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Machine Learning Methods to Identify Aggressive Behavior in Social Media
With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Brief Review on the Role of Blockchain in Supply Chain Management
Blockchain is a proficient technology when used in combination with other intelligent technologies which gives an opportunity to an organization to rethink about improvement of their supply chain internal and external processes. It helps in improvement of transparency and provenance by removing shortfalls and building a better organizational control overall. However, blockchain faces numerous challenges, e.g., transaction speed, decentralization, scalability, interoperability, and lack of standardization that could affect its adoption across organizations. However, a greater number of research are required to overcome the governance, standardization, and technological challenges involved within. Concisely, blockchain in supply chain is still in initial phase, many improvements are needed for better adaptation of blockchain using Machine Learning, Neural Network algorithms to make optimized computation decision of blockchain framework. In this paper, we studied and discussed about blockchain and its type, consensus mechanism, blockchain in supply chain, key issues of blockchain and supply chain and intelligence in blockchain. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Global Governance of Artificial Intelligence: Ethical, Legal Challenges and Changes in Economy and Business
Artificial intelligence (AI) and global governance are an inclusive platform to discover the policy challenges worldwide augmented by artificial intelligence. The platform has three predominant subjects: AI and the global order, governance of AI, insights on the platform consider for mapping of AI futures. AI has great impact in revolution of geopolitical order and the reaction of multifaceted organizations which minimize AI risks and unpremeditated significances and its social aids are maximized through governance structures. It focusses on setups, collaborations, and tensions between different actors responsible for plan, deployment, support and governance of AI. AI improves the benefit for human well-being, productivity, social good, and safety with substantial risks for workers, developers, firms, and governments. The actors and organization begin to realize the ethical, legal, and regulatory challenges associated with AI. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of AI Privileges to Combat Widen Threat of Flavivirus
In order to prevent the extraordinary spread of sickness caused by Flavivirus, the healthcare business as well as public health are working tirelessly. Individual lives have been affected, but mosquito-infested public locations have made a considerable influence on the general publics health. Site adaptability, climate change, and inadequate healthcare services and surveillance all contribute to the spread of the virus. The potential dangers of this virus, on the other hand, have been uncovered through extensive and ongoing research in the healthcare business. Modern healthcare facilities may benefit from the reasoning capabilities and ever-evolving analysis techniques provided by artificial intelligence. More conclusive findings have been demonstrated in the realm of AI applications in healthcare domains such as cancer, neurology, and cardiology. A number of research works have justified the use of AI-oriented algorithms for intelligently handling unstructured and huge healthcare data. When it comes to using artificial intelligence (AI) to identify, forecast, diagnose, and treat disease using data from public health and biological databases, the current effort aims to undertake an extensive examination. There may be issues in integrating assistive technology into the current healthcare system, as well. Because of this review, we hope that by merging AI research with clinical and public health specialists, critical knowledge may be extracted from data in order to unchain the relevant information of Flavivirus disease from its chains. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Traffic Management in Forest and Ecosystem Conservation. A Study on NH 766 Through Bandipore National Park and Proposing a Traffic Management Plan with Alternate Route Consideration
Transportation network is inevitable in the developing world. In India where we have a rich forest cover, many of the roads are passing through eco-sensitive areas such as national parks and wildlife sanctuaries. There are issues being reported due to these roads passing through the eco-sensitive areas such as animal deaths due to road accidents, loss of habitats, fragmentation of ecosystems, and loss of forest cover. The CalicutKollegal national highway, NH766 is passing through Bandipore national park on the stretch which connects Sultan Bathery and Gundelpette. Recently, a conflict had risen between environmental activists and the public for imposing a complete traffic ban along the NH766 passing through the Bandipore NP. A baseline study had conducted on the NH766, and the impact of the same on the ecosystem existing is analyzed through the data collected. A network analysis is performed on the alternate route available for bypassing the traffic. Traffic management plan and policies are derived out of the analysis on the baseline data collected and the inferences drawn from the network analysis performed on the alternate routes. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative Study of Graph Theory for Network System
The historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Environmental Concern in TPB Model for Sustainable IT Adoption
Rapid advancement in technology and continuous environmental degradation has attracted the attention of practitioners toward sustainable solutions. This study aims to investigate educated millennial beliefs and behavior toward sustainable IT practices. The Theory of Planned Behavior (TPB) model deployed in the study was extended through perceived environmental responsibility. A survey was conducted to examine the sustainable IT adoption behavior of millennial in the National Capital Region, Delhi India. Variance based partial least square structure equation modeling was employed to evaluate the hypothesized model. Findings of the study confirm environmental concern (ER) a precursor for attitude (ATT), perceived behavioral control (PBC), and subjective norm (SN). Further, there is a significant positive influence of ATT, PBC, and SN on the adoption intention of sustainable IT practices, followed by the effect of adoption intention on actual adoption behavior. Study disseminates valuable insights to policymakers and marketers to formulate strategies and policies to attain sustainability through sustainable IT practices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
