons as well as analysis established the claims with tables and graphs.

MSuPDA: A memory efficient algorithm for sequence alignment

AbstractSpace complexity is a million dollar question in DNA sequence alignments. In this regard, memory saving under pushdown automata can help to reduce the occupied spaces in computer memory. Our proposed process is that anchor seed (AS) will be selected from given data set of nucleotide base pairs for local sequence alignment. Quick splitting techniques will separate the AS from all the DNA genome segments. Selected AS will be placed to pushdown automata’s (PDA) input unit. Whole DNA genome segments will be placed into PDA’s stack. AS from input unit will be matched with the DNA genome segments from stack of PDA. Match, mismatch and indel of nucleotides will be popped from the stack under the control unit of pushdown automata. During the POP operation on stack, it will free the memory cell occupied by the nucleotide base pair.Space complexity is a million dollar question in DNA sequence alignments. In this regard, memory saving under pushdown automata can help to reduce the occupied spaces in computer memory. Our proposed process is that anchor seed (AS) will be selected from given data set of nucleotide base pairs for local sequence alignment. Quick splitting techniques will separate the AS from all the DNA genome segments. Selected AS will be placed to pushdown automatas (PDA) input unit. Whole DNA genome segments will be placed into PDAs stack. AS from input unit will be matched with the DNA genome segments from stack of PDA. Match, mismatch and indel of nucleotides will be popped from the stack under the control unit of pushdown automata. During the POP operation on stack, it will free the memory cell occupied by the nucleotide base pair.

An Efficient Audio Watermarking Algorithm in Frequency Domain for Copyright Protection

Digital Watermarking plays an important role for copyright protection of multimedia data. This paper proposes a new watermarking system in frequency domain for copyright protection of digital audio. In our proposed watermarking system, the original audio is segmented into non-overlapping frames. Watermarks are then embedded into the selected prominent peaks in the magnitude spectrum of each frame. Watermarks are extracted by performing the inverse operation of watermark embedding process. Simulation results indicate that the proposed watermarking system is highly robust against various kinds of attacks such as noise addition, cropping, re-sampling, re-quantization, MP3 compression, and low-pass filtering. Our proposed watermarking system outperforms Cox’s method in terms of imperceptibility, while keeping comparable robustness with the Cox’s method. Our proposed system achieves SNR (signal-to-noise ratio) values ranging from 20 dB to 28 dB, in contrast to Cox’s method which achieves SNR values ranging from only 14 dB to 23 dB.

An Efficient Distributed Bioinformatics Computing System for DNA Sequence Analysis on Encoding System

This paper provides an effective design of search technique of a distributed bioinformatics computing system for analysis of DNA sequences using OPTSDNA algorithm. This system could be used for disease detection, criminal forensic analysis, gene prediction, genetic system and protein analysis. Different types of distributed algorithms for the search and identification for DNA segments and repeat pattern in a given DNA sequence are developed. The search algorithm was developed to compute the number of DNA sequence which contains the same consecutive types of DNA segments. A distributed subsequence identifications algorithm was designed and implemented to detect the segment containing DNA sequences. Sequential and distributed implementation of these algorithms was executed with different length of search segments patterns and genetic sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequence into database. DNA sequences of different lengths were tested by using this algorithm. These input DNA sequences varied in size from very small to very large. The performance of search technique distributed system is compared with sequential approach

Enhanced Edge Localization and Gradient Directional Masking for Moving Object Detection

Moving object detection has been widely used in intelligent video surveillance system. This paper proposes a new moving object detection method based on enhanced edge localization mechanism and gradient directional masking. In our proposed method, initially gradient map images are generated from the input image and the background image using gradient operator. The gradient difference map is then calculated from gradient map images. Finally, the moving object is extracted by using appropriate directional masking and thresholding. Simulation results indicate that the proposed method outperforms conventional edge based methods under different illumination conditions including indoor, outdoor, and foggy cases to detect moving object. In addition, it is computationally faster and applicable for real-time processing.

Facial Features Approximation for Expression Detection in Human-Robot Interface

This paper presents a facial expression recognition system employing Bézier curves approximation technique. The system is based on facial features extraction using the knowledge of the face geometry and approximated by 3rd order Bézier curves representing the relationship between the motion of features and changes of expressions. For face detection, color segmentation based on the novel idea of fuzzy classification has been employed that manipulates ambiguity in colors. Experimental results demonstrate that this method can recognize the facial expressions with an accuracy of more than 90% in all cases. Finally the system has been implemented using a manipulator robot and issuing facial expression commands.