Yaser Daanial Khan

Iris Recognition Using Image Moments and k-Means Algorithm

This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

A Novel Modeling in Mathematical Biology for Classification of Signal Peptides

The molecular structure of macromolecules in living cells is ambiguous unless we classify them in a scientific manner. Signal peptides are of vital importance in determining the behavior of newly formed proteins towards their destined path in cellular and extracellular location in both eukaryotes and prokaryotes. In the present research work, a novel method is offered to foreknow the behavior of signal peptides and determine their cleavage site. The proposed model employs neural networks using isolated sets of prokaryote and eukaryote primary sequences. Protein sequences are classified as secretory or non-secretory in order to investigate secretory proteins and their signal peptides. In comparison with the previous prediction tools, the proposed algorithm is more rigorous, well-organized, significantly appropriate and highly accurate for the examination of signal peptides even in extensive collection of protein sequences.

A Prediction Model for Membrane Proteins Using Moments Based Features.

The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.

iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC

Among all the post-translational modifications (PTMs) of proteins, Phosphorylation is known to be the most important and highly occurring PTM in eukaryotes and prokaryotes. It has an important regulatory mechanism which is required in most of the pathological and physiological processes including neural activity and cell signalling transduction. The process of threonine phosphorylation modifies the threonine by the addition of a phosphoryl group to the polar side chain, and generates phosphothreonine sites. The investigation and prediction of phosphorylation sites is important and various methods have been developed based on high throughput mass-spectrometry but such experimentations are time consuming and laborious therefore, an efficient and accurate novel method is proposed in this study for the prediction of phosphothreonine sites. The proposed method uses context-based data to calculate statistical moments. Position relative statistical moments are combined together to train neural networks. Using 10-fold cross validation, 94.97% accurate result has been obtained whereas for Jackknife testing, 96% accurate results have been obtained. The overall accuracy of the system is 94.4% to sensitivity value 94% and specificity 94.6%. These results suggest that the proposed method may play an essential role to the other existing methods for phosphothreonine sites prediction.

A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes

Membrane proteins are vital mediating molecules responsible for the interaction of a cell with its surroundings. These proteins are involved in different functionalities such as ferrying of molecules and nutrients across membrane, recognizing foreign bodies, receiving outside signals and translating them into the cell. Membrane proteins play significant role in drug interaction as nearly 50% of the drug targets are membrane proteins. Due to the momentous role of membrane protein in cell activity, computational models able to predict membrane protein with accurate measures bears indispensable importance. The conventional experimental methods used for annotating membrane proteins are time-consuming and costly and in some cases impossible. Computationally intelligent techniques have emerged to be as a useful resource in the automation of prediction and hence the annotation process. In this study, various techniques have been reviewed that are based on different computational intelligence models used for prediction process. These techniques were formulated by different researchers and were further evaluated to provide a comparative analysis. Analysis shows that the usage of support vector machine-based prediction techniques bears more assiduous results.

Content-based image retrieval using extroverted semantics: a probabilistic approach

This paper presents a novel content-based image retrieval technique based on Gaussian mixture probability model. The proposed technique provides the solution toward matching arbitrary images based on color, shape and texture. Glyph structure of the image, which inclines on the shape and texture attributes, is modeled and used for content matching. Gaussian mixture model is applied to quantify the glyph structure in terms of its parameters. The formed probability density functions based on the glyph structure are refined using expectation maximization. Finally, the parameters yielded by the Gaussian mixture model allow us to perform comparison between arbitrary images based on their semantic details. It is concluded from the experimental results that relatively similar images have comparable parameters while the parameters of discordant images deviate with each other. In this way, for a certain arbitrary image, the set of resembling images is obtained from a large image base. In addition, the results show that this set is narrowed or broadened on the basis of a divergence ratio which marks the functional difference between the parameters of the images being compared.

Petri net-based modeling and control of the multi-elevator systems

This paper extends the Petri net (PN)-based modeling of multi-elevator control system for M floors and N elevators which provides the generic PN model of the system. A new class of Petri nets is introduced known as elevator control Petri net (ECPN) for such purpose. The model of the multi-elevator control system is developed through components, whereas the model of each elevator is defined as a component. The interaction between these elevators is implemented through control places (CPs) of its PN model. A bottom-up modeling approach is adopted by adding the CPs and using the arc-addition operator to the single-elevator modules. Mixture of collective and selective approaches, that is, collective-selective/up–down approach, is used for the control. The proposed Petri net class in the paper resolves the bunching problem among multiple elevators. The bunching problem is tackled by introducing the request places with the capacity of one in the ECPN. A case study of ECPN is also presented by taking the two elevators and four-floor model, and it is analyzed by the incidence matrix–based invariant method.

Prediction of N-linked glycosylation sites using position relative features and statistical moments.

Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen’s recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew’s correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.

An Efficient Algorithm for Recognition of Human Actions

Recognition of human actions is an emerging need. Various researchers have endeavored to provide a solution to this problem. Some of the current state-of-the-art solutions are either inaccurate or computationally intensive while others require human intervention. In this paper a sufficiently accurate while computationally inexpensive solution is provided for the same problem. Image moments which are translation, rotation, and scale invariant are computed for a frame. A dynamic neural network is used to identify the patterns within the stream of image moments and hence recognize actions. Experiments show that the proposed model performs better than other competitive models.

A treatise to vision enhancement and color fusion techniques in night vision devices

In this article a vast literary and ample historic review has been examined to provide in detail introduction, knowledge and comparison of night vision imaging techniques. It unveils night vision enhancement methods described in the recent times such as contrast enhancement; color transfer based clustering, fast color contrast enhancement and pseudo-color fusion algorithm for self-adaptive enhancement system along many others. Furthermore, the scientific and mathematical details are elaborated along with the mechanisms used image fusion techniques, color mapping, histogram matching and statistical evaluation. Conclusively, the channel based color fusion technique stood out through statistical and perceptual analysis.