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Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach
Experts in the finance system have long found it difficult to estimate stock values. Despite the Efficient - market hypothesis Principle claim that it is difficult to anticipate share prices with any degree of precision, research has demonstrated that share price movements could be anticipated with the proper levels of precision provided the correct parameters are chosen and the proper predictive models are created. individuals who are adaptable. The share market is unpredictable in essence, making its forecasting a difficult undertaking. Stock prices are affected by more than economic reasons. In this project, Arima, LSTM and Prophet models are used to predict the future way of behaving share price, the datasets has been obtained from NSE, share price prediction algorithms have been created and tested. According to the empirical findings, the LSTM model would be used to anticipate share prices rather well over a substantial amount of time with exactness. 2022 IEEE. -
Implementation of tokenization in natural language processing using NLTK module of python
With the advancement of technologies, now it is possible to analyze the large amount of unstructured text circulated online with various tools and methods for understanding the changes as well to infer meaningful insights from the text data. In this work, the aim is to understand how Python can be used for text analytics by the help of various libraries available in it. The natural language processing (NLP) is being used to analyze and synthesize natural language and speech in Python. 2023 Scrivener Publishing LLC. -
Implementation of vendor-managed inventory in hospitals: an empirical investigation
This research aims to determine critical success components for implementing the vendor-managed inventory (VMI) and test their influence on the inventory in Indian hospitals. The independent and dependent components of the research are identified from the extensive literature review. The independent variables are top management commitment, supply chain strategy, business process integration, continuous improvement, resource sharing, and information technologies adoption. The dependent variable identified is the adoption of vendor-managed inventory. The study results suggest that the proposed latent variables significantly impact the VMI and significantly contribute to VMIs implementation and sustainability. The study highlights the importance of VMI in Indian hospitals, and therefore, it will help the management focus on the VMI for enhanced operational efficiency. Previous studies have not empirically tested the impact of the suggested practices for VMI in Indian hospitals. The analysis would help evaluate VMI adoption in Indian hospitals. Copyright 2024 Inderscience Enterprises Ltd. -
Implementation Strategies for Green Computing
In this chapter, we look at how renewable energy sources can be integrated into the planning, design, and construction of long-term sustainability in green buildings. When it comes to establishing a framework for environmentally friendly building, there are two primary schools of thought. One is related to the use of conventional architecture and low-energy construction material. The fundamental focus of green building design is on using renewable energy solutions for the purpose of managing energy protection. When referring to a green building, either sustainable construction or green construction may be used instead. To guarantee a structure will last for its intended purpose and the environment will not be harmed in the process, sustainable construction practices should be included from the start. Additionally, the economics of renewable energy are presented in this chapter with eco-friendly construction practices that make use of renewable energy sources. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Implementing a programmable drop voltage controller vlsi
This study offers a new synchronized practice area door array (FPGAs), to minimize electricity usage. Concurrent bit-serial architecture is shown in the figure to minimize energy consumption and timing synchronization of switching structures. Researchers offer a fine-grained energy control system with each Look-up database to minimize the Static energy by the channel length, which is now equivalent to the dynamical one (LUT). A 90 nm Processor is the planned field-programmable VLSI. Its electricity consumption is 42 percent lower than that of sequential design. 2021, SciTechnol, All Rights Reserved. -
Implementing artificial intelligence agent within connect 4 using unity3d and machine learning concepts
Nowadays, we come across games that have unbelievably realistic graphics that it usually becomes hard to distinguish between reality and the virtual world when we are exposed to a virtual reality gaming console. Implementing the concepts of Artificial Intelligence (AI) and Machine-Learning (ML) makes the game self-sustainable and way too intelligent on its own, by making use of self-learning methodologies which can give the user a better gaming experience. The use of AI and ML in games can give a better dimension to the gaming experience in general as the virtual world can behave unpredictably, thus improving the overall stigma of the game. In this paper, we have implemented Connect-4, a multiplayer game, using ML concepts in Unity3D. The machine learning toolkit ML-Agents, which depends on Reinforcement Learning (RL) technique, is provided using Unity3D. This toolkit is used for training the game agent which can distinguish its good moves and mistakes while training, so that the agent will not go for same mistakes over and over during actual game with human player. With this paper, authors have increased intelligence of game agent of Connect 4 using Reinforcement Learning, Unity3D and ML-Agents toolkit. BEIESP. -
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE. -
Implementing Innovative Weed Detection Techniques for Environmental Sustainability
Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture. 2024, Institute for Environmental Nanotechnology. All rights reserved. -
Implementing learner-centric approaches in high-density classrooms: A TEAMS-aligned hybrid model for management education
