International Journal on Recent and Innovation Trends in Computing and Communication
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Implementing Blockchain-Based Secure Data Provenance Mechanisms for Ensuring Data Integrity and Authenticity in Cloud Networks
Get PDFThis research focuses on establishing techniques to prove the integrity and genuineness of data in cloud networks through blockchain. With cloud computing paving its way into the new technological era, the importance of proper security measures to safeguard data and the history of that data has become exceedingly critical. This research presents an innovative approach to developing a system that uses blockchain to store data’s pedigree in cloud environments securely. The methodology comprises system architecture, which is elaborated into three layers: blockchain, cloud integration, and user interface. The type of smart contract implementation used in cloud networks is elucidated along with data capture methods, integrityCheck procedures, and the available authentications and consensus frameworks. The results also reveal that the proposed methods achieve higher levels of data security with better protection against data tampering and unauthorized data access. Throughput and scalability analysis depicts the possibility of high performance, while usability studies indicate the applicability to various organizations. The comparison shows greater efficiency with the existing solutions, for example, by the presence of complete lists of features and instantaneous monitoring. Nevertheless, this paper revealed some unresolved issues, such as cross-cloud integration and privacy concerns, and added significant value to the literature on cloud security. The conclusions drawn from the research show that there is a strong possibility that blockchain-based provenance applications can transform data handling and protection with the advancement of cloud solutions and structures
Adaptive LSTM-Based Model for Accurate Forecasting of Workload and Resource Variability in Cloud Computing
Get PDFCloud/edge computing systems play a crucial role in providing a wide range of services for Internet users. Despite their numerous advantages, providers of these systems face certain challenges, such as accurately predicting large-scale workloads and resource usage traces. The complexity of cloud computing environments makes it difficult for traditional models to accurately predict these traces due to their highly variable nature. Traditional models struggle to handle nonlinear characteristics and long-term memory dependencies. To address this issue, this study proposes an integrated prediction method that combines Bi-directional and Grid Long Short-Term Memory network (BGLSTM) models to predict workload and resource usage traces. The proposed method first smooths the traces using a Savitzky-Golay filter to eliminate extreme points and noise interference. Subsequently, an integrated prediction model is established to achieve accurate predictions for highly variable traces. The effectiveness and adaptability of the BG-LSTM model for different traces are demonstrated through extensive experiments using real-world workload and resource usage traces from Google Cloud data centers. The performance results indicate that BG-LSTM outperforms typical prediction methods in accurately predicting highly variable real-world cloud systems
Intelligent Water Management in Precision Agriculture Using Machine Learning
Get PDFIndia's economy heavily relies on agriculture, with a significant portion of the population engaged in agriculture-based businesses. Automation in agriculture holds the potential to enhance crop production quality and quantity while reducing resource consumption. However, the high investment costs associated with agricultural automation present a major challenge. To address this, a Machine Learning (ML) technique is proposed to optimize the costs associated with automating agricultural irrigation systems.The proposed ML model aims to reduce the implementation and operational costs of the remote sensor network used for irrigation. A dataset comprising soil moisture and temperature data was utilized, with the irrigation treatment type serving as the target variable. The dataset underwent preprocessing to ensure suitability for learning, followed by the application of k-means clustering for behavioral data analysis. This clustering technique grouped similar sensor readings, improving learning performance in terms of accuracy and training time.Two machine learning algorithms were then implemented to train the model and predict irrigation treatments, effectively minimizing the cost of deploying and maintaining sensors in the field. While the current system demonstrates significant cost efficiency, its overall performance remains reliant on the accuracy of the prediction model. Future work will focus on further enhancing the prediction accuracy to ensure optimal performance.This study highlights the potential of intelligent water management systems in precision agriculture, demonstrating how machine learning can contribute to sustainable and cost-effective agricultural practices.  
Integrated Approaches for Modern Power Systems State Estimation and Monitoring
Get PDFWhen paired with the findings of state estimation, the data obtained from monitoring metres enables utilities to identify regions with high demand or congestion, which in turn enables effective load management and control measures to be implemented. It is possible for utilities to monitor the performance of renewable energy sources thanks to the strategic deployment of monitoring metres. There are a number of obstacles that must be navigated in order to achieve optimal planning for monitoring metres and state estimates. The power distribution systems are the backbone of the energy supply. They ensure that the electricity is delivered from the transmission networks to the end users in an effective and dependable manner. The need of efficient monitoring, control, and optimisation is only going to grow more pressing as these systems continue to become more intricate and interdependent on one another. In the field of electricity distribution, optimal design of monitoring metres and state estimate methodologies has emerged as an important area of attention, with the goals of improving system performance, enhancing decision-making procedures, and ensuring the dependable functioning of distribution networks. Monitoring metres are an extremely important component in the process of collecting real-time data from several locations across the electricity distribution network. These metres give vital information on a variety of characteristics, including voltage levels, current flows, power quality, and performance indicators for the system. The information that is gathered from these metres is very useful for optimising the regulation of power flow, recognising irregularities in the network's behaviour, locating defects, and monitoring the network's behaviour. However, the positioning of monitoring metres in a strategic location is necessary in order to make the most of their usefulness and efficiency. The most effective method of planning for monitoring metres is identifying the points throughout the distribution system at which these metres may be installed in the most optimal places. Experimental experiments are carried out in order to verify the efficacy of the methods for improving power distribution via the optimisation of monitoring metres and state estimates
