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The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
Crude oil is a valuable asset class which forms the nucleus of the energy core of the transport sector for any country. According to report [1], crude oil helps in meeting 93% of energy needs for the transportation sector globally. It has been projected across various forums that crude oil along with coal and natural gas is going to satisfy world energy needs for the forthcoming years. Consequently, it has been observed that fluctuations in crude oil prices tilt the economies of scale across the world. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE. -
Reading behind the tweets: A sentiment Clustering Approach
Market sentiment influence crude oil future prices in direct or indirect way. In order to measure the polarity of market sentiment various techniques has been deployed by industry and academia alike. This pilot study successfully introduced two instruments, namely topic modeling and Sentiment clustering, to unearth the prevailing sentiments behind crude oil future pricesThree main conclusions that can be drawn from empirical results are. First, the K-Means clustering algorithm is an effective technique for sentiment clustering compared to Louveian and MDS clustering techniques. Second sentiment polarity-related positive sentiments have shown more variations in comparison to neutral and negative sentiments. Third It is possible to extract the keywords related to essential factors influencing crude oil prices using the LDA technique under topic modeling 2022 IEEE. -
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. -
Women at work: The cultural and creative industries
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Effectiveness of gamification in facilitating microlearning for gen Z
This chapter offers a thorough examination of the uses, advantages, and difficulties of gamification in higher education. In contrast to game-based learning, gamification uses specific game features to improve the learning experience. This chapter investigates the use of gamification to engage and inspire Generation Z (Gen Z) pupils with the goal of enhancing their academic performance. It underlines the necessity for game development that increases motivation and engagement in educational settings and highlights the measurement of student progress based on completed activities. Effective instructional approaches are crucial in a time where there is a constant stream of information and people have short attention spans. A promising approach to overcoming these difficulties in both online and offline education utilizing ICT technologies is offered as gamified microlearning, which combines microlearning and games. 2024, IGI Global. -
Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management
Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption. 2021 IEEE. -
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Intensity of hospital waste generation and disposal in the selected hospitals in Kerala, India: an analysis based on hospital ownership
Management of hospital wastes has been considered as an integral part of hospital hygiene and infection control, which in turn depends on the intensity of waste generation and disposal. This study analyses the ownership-wise intensities of hospital waste generated, treated and disposed in the selected hospitals in the state of Kerala, India. These intensities are examined using secondary data collected from four districts of Kerala for the period from 2010 to 2014. The intensity of hospital waste generation is measured on the basis of per bed per kilogram per day and also per patient per kilogram per day basis. The study shows that private hospitals are producing significantly higher amount of waste than government and co-operative hospitals. However, private hospitals are found to be more efficient compared to government hospitals in treating and disposing the hospital waste. It is also found that the co-operative hospitals are well-organized in treating and disposing the liquid waste compared with other hospitals in Kerala. 2023, The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature. -
STUDY ON ORGANIZATIONAL CULTURE IN RELATION TO HUMAN RESOURCE MANAGEMENT PRACTICES IN HOTEL INDUSTRY IN BANGALORE
This research focuses and examines the relationship between organizational culture and human resource management practices in hotel industry in Bangalore. Organizational culture is palpable in any organization which has a diversified work force. Hotel industry is built with employees with various values and beliefs. These factors reflect in the culture and are related with the various human resource management practices. There is a significant relationship between organizational culture and human resource management practices in the hotel industry in Bangalore. There were eleven dimensions of organizational culture and thirteen dimensions of human resource management practices that were considered for examining the relationship between organizational culture and human resource management practices. The sample for the study was 135 mid-level managers of different category of hotels in the geographical location of Bangalore. Organizational culture survey scale developed by Pareek, U. (2003) and human resource management practices scale developed by Sebastian, S., & Patrick, H. A. (2010) was administered for this study. This research concludes by the finding fact that there is significant relationship between organizational culture and human resource management practices in the hotel industry. The study also shows that the culture in hotel industry is narcissistic. Keywords: Organizational culture, Human resource management practices, hotel industry -
Workplace bullying in the service sector
