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Phytochemistry and antigenotoxic properties of six ethnobotanically important members from the family Zingiberaceae
Genotoxicity is considered as a potential cause of various diseases including cancer. During the last decade, herbal extracts attained a great deal of attention due to its safe and effective applications against various DNA damaging agents. However, the mechanism of DNA strand breaks by various mutagens and genotoxins is often correlated with the generation of Reactive Oxygen Species (ROS). Herbal extracts constitute a number of phytochemicals and those are reported to have considerable antioxidant properties, which are in turn capable of neutralizing ROS mediated DNA damage. The botanical family Zingiberaceae is reported to have significant antioxidant and antigenotoxic potential by various researchers. Among a number of species belonging to this family, six species, namely Alpinia galanga, A. zerumbet, Curcuma amada, C. caesia, Zingiber officinale, and Z. zerumbet, attract notable attention due to their remarkable ethnobotanical and medicinal importance. This chapter deals with phytochemical composition, antioxidant, and antigenotoxic properties of these six Zingiberaceous plant extracts. 2020 by IGI Global. All rights reserved. -
Impact of Meltdown and Spectre Threats in Parallel Processing
Threat characterization is critical for associations, as it is an imperative move towards execution of data security. Vast majority of the current threat characterizations recorded threats in static courses without connecting risks to information system zones. The aim of this paper is to represent each threat in different areas of the information system the methodology to solve the problem. Data security is habitually represented to different kinds of threats which may cause distinctive types of harms that can prompt to critical monetary losses. Data security problems can go from small losses to entire data framework destruction. The effect of various threats vary extensively: some manipulate the integrity or confidentiality of information while others manipulate the accessibility of a framework. At present, associations are trying to comprehend what are the threats to their data resources are and what are the ways to get the significant intends to combat them which keep on representing a challenge. Springer Nature Switzerland AG 2020. -
Quantum optimization for machine learning
Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Supply chain leadership in emerging markets: Understanding the role of trust, information management, and collaboration
The massive growth of emerging economies in last two decades has attracted many global companies to expand their physical presence in these countries. But the ability to take advantage of those opportunities is only available to companies that appreciate the environmental challenges and complexity of the region. The lexicon of extant literature focuses on enhancing supply chain leadership and development of efficient and effective strategies in developed economies, yet the corresponding literature in emerging economies is very fragmented. The aim of this chapter is to synthesize the current literature to understand the phenomenon including its definitions, dimensions, and constructs and to propose a conceptual model for successful supply chain leadership in emerging markets. The study tries to understand and establish the impact of various factors of supply chain leadership, which leads to sustainable supply chain performance. Collaboration and information management emerge as the major drivers for supply chain leadership in emerging markets and identifies trust as a mediating factor. 2020 by IGI Global. All rights reserved. -
Introduction to quantum machine learning
Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Modeling destination competitiveness: The unfamiliar shift for destination rebranding, restructuring, and repositioning with DMOs
Tourism is a tactical economic practice across the globe, but the urban and provincial transformations in the industry are strongly contemplated in the light of an unfamiliar shift in tourism business. This chapter discusses an integrated concept with a framework relating systematic approach of managing the destination and its competitiveness. An investigation on the impact on tourism and the recent narrative of national, regional, and local planning approach directs towards efficient destination management organizations (DMO) in practice for future development. This has proceeded by the formation of a competitive approach, emphasizing on the DMO roles and responsibilities helpful for a destination management during an unfamiliar business trend. Modeling destination competitiveness demands an absolute mechanism through destination rebranding, restructuring, and repositioning with DMOs for enabling competency. 2018, IGI Global. -
Handwritten digit recognition using convolutional neural networks
Optical character recognition (OCR) systems have been used for extraction of text contained in scanned documents or images. This system consists of two steps: character detection and recognition. One classification algorithm is required for character recognition by their features. Character can be recognized using neural networks. The multilayer perceptron (MLP) provides acceptable recognition accuracy for character classification. Moreover, the convolutional neural network (CNN) and the recurrent neural network (RNN) are providing character recognition with high accuracy. MLP, RNN, and CNN may suffer from the large amount of computation in the training phase. MLP solves different types of problems with good accuracy but it takes huge amount of time due to its dense network connection. RNNs are suitable for sequence data, while CNNs are suitable for spatial data. In this chapter, a CNN is implemented for recognition of digits from MNIST database and a comparative study is established between MLP, RNN, and CNN. The CNN provides the higher accuracy for digit recognition and takes lowest amount of time for training the system with respect to MLP and RNN. The CNN gives better result with accuracy up to 98.92% as the MNIST digit dataset is used, which is spatial data. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Towards connected government services: A cloud software engineering framework