Learner-Centric Approaches (LCAs) are increasingly used in higher education to enhance engagement, critical thinking, and deeper learning. This chapter examines LCAs in a Bachelor of Business Administration (BBA) program at a large institution with class sizes of 80 students. To address challenges in personalized education, the program employs a hybrid model: large lectures for theory and smaller tutorial groups of 40 students for interactive LCAs. This approach balances personalized learning with logistical demands. The chapter evaluates the model's effectiveness through student outcomes, faculty adaptability, and engagement, while examining scalability and alignment with the TEAMS framework-Transparency, Empowerment, Attainability, Mentorship, and Sustainability. It offers practical insights for implementing scalable, learner-focused strategies in high-density classrooms. 2026, IGI Global Scientific Publishing. All rights reserved. -
Implementing learning analytics in educational systems to effectively integrate and cater to different learning styles
This research deals with developing an educational system that will contain advanced learning analytics and technologies such as virtual reality (VR), augmented reality (AR), among others, to ensure learning styles are well catered for, including auditory, visual, kinesthetic, and digital. With personalized education at an increased height in this present dispensation, the study delves into how educational frameworks can be leveraged to support individualized learning preferences, which would in turn optimize student performance by applying tailored instructional methods. By combining LMS data with educator and student voices, the report shows how these adaptive technologies have a significantly positive impact on engagement and academic performance. It also considers affordability, scalability, data privacy, and training challenges to focus on holistic implementation at the institutional level. It is imperative that the students in higher education benefit from customized, technology-based learning environments oriented towards their personalized needs. 2025, IGI Global Scientific Publishing. -
Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases
The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. 2025 IEEE. -
Implementing privacy and data confidentiality within the framework of the Internet of Things
Throughout the current and future worldwide Web network infrastructure, the notion of the Internet of Things (IoT) foresees the pervasive interconnection and cooperation of intelligent things. As such, the IoT is simply the next logical step in the expansion of the Web into the real world, ushering in a plethora of unique services that will enhance peoples lives, give rise to entirely new economic sectors and smarten up the physical infrastructure upon which we rely, including buildings, cities and transportation networks. As smart devices permit widespread information collection or tracking, the IoT will not be able to reach its full potential if the vision for the IoT is not implemented appropriately. These helpful characteristics are countered by concerns over confidentiality, which have, to date, hindered the viability of IoT aspirations. In the face of widespread surveillance, the management of private information and the development of tools to limit or evade pervasive monitoring and analysis are two examples of the new difficulties brought about by such dangers. This paper considers the privacy concerns raised by the Internet of Things in depth. Henry Stewart Publications 2398-1679 (2023). -
Implementing Quality Healthcare Strategies for Improving Service Delivery at Private Hospitals in India
Healthcare is becoming the largest growing sector of India because of its huge coverage, providing services and investment by public and private players. In India growth of private hospitals have totally changed the scenario of health care delivery. This study explores the effectiveness of the strategies to provide quality health care and thereby improving the service delivery in Private Hospitals. In total 122 responses were collected after administering the questionnaires. The findings of this study reveals that quality health care strategies has positive impact on service delivery. Quality health care strategies showed a different kind of associations with three measures of quality namely structure, process and outcome measures. The implications from the study provides the need of multifaceted approach for implementing quality improvement strategies and adoption of the model for the same. This study recommends a blend of quality improvement programs with increased ICT (Information and Communication Technology) applications for enhancing the turnaround time. Further study can be conducted on other healthcare quality dimensions and strategic interventions that can enhance the quality of health care and clinical outcomes in Private Hospitals in India. 2017 Indian Institute of Health Management Research. -
Implementing quality healthcare strategies for improving service delivery at private hospitals in India /
Journal of Health Management, ISSN No. 0972-0634. -
Implementing smart cyber-physical systems in industrial and urban applications: A practical approach
World urbanization, at an accelerated rate, leads to a growing need for innovative cities that consider advances in AI and cyber-physical systems (CPS). A smart city is a development of a traditional environment of an urban setting, enhancing it with information and communication technology (ICT) and CPS to improve the quality of life, sustainability, and efficiency of the inhabitants. This chapter will cover the major constituents, challenges, and opportunities associated with the development of smart cities from the historically congested and ad hoc planned cities. A smart city connects a digitally empowered environment through sensors, processors, and communication systems integrated into urban infrastructures, allowing continuous monitoring of public health, mobility, energy consumption, and so on. The combination of AI and data analytics with smart city technologies will help optimize services in cities, reduce environmental effects, and accelerate socioeconomic development and decision-making. Improvement to cities is still a debatable issue, and there are additional obstacles to be overcome, such as infrastructural inadequacy, budgetary constraints, and technical issues. Realization of the full potential of smart cities will occur through successful resolution of the aforementioned issues. All the issues of this chapter can be addressed by adopting a multidisciplinary approach emphasizing sustainable designs, publicprivate sector partnerships, and regulatory frameworks. If ignored, such problems will definitely lead to adverse effects on implementation, an increase in socioeconomic inequality, and damage to the environment. This chapter relies on the secondary methodology of research and assimilates knowledge from journal articles, literature, and earlier research regarding smart cities, CPS, and AI applications. It defines current trends, recognizes long-standing problems, and suggests ways to bridge them, along with some directions for future research. This will help in understanding the ability of AI to make smart city adaptation strong concerning population growth, health crises, and climate change. It will also strengthen and connect the urban landscape of the future. Thus, by solving these problems and their consequent impacts, smart cities can totally transform urban life. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Implementing strategic responses in the COVID-19 market crisis: a study of small and medium enterprises (SMEs) in India