Transcending Nothingness - The Existential Heroes of Ernest Hemmingway
Get PDFAn Existential hero is portrayed in literature as the one who has transcended above the meaninglessness of life and has accepted it with its original harshness and reality. The characters presented in the works of Arthur Miller, Tennessee Williams and Hemmingway show stark similarities with the existential characters of Kafka, Camus and Sartre. The European writers regard this concern "with the meaning of identity in the modern world, the nature of good and evil, the possibility of fulfilment in the contemporary society". Hemmingway strongly believed in living a life fully and heroically and he felt that even in the dystopian world after World War man was powerful enough to realize his moral purpose . His true heroes are the ones who have accepted their responsibility towards the world where notions of ultimate truth and reality do not exist anymore
Online News Platform Preference Among Communication and Performing Arts Undergraduate Students from Three Selected Universities in South-East, Nigeria
Get PDFThe internet is here to stay, and news consumption is a daily activity for men. Information must satisfy the eternal curiosity of the human mind. The readily available information online, its ease of access, and its constant and speedy updates serve to satisfy man's constant search for information. The researchers surveyed 4435 students enrolled in Mass Communication, Fine Art, Theatre and Film Studies at three universities from South East, Nigeria during the 2023–2024 academic session: University of Nigeria Nsukka, Imo State University, and Gregory University in Uturu. The researchers determined a sample size of 383 using the Taro Yamane formula and used proportionate sampling to identify the respondents for each group. The researchers analysed the collected data using mean and standard deviation. The research found that students visit multiple platforms for online news. Entertainment news was the content that most captivated the students. Students prefer Premium Times for its meticulous attention to detail, their social media handles for their convenient accessibility, and Arise TV for its comprehensive analysis. The study suggests that, given the respondents' consistent preference for Premium Times' online news stories, their social media handles' news stories, and Arise TV's news stories across various online media platforms, all media organisations should consider posting their news stories online to cater to the majority of youths who prefer online reading
Implements and Integrates Data-Centric Applications based on Big Data Analysis Service on Big Data Platforms
Get PDFBig data is now an integral part of many fields of research due to the rapid advancement of digital tools for data processing. With the system's help, the business analysis process may be quickly developed and implemented with flexibility, allowing for faster contact and reaction times. It also helps with the management and development of smart data analysis programmes, which are constantly evolving. The suggested method enables users to do batch and streaming computations concurrently by combining online-streaming analysis with offline-batch models. Big data analysis using cloud capabilities and user-customizable workflow techniques are also available. Cloud workflow system modelling, application customisation, dynamic construction, and scheduling are all included in this study. To improve efficiency, it is suggested to use a chain workflow foundation mechanism that combines multiple analytic components into one. To ensure the system's analytical competence, four real-world application examples are given. The results of the experiments demonstrate that the suggested system makes good use of data analysis methods and can handle numerous users logging in at the same time. Network operators have used the suggested SaaS workflow solution, and it has proven successful for them
Extensive Experience in Aws Services in Develop and Deploying and Highly Available, Scalable and Fault Tolerant Systems
Get PDFDistributed systems' reliability and high availability have long been essential concerns. If you want to keep your customers happy and keep money coming in, you need to make sure your cloud services are always available and trustworthy. There have been numerous suggestions for improving the cloud's availability and reliability, but no thorough studies that address the whole issue have been carried out. A system's fault-tolerance level determines how well it can continue operating even if some part of it fails. We need a lot more software choices, and mission-critical systems must be up at all times. To buy something, for instance, we might like to check out an online store. It doesn't matter if it's 3 in the morning on a holiday or 9 in the morning on a Monday; what matters is that the site is accessible and prepared to take our payment. Many companies can't afford to fail if these expectations aren't met. Thousands of dollars could be lost for every minute that an active e-commerce site is down, even with the most careful estimations. Companies and groups invest much in developing software systems that can deal with errors for reasons like these. The infrastructure offered by Amazon Web Services (AWS) is perfect for developing software systems that can withstand failures. Having said that, this feature is not exclusive to our platform. Any platform can host a fault-tolerant software system if enough effort and time are put into it. Amazon Web Services (AWS) stands out from the competition because it lets you construct resilient systems with little to no initial investment and human intervention
Artificial Intelligence in Utilities: Predictive Maintenance and Beyond
Get PDFArtificial Intelligence (AI) and Machine Learning (ML) are transforming the utility industry, offering significant advancements in areas such as predictive maintenance, demand forecasting, and operational optimization. By leveraging AI-driven analytics, utilities can predict equipment failures, optimize maintenance schedules, forecast energy demand, and improve grid stability. Case studies from Duke Energy, Siemens, and Constellation Energy highlight the real-world benefits of AI in reducing costs, improving reliability, and enhancing customer satisfaction. However, challenges such as data quality, system integration, and regulatory compliance must be addressed for full-scale AI adoption. Future innovations, including self-healing grids and AI integration with renewable energy, underscore AI's potential to revolutionize utility operations and contribute to a more sustainable, reliable, and efficient energy landscape
Metric Fuzziness and Eigenvalue Theory
Get PDFThis article is dedicated to the exploration of fuzzy eigenvalues and fuzzy eigenvectors within the context of a fuzzy metric space. To facilitate this discussion, we introduce a specific metric for this space. Furthermore, we provide comprehensive definitions for fuzzy eigenvalues and fuzzy eigenvectors, focusing on their application to fuzzy square matrices. In the course of our exploration, we establish a series of theorems pertaining to fuzzy eigenvalues and eigenvectors within a fuzzy metric space. To enhance understanding, we illustrate these theorems with practical examples