Cloud computing technologies are being used highly successfully in large-scale businesses. Therefore, it is useful for governments to adopt cloud-driven multi-channel, and multiple devices to offer their services such as e-tax, e-vote, e-health, etc. Since these applications require open, flexible, interoperable, collaborative, and integrated architecture, service-oriented architecture approach can be usefully adopted to achieve flexibility and multi-platform and multi-channel integration. However, its adoption needs to be systematic, secure, and privacy-driven. In this context, micro services architecture (MSA), a direct offshoot of SOA, is also a highly attractive mechanism for building and deploying enterprise-scale applications. This chapter proposes a systematic framework for cloud e-government services based on the cloud software engineering approach and suggests a cloud adoption model for e-government, leveraging the benefits of MSA patterns. The proposed model is based on a set of evaluated application characteristics that, in turn, support emerging IT-based technologies. 2021 by IGI Global. All rights reserved. -
Does fdi intensify economic growth? Evidence from china and India
[No abstract available] -
Trade in renewable energy technologies: A comparison of India and China
[No abstract available] -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Innovations in teaching-learning and evaluation: An overview of processes undertaken at CHRIST (Deemed to be University)
The term 'teaching-learning' intrinsically expresses the ongoing learning process that every educator constantly experiences; to teach is to learn and to engage in knowledge updation continually. Indeed, it may be argued that the very basis of being a teacher is the facilitation of one's own learning opportunities and skill sets. In investigating the evolution of teaching-learning processes at CHRIST, one may define the university's growth using the key concept of 'innovation '. Whether it be the humanities, social sciences, life sciences, or business studies, innovations in teaching-learning methods are imperative in any globally conscious education system today. Two of the key areas of focus in terms of innovations in the teaching learning process are the practical application of knowledge and learnt skills. 2021, IGI Global. -
FISCAL DECENTRALIZATION IN INDIA AND CHINA: Experiences in service delivery
[No abstract available] -
Citizen data in distributed computing environments: Privacy and protection mechanisms
Data security is paramount in the increasingly connected world. Securing data, while in transit and rest, and while under usage, is essentialfor deriving actionable insights out of data heaps. Incorrect or wrong data can lead to incorrect decisions. So, the confidentiality and integrity of data have to be guaranteed through a host of technology-inspired security solutions. Organizational data is kept confidentially by the businesses and governments, often in distant locations (e.g., in cloud environments), though more sensitive data is normally kept in house. As the security mechanisms are getting more sophisticated, cyber security attacks are also becoming more intensive, so there is a constant battle between the organisations and the hackers to be one step ahead of the other. In this chapter, the aim is to discuss various mechanisms of accomplishing citizens ' data confidentiality and privacy and to present solution approaches for ensuring impenetrable security for personal data. 2021 by IGI Global. All rights reserved. -
Threats and security issues in smart city devices
The main objective of this chapter is to discuss various security and privacy issues in smart cities. The development of smart cities involves both the private and public sectors. The theoretical background is also discussed in future growth of smart city devices. Thus, the literature survey part discusses different smart devices and their working principle is elaborated. Cyber security and internet security play a major role in smart cities. The primary solution of smart city security issues is to find some encryption methods. The symmetric and asymmetric encryption algorithm is analyzed and given some comparative statement. The final section discusses some possible ways to solve smart city security issues. This chapter showcases the security issues and solutions for smart city devices. 2022, IGI Global. -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
A novel approach using steganography and cryptography in business intelligence
In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy. 2021, IGI Global. -
Business transaction privacy and security issues in near field communication
The main objective of this chapter is to discuss various security threats and solution in business transactions. The basic working principle and theoretical background of near field communication (NFC) technology is discussed. A component of NFC communication section is to be discussed on various NFC operation modes and RFID tags. NFC technology is used in various fields such as electronic toll collection and e-payment collection for shopping. This device-to-device payment system is facing major security issues. This NFC communication data is transferred from one terminal to another terminal by using short-range radio frequency. Data hackers try to access this radio frequency and attack the business transaction. This hybrid encryption algorithm is used to solve business transaction data security issues. This chapter deals with both key encryption and data encryption processes. 2021, IGI Global. -
Genome analysis for precision agriculture using artificial intelligence: a survey
Precision agriculture is a farm management technique which uses the help with the help of information technology to ensure that the crops and soil receive exactly what is required for optimum health and productivity. Genome analysis in plants helps to identify the plant structure and physiological traits. The identification of the right plant genome and the resulting traits help to optimize the cultivation of the plant for better productivity and adaptability. Genome analysis helps the biologist edit the plant genetic makeup structure to make the plant to adapt to the current conditions and thereby reducing the use of fertilizers. For precision agriculture, artificial intelligence techniques help to understand the relationships between plant genome and soil nutrient conditions that help in precision farming effectively reducing the usage of fertilizers by modifying the plants to adapt with the current soil characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.