Muazzam A. Khan

Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.

Enzyme catalysis is one of the most essential and striking processes among of all the complex processes that have evolved in living organisms. Enzymes are biological catalysts, which play a significant role in industrial applications as well as in medical areas, due to profound specificity, selectivity and catalytic efficiency. Refining catalytic efficiency of enzymes has become the most challenging job of enzyme engineering, into acidic and alkaline. Discrimination of acidic and alkaline enzymes through experimental approaches is difficult, sometimes impossible due to lack of established structures. Therefore, it is highly desirable to develop a computational model for discriminating acidic and alkaline enzymes from primary sequences. In this study, we have developed a robust, accurate and high throughput computational model using two discrete sample representation methods Pseudo amino acid composition (PseAAC) and split amino acid composition. Various classification algorithms including probabilistic neural network (PNN), K-nearest neighbor, decision tree, multi-layer perceptron and support vector machine are applied to predict acidic and alkaline with high accuracy. 10-fold cross validation test and several statistical measures namely, accuracy, F-measure, and area under ROC are used to evaluate the performance of the proposed model. The performance of the model is examined using two benchmark datasets to demonstrate the effectiveness of the model. The empirical results show that the performance of PNN in conjunction with PseAAC is quite promising compared to existing approaches in the literature so for. It has achieved 96.3% accuracy on dataset1 and 99.2% on dataset2. It is ascertained that the proposed model might be useful for basic research and drug related application areas.

A compression sensing and noise-tolerant image encryption scheme based on chaotic maps and orthogonal matrices

Abstract With the evolution of technologies, the size of an image data has been significantly increased. However, traditional image encryption schemes cannot handle the emerging problems in big data such as noise toleration and compression. In order to meet today’s challenges, we propose a new image encryption scheme based on chaotic maps and orthogonal matrices. The main core of the proposed scheme is based on the interesting properties of an orthogonal matrix. To obtain a random orthogonal matrix via the Gram Schmidt algorithm, a well-known nonlinear chaotic map is used in the proposed scheme to diffuse pixels values of a plaintext image. In the process of block-wise random permutation, the logistic map is employed followed by the diffusion process. The experimental results and security analyses such as key space, differential and statistical attacks show that the proposed scheme is secure enough and robust against channel noise and JPEG compression. In addition to complete encryption for higher security, it also supports partial encryption for faster processing as well.

A survey of multicast routing protocols for vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) are autonomous and self-configurable wireless ad hoc networks and considered as a subset of Mobile Ad Hoc Networks (MANETs). MANET is composed of self-organizing mobile nodes which communicate through a wireless link without any network infrastructure. A VANET uses vehicles as mobile nodes for creating a network within a range of 100 to 1000 meters. VANET is developed for improving road safety and for providing the latest services of intelligent transport system (ITS). The development and designing of efficient, self-organizing, and reliable VANET are a challenge because the nodes mobility is highly dynamic which results in frequent network disconnections and partitioning. VANET protocols reduce the power consumption, transmission overhead, and network partitioning successfully by using multicast routing schemes. In multicasting, the messages are sent to multiple specified nodes from a single source. The novel aspect of this paper is that it categorizes all VANET multicast routing protocols into geocast and cluster-based routing. Moreover, the performance of all protocols is analyzed by comparing their routing techniques and approaches.

DeVANET: Decentralized Software-Defined VANET Architecture

Vehicular adhoc networks come out to be a promising solution for ensuring traffic and road safety on highways. However this area induces communication challenges such as topology dynamics, and connectivity losses. Todays Vanet demand a sound planning to make architectural level decisions. Integrating Vanet with emerging Software defined Networking brings ground-breaking networking innovation. Recent researches in SDN based Vanet are useful but their performance goals drops in large scale VANETs. In this research we are going to exploit SDN planes by partitioning Vanet to work in distributed manner. The proposed architecture is tested on VEINS testbed which provides interactive environment to perform road traffic simulations. Simulation results show an increase in performance gains as compared to traditional Vanet architectures.

Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels

Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

Person identification using vascular and non-vascular retinal features

Novel methods for personal identification using retinal images.Vascular based method involves the use of vessel properties of retinal images with improved vessel segmentation algorithm by catering pathological lesions.Non-vascular based method uses novel structural features structure to perform person identification. Display Omitted Retina recognition is the most stable and reliable biometric system due to its stability, uniqueness and non-replicable nature of vascular pattern. On the other hand, the complexity of vascular pattern in diseased retina makes the extraction of blood vessels very hard, which majorally effects the recognition rate. The main aim of this paper is to design a robust retinal recognition system with reduced computational complexity and to explore novel retinal features. This paper presents two different approaches for retinal recognition; one is vascular-based feature extraction with an improved vessel segmentation algorithm and second is non-vascular based feature extraction. Vascular-based method uses vessel properties of retinal images and aims to improve the efficiency of retinal recognition system. Whereas, non-vascular based method intends to analyze non-vessel properties of retinal images in order to reduce time complexity. The proposed system is assessed on two local and three public databases.