Context: Bullying is a problem that people, the world over, grapple with. It is manifest in different forms among different sections of people. Despite its prevalence, workplace bullying has not received much attention in scholarly literature in India. It is also not widely acknowledged as a threat to individual and organizational well-being. The purpose of this study is to add to the existing body of literature on the topic and to draw attention to the gravity of the issue. Aims: The primary objectives are to identify if there exist variations in its incidence on the basis of gender and years of experience, to identify the source of negative behavior, and the type of bullying that is most prevalent. Settings and Design: The study is a type of cross-sectional, descriptive study. Subjects and Methods: Data have been collected from a sample of 84 respondents using the Work Harassment Scale. All respondents are white-collar employees of the service sector in the cities of India. The data were analyzed using IBM SPSS v25. Results: The results find that there is no difference in the incidence of bullying on the basis of either gender or years of experience. Moreover, the source of negative behavior is generally one's superiors, and the most prevalent type is 'verbal aggression.' Conclusions: The study concludes with suggestions of steps to be implemented at the national and organizational level, to combat the problem. 2022 Authors. All rights reserved. -
Cloud based ERP Model using Optimized Load Balancer
Enterprise Resource Planning (ERP) and Cloud computing are turning out to be increasingly more significant in the field of Information Technology (IT) furthermore, Communication. These are two distinct segments of current data frameworks, and there are a few inside and out examinations about Enterprise Resource Planning on cloud computing framework. ERP frameworks are related with a few issues, for example, shared synchronization of multi-composed assets, constrained customization, massive overhauling cost, arrangement mix, industry usefulness, reinforcement support and innovation refreshes. These issues render ERP frameworks execution excruciating, complex and time-devouring and create the need for a huge change in ERP structure to upgrade ERP frameworks foundation and usefulness. Cloud Computing (CC) stages can defeat ERP frameworks inconsistencies with financially savvy, redid and profoundly accessible figuring assets. The objective of this examination is to blend ERP and CC benefits to lessen the factor of consumption cost and execution delays through a proposed system. For this reason, investigate the unmistakable issues in current ERP frameworks through a complete correlation between ERP when moving to CC condition. Also, a conventional structure is proposed for 'Cloud-based ERP frameworks'. 2020 IEEE. -
Sentimental analysis on voice using AWS comprehend
Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file. 2020 IEEE. -
A Metal-Free KOtBu-Mediated Protocol towards the Synthesis of Quinolines, Indenoquinolines and Acridines
An expeditious strategy has been developed for the synthesis of diverse quinolines, indenoquinolines and acridines using KOtBu-mediated reaction conditions. The designed process utilizes 2-aminoaryl carbaldehydes/2-aminoaryl ketones and methyl/methylene group containing ketones as readily available feedstock. The chemical transformation was affected at room temperature within a short duration of time to obtain diverse N-heterocycles yields up to 92 %. The established process also exhibits considerable functional group tolerance with an operational simplicity. 2024 Wiley-VCH GmbH. -
Applications of neuroscience in education practices: A research review in cognitive neuroscience
The human brain is the most complex and mysterious organ in the body responsible for learning. Applications of neuroscience and genetics need to be comprehended to modulate teaching and learning practices in education. Considering the scope for application of advanced sciences in education practices, this book chapter simplifies and reviews ten critical research findings relevant for students and teachers for classroom applications and for modulating learning patterns for different age groups. The concept is also relevant for parents and the academic fraternity at large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
ENHANCING FOREST ECOSYSTEM RESILIENCE TO CLIMATE CHANGE WITH VANET AND INTEGRATED NATURAL RESOURCES MODELLING
Forest ecosystems are immediately threatened by rising global temperatures and changing climatic patterns. Periodic assessments also contribute to a reduction in the frequency of monitor-ing, which could cause environmental changes to go unnoticed. This work develops a novel real-time monitoring and early warning system to meet this difficulty. By integrating Vehicular Ad Hoc Networks (VANET) with sophisticated natural resources modelling, the proposed method aims to revolutionise the way forest ecosystems are managed. This study strives to design and implement a comprehensive system that harnesses the power of VANET to collect real-time data from sensors deployed on vehicles, and integrates advanced modelling to predict, assess, and mitigate risks to forest ecosystems. The proposed method involves deploying a network of vehicles equipped with environmental sensors within VANET. These sensors continuously collect data on crucial environmental parameters, such as temperature, humidity, air quality, and spatial information. The data are transmitted through a secure VANET communication protocol to a centralised processing unit, where it is integrated with climate models and ecosystem dynamics models. Resilience metrics and thresholds are defined to trigger a tiered early warning system. Preliminary testing of the system demonstrates promising accuracy and responsiveness. The integrated approach allows for dynamic risk assessment, enabling the identification of potential threats such as extreme weather events, invasive species, or disease outbreaks. Early warnings prompt adaptive management strategies, showcasing the systems potential to significantly enhance forest ecosystem resilience. This research presents a pioneering solution to the escalating challenges faced by forest ecosystems in the time of climate change. The real-time monitoring, early warning system, amalgamating VANET and integrated modelling, stand as a robust tool for forest managers, policymakers, and communities to proactively address environmental changes. The findings underscore the systems potential to transform forest management practices, marking a critical step toward sustainable and resilient ecosystems. 2024, Scibulcom Ltd. All rights reserved. -
