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Cybercrime involves unlawful activities done by the individual in cyberspace using the internet. It is cyberbullying, financial theft, code-hack, cryptojacking, hacking, etc. The main difference between cybercrime and cyberattack is that cybercrime victims are humans. The crime associated with the latter is that of a computer network, hardware or software. Cyberattack activities include ransomware, viruses, worms, SQL injection, DDoS attacks, and government and corporate are potential targets. Cyber security provides a specialised approach to the protection of computer systems from cybercrimes and cyberattacks. As of now, no cyber defence is 100% safe. What is considered safe today may not be secure tomorrow. Blockchain enables a new way of recording transactions or any other digital interaction within the network with security, transparency, integrity, confidentiality, availability, and traceability. This chapter explains in detail about cyber risks and how blockchain can be used to avoid risks in financial and insurance frauds.

e-Governance is a medium to offer various services to citizens through a web portal, that exists in many countries nowadays. The existing e-Governance technology is a vast, centrally managed database and a set of applications that connect to it via web interfaces. Despite the modernisation of services, it remains with the lack of transparency. Thus, the existing infrastructure of e-Governance paves the way for corrupt practises by the bureaucrats. e-Governance needs a powerful underlying technology which doesn''t provide any way to allow tampering of the record and which in turn eliminates corruption. In this paper, we took land registration as a use-case for building e-Governance by keeping Blockchain as an underlying technology, to put off the corrupt practices and to bring transparency. Once transactions in land registration added to the Blockchain, it is immutable as it is cryptographically secured. Besides, the blockchain technology is secured as the ledger is distributed over the network. If a hacker wants to modify the ledger, he needs to hack every node in the blockchain network. Hyperledger Fabric, a permissioned Blockchain adopted for implementation and Hyperledger Caliper for performance analysis with these evaluation metrics such as throughput, latency and execution time.

Sentiment Analysis and classification becomes a key trend in the human world in analyzing the nature and quality of the product, people's emotion, inference about products, and movies. Sentiment Analysis is the process of classification as it classifies the inference or review into positive or negative. Since the data that are labeled are very expensive and difficult to gather, it is hard. Also, the sarcastic data and homonyms are difficult to be identified. Hence the assumption of reviews will be wrong. The solution to identify the sarcastic words and the words with different meanings happens with the help of Recurrent Neural Networks.

In this paper, we proposed a novel classification method for face recognition which adopts the functionalities of linear discriminant and regression. Linear discriminant and regression analysis methods have benefits regarding minimising time, memory usage and better feature extraction. Linear regression and discriminant classification (LRDC) makes use of the principle that a sample class lie in a linear subspace, proposed method represents a predicted image as a linear combination of class-specific galleries. LRDC belongs to the category of nearest subspace classification and finds the set of optimal discriminant projection vectors by adopting singular value decomposition (SVD) and null space, and the decision made for a class with the minimum distance. LRDC is extensively evaluated by applying it to different classified datasets and compared with the state-of-the-art algorithms.

In this paper, a new job aware scheduling algorithm for IaaS cloud is proposed. As we know IaaS cloud provides an increase in computing power, storage capacity and lowering the hardware cost and also it offers cost efficiency, scalability, elasticity and dynamic service according to requested application. Scheduling in cloud is vital as it plays an important role for ripe the benefits in-terms of cost and make-span. In scheduling, the jobs are mapped based on the characteristics and user requirements. Parameters like cost, load and resource are to be considered while scheduling. In IaaS cloud, the users pay for the resources they need for computation and the resources should be utilized efficiently for the benefit of both users and providers. Hence, scheduling should consider the jobs cost and has to fully utilize the resources to reduce the make-span, cost and increase the throughput of the system. Aggrandized job aware scheduling algorithm does load balancing in cloud with respect to the services based on resource and cost. The parameters such as make-span, number of tasks executed and cost for execution are considered to evaluate the performance of proposed algorithm.

The recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. It is the process of taking in raw data and making actions based on the category of the data. In this paper, we present a tool for recognizing handwritten mathematical expressions. The proposed architecture aims at handling the handwritten expressions by performing segmentation of the input based on each pen ups and pen downs followed by symbol classification. As a classifier, an Extreme Learning Machine and Support Vector machines are used, the classifier which produces a best accuracy is selected and then the symbols are trained among various handwritten mathematical expression and a promising result are achieved at symbol classification stage. Once the symbols are classified, the corresponding output is converted to LaTex format.

E-Learning is the process of extending the resources to different locations by using multimedia communications. Many e-Learning methodologies are available and based on client-server, peer-to-peer and using Grid Computing concepts. To establish e-Learning process, systems should satisfy these needs, i) high storage for storing, ii) high network throughput for faster transfer and iii) efficient streaming of materials. The first and second needs are satisfied by using Grid and P2P technologies and the third need can be achieved by an efficient video compression algorithm. This study proposes a framework, called Optimized Grid Based e-Learning (OgBeL), which adopts both Grid and P2P technology. To reduce the e-Learning material size for efficient streaming, a light weight compression algorithm called ＂dWave＂ is embedded in ＂OgBeL＂. The behavior of framework is analyzed in terms of time taken to transfer files using in-use grid protocols and in networks combined with grid and P2P.

