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Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The mediating role of parental playfulness on parentchild relationship and competence among parents of children with ASD
Purpose: The difficulties of a child diagnosed with autism spectrum disorder (ASD) can lead to behaviours that are quite challenging for parents to understand and address. Most of the parental studies of ASD focus on the challenges faced by the parents. This study aims to adopt a strength-based model that investigates the mediating role of parental playfulness in the association between parentchild relationship and parental competence. Design/methodology/approach: This study is a quantitative study that adopts a correlational research design. The mediation analysis explores the role of parental playfulness as a mediator in the association between parentchild relationship and parental competence. The sample consisted of 120 parents of children diagnosed with ASD from India, selected using a purposive sampling technique. Findings: The mediation analysis results indicate that playfulness among parents of children with ASD was found to function as a partial mediator in the relationship between parentchild relationship and parental competence. This could suggest that more playful parents have better parentchild relationships and are competent in parenting. Research limitations/implications: These findings have importance in understanding the role of playful interaction on parentchild relationships and parenting competence, having implications for further research. Enabling playfulness in parenting will enhance children and parents to promote their relationship and thus feel competent to bring positive light in their lives. Practical implications: Most often, the clinicians are concerned with addressing only the autistic symptoms; it is also essential to look into parental well-being. Practical playful interaction training should help parents establish a rapport, understand, adjust and adapt with their child. Social implications: Practical intervention and training plans can be suggested to all family members to improve the condition of the child and the familys general well-being. As the study focused on the clinical population, the findings could provide useful inputs for mental health professionals and counsellors. Originality/value: There are some theoretical and empirical evidence that support positive outcomes of playfulness on personal well-being (Atzaba Poria, in press; Yue et al., 2016; Proyer, 2014). Although there has been some interest in the impact of childrens playfulness on their development (Bundy, 1997), little is known about the influence of parental playfulness on parents and children. Therefore, addressing these gaps, this empirical study focusses on investigating the role of parental playfulness in parentchild relationship and parental competence, rather than considering external challenges of parents based on the ASD childs behavioural challenges and autistic features. 2021, Emerald Publishing Limited. -
Machine Learning Techniques in Predicting Heart Disease a Survey
The heart serves an important role in living creatures. Diagnosis and forecast of cardiac illnesses demand greater precision, perfection, and accuracy because such tiny mistakes can lead to weariness and death. Numerous heart-related deaths have occurred, and the incidence rates have been rising over time. Predicting the development of heart disorders is important to work in the medical industry. Every month, many databases related to the patient are kept. The information gathered can be used to predict the occurrence of future diseases. This article gives an outline of cardiovascular diseases and modern treatments. Also, the focus of this research is to outline some current research on applying machine learning techniques to predict heart disease, analyze the many machine learning algorithms employed, and determine which technique(s) are useful and efficient. Artificial neural network (ANN), decision tree (DT), fuzzy logic, K-nearest neighbor (KNN), Naive bayes (NB), and support vector machine (SVM) are data mining and machine learning approaches used to predict cardiac disease. This paper includes an overview of the present method based on features, the algorithms are compared, and the most accurate algorithm is analyzed. 2022 IEEE. -
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE. -
Educational Deprivation of the Tribes Insights from the Block-level Study
The paper examines the nature of tribal deprivation, with specific focus on the issue of education. The research delves into the supply- and the demand-side factors, which determined the state of education within a region. Reaffirming the deprivation faced by the tribal communities, the study identifies specific factors that cause marginalisation. It points to the failure of the uniform tribal development programme to deal with the context-specific problems and thereby achieving the targeted results. The paper suggests the importance of not assuming the homogeneity of tribal societies, and need for public policies that are sensitive to this fact, in order to translate the goal of empowerment into a reality. 2023 Economic and Political Weekly. All rights reserved. -
Blockchain Scalability: Solutions, Challenges and Future Possibilities
In recent years, blockchain has received a lot of interest and has also been widely adopted. Yet, blockchain scalability is proving to be a difficult problem. To create a new node in platforms like Bitcoin takes few days of time. This scalability problem has few proposed solutions. The present alternatives to blockchain scalability are divided into two groups in this paper: first layer and second layer techniques. Second layer solutions suggest procedures that are deployed outside of the blockchain, while first layer methods propose adjustments to the blockchain (i.e., altering the blockchain design, such as block size). We concentrate on sharding as a viable first-layer solution to the scalability problem. The thought behind sharding is to split the blockchain network into numerous groups, each processing a different set of transactions. Furthermore, we compare few of the already available sharding-based blockchain solutions and present a performance-based comparative analysis in form of the benefits and drawbacks of the existing solutions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images
In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analytical Study of Security Enhancement Methods on Diverse Cloud Computing Platforms
Cloud storage is a convenient and virtually limitless storage option for the bulk of data technology is producing in recent times. Data security in cloud is not so robust as data owners need to depend upon the service providers for the safe storage. In this paper, we have identified few broadly used cloud computing paradigms: mobile cloud, cloud-based IoT and multi-tenant cloud. Mobile cloud helps reduce the data storage overhead on the mobile device and give users access to their personal data as and when required through cloud access. Cloud-based IoT helps the network of IoT devices, which is growing exponentially, to create on-demand cloud repositories. Multi-tenant cloud platforms are cloud environment accessed by more than one user. Few recent and related research work which aims at enhanced security from all these three paradigms is discussed and analysed. Encryption and similar network securing methods are used for mobile cloud and cloud-based IoT. For multi-tenant cloud, the objective is to keep the user spaces separate to keep their resources confidential. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
She Shores: A Study on the Lives, Challenges and Resilience of Women of the Koli Fishing Community in Mumbai
