Creative commons; Creative work; Rights and responsibilities; Self-plagiarism; Verbatim lifting
The power of cognition of human beings is beyond the imagination of any cognitive person. As gifted and nurtured property, the intellect of human beings has the potential to be original, creative, and innovative. Has the human being got absolute control over her/his intellect? Can human beings possess absolute rights over any product of her/his intellect? How far is a human being indebted to society? If human beings are not given due credit to the product of her/his intellect, the enthusiasm to be more creative and productive may take a coarser path. Human beings have the fundamental right to use her/his intellect to live a life of their choice enjoying economic and non-economic benefits. The right to intellectual property is fundamental to human beings. Hence, any infringement of intellectual property has to be dealt with appropriately. At the same time, human beings should be indebted to society for nurturing their intellect. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Shukla S.; George J.P.; Tiwari K.; Kureethara J.V.
SpringerBriefs in Applied Sciences and Technology, pp. 73-81.
Springer Science and Business Media Deutschland GmbH
Shukla S., Department of Data Science, Christ University, Karnataka, Bengaluru, India; George J.P., Christ University, Karnataka, Bengaluru, India; Tiwari K., Christ University, Karnataka, Bengaluru, India; Kureethara J.V., Centre for Research, Christ University, Karnataka, Bengaluru, India
Efficacy of Nanomaterials and Its Impact on Nosocomial Infections
Antimicrobial agents; Detection methods; Microbial pathogens; Nanomaterials
Nanotechnology provides the ability to manipulate the properties of materials by using their size, and this has lead research towards a massive amount of plausible uses for nanomaterials. Irresistible maladies can occur, and they create an impressive burden on general wellbeing worldwide. The incident of these ailments is higher in developing nations. Irresistible maladies might be caused by microscopic organisms, infections, and protozoa, and the diseases they cause are often resistant to traditional treatment bringing about protracted contamination and higher mortality risk. In connection to that, the patients infected with these smaller scale creatures, that may prove resilient for an extended period of time, can be transmitters of these diseases to others. The recuperating of irresistible maladies is possible by metal-based nanoparticles that are plausible therapeutics for the treatment of irresistible ailments and their natural productivity. Metal-based nanoparticles that have been accounted for with antibacterial movement include silver, iron, iron oxide, copper oxide, zinc oxide, aluminum oxide, titanium dioxide, gold, and gallium nanoparticles. Present day improvements in nanotechnology enable us to handle this issue at two levels: diagnostics and treatment. Elimination of irresistible microorganisms requires effortless and exact recognition of the irresistible agents for suitable treatment. Various nanomaterials have been considered for the management of and cautious measures for irresistible ailments. Recently, nanomaterials have improved the treatment, diagnostics, and avoidance of irresistible illnesses. Built nanoparticles have been progressively utilized in irresistible infection management caused by microorganisms. Progress in nanoparticle-based frameworks involve a confident research region with basic ramifications for the recuperating of bacterial contaminations. Nanosystems have been shown to be beneficial, and different approaches dependent on nanoparticles have been expanded to see unambiguous agents. Various purpose-of-care (POC) tests have been anticipated that can propose results earlier, simpler, and at less expense than known strategies and can even be used in difficult to reach areas for viral determination. Quorum sensing is a boosts reaction substance formulation strategy interrelated with population density that microorganisms use to authorize biofilms development. Research is ongoing concerning the antimicrobial movement of nanoparticles, contrasting it by methods for and the motivation behind the natural extract of therapeutic plants, and concentrating on anti-toxin protections of pathogenic microscopic organisms. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Kalitha Parveen P.; Christobher S.; Balasubramanian B.; Kaliannan D.; Pappusamy M.; Meyyazhagan A.
Nanotechnology in the Life Sciences, pp. 237-260.
Springer Science and Business Media B.V.