Blockchain evolved as a core technology of Bitcoin, and earned a significant attraction for entrepreneurs and researchers nowadays. The blockchain is an open, immutable distributed public ledger that allows transactions to take place in a decentralised manner without a need for a trusted third party. Bitcoin, the first successful cryptocurrency, is a peer-to-peer electronic payment system. The thought for Bitcoin started as a means for making a secure currency that had no centralised control. Blockchain application is not only restricted to Bitcoin, but it also ignited the idea of using it for a wide range of fields such as ehealth, governance, arts, culture, education, electricity trading, etc. To apply this technology to many areas, one should have a complete understanding of what it is, this paper is meant to give a quick synopsis of blockchain technology (BT).

Concurrency bugs are often due to inadequate synchronization that fail to prevent specific (undesirable) thread interleavings. Such errors, often referred to as Heisenbugs, are difficult to detect, prevent, and repair. In this paper, we present a new technique to increase program robustness against Heisenbugs. We profile correct executions from provided test suites to infer fine-grained atomicity properties. Additional deadlock-free locking is injected into the program to guarantee these properties hold on production runs. Notably, our technique does not rely on witnessing or analyzing erroneous executions. The end result is a scheme that only permits executions which are guaranteed to preserve the atomicity properties derived from the profile. Evaluation results on large, real-world, open-source programs show that our technique can effectively suppress subtle concurrency bugs, with small runtime overheads (typically less than 15%).

Video streaming applications have newly attracted a large number of participants in a distribution network. Traditional client-server based video streaming solutions sustain precious bandwidth provision rate on the server. Recently, several P2P streaming systems have been organized to provide on-demand and live video streaming services on the wireless network at reduced server cost. Peer-to-Peer (P2P) computing is a new pattern to construct disseminated network applications. Typical error control techniques are not very well matched and on the other hand error prone channels has increased greatly for video transmission e.g., over wireless networks and IP. These two facts united together provided the essential motivation for the development of a new set of techniques (error concealment) capable of dealing with transmission errors in video systems. In this paper, we propose an flexible multiple description coding method named as Flexible Spatial-Temporal (FST) which improves error resilience in the sense of frame loss possibilities over independent paths. It introduces combination of both spatial and temporal concealment technique at the receiver and to conceal the lost frames more effectively. Experimental results show that, proposed approach attains reasonable quality of video performance over P2P wireless network., Comment: 11 pages, 8 figures, journal

Scheduling of jobs is essential with distribution of load on processors and dynamic allocation of resources in order to get maximum benefit in terms of make-span. In scheduling the mapping of tasks are done based on its characteristics and user requirements. Many task parameters such as cost, load and required resources for the task completion are to be considered while scheduling. In cloud, the resources should be utilized efficiently and hence scheduling should consider the resource utilization to reduce the execution time and thereby increasing the throughput of the system. In this paper, we proposed a new scheduling algorithm supporting load balancing in cloud with respect to various types of quality services based on resources. To evaluate the scheduling algorithm, the performance metrics such as execution time, average execution time of each resource and number of tasks assigned to each resource are taken into consideration.

Discriminant Analysis is utilised in working out which specific classification, a data pertains to on the basis of its needed features. Linear Discriminant Analysis(LDA) achieves the maximum class separability by projecting high-dimensional data onto a lower dimensional space. However, LDA suffers from small sample size(SSS) problem where the dimensionality of feature vector is very large compared to the number of available training samples. Regularized Discriminant Analysis(RDA) handles SSS problem of LDA with an introduction of regularization parameter(λ) and has the ability to reduce the variance. One important issue of RDA is how to automatically estimate an appropriate regularization parameter. In this paper, we propose a new algorithm to enhance the performance of RDA by effectively estimating an appropriate regularization parameter in order to reduce training time and error rate. Experiments are done using various benchmark datasets to verify the effectiveness of our proposed method with the state-of-the-art-algorithm.

This paper presents a novel method for pattern recognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for pattern recognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for pattern recognition.

The growth of data has increased exponentially in recent years. In this context, data-center scale computer systems are built to meet the high storage and processing demands of these applications. Such systems are composed of hundreds, thousands, or even millions of commodity computers connected through a LAN housed in a data center. It has a much larger scale than a traditional computer cluster. Hadoop enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Strength of Hadoop is in its ability to detect and handle failures. The original Hadoop native task scheduler implicitly assumes that cluster nodes are homogeneous. This assumption is used to identify a slow task and re-execute it. However, this assumption does not hold where the cluster nodes are heterogeneous, since speculatively identifying a slow task will give rise to erroneous conclusions. In MapReduce, the sub-task is transferred to a node for execution. The input to the subtask, if not present in the node, must be transferred from another node in the network. Transferring data takes time and delays execution. In this paper, we have proposed a methodology for improving Hadoop in-terms of Heterogeneity and Data Locality. The performance of improved version can be measured using these metrics, i)execution time, ii)response time, iii)tasks submitted, iv)time related to jobs i.e. arrival, start and completion, v)completed task, vi)fairness, vii)locality and viii)mean completion time.

