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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. -
Therapy recommendation based on level of depression using social media data
Social media is a massive platform with currently over 100 million registered users. It is a platform where individuals express themselves along with their interests. These expressions of individual can be used to identify their mental status. That being said, depression and anxiety are the dominant cause for illness and ill-health across the world. Studies show that users mental health can be predicted by their everyday use of language. This paper examines the tweets for analyzing the linguistic and behavioral features for classifying the levels of depression among the users. In order to classify the levels of depression, a knowledge base of the words that are associated with depression/anxiety has been created. The model evaluated this using simple text mining techniques to measure the mental health status of the users and provide appropriate recommendations. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Wearable Sensors for Pervasive and Personalized Health Care
Healthcare systems are designed to provide commendable services to cater health needs of individuals with minimum expenditure and limited use of human resources. Pervasive health care can be considered as a major development in the healthcare system which aims to treat patients with minimal human resources. This provides a solution to several existing healthcare problems which might change the future of the healthcare systems in a positive way. Pervasive health care is defined as a system which is available to anyone at any point of time and at any place without any location constraints. At a broader definition, it helps in monitoring the health-related issues at a home-based environment by medical stakeholders which is very beneficial in case of emergency situations. This chapter elaborates architecture of IoT, how wearable sensors can be used to help people to get personalized and pervasive healthcare systems, and it also gives a detailed working of different types of IoT-enabled wearable devices for pervasive health care. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
On some classes of equitable irregular graphs
Graph labeling techniques are used by data scientists to represent data points and their relationships with each other. The segregation/sorting of similar datasets/points are easily done using labeling of vertices or edges in a graph. An equitable irregular edge labeling is a function $$f: E(G) \rightarrow N$$ (not necessarily be injective) such that the vertex sums of any two adjacent vertices of $$G$$ differ by at most one, where vertex sum of a vertex is the sum of the labels under $$f$$ of the edges incident with that vertex. A graph admitting an equitable irregular edge labeling is called an equitable irregular graph (EIG). In this paper, more classes of equitable irregular graphs are presented. We further generalize the concept of equitable irregular edge labeling to $$k$$-equitable irregular edge labeling by demanding the difference of the vertex sum of adjacent vertices to be $$k \ge 1$$. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Adaptive artificial bee colony (aabc)-based malignancy pre-diagnosis
Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A comparative study of text mining algorithms for anomaly detection in online social networks
Text mining is a process by which information and patterns are extracted from textual data. Online Social Networks, which have attracted immense attention in recent years, produces enormous text data related to the human behaviours based on their interactions with each other. This data is intrinsically unstructured and ambiguous in nature. The data involves incorrect spellings and inaccurate grammars leading to lexical, syntactic and semantic ambiguities. This causes wrong analysis and inappropriate pattern identification. Various Text Mining approaches are being used by researchers which can help in Anomaly Detection through Topic Modeling, identification of Trending Topics, Hate Speeches and evolution of the communities in Online Social Networks. In this paper, a comparative analysis of the performance of four classification algorithms, Support Vector Machine (SVM), Rocchio, Decision Trees and K-Nearest Neighbour (KNN) for a Twitter data set is presented. The experimental study revealed that SVM outperforms better than other classifiers, and also classifies the dataset into anomalous and non-anomalous users opinions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Treexpan instantiation of xpattern framework
Most of the data generated from social media, Internet of Things, etc. are semi-structured or unstructured. XML is a leading semi-structured data commonly used over cross-platforms. XML clustering is an active research area. Because of the complexity of XML clustering, it remains a challenging area in data analytics, especially when Big Data is considered. In this paper, we focus on clustering of XML based on structure. A novel method for representing XML documents, Compressed Representation of XML Tree, is proposed following the concept of frequent pattern tree structure. From the proposed structure, clustering is carried out with a new algorithm, TreeXP, which follows the XPattern framework. The performances of the proposed representation and clustering algorithm are compared with a well-established PathXP algorithm and found to give the same performance, but require very less time. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Implementation of integer factorization algorithm with pisano period
The problem of factorization of large integers into the prime factors has always been of mathematical interest for centuries. In this paper, starting with a historical overview of integer factorization algorithms, the study is extended to some recent developments in the prime factorization with Pisano period. To reduce the computational complexity of Fibonacci number modulo operation, the fast Fibonacci modulo algorithm has been used. To find the Pisano periods of large integers, a stochastic algorithm is adopted. The Pisano period factorization method has been proved slightly better than the recently developed algorithms such as quadratic sieve method and the elliptic curve method. This paper ideates new insights in the area of integer factorization problems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Classification of financial news articles using machine learning algorithms
The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Efficient handwritten character recognition of modi script using wavelet transform and svd
MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A predictive model on post-earthquake infrastructure damage
Disaster management initiatives are employed to mitigate the effects of catastrophic events such as earthquakes. However, post-disaster expenses raise concern for both the government and the insurance companies. The paper provides insights about the key factors that add to the building damage such as the structural and building usage properties. It also sheds light on the best model that can be adopted in terms of both accuracy and ethical principles such as transparency and accountability. From the performance perspective, random forest model has been suggested. From the perspective of models with ethical principles, the decision tree model has been highlighted. Thus, the paper fulfills to propose the best predictive model to accurately predict the building damage caused by earthquake for incorporation by the insurance companies or government agency to minimize the post-disaster expenses involved in such catastrophic event. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.