Augmented Reality Based Medical Education
The education in medical field requires both theoretical knowledge and practical knowledge. It is important for medical student to acquire effective practical skills. Since the students apply the theoretical knowledge in practical manner in human body. Human body is very volatile, gentle, and difficult system. If a student apply trial in the humans for practical knowledge, there may cause the human error which leads to death of the person. To avoid this, the proposed system 'Augmented Reality Based Medical Education' is useful. Augmented reality makes the learning process more interactive and interesting. It can reproduce specific circumstances that assist students to rehearse with virtual objects that look like the human body and organ. Like traditional learning, it does not require real patients. By this way, augmented reality prevents risk of human life. Medical education with augmented reality extensively provides real time experiences. It has low risks and also affordable. When any human error occurs, there is no human loss. So the human life can be prevented by the system. The proposed system is developed using tools like Unity which is the complete platform for the developing our application, Vuforia-developer portal, a tool to create image target and Blender which is used to create 3D objects. 2023 IEEE. -
The influence of social media on investment decision-making: examining behavioral biases, risk perception, and mediation effects
The increasing use of social media platforms for investment-related information and advice has raised concerns about the impact of social media on investment choices. In this paper, we investigated the role of behavioral biases and risk perception in investment decisions. Specifically, this paper aims to explore the impact of social media on these factors and their influence on investment decisions. To achieve this aim, we investigated the existing works on the impact of social media on investment decisions, including its influence on behavioral biases and risk perception. We also collected data through an online survey from individual investors who use social media for investment-related information and advice. The survey measured their investment decisions, behavioral biases, risk perception, and the impact of social media on these factors. The valuable insights offered by this paper shed light on how social media affects the decisions made regarding investments and extend our understanding of the role of behavioral biases and risk perception in this context. Our results indicate that social media has a significant impact on the investment-related behaviors and perceptions of individual investors. Specifically, social media can exacerbate the effects of behavioral biases, such as herding and overconfidence bias, and influence risk perception. Moreover, the paper highlights the significance of managing social media use to make rational investment decisions. The paper's results can help individual investors make more informed investment decisions by understanding the impact of social media on their investment-related behaviors and perceptions. Moreover, the paper provides useful information to policymakers and financial regulators to develop guidelines for the responsible use of social media in the investment industry. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. -
Smart Satellites: Unveiling the Power of Artificial Intelligence in Space Communication-A Study
The incorporation of Artificial Intelligence (AI) into space and satellite communication represents a paradigm shift in the way we explore, navigate, and communicate beyond our planet. This article is about the impact of AI on satellite operations, and the broader field of its communication to the earth. The article explores how AI enhances spacecraft autonomy, mitigates signal degradation, and improves the overall reliability of communication performance Satellite communication benefits from AI-driven advancements, in the areas of signal processing and optimization. Furthermore, examines the integration of AI in space-based challenges and opportunities associated with large-scale satellite networks. AI playing a crucial role in detecting and mitigating cyber security threats in space communication systems. This paper comes up with the perception into the future trends and potential advancements in AI applications for space and satellite communication. 2024 IEEE. -
Challenges of Indian girls with maternal schizophrenia
Schizophrenia, earlier known as dementia praecox, is considered to be one of the most devastating mental illnesses due to its impact on the individual as well as family members. The Indian context characterized by ones rootedness to family, warrant enquiry about difficulties and burnouts faced by girl children.When it is the mother who is suffering from the illness, there tends to be a huge lag in terms of primary care giving. A disturbed home environment along with inadequate parenting have shown to adversely affect the girl children. The present qualitative research study aimed to explore challenges faced by the girl children with maternal schizophrenia with the help of 43 Mental Health Professionals (MHPs) across India. Interpretative Phenomenological Approach (IPA)was adopted and interviews were conducted using a validated interview guide. Thematic analysis revealed that girl children whose mothers are diagnosed with schizophrenia faced challenges in self, family and social sphere of life. Neglect, self blame and the question why me were recurrent themes.They experienced difficulties in cognitive, behavioral and social domains. The added burden of family responsibilities and social stigma made the surroundings challenging.Exploring the world of girls with maternal schizophrenia would deepen our understanding about impact of schizophrenia on family members and aid us develop interventions to support the care givers. 2019 Oriental Scientific Publishing Company. All rights reserved.