Kalitha Parveen P., PG Department of Zoology, Hajee Karutha Rowther Howdia College of Arts and Science, Tamil Nadu, Uthamapalayam, India; Christobher S., Department of Zoology, Nallamuthu Gounder Mahalingam College, Tamil Nadu, Pollachi, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, South Korea; Kaliannan D., Zoonosis Research Center, Department of Infection Biology, School of Medicine, Wonkwang University, Iksan, South Korea; Pappusamy M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India
And regulations; Cybersecurity; Data leak; Data loss; Data security; Data security laws
Corporates, industries, and governments have completely digitized their infrastructure, processes, and data or running towards completed digitalization. This data could be text files, different types of databases, accounts, and networks. The data living in the digital format needs to be preserved and also protected from unauthorized access. If this data remains open for access, any unauthorized user can destroy, encrypt, or corrupt the data, making the data unusable. There are implications of data security threats such as data breaches and data spills, beyond cost and can spell doom for the business. Hence the data needs to be protected from such threats. Data security is a mechanism through which data is protected and prevented from loss due to unauthorized access. It is a mix of practices and processes to ensure data remains protected from unauthorized use and readily accessible for authorized use. Data Security is essential for achieving data privacy in general. To define appropriate security measures, we must define the difference between a data breach and a data leak. Data security mechanisms could be data-centric such as identity and access management, encryption and tokenization, and backup and recovery. A defined data governance and compliance can also ensure data security. This chapter will explain why there is a need for data security, methods and processes to achieve data security, and touch upon some of the data security laws and regulations. We will also see a case study on how hackers exploited a vulnerability to mount a data security attack worldwide and how data security mechanisms could have prevented it. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
SpringerBriefs in Applied Sciences and Technology, pp. 41-59.
Group Key Management Techniques forSecure Load Balanced Routing Model
Ad hoc networks; Cryptography; Group key; Wireless sensors
Remote sensor organizations (WSNs) assume a vital part in giving ongoing information admittance to IoT applications. Be that as it may, open organization, energy limitation, and absence of brought together organization make WSNs entirely defenseless against different sorts of pernicious assaults. In WSNs, recognizing vindictive sensor gadgets and dispensing with their detected data assume a vital part for strategic applications. Standard cryptography and confirmation plans cannot be straightforwardly utilized in WSNs on account of the asset imperative nature of sensor gadgets. In this manner, energy productive and low idleness procedure is needed for limiting the effect of malignant sensor gadgets. In this research work presents a secured and burden balanced controlling contrive for heterogeneous bunch-based WSNs. SLBR shows a predominant trust-based security metric that beats the issue when sensors proceed to influence from extraordinary to terrible state and the other way around; besides, SLBR alters stack among CH. In this way, underpins fulfilling superior security, allocate transmission, and vitality efficiency execution. Trials are driven to calculate this presentation of developed SLBR demonstrate over existing trust-based controlling show, particularly ECSO. The result accomplished appears SLBR demonstrate fulfills favored execution over ECSO as distant as vitality capability (i.e., arrange lifetime considering to begin with sensor contraption downfall and total sensor contraption passing), correspondence overhead, throughput, allocate planning idleness, and harmful sensor contraption mis-classification rate and recognizable verification. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Studies in Computational Intelligence, Vol-1029, pp. 65-76.
Bondada P., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST Deemed to be University, Bengaluru, India
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Intelligent transportation system; Machine learning; Parameter optimization; Traffic flow prediction; Urban traffic flow
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Neelakandan S.; Prakash M.; Bhargava S.; Mohan K.; Robert N.R.; Upadhye S.
Studies in Systems, Decision and Control, Vol-412, pp. 111-127.
Neelakandan S., Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India; Prakash M., Data Science and Analytics Center, Karpagam College of Engineering, Coimbatore, India; Bhargava S., Department of Computer Science and Engineering, Poornima College of Engineering, Rajasthan, Jaipur, India; Mohan K., Lecturer Information Technology, University of Technology and Applied Sciences, Shinas, Oman; Robert N.R., Department of Computer Science, Christ University, Bangalore, 560029, India; Upadhye S., Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Maharashtra, Nagpur, India
Impact of Blockchain Technology in the Healthcare Systems
Blockchain; Decentralization; Health care; Scalability; Security
The healthcare industry is one of the most important industries in the world which is in dire need of a restructuring process because of its poor and outdated techniques of data management. Healthcare system has adopted a centralized environment and deals with a lot of intermediaries which makes it prone to issues of single point of failure, lack of traceability of transactions, and privacy issues such as data leakage. Blockchain is a relatively new technology which is able to tackle the obsolete methods and practices existing in the healthcare industry. In this chapter, we analyzed the applications of blockchains in the healthcare industry which can solve the issues prevalent in the healthcare industry. The aim of this chapter is to reveal the potential benefits that comes from using blockchain technology in the healthcare industry and identify the various challenges that this technology has. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Anand G.; Prajeeth A.; Gautam B.; Rahul; Monika
Lecture Notes on Data Engineering and Communications Technologies, Vol-101, pp. 709-729.