An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform, chaotic maps and substitution box

Abstract Wireless Sensor Networks (WSN) is widely deployed in monitoring of some physical activity and/or environmental conditions. Data gathered from WSN is transmitted via network to a central location for further processing. Numerous applications of WSN can be found in smart homes, intelligent buildings, health care, energy efficient smart grids and industrial control systems. In recent years, computer scientists has focused towards findings more applications of WSN in multimedia technologies, i.e. audio, video and digital images. Due to bulky nature of multimedia data, WSN process a large volume of multimedia data which significantly increases computational complexity and hence reduces battery time. With respect to battery life constraints, image compression in addition with secure transmission over a wide ranged sensor network is an emerging and challenging task in Wireless Multimedia Sensor Networks. Due to the open nature of the Internet, transmission of data must be secure through a process known as encryption. As a result, there is an intensive demand for such schemes that is energy efficient as well as highly secure since decades. In this paper, discrete wavelet-based partial image encryption scheme using hashing algorithm, chaotic maps and Hussain’s S-Box is reported. The plaintext image is compressed via discrete wavelet transform and then the image is shuffled column-wise and row wise-wise via Piece-wise Linear Chaotic Map (PWLCM) and Nonlinear Chaotic Algorithm, respectively. To get higher security, initial conditions for PWLCM are made dependent on hash function. The permuted image is bitwise XORed with random matrix generated from Intertwining Logistic map. To enhance the security further, final ciphertext is obtained after substituting all elements with Hussain’s substitution box. Experimental and statistical results confirm the strength of the anticipated scheme.

A Novel Real Time Framework for Cluster Based Multicast Communication in Vehicular Ad Hoc Networks

In a vehicular ad hoc network (VANET), the vehicles communicate with each other to develop an intelligent transport system (ITS) which provides safety and convenience while driving. The major challenge of VANET is that the topology changes dynamically due to the high speed and unpredictable mobility of vehicles resulting in an inefficient real time message dissemination, especially in emergency scenarios such as in the accident event where it can cause high level of destruction. To the best of our knowledge, there is no such mechanism in existing literature which can handle real time multicast communication in VANET for both urban and highway scenarios. In this paper, we propose a novel real time vehicular communication (RTVC) framework which consists of a VANET cluster scheme (VCS) and VANET multicast routing (VMR) to achieve efficient vehicle communication within both urban and highway scenarios. The RTVC framework develops stable communication links and achieves high throughput with low overhead despite high mobility by combining the multicast routing with a unique cluster based scheme. In VCS, the cluster head (CH) is elected upon cluster threshold value (CTV) to disseminate the messages within the cluster members (CMs) and to other cluster heads by intercluster communication, which reduces the network overhead. In addition, the vehicles cluster head election (VCHE) procedure is proposed to reduce the number of CHs and CMs switches which results in lower overhead of maintaining the clusters. Moreover, another novelty of the framework is that the CTV of VCHE can be adjusted by speed adjustment factor (SAF) to achieve the desired cluster stability depending upon the required VANET application. The simulation results illustrate that the proposed framework has achieved the goal of stable, efficient, and real time communication despite highly dynamic environment of VANET.

AMVR: A multicast routing protocol for autonomous military vehicles communication in VANET

Unmanned military vehicles (UMVs) and autonomous robots became part of modern warfare strategy to perform military combat missions and dangerous war field operations. The military vehicles (MVs) need to communicate with each other to achieve several required military tasks collectively. It has been achieved by proposing an autonomous military vehicles routing (AMVR) protocol to develop a vehicular ad hoc network (VANET) among all military manned and unmanned vehicles to meet the challenges of modern warfare. AMVR protocol performs multicast communication among unmanned and manned military vehicles in combination to develop strong coordination among them. The proposed protocol performs the message dissemination among MVs in two tier structure i.e. T1 and T2 which reduces the network overhead by distributing it among the two tiers. The UMVs are grouped in to T1 because these vehicles have the capability to arrange them at front autonomously with uniform distance by sharing speed and direction which avoids the occurrence of network fragmentation also. Hence, the UMVs maintain the stable radio links of VANET within dynamic environment of war field. The event detection messages (EDMs) are disseminated from unmanned vehicles to manned military vehicles (MMVs) of T2. The proposed protocol performs multicast communication to achieve high throughput and efficient dissemination of EDMs among all or specific group of military vehicles. The store and carry approach is adopted to inform incoming MVs about the current situation of war field. The simulation results illustrate that the proposed protocol has achieved the goal of EDMs dissemination among all UMVs and MMVs efficiently despite of dynamic battlefield environment.

A novel image encryption based on Lorenz equation, Gingerbreadman chaotic map and S 8 permutation

Internet is used as the main source of communication throughout the world. However due to public nature of internet data are always exposed to different types of attacks. To address this issue many researchers are working in this area and proposing data encryption techniques. Recently a new substitution box has been proposed for image encryption using many interesting properties like gingerbread-man chaotic map and S8 permutation. But there are certain weaknesses in aforesaid technique which does not provide sufficient security. To resolve the security issue an enhanced version of existing technique is proposed in this paper. Lorenz chaotic map based confusion and diffusion processes in existing technique are employed. Lorenz map is used to remove strong correlation among the plain text image pixels. In diffusion stage a random matrix is generated through lorenz chaotic map and XORed with shuffled image. It the end, existing gingerbread-man chaotic map based S-box is applied to extract the final cipher text image. The proposed enhanced scheme is analysed by statistical analysis, key space analysis, information entropy analysis and differential analysis. In order to ensure the robustness and higher security of proposed scheme, results via Number of Pixel Rate Change (NPRC)and Unified Average Change Intensity (UACI) tests are also validated. 9