ML-Based Prediction Model for Cardiovascular Disease
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Novel Approach for Segmenting Coronary Artery from Angiogram Videos
This paper addresses the research focuses on coronary artery disease; it is one of the major heart diseases affecting the people all around the world in the recent era. This heart disease is primarily diagnosed using a medical test called angiogram test. During the angiogram procedure the cardiologist often physically selects the frame from the angiogram video to diagnose the coronary artery disease. Due to the waning and waxing changeover in the angiogram video, its hard for the cardiologist to identify the artery structure from the frame. So, finding the keyframe which has a complete artery structure is difficult for the cardiologist. To help the cardiologist a method is proposed, to detect the keyframe which has segmented artery from the angiogram video. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Mining Approaches forHealthcare Decision Support Systems
Data mining is a user-friendly approach to locating previously unknown or hidden information in data. The employment of data mining technologies in the healthcare system may result in the finding of relevant data. Data mining is used in healthcare medicine to construct learning models that predict a patients condition. Data mining technologies have the potential to benefit all stakeholders in the healthcare industry. For example, data mining may aid health providers in detecting theft and fraud, medical organizations in making customer service management decisions, physicians in discovering effective therapies and best practices, and customers in obtaining suitable and less expensive healthcare. Contemporary systems, due to their complexity and size, are unable to control and analyze the huge amounts of data generated by healthcare operations. Data mining is a technique and mechanism for converting a large amount of data into useful information. The fundamental purpose of this research is to look at what makes clinical data mining unique, to give an overview of existing clinical decision support systems, to identify and select the most common data mining algorithms used in modern Health and Demographic Surveillance System (HDSS), and to compare different data mining algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Growth of cerium oxide nanorods by hydrothermal method and electrochromic properties of CeO2/WO3 hybrid thin films for smart window applications
Innovative electrochromogenic nanomaterials such as composite materials and also hybrid films can improve electrochromic performance, because of their potential application qualities in electronic, low-power screens, automotive anti-reflect mirrors, and smart windows. In this study, we used a hydrothermal method to used grow the CeO2 nanorods both with and without HCl added to the solution. And also, DC magnetron sputtering was used to deposit the tungsten oxide films on the cerium oxide nanorods. The surface plasmon effect changes with the size of CeO2 Nanorods, and this phenomenon influences electrochromic outcomes. The electrochromic characteristics of CeO2/WO3 nanostructures on FTO-coated glass are examined in the visible spectrum to use a 0.5 M concentration of H2SO4 as such electrolyte. At 600 nm, these structures produce significant optical modulation (50 %) and coloring efficiency (11.60 cm2/C at 700 nm). 2022 -
Search Engine Optimization for Digital Marketing to Raise the Rank, Traffic, and Usability of the Website
According to the Content Marketing Institute, 93% of online experiences start with search. That is the explanation search. Thats why search promoting is a crucial procedure for all organizations to improve and develop their organizations. At that time the marketers and the clients who paid for advertisements started analyzing SEO and SEM. Web crawler promoting expands the perceivability of sites through SEO or through paid publicizing with the plan of expanding traffic to the site. SEM eludes to all advertising exercises that utilization web index innovation for promoting purposes. These incorporate SEO, paid postings and advertisements, and other web crawler related administrations and capacities that will expand reach and introduction of the site, bringing about more prominent traffic. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification on Alzheimers Disease MRI Images with VGG-16 and VGG-19
Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition
Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Customized SEIR Mathematical Model to Predict the trends of Vaccination for Spread of COVID-19
The uncertainty in life plans, restrictions on physical classrooms, loss of jobs, large number of infections and deaths due to COVID-19 are some significant causes of concern for the public as well as Governments all over the globe. Moreover, the exponential increase in the number of infected people in a short time is responsible for the collapse of the health industry during the pandemic caused by COVID-19. The health experts recommended that the quick and early diagnosis followed by treatment of patients in isolation is a way to minimize its spread and save lives. The objective of this research is to propose a customized SEIR model to predict the trends of vaccination in the USA. The experimental results prove that the Moderna vaccine reports the efficacy of 93%, which is higher than the Pfizer and Johnson and Johnson vaccines. 2022 ACM. -
Social Characteristics and Its Relationship with Intent to Stay-with Reference to Financial Sectors