Anand G., Department of Computational Sciences, CHRIST (Deemed to be University), Bangalore, India; Prajeeth A., Department of Computer Science and Engineering, Delhi Technological University, Delhi, 110042, India; Gautam B., Department of Computer Science and Engineering, Delhi Technological University, Delhi, 110042, India; Rahul, Department of Software Engineering, Delhi Technological University, Delhi, 110042, India; Monika, Department of Computer Science, Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, Delhi, 110096, India
Supervised Learning-Based Data Classification and Incremental Clustering
Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, Tell me who your neighbors are, and it will tell you who you are. The supervised learning-based data classification and incremental clustering technique is a simple yet powerful approach with applications in computer vision, pattern recognition, optical character recognition, facial recognition, genetic pattern recognition, and other fields. Its also known as a slacker learner because it doesnt develop a model to classify a given test tuple until the very last minute. When we say yes or no, there may be an element of chance involved. However, the fact that a diner can recognise an invisible food using his senses of taste, flavour, and smell is highly fascinating. At first, there can be a brief data collection phase: what are the most noticeable spices, aromas, and textures? Is the flavour of the food savoury or sweet? This information can then be used by the diner to compare the bite to other items he or she has had in the past. Earthy flavours may conjure up images of mushroom-based dishes, while briny flavours may conjure up images of fish. We view the discovery process through the lens of a slightly modified adage: if it smells like a duck and tastes like a chicken, youre probably eating chicken. This is a case of supervised learning in action. Machine learning can benefit from supervised learning, which is a concept that can be applied to it (ML). 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 33-72.
Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Real-Time Application with Data Mining and Machine Learning
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 129-157.
Machine learning is a subset of AI. Its a research project aimed at gathering computer programscapable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the datas underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 23-32.
Play and Play Spaces for Global Health, Happiness, and Well-Being
Happiness; Health; Lifespan; Play; Playspaces; Well-being
Play has a significant role in an individuals learning and holistic development. Play and recreation are a need and right. Research on play indicates that the significance of play is neglected among the current generation. Play spaces are shrinking, and physical play is becoming extinct in most communities. This current scenario may or have led to poor physical and mental health outcomes. The proposed book chapter aims to present play and play spaces in physical and mental health. The literature of play theories in child development shows the role of play in socioemotional, physical, and cognitive development. The current paper brings together literature on play across the lifespan, highlighting how play and recreation impacts children, youth, adults, and older adults physical and mental health. The change in lifestyle patterns has contributed to the neglect of play and recreation. The paper throws light on the need for the attention of professionals and policymakers for interventions and advocacy at both local and global levels in promoting play and preserving natural play spaces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022.
Kumar P.A.; Syama S.; Srinivasan S.; Thomas S.; Jose K.A.
Handbook of Health and Well-Being: Challenges, Strategies and Future Trends, pp. 129-154.
Kumar P.A., Department of Psychology, Christ University, Bengaluru, India; Syama S., School of Education, Bengaluru, Christ, India; Srinivasan S., Department of Psychology, Christ University, Bengaluru, India; Thomas S., Department of Psychology, Christ University, Bengaluru, India; Jose K.A., Department of Psychology, Christ University, Bengaluru, India
Review on Segmentation of Facial Bone Surface from Craniofacial CT Images
Computed tomography; Craniofacial image analysis; Deep learning; Machine learning; Mandible segmentation; Orthognathic surgery; Segmentation
Three-dimensional (3D) representation of facial bone surface is needed in the virtual surgical planning for orthognathic surgery. Segmentation of facial bone surface from computed tomography images is first step in developing the 3D model. With the advent in the computer vision techniques, various automatic and semi-segmentation algorithms were developed in the recent years for segmentation of facial bone surface from craniofacial computed tomography images. In the proposed paper, the various segmentation techniques for extracting bone surface from 3D CT images for corrective jaw surgery available in the literature have been discussed. By reviewing all the methods available in the literature, it is found that each method has its own merits and demerits. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-117, pp. 717-738.
Varghese J., Department of Computer Science, Christ Deemed To Be University, Karnataka, Bangalore, India, Christ Academy Institute for Advanced Studies, Karnataka, Bangalore, India; Saleema J.S., Department of Computer Science, Christ Deemed To Be University, Karnataka, Bangalore, India
Data Classification and Incremental Clustering Using Unsupervised Learning
Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 73-99.