Purpose: The COVID-19 pandemic presents unprecedented challenges for small and medium enterprises (SMEs) in emerging economies. This paper aims to examine how India's SMEs implement their strategic responses in this crisis. Design/methodology/approach: The study uses dynamic capability theory to explore the strategic responses of SMEs. Strategy implementation theory helps to explain how they implement innovative practices for outcomes. A research model defines the COVID-19 challenges, strategic responses and performance outcomes. The study reports the findings of an initial pilot study of 75 firms and follow-up case study results in the context of COVID-19. Findings: Firms choose their approaches according to their perceived market risks. Case studies illustrate that firms display diverse attitudes depending on their strategic direction, leadership vision and organizational culture. They achieve different outcomes by implementing specific styles of risk management practices (e.g. risk-averting, risk-taking and risk-thriving). Research limitations/implications: Although the study context is Indian SMEs, the findings suggest meaningful lessons for other emerging economies in similar crisis events. The propositions may be extended to future research in broad contexts. Practical implications: Even in the extraordinary COVID-19 market crisis, SMEs with limited resources display their strategic potential by recognizing their unique capabilities, translating them into effective actions and achieving desirable outcomes. Social implications: In the COVID-19 pandemic, top leaders' mental attitude, strategic perspective and routine practices are contagious. Positive leadership motivates both internal and external stakeholders with an enormous level of collaboration. Originality/value: This rare study of Indian SMEs provides a theoretical framework for designing a pilot survey and conducting a case study of multiple firms. Based on these findings, testable propositions are articulated for future research in diverse organizational and national contexts. 2021, Emerald Publishing Limited. -
Implementing the Happy Wisdom Framework in Modern Workplaces: Nurturing Joyful Insights
Happiness in the form of pleasant moods and emotions, well- being, and positive attitudes has been attracting increasing attention throughout psychology research. This chapter introduces the happy wisdom framework, an integrative approach designed to nurture joyful insights and address the multifaceted challenges of modern workplaces. By combining the principles of wisdom and happiness, the authors propose a comprehensive strategy for enhancing individual well- being, team dynamics, and organizational culture. The framework builds on established conceptualizations of wisdom and happiness, drawing from extensive theoretical and empirical research. It identifies key workplace issues at different levels, including individual stress and lack of meaning, team incivility and abusive supervision, and organizational deficits in ethical, social, and environmental responsibility. The chapter concludes by presenting practical insights and recommendations for integrating the happy wisdom framework into workplace practices that benefit employees and organizations alike. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Implication of big data in hospitality with special reference to ecoresorts in Karnataka
Big data with its velocity has revolutionized several industries, and the hospitality industry is no exception. Operational efficiency and services of ecoresorts can always be improved with customer reviews, and big data provide unprecedented opportunities toimplement it. Socialmedia has alwaysbeen instrumental in capturing customer feedback and understanding booking patterns. On this note, it has been aimed to understand the visitors' sentiment and to identify satisfactory indicators for this business. In this scholarly work, Tripadvisor website has been adopted as the collection platform. A total of 15 resorts with 7,235 reviews have been considered for the same. It has been intended to capture up-to-date data available till April 2024. The five-stage model has been considered for the smooth execution of the analysis which includesweb scraping for data extraction, data pre-processing, data storage, sentiment analysis, and understanding key insights. More specifically, with the help of Natural Language Processing (NLP), text analytics was executed to understand customers' sentiments. In total,150 high-frequencywords have been captured.The outcome of the study also revealed nine satisfactory indicators and those are "Variety of Experiences"; "Quality of Accommodation"; "Quality of Food"; "Cleanliness of the Resort"; "Level of Service by Staff"; "Service Staff were Helpful"; "Natural Environment of the Resort"; "Safety and Security;" and "Good Opportunities to enjoy local Cuisine". 2025 Shivi Khanna, Tulasi B., Nagarjuna G. and Bidisha Sarkar. All rights reserved. -
Implication of emotional labor, cognitive flexibility, and relational energy among cabin crew: A review
The primary aim of the civil aviation industry is to provide a secured and comfortable service to their customers and clients. This review concentrates on the cabin crew members, who are the frontline employees of the aviation industry and are salaried to smile. The objective of this review article is to analyze the variables of emotional labor, cognitive flexibility, and relational energy using the biopsychosocial model and identify organizational implications among cabin crew. Online databases such as EBSCOhost, JSTOR, Springerlink, and PubMed were used to gather articles for the review. The authors analyzed 17 articles from 2001 to 2016 and presented a comprehensive review. The review presented an integrative approach and suggested a hypothetical model that can prove to be a signitficant contribution to the avaition industry in particular and to research findings of aviation psychology. 2018 Indian Journal of Occupational and Environmental Medicine | Published by Wolters Kluwer-Medknow.