One of the challenging tasks of the HR management of the organization is to design the job in such a way that facilitates a good work culture/atmosphere for the employees to ensure their stay in the organization. The present study analyzed the role of social characteristics of the job with their intention to leave among the employees working in the finance sector. Primary data were collected from 250 employees working at all levels of management in the finance and banking sector in Indias southwest region through the Convenience sampling method. Morgeson and Humphrey (2006) developed the work design questionnaire which was adopted and used for data collection. Hierarchical multiple regression used for applied for data analysis. The results show that social characteristics cannot predict intent to stay. Also, age and gender do not have a significant role as mediating factors to social characteristics and intent to stay. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Efficient Underwater Image Restoration Model for Digital Image Processing
Digital image processing (DIP) is showing a massive growth intodays trending world particularly, in the field of biological research. Underwater image analysis plays a vital role, where the images are easily prone to attenuation and haziness. Capturing underwater images has always been a challenging job due to dispersion and scattering of light inside water on a high scale. Several image enhancement and restoration methodologies are currently available to address these issues, where hazing and color diffusion are viewed as a common phenomenon in it. Such procedures normally includes two basic methodologies in it, namely dehazing and contrast or color enhancement, which improves the overall output of the degraded image. However, the quality and processing time of the images can still be enhanced with additional techniques incorporated to it. This work is intended toward proposing one such channel called improvised bright channel prior for dehazing the underwater images. The technique further improves on the existing methodologies by estimating the atmospheric light and refining the transmittance of the image along with image restoration. The experimental results show that the improvised bright channel prior methodology is found to perform better in dehazing underwater images with a balanced intensity in terms of dark and white patches obtained from it. When comparing and contrasting the processing time of the proposed methodology with the existing techniques, it is found that improvised bright channel prior performs better. Also, the quality of the dehazed underwater image obtained from the proposed channel is found to be effective when compared with the existing channels. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Digital Platforms and Techniques for Marketing in the Era of Information Technology
Digital marketing is the promotion of a product or service through at least one form of electronic media. This form of marketing is distinct from traditional marketing, but it uses some of the ideologies of traditional marketing. This research article examines the various technologies and platforms used in digital marketing that allow any organization or business to do this form of marketing and study what works for them and what does not. The article also explores the recent advancements in digital marketing due to the increase in users and the vast amount of data collected from these users. The two main advancements analyzed and discussed in this paper are machine learning (ML) and artificial intelligence (AI) tools. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Alkali-Activated Materials - A Review for Sustainable Construction
New, sustainable low-Carbon Dioxide (CO2) construction materials must be developed for the global building sector to decrease its environmental impact. During the last several decades, Alkali-activated Materials (AAMs) is a Portland cement-free form, have been intensively researched as a potential alternative for ordinary Portland cement concrete (OPCC), with the objective of lowering CO2 emissions while repurposing a large volume of industrial waste by-products. The suitability of using AAMs made up of industrial waste by-products such as blast furnace slag (BFS), calcined clay (metakaolin), and fly ash (FA) was investigated in this study utilizing a performance-based approach that was unaffected by binder chemistry, history, or environmental effect, Binder paste microstructural assessment and influence on engineering effectiveness, including fresh and hardened characteristics of these materials, In the Viewpoints area, we analyze specific premature phase and long-phase performance of AAMs, as well as Upcoming scientific breakthroughs are also discussed in the Viewpoints section. 2022 American Institute of Physics Inc.. All rights reserved. -
Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company
Machine learning models are being used for better insights and decision making across many industries today. It shows to be quite useful for businesses in the ecommerce industry as well due to the vast amount of data generated and its potential. This research aimed to find insights on future sales of an ecommerce company [1]. The vast number of variables including both categorical and continuous variables under product data, customer information, transaction information, led us to implement a prediction model using regressors rather than just time series forecasting techniques. First an RFM (Recency, Frequency and Monetary) based clustering algorithm was used to get customer related information and then integrate those results into a regressor to achieve the desired goal of prediction of sales. Two schemes were tested one being predictions on individual clusters and the other where the clusters were one hot encoded back into the main data. Results show quite high accuracy of prediction. The high R-squared also indicated that our hypothesis of including the variables contributed significantly to the predicted sales values was correct in this case. This research fulfills an identified need to understand how machine learning algorithms can be implemented by multiple algorithms being integrated in sequential and logical orders thus helping derive business specific strategies rather than making it a mere technical process by providing empirical results about how the predicted sales values along with given inputs can contribute in business decision making relating to marketing, inventory management, dynamic pricing or many more such strategies. 2022 ACM.