This study delves into the lives of women from the Koli fishing community in Mumbai, aiming to illuminate their unique life experiences and the daily struggles that often remain hidden beneath their prosperous facade. It endeavours to examine their agency and adaptive strategies employed to navigate these challenges. The research was conducted in Pachubandar, Vasai, located in the western suburbs of Mumbai, which stands as one of the prominent Koli settlements in the city. Employing a qualitative research approach coupled with an exploratory research design, the study engaged ten participants, comprising seven Koli women and three key informants from the community. Additionally, an observational analysis of four retail and wholesale fish markets in Mumbai was conducted to gain insight into the working conditions of Koli fisherwomen. This study adopts a gender-focused perspective to scrutinise the contextual vulnerabilities that shape the lives of Koli women. It underscores the paradox wherein, despite playing a pivotal role in sustaining both their families and the traditional fishing occupation, their contributions often go unnoticed. The Koli women face severe deprivation due to their limited access to property and decision-making authority. They find themselves entangled within traditional norms and patriarchal structures, which impede their access to essential assets and diverse livelihood resources. Although they significantly contribute to the fishery sector, their struggles, needs, and aspirations are frequently disregarded due to their lack of representation and involvement in decision-making bodies. The majority of these women work under precarious conditions, devoid of proper infrastructure, resources, and security. Furthermore, the evolving dynamics within the fishery sector, driven by rapid urbanisation and modernisation, have a profound impact on the lives and traditional livelihoods of Koli women. They now confront issues such as dwindling fish catches due to environmental degradation, heightened market competition, reduced livelihood spaces brought about by shifting urban and coastal landscapes, altered labour relations, and technological advancements. Consequently, they find themselves caught between the conflicting forces of tradition and modernity. The research also sheds light on the strategies devised by Koli women to resist and adapt to the uncertainties and challenges they encounter, ultimately safeguarding their livelihoods through self-organisation. The study emphasises the imperative to acknowledge their contributions as 'visible work' and advocates for the incorporation of gender considerations when formulating policies and development strategies within the fisheries sector. 2024 Meghna Roy. -
Automated Verification of Open/Closed Principle: A Code Analysis Approach
The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE. -
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Pattern of Carbon Dioxide Emission, Economic Growth and Energy Consumption in South-Asian Countries: An Empirical Analysis
The main aim of this chapter is to analyse the pattern of environmental pollution as represented by per capita carbon dioxide emission (PCCO2), per capita gross domestic product (PCGDP) and per capita energy consumption (PCEC) and their nexus in case of South-Asian countries for the time period 19912014. Econometric tools such as panel co-integration and fully modified ordinary least squares have been used to study the relations. A positive significant relationship has been observed between PCGDP and PCCO2 emission. In addition, an increase in PCEC also has a positively significant impact on PCCO2 emission. Therefore, the governments of all the countries need to come together and take steps to curb the rising carbon emission since neither the problem nor the responsibility is restricted to one country alone. There is a need for countries to increase the consumption of renewable energy and explore alternate options that are fewer dependents on coal or any other fossil fuel. On priority, economies in South-Asian region should focus on sustainable economic activities by balancing growth of economy with clean environment. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
ETHICAL CONFLICTS AMONG THE LEADING MEDICAL AND HEALTHCARE LEADERS
Today, the whole world is fighting the COVID-19 pandemic. In these circumstances, medical professionals are being viewed as the frontline warriors who are risking their lives for the sake of helping, caring, and curing these patients. However, in these difficult times, there are few medical professionals and health care providers who are taking advantage of this situation and taking advantage of distressed and distraught patients at will. A conflict between professional and personal ethical values makes them depressed and puzzled. It is tough for them to maintain a good image of their profession and business. The objectives of this study are to review the ethical conflict amid the ongoing Covid pandemic and post-Covid pandemic (vaccination period) in the context of medical professionals and health care providers. The paper is designed based on a literature review. Almost fifty-two research papers, articles, survey reports, and newspapers were studied in the context of ethics in business/profession. After reviewing moral distress is ongoing and post-pandemic period, the researchers have tried to present the medical professionals and health care providers' critical situation to give priority to their professional ethics or personal interest. School of Engineering, Taylor's University -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE. -
Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Impact of Digital Media Marketing on Consumer Buying Decisions
Digital Marketing has become one of the most discussed topics in the field of management in the recent past. With the advent of social media, digital marketing has even garnered more attention. It has directly or indirectly influenced the buying behaviour of the customers also. This paper has tried to understand the impact of digital marketing in influencing the impulsive buying behaviour of the customers. 2024 IEEE. -
Determining the Antecedents and Consequences of Brand Experience: A Study to develop a Conceptual Framework
In the marketing literature, one of the most talked- about subjects is brand experience (BE). Through an examination of the numerous studies conducted by BE researchers, this report attempted to determine the significance of BE in the body of recent literature. This paper culminates in the creation of a conceptual framework that prospective investigators might utilize to discern the diverse pathways inside BE. 2024 IEEE. -
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Organization justice impact on employee work engagement
Research methodology: For the study 200 employees of selected Educational Institutions in North NCR was taken as respondents. Data was collected using standard questionnaire containing standard scaled of distributive, procedural, interactional, trust and employee engagement. The relationships between justice perceptions and work engagement were analyzed using correlations and regression analysis. Findings: The analysis of the study indicates that there is a strong and positive relationship among organization justice and employee engagement. The study also indicates that procedural, interactional and distributive justice are inter related with each other. Further, distributive and interactional justice take precedence over procedural justice in determining job engagement, while distributive justice plays the most important role in determining organization engagement (OE), followed by procedural and interactional justice. Limitations: This paper adds to the very small number of studies that have investigated the role of interactional justice in enhancing job and OEs. It has also established inter-relationships between the three dimensions of organizational justice and their individual roles in determining job and OEs. 2020 SERSC.