Corporate Social Responsibility and Sustainability Development Mapping: Practical Application Beer and Nigel Roome Model
Corporate social responsibility; Cybernetics model; Holistic view; Mapping theoretical model; Stakeholder approach; Sustainability development
Toyota is one of the highly appreciated companies for CSR performance in India. The central theme of this research paper is to apply Beers and Nigel Roome model of sustainability to the Japanese automobile manufacturer operating in India. In the first part of analysis, we have discussed the relevance of Beers model to the Toyota CSR activity. In the second part of analysis, we make an effort to take forward the work of Basavaraj et al. (2018) who framed the questionnaire to map the Nigel Roome model to Toyota CSR case. The contribution of this case is to bring the new dimension in data presentation and analysis of Roome theoretical model on weak sustainability versus strong sustainability. The case analysis provides new insights for CSR managers to map their performance pictorially using Tableau software. Finally, we conclude, Toyota follows significant components of cybernetic model. Due to conscious effort of Toyota, India has positioned itself substantially well in the CSR ranking. 2022, Springer Nature Singapore Pte Ltd.
CSR, Sustainability, Ethics and Governance, pp. 11-38.
Basavaraj S., School of Business and Management, CHRIST (Deemed To Be University), Bangalore, India; P S., School of Business and Management, CHRIST (Deemed To Be University), Bangalore, India
Introduction to Data Mining and Knowledge Discovery
Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. Its an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 1-22.
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Alzheimers disease; Artificial intelligence; Autism spectrum disorder; Dementia; Epilepsy; Machine learning; Mild cognitive impairment; Multiple sclerosis; Neurological disorder; Parkinsons disease; Schizophrenia; Stroke
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinsons disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each others field, leading to fruitful collaborations and effective solutions. 2022 Elsevier Inc. All rights reserved.
Chandra J.; Rangaswamy M.; Banerjee B.; Prajapati A.; Akhtar Z.; Sakauye K.; Joseph A.
Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, pp. 243-272.
Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Rangaswamy M., Department of Psychology, CHRIST (Deemed to be University), Bangalore, India; Banerjee B., Institute for Intelligent Systems, Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States, Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States; Prajapati A., Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN, United States; Akhtar Z., Department of Network and Computer Security, State University, New York Polytechnic Institute, Utica, NY, United States; Sakauye K., Department of Psychiatry, University of Tennessee, Health Science Center, Memphis, TN, United States; Joseph A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Feature Subset Selection Techniques with Machine Learning
Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapterdescribes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learningtasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizesthe performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 159-175.
Data Mining-Based Variant Subset Features
A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 177-193.
Research Intention Towards Incremental Clustering
Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. Thats why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges.To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 101-127.
An Overview of Augmenting AI Application in Healthcare
Artificial intelligence; COVID-19; Ethics; Healthcare; Neural networks
Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-117, pp. 397-407.
Chellasamy A., School of Business and Management, CHRIST University, Bangalore, India; Nagarathinam A., School of Business and Management, CHRIST University, Bangalore, India
AI Based Technologies for Digital and Banking Fraud During Covid-19
Artificial intelligence; COVID 19; Digital frauds; Digital transactions; Pandemic
The only viral thing today is the Covid 19 virus, which has severely disrupted all the economic activity around globe because of which all the businesses are experiencing irrespective of its domain or country of origin. One such major paradigm shift is contactless business, which has increased digital transaction. This in turn has given hackers and fraudsters a lot of space to perform digital scams line phishing, spurious links, malware downloads etc. These frauds have become undesirable part of increased digital transactions, which needs immediate attention and eradication from the system with instant results. In this pandemic situation where, social distancing is key to restrict the spread of the virus, digital payments are the safest and most appropriate payment method, and it needs to be safe and secure for both the parties. Artificial intelligence can be a saviour in this situation, which can help combat the digital frauds. The present study will focus on the different kinds of frauds which customers and facing, and most possible ways Artificial intelligence can be incorporated to identify and eliminate such kind of frauds to make digital payments more secure. Findings of the study suggest that inclusion of AI did bring a change in the business environment. AI used for entertainment has become an essential part in business. Transfiguration from process to platform focused business. The primary requirement of AI is to study the customer experience and how to give a better response for improving the satisfaction. But recently AIs are used not only for customer support, but its been observed that businesses have taken it as marketing strategy to increase demand and sales. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Studies in Computational Intelligence, Vol-1038, pp. 443-459.
Sinha M., CHRIST University, Bangalore, India; Chacko E., Jain (Deemed to be) University, Bangalore, India; Makhija P., Jain (Deemed to be) University, Bangalore, India