This Grid is a computing centric distributed environment that integrates computing, storage and other resources to enable execution of applications that cannot run on a single resource. The vision of grid computing is that, every users can gain access to the computing resources inspite of its location or underlying technologies. Resource management and scheduling plays a critical role in achieving high utilization of resources in grid computing environments. The goal of scheduling is to achieve maximum throughput with available computing resources. Condor-G is a High-throughput scheduler supported by Grid Resource Allocation Management (GRAM) component of Globus. It uses non-dedicated resources to schedule the jobs. Processing huge amounts of data on large and scalable computational infrastructures is gaining increasing importance. Apache Hadoop, a data centric framework allows distributed processing of large sets of data across clusters of computers offering local computation, storage and failure handling thereby delivering a highly available service. In this paper, we have proposed a concept of Improvisioning Hadoop in Globus Toolkit, for efficient usage of resources, job management (scheduling) and faster execution. The performance of the proposed toolkit will improve in-terms of Job Execution, Resource Handling and Cluster Formation. electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

In healthcare industry, the patient’s medical data plays a vital role because diagnosis of any ailments is done by using those data. The high volume of medical data leads to scalability and maintenance issues when using healthcare provider’s onsite picture archiving and communication system (PACS) and network oriented storage system. Therefore a standard is needed for maintaining the medical data and for better diagnosis. Since the medical data reflects in a similar way to individuals personal information, secrecy should be maintained. Maintaining secrecy can be done by encrypting the data, but as medical data involves images and videos, traditional text based encryption/decryption schemes are not adequate for providing confidentiality. In this paper, we propose a method for securing the DICOM format medical archives by providing a better confidentiality. Our contribution in this method is of twofold: (1) Development of Chaotic based Arnold Cat Map for encryption/decryption of DICOM files and (2) Applying diffusion for those encrypted files. By applying this method, the secrecy of medical data is maintained and is tested with various DICOM format image archives by studying the following parameters i) PSNR-for quality of images and ii) Key-for security.

E-learning is the process of stretching learning or carrying instructional resource sharing gateways, to locations away from a classroom to another classroom, by using multimedia communications. The process of e-learning will cause some needs, such as i) high storage capacity systems for storing e-learning materials, ii) high network throughput for faster and efficient access of materials and iii) efficient streaming of e-learning materials which are in multimedia format. The first and second needs can be tackled by grid solutions and last one by embedding efficient video compression algorithm in grid. In this paper we proposed a framework called gBeL [Grid Based E-Learning] framework, which can fulfill the above said needs by adopting the grid technology and efficient streaming by reducing the video size by using video compression algorithm and embedding it in gBeL framework.

In this paper, we propose a technique which enhances continuous flow of streaming despite packet loss, error resilience and quality of service using multiple descriptions coding (MDC). The main aim of MDC is to encode a video into multiple frames called descriptions, supporting multiple stages of decoding and when compared to layered coding techniques, each description can be decoded separately to estimate the model. In this proposed work we improve the error resilience and user heterogeneity using new multiple description coding method called Hybrid Interpolation Scalable MDC (HIS-MDC) technique and using both spatial and temporal interpolation schemes corrupted portions of a frame are concealed. Experimental results show that, proposed approach attains reasonable quality of video performance compared with previous multiple description methods.

Nowadays digital libraries have become the source of information, sharing across the globe in the fields of education, research and knowledge. The full usage of digital libraries will be realized only when people can have access to the material from any location. The advantage of multimedia is that people of all ages can understand more clearly by seeing or hearing rather than reading. Considering the exponential growth in various technologies, developing a multimedia digital library in wireless is not an complicated task. Grid computing enables the virtualization data resources, process network bandwidth and storage capacities to create a single system image granting the user a seamless access to vast IT capabilities. By adopting peer-to-peer overlay networks, which are taking a central position in information systems, the storage space problem can be solved and by using grid computing the security can be maintained. In this article, we propose a framework for wireless multimedia digital library, built using Grid and P2P technology. Using this proposed framework, the digital data is stored in a cluster built of commodity components and users can access those data from anywhere, anytime securely. Advantages of this framework are: (i) existing capital investments are used for storing the multimedia files, (ii) increased access to data, (iii) balancing workloads among different systems connected in pGrid nodes, (iv) authenticated and secured transfer of files. Benchmarks used to test this framework are: (i) file size vs. download time, (ii) simultaneous connections, (iii) band-width utilization, (iv) security, (v) scalability, and